Skip main navigation
×

Heart Disease and Stroke Statistics—2013 Update

A Report From the American Heart Association
and on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee
Originally publishedhttps://doi.org/10.1161/CIR.0b013e31828124adCirculation. 2013;127:e6–e245

Table of Contents

Summary e7

1. About These Statistics e11

Cardiovascular Health

2. American Heart Association’s 2020 Impact Goals e14

Health Behaviors

3. Smoking/Tobacco Use e32

4. Physical Inactivity e37

5. Nutrition e45

6. Overweight and Obesity e59

Health Factors and Other Risk Factors

7. Family History and Genetics e68

8. High Blood Cholesterol and Other Lipids e72

9. High Blood Pressure e77

10. Diabetes Mellitus e87

11. Metabolic Syndrome e98

12. Chronic Kidney Disease e104

Conditions/Diseases

13. Total Cardiovascular Diseases e109

14. Stroke (Cerebrovascular Disease) e132

15. Congenital Cardiovascular Defects and Kawasaki Disease e153

16. Disorders of Heart Rhythm e159

17. Subclinical Atherosclerosis e175

18. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris e185

19. Cardiomyopathy and Heart Failure e199

20. Valvular, Venous, Aortic, and Peripheral Artery Diseases e205

Outcomes

21. Quality of Care e215

22. Medical Procedures e229

23. Economic Cost of Cardiovascular Disease e234

Supplemental Materials

24. At-a-Glance Summary Tables e238

25. Glossary e243

Summary

Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on heart disease, stroke, and other cardiovascular disease–related morbidity and mortality and the risks, quality of care, medical procedures and operations, and costs associated with the management of these diseases in a single document. Indeed, since 1999, the Statistical Update has been cited >10 500 times in the literature, based on citations of all annual versions. In 2011 alone, the various Statistical Updates were cited ≈1500 times (data from ISI Web of Science). In recent years, the Statistical Update has undergone some major changes with the addition of new chapters and major updates across multiple areas, as well as increasing the number of ways to access and use the information assembled.

For this year’s edition, the Statistics Committee, which produces the document for the AHA, updated all of the current chapters with the most recent nationally representative data and inclusion of relevant articles from the literature over the past year. This year’s edition also implements a new chapter organization to reflect the spectrum of cardiovascular health behaviors and health factors and risks, as well as subsequent complicating conditions, disease states, and outcomes. Also, the 2013 Statistical Update contains new data on the monitoring and benefits of cardiovascular health in the population, with additional new focus on evidence-based approaches to changing behaviors, implementation strategies, and implications of the AHA’s 2020 Impact Goals. Below are a few highlights from this year’s Update.

The 2013 Update Expands Data Coverage of the Epidemic of Poor Cardiovascular Health Behaviors and Their Antecedents and Consequences

  • Adjusted population attributable fractions for cardiovascular disease (CVD) mortality were as follows1: 40.6% (95% confidence interval [CI], 24.5–54.6) for high blood pressure; 13.7% (95% CI, 4.8–22.3) for smoking; 13.2% (95% CI, 3.5–29.2) for poor diet; 11.9% (95% CI, 1.3–22.3) for insufficient physical activity; and 8.8% (95% CI, 2.1–15.4) for abnormal glucose levels.

  • Despite 4 decades of progress, in 2011, among Americans ≥18 years of age, 21.3% of men and 16.7% of women continued to be cigarette smokers. In 2011, 18.1% of students in grades 9 through 12 reported current cigarette use.

  • The percentage of the nonsmoking population with detectable serum cotinine (indicating exposure to secondhand smoke) declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008, with declines higher for those 3 to 11 years of age (–53.6%) and those 12 to 19 years of age (–46.5%) than for those 20 years of age and older (–36.7%).

  • The proportion of youth (≤18 years of age) who report engaging in no regular physical activity is high, and the proportion increases with age. In 2011, among adolescents in grades 9 through 12, 17.7% of girls and 10.0% of boys reported that they had not engaged in ≥60 minutes of moderate-to-vigorous physical activity, defined as any activity that increased heart rate or breathing rate, even once in the previous 7 days, despite recommendations that children engage in such activity 7 days per week.

  • Thirty two percent of adults reported engaging in no aerobic leisure-time physical activity.

  • Data from the National Health and Nutrition Examination Survey (NHANES) indicate that between 1971 and 2004, average total energy consumption among US adults increased by 22% in women (from 1542 to 1886 kcal/d) and by 10% in men (from 2450 to 2693 kcal/d).

  • The increases in calories consumed during this time period are attributable primarily to greater average carbohydrate intake, in particular, of starches, refined grains, and sugars. Other specific changes related to increased caloric intake in the United States include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, commercially prepared (especially fast food) meals, and higher energy-density foods.

  • The estimated prevalence of overweight and obesity in US adults (≥20 years of age) is 154.7 million, which represents 68.2% of this group in 2010. Fully 34.6% of US adults are obese (body mass index ≥30 kg/m2). Men and women of all race/ethnic groups in the population are affected by the epidemic of overweight and obesity.

  • Among children 2 to 19 years of age, 31.8% are overweight and obese (which represents 23.9 million children) and 16.9% are obese (12.7 million children). Mexican American boys and girls and African American girls are disproportionately affected. Over the past 3 decades, the prevalence of obesity in children 6 to 11 years of age has increased from ≈4% to >20%.

  • Obesity (body mass index ≥30 kg/m2) is associated with marked excess mortality in the US population. Even more notable is the excess morbidity associated with overweight and obesity in terms of risk factor development and incidence of diabetes mellitus, CVD end points (including coronary heart disease, stroke, and heart failure), and numerous other health conditions, including asthma, cancer, end-stage renal disease, degenerative joint disease, and many others.

Prevalence and Control of Cardiovascular Health Factors and Risks Remains an Issue for Many Americans

  • An estimated 31.9 million adults ≥20 years of age have total serum cholesterol levels ≥240 mg/dL, with a prevalence of 13.8%.

  • Based on 2007 to 2010 data, 33.0% of US adults ≥20 years of age have hypertension. This represents 78 million US adults with hypertension. The prevalence of hypertension is nearly equal between men and women. African American adults have among the highest prevalence of hypertension (44%) in the world.

  • Among hypertensive adults, ≈82% are aware of their condition and 75% are using antihypertensive medication, but only 53% of those with documented hypertension have their condition controlled to target levels.

  • In 2010, an estimated 19.7 million Americans had diagnosed diabetes mellitus, representing 8.3% of the adult population. An additional 8.2 million had undiagnosed diabetes mellitus, and 38.2% had prediabetes, with abnormal fasting glucose levels. African Americans, Mexican Americans, Hispanic/Latino individuals, and other ethnic minorities bear a strikingly disproportionate burden of diabetes mellitus in the United States.

  • The prevalence of diabetes mellitus is increasing dramatically over time, in parallel with the increases in prevalence of overweight and obesity.

  • On the basis of NHANES 2003–2006 data, the age-adjusted prevalence of metabolic syndrome, a cluster of major cardiovascular risk factors related to overweight/obesity and insulin resistance, is ≈34% (35.1% among men and 32.6% among women).

Rates of Death Attributable to CVD Have Declined, but the Burden of Disease Remains High

  • The 2009 overall rate of death attributable to CVD (International Classification of Diseases, 10th Revision, codes I00–I99) was 236.1 per 100 000. The rates were 281.4 per 100 000 for white males, 387.0 per 100 000 for black males, 190.4 per 100 000 for white females, and 267.9 per 100 000 for black females.

  • From 1999 to 2009, the relative rate of death attributable to CVD declined by 32.7%. Yet in 2009, CVD (I00–I99; Q20–Q28) still accounted for 32.3% (787 931) of all 2 437 163 deaths, or 1 of every 3 deaths in the United States.

  • On the basis of 2009 death rate data, >2150 Americans die of CVD each day, an average of 1 death every 40 seconds. About 153 000 Americans who died of CVD (I00–I99) in 2009 were <65 years of age. In 2009, 34% of deaths attributable to CVD occurred before the age of 75 years, which is well before the average life expectancy of 78.5 years.

  • Coronary heart disease alone caused ≈1 of every 6 deaths in the United States in 2009. In 2009, 386 324 Americans died of coronary heart disease. Each year, an estimated ≈635 000 Americans have a new coronary attack (defined as first hospitalized myocardial infarction or coronary heart disease death) and ≈280 000 have a recurrent attack. It is estimated that an additional 150 000 silent first myocardial infarctions occur each year. Approximately every 34 seconds, 1 American has a coronary event, and approximately every 1 minute, an American will die of one.

  • From 1999 to 2009, the relative rate of stroke death fell by 36.9% and the actual number of stroke deaths declined by 23.0%. Yet each year, ≈795 000 people continue to experience a new or recurrent stroke (ischemic or hemorrhagic). Approximately 610 000 of these are first attacks, and 185 000 are recurrent attacks. In 2009, stroke caused ≈1 of every 19 deaths in the United States. On average, every 40 seconds, someone in the United States has a stroke and dies of one approximately every 4 minutes.

  • In 2009, 1 in 9 death certificates (274 601 deaths) in the United States mentioned heart failure. Heart failure was the underlying cause in 56 410 of those deaths in 2009. The number of any-mention deaths attributable to heart failure was approximately as high in 1995 (287 000) as it was in 2009 (275 000). Additionally, hospital discharges for heart failure remained essentially unchanged from 2000 to 2010, with first-listed discharges of 1 008 000 and 1 023 000, respectively.

The 2013 Update Provides Critical Data About Cardiovascular Quality of Care, Procedure Utilization, and Costs

In light of the current national focus on healthcare utilization, costs, and quality, it is critical to monitor and understand the magnitude of healthcare delivery and costs, as well as the quality of healthcare delivery, related to CVD risk factors and conditions. The Statistical Update provides these critical data in several sections.

Quality-of-Care Metrics for CVDs

Quality data are available from the AHA’s “Get With The Guidelines” programs for coronary artery disease and heart failure and from the American Stroke Association/AHA’s “Get With The Guidelines” program for acute stroke. Similar data from the Veterans Healthcare Administration, national Medicare and Medicaid data, and Acute Coronary Treatment and Intervention Outcomes Network (ACTION)–“Get With The Guidelines” Registry data are also reviewed. These data show impressive adherence to guideline recommendations for many, but not all, metrics of quality of care for these hospitalized patients. Data are also reviewed on screening for CVD risk factor levels and control.

Cardiovascular Procedure Use and Costs

  • The total number of inpatient cardiovascular operations and procedures increased 28%, from 5 939 000 in 2000 to 7 588 000 in 2010 (National Heart, Lung, and Blood Institute computation based on National Center for Health Statistics annual data).

  • The total direct and indirect cost of CVD and stroke in the United States for 2009 is estimated to be $312.6 billion. This figure includes health expenditures (direct costs, which include the cost of physicians and other professionals, hospital services, prescribed medications, home health care, and other medical durables) and lost productivity that results from morbidity and premature mortality (indirect costs).

  • By comparison, in 2008, the estimated cost of all cancer and benign neoplasms was $228 billion ($93 billion in direct costs, $19 billion in morbidity indirect costs, and $116 billion in mortality indirect costs). CVD costs more than any other diagnostic group.

The AHA, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current data available in the Statistics Update.

Finally, it must be noted that this annual Statistical Update is the product of an entire year’s worth of effort by dedicated professionals, volunteer physicians and scientists, and outstanding AHA staff members, without whom publication of this valuable resource would be impossible. Their contributions are gratefully acknowledged.

Alan S. Go, MD

Melanie B. Turner, MPH

On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Note: Population data used in the compilation of NHANES prevalence estimates are for the latest year of the NHANES survey being used. Extrapolations for NHANES prevalence estimates are based on the census resident population for 2010 because this is the most recent year of NHANES data used in the Statistical Update.

Acknowledgments

We wish to thank Lucy Hsu, Michael Wolz, Sean Coady, and Laurie Whitsel for their valuable comments and contributions. We would like to acknowledge Karen Modesitt and Lauren Rowell for their administrative assistance.

Reference

Footnotes

*The findings and conclusions of this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention

The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.

The American Heart Association requests that this document be cited as follows: Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Magid D, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER, Moy CS, Mussolino ME, Nichol G, Paynter NP, Schreiner PJ, Sorlie PD, Stein J, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation. 2013;127:e6-e245.

A copy of the document is available at http://my.americanheart.org/statements by selecting either the “By Topic” link or the “By Publication Date” link. To purchase additional reprints, call 843-216-2533 or e-mail: .

Expert peer review of AHA Scientific Statements is conducted by the AHA Office of Science Operations. For more on AHA statements and guidelines development, visit http://www.my.americanheart.org/statements and select the “Policies and Development” link.

Permissions: Multiple copies, modification, alteration, enhancement, and/or distribution of this document are not permitted without the express permission of the American Heart Association. Instructions for obtaining permission are located at http://www.heart.org/HEARTORG/General/Copyright-Permission-Guidelines_UCM_300404_Article.jsp. A link to the “Copyright Permissions Request Form” appears on the right side of the page.

References

  • 1. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults.JAMA. 2012; 307:1273–1283.CrossrefMedlineGoogle Scholar

Disclosures

Writing Group Disclosures

Writing Group MemberEmploymentResearch GrantOther Research SupportSpeakers’ Bureau/HonorariaExpert WitnessOwnership InterestConsultant/Advisory BoardOther
Alan S. GoKaiser PermanenteNoneNoneNoneNoneNoneNoneNone
Emelia J. BenjaminBoston University School of MedicineNIH†NoneNoneNoneNoneNoneAssociate Editor of Circulation
Jarett D. BerryUT SouthwesternAHA†; NIH†NoneMerck†NoneNoneNoneNone
William B. BordenWeill Cornell Medical College/U.S. Department of Health and Human ServicesNoneNoneNoneNoneNoneNoneNone
Dawn M. BravataDepartment of Veteran AffairsNIH/NHLBI†NoneNoneNoneNoneNoneNone
Shifan DaiCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Earl S. FordCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Caroline S. FoxNational Heart Lung and Blood InstituteNoneNoneNoneNoneNoneNoneNone
Sheila FrancoCenters for Disease Control and Prevention/National Center for Health StatisticsNoneNoneNoneNoneNoneNoneNone
Heather J. FullertonUniversity of California, San FranciscoNoneNoneNoneNoneNoneNoneNone
Cathleen GillespieCenters for Disease Control and PreventionNoneNoneNoneNoneNoneNoneNone
Susan M. HailpernIndependent ConsultantNoneNoneNoneNoneNoneNoneNone
John A. HeitMayo ClinicNoneNoneNoneNoneNoneDaiichi Sankyo*; GTC Biotherapeutics*; Janssen Pharmaceuticals*None
Virginia J. HowardUniversity of Alabama at BirminghamNIH†NoneNoneNoneNoneNoneNone
Mark D. HuffmanNorthwestern University Feinberg School of MedicineNIH/NHLBI†; Scientific Therapeutic Information*NoneNoneNoneNoneNoneNone
Brett M. KisselaUniversity of CincinnatiNoneNoneNoneNoneNoneNoneNone
Steven J. KittnerUniversity of Maryland School of MedicineNoneNoneNoneNoneNoneNoneNone
Daniel T. LacklandMedical University of South CarolinaNoneNoneNoneNoneNoneNoneNone
Judith H. LichtmanYale UniversityNoneNoneNoneNoneNoneNoneNone
Lynda D. LisabethUniversity of MichiganNHLBI; NINDS†NoneNoneNoneNoneNoneNone
David MagidColorado Permanente Medical GroupNoneNoneNoneNoneNoneNoneNone
Gregory M. MarcusUniversity of California, San FranciscoForest Research Institute*; Gilead*; Medtronic*; SentreHeart†; Zoll*NoneSt. Jude Medical*NoneNoneNoneNone
Ariane MarelliMcGill University Health CenterNoneNoneNoneNoneNoneNoneNone
David B. MatcharDuke University Medical Center/Duke-NUS Graduate Medical SchoolNoneNoneNoneNoneNoneAstraZeneca*; Boehringer Ingelheim*None
Darren K. McGuireUT Southwestern Medical CenterBoehringer Ingelheim*; Bristol-Meyers Squibb*; Daiichi Sankyo*; Eli Lilly*; Merck*; Orexigen†; Roche/Genentech*; Takeda*NoneNoneExpert Witness for Takeda in class action suit for Actos*NoneBoehringer Ingelheim*; Daiichi Sankyo*; Genentech*; Janssen Pharmaceuticals†; Regeneron Pharmaceuticals*; Sanofi-Aventis*None
Emile R. Mohler IIIUniversity of PennsylvaniaAHA†; Department of Defense†; NIH†NoneNoneCytoVas*; FloxiMedical*GlaxoSmithKline*; Merck*; Roche*; Takeda*NoneNone
Claudia S. MoyNational Institutes of HealthNoneNoneNoneNoneNoneNoneNone
Dariush L. MozaffarianHarvard School of Public Health/Brigham and Women’s Hospital/Harvard Medical SchoolNIH†; GlaxoSmithKline†; Pronova†; Sigma Tau†NoneBunge*; International Life Sciences Institute*; Nutrition Impact*; Pollock Institute*; Sprim*NoneHarvard has filed a provisional patent application that has been assigned to Harvard, listing Dr. Mozaffarian as a co-inventor for use of trans-palmitoleic acid to prevent and treat insulin resistance, type 2 diabetes and related conditions*; Royalties from UpToDate for an online chapter*Foodminds*; McKinsey Health Systems Institute*; Unilever North America*None
Michael E. MussolinoNational Heart, Lung, and Blood InstituteNoneNoneNoneNoneNoneNoneNone
Graham NicholUniversity of WashingtonNoneNoneNoneNoneNoneNoneNone
Nina P. PaynterBrigham and Women’s HospitalCelera Corp.†; NHLBI†; NIH/NCRR†NoneNoneNoneNoneNoneNone
Veronique L. RogerMayo ClinicNoneNoneNoneNoneNoneNoneNone
Pamela J. SchreinerUniversity of MinnesotaNoneNoneNoneNoneNoneNoneNone
Paul D. SorlieNational Heart, Lung, and Blood Institute, NIHNoneNoneNoneNoneNoneNoneNone
Joel SteinColumbia UniversityMyomo, Inc.*; Tibion, Inc.*; Tyromotion, Inc.*NoneQuantiaMF*NoneNoneMyomo, Inc.*None
Tanya N. TuranMedical University of South CarolinaNIH/NINDS†NoneNoneNoneNoneNoneNone
Melanie B. TurnerAmerican Heart AssociationNoneNoneNoneNoneNoneNoneNone
Salim S. ViraniDepartment of Veterans AffairsMerck†; NFL Charities†; NIH†; VA†NoneNoneNoneNoneNoneNone
Nathan D. WongUniversity of California, IrvineBristol-Myers Squibb†; Merck†NoneNoneNoneNoneAbbott Pharmaceuticals*None
Daniel WooUniversity of CincinnatiNIH†NoneNoneNoneNoneNoneNone

This table represents the relationships of writing group members that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all members of the writing group are required to complete and submit. A relationship is considered to be “significant” if (a) the person receives $10 000 or more during any 12-month period, or 5% or more of the person’s gross income; or (b) the person owns 5% or more of the voting stock or share of the entity, or owns $10 000 or more of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.

*Modest.

†Significant.

1. About These Statistics

The American Heart Association (AHA) works with the Centers for Disease Control and Prevention’s (CDC’s) National Center for Health Statistics (NCHS); the National Heart, Lung, and Blood Institute (NHLBI); the National Institute of Neurological Disorders and Stroke (NINDS); and other government agencies to derive the annual statistics in this Heart Disease and Stroke Statistical Update. This chapter describes the most important sources and the types of data we use from them. For more details, see Chapter 25 of this document, the Glossary.

The surveys used are:

  • Behavioral Risk Factor Surveillance System (BRFSS)—ongoing telephone health survey system

  • Greater Cincinnati/Northern Kentucky Stroke Study (GCNKSS)—stroke incidence rates and outcomes within a biracial population

  • Medical Expenditure Panel Survey (MEPS)—data on specific health services that Americans use, how frequently they use them, the cost of these services, and how the costs are paid

  • National Health and Nutrition Examination Survey (NHANES)—disease and risk factor prevalence and nutrition statistics

  • National Health Interview Survey (NHIS)—disease and risk factor prevalence

  • National Hospital Discharge Survey (NHDS)—hospital inpatient discharges and procedures (discharged alive, dead, or status unknown)

  • National Ambulatory Medical Care Survey (NAMCS)—physician office visits

  • National Home and Hospice Care Survey (NHHCS)—staff, services, and patients of home health and hospice agencies

  • National Hospital Ambulatory Medical Care Survey (NHAMCS)—hospital outpatient and emergency department (ED) visits

  • Nationwide Inpatient Sample of the Agency for Healthcare Research and Quality (AHRQ)—hospital inpatient discharges, procedures, and charges

  • National Nursing Home Survey (NNHS)—nursing home residents

  • National Vital Statistics System—national and state mortality data

  • World Health Organization (WHO)—mortality rates by country

  • Youth Risk Behavior Surveillance System (YRBSS)—health-risk behaviors in youth and young adults

Disease Prevalence

Prevalence is an estimate of how many people have a disease at a given point or period in time. The NCHS conducts health examination and health interview surveys that provide estimates of the prevalence of diseases and risk factors. In this Update, the health interview part of the NHANES is used for the prevalence of cardiovascular diseases (CVDs). NHANES is used more than the NHIS because in NHANES, angina pectoris (AP) is based on the Rose Questionnaire; estimates are made regularly for heart failure (HF); hypertension is based on blood pressure (BP) measurements and interviews; and an estimate can be made for total CVD, including myocardial infarction (MI), AP, HF, stroke, and hypertension.

A major emphasis of this Statistical Update is to present the latest estimates of the number of people in the United States who have specific conditions to provide a realistic estimate of burden. Most estimates based on NHANES prevalence rates are based on data collected from 2007 to 2010 (in most cases, these are the latest published figures). These are applied to census population estimates for 2010. Differences in population estimates based on extrapolations of rates beyond the data collection period by use of more recent census population estimates cannot be used to evaluate possible trends in prevalence. Trends can only be evaluated by comparing prevalence rates estimated from surveys conducted in different years.

Risk Factor Prevalence

The NHANES 2007– 2010 data are used in this Update to present estimates of the percentage of people with high lipid values, diabetes mellitus (DM), overweight, and obesity. The NHIS is used for the prevalence of cigarette smoking and physical inactivity. Data for students in grades 9 through 12 are obtained from the YRBSS.

Incidence and Recurrent Attacks

An incidence rate refers to the number of new cases of a disease that develop in a population per unit of time. The unit of time for incidence is not necessarily 1 year, although we often discuss incidence in terms of 1 year. For some statistics, new and recurrent attacks or cases are combined. Our national incidence estimates for the various types of CVD are extrapolations to the US population from the Framingham Heart Study (FHS), the Atherosclerosis Risk in Communities (ARIC) study, and the Cardiovascular Health Study (CHS), all conducted by the NHLBI, as well as the GCNKSS, which is funded by the NINDS. The rates change only when new data are available; they are not computed annually. Do not compare the incidence or the rates with those in past editions of the Heart Disease and Stroke Statistics Update (also known as the Heart and Stroke Statistical Update for editions before 2005). Doing so can lead to serious misinterpretation of time trends.

Mortality

Mortality data are presented according to the underlying cause of death. “Any-mention” mortality means that the condition was nominally selected as the underlying cause or was otherwise mentioned on the death certificate. For many deaths classified as attributable to CVD, selection of the single most likely underlying cause can be difficult when several major comorbidities are present, as is often the case in the elderly population. It is useful, therefore, to know the extent of mortality attributable to a given cause regardless of whether it is the underlying cause or a contributing cause (ie, its “any-mention” status). The number of deaths in 2009 with any mention of specific causes of death was tabulated by the NHLBI from the NCHS public-use electronic files on mortality.

The first set of statistics for each disease in this Update includes the number of deaths for which the disease is the underlying cause. Two exceptions are Chapter 9 (High Blood Pressure) and Chapter 19 (Cardiomyopathy and Heart Failure). High BP, or hypertension, increases the mortality risks of CVD and other diseases, and HF should be selected as an underlying cause only when the true underlying cause is not known. In this Update, hypertension and HF death rates are presented in 2 ways: (1) As nominally classified as the underlying cause and (2) as any-mention mortality.

National and state mortality data presented according to the underlying cause of death were computed from the mortality tables of the NCHS World Wide Web site, the Health Data Interactive data system of the NCHS, or the CDC compressed mortality file. Any-mention numbers of deaths were tabulated from the electronic mortality files of the NCHS World Wide Web site and from Health Data Interactive.

Population Estimates

In this publication, we have used national population estimates from the US Census Bureau for 2010 in the computation of morbidity data. NCHS population estimates for 2009 were used in the computation of death rate data. The Census Bureau World Wide Web site1 contains these data, as well as information on the file layout.

Hospital Discharges and Ambulatory Care Visits

Estimates of the numbers of hospital discharges and numbers of procedures performed are for inpatients discharged from short-stay hospitals. Discharges include those discharged alive, dead, or with unknown status. Unless otherwise specified, discharges are listed according to the first-listed (primary) diagnosis, and procedures are listed according to all listed procedures (primary plus secondary). These estimates are from the NHDS of the NCHS unless otherwise noted. Ambulatory care visit data include patient visits to physician offices and hospital outpatient departments and EDs. Ambulatory care visit data reflect the first-listed (primary) diagnosis. These estimates are from NAMCS and NHAMCS of the NCHS.

International Classification of Diseases

Morbidity (illness) and mortality (death) data in the United States have a standard classification system: the International Classification of Diseases (ICD). Approximately every 10 to 20 years, the ICD codes are revised to reflect changes over time in medical technology, diagnosis, or terminology. Where necessary for comparability of mortality trends across the 9th and 10th ICD revisions, comparability ratios computed by the NCHS are applied as noted.2 Effective with mortality data for 1999, we are using the 10th revision (ICD-10). It will be a few more years before the 10th revision is used for hospital discharge data and ambulatory care visit data, which are based on the International Classification of Diseases, Clinical Modification, 9th Revision (ICD-9-CM).3

Age Adjustment

Prevalence and mortality estimates for the United States or individual states comparing demographic groups or estimates over time either are age specific or are age adjusted to the 2000 standard population by the direct method.4 International mortality data are age adjusted to the European standard.5 Unless otherwise stated, all death rates in this publication are age adjusted and are deaths per 100 000 population.

Data Years for National Estimates

In this Update, we estimate the annual number of new (incidence) and recurrent cases of a disease in the United States by extrapolating to the US population in 2010 from rates reported in a community- or hospital-based study or multiple studies. Age-adjusted incidence rates by sex and race are also given in this report as observed in the study or studies. For US mortality, most numbers and rates are for 2009. For disease and risk factor prevalence, most rates in this report are calculated from the 2007–2010 NHANES. Because NHANES is conducted only in the noninstitutionalized population, we extrapolated the rates to the total US population in 2008, recognizing that this probably underestimates the total prevalence, given the relatively high prevalence in the institutionalized population. The numbers and rates of hospital inpatient discharges for the United States are for 2010. Numbers of visits to physician offices, hospital EDs, and hospital outpatient departments are for 2010. Except as noted, economic cost estimates are for 2009.

Cardiovascular Disease

For data on hospitalizations, physician office visits, and mortality, CVD is defined according to ICD codes given in Chapter 25 of the present document. This definition includes all diseases of the circulatory system, as well as congenital CVD. Unless so specified, an estimate for total CVD does not include congenital CVD. Prevalence of CVD includes people with hypertension, heart disease (HD), stroke, peripheral artery disease (PAD), and diseases of the veins.

Race

Data published by governmental agencies for some racial groups are considered unreliable because of the small sample size in the studies. Because we try to provide data for as many racial groups as possible, we show these data for informational and comparative purposes.

Contacts

If you have questions about statistics or any points made in this Update, please contact the AHA National Center, Office of Science & Medicine at [email protected]. Direct all media inquiries to News Media Relations at or 214-706-1173.

We do our utmost to ensure that this Update is error free. If we discover errors after publication, we will provide corrections at our World Wide Web site, http://www.heart.org/statistics, and in the journal Circulation.

Abbreviations Used in Chapter 1

AHA

American Heart Association

AHRQ

Agency for Healthcare Research and Quality

AP

angina pectoris

ARIC

Atherosclerosis Risk in Communities Study

BP

blood pressure

BRFSS

Behavioral Risk Factor Surveillance System

CDC

Centers for Disease Control and Prevention

CHS

Cardiovascular Health Study

CVD

cardiovascular disease

DM

diabetes mellitus

ED

emergency department

FHS

Framingham Heart Study

GCNKSS

Greater Cincinnati/Northern Kentucky Stroke Study

HD

heart disease

HF

heart failure

ICD

International Classification of Diseases

ICD-9-CM

International Classification of Diseases, Clinical Modification, 9th Revision

ICD-10

International Classification of Diseases, 10th Revision

MEPS

Medical Expenditure Panel Survey

MI

myocardial infarction

NAMCS

National Ambulatory Medical Care Survey

NCHS

National Center for Health Statistics

NHAMCS

National Hospital Ambulatory Medical Care Survey

NHANES

National Health and Nutrition Examination Survey

NHDS

National Hospital Discharge Survey

NHHCS

National Home and Hospice Care Survey

NHIS

National Health Interview Survey

NHLBI

National Heart, Lung, and Blood Institute

NINDS

National Institute of Neurological Disorders and Stroke

NNHS

National Nursing Home Survey

PAD

peripheral artery disease

WHO

World Health Organization

YRBSS

Youth Risk Behavior Surveillance System

See Glossary (Chapter 25) for explanation of terms.

References

References

  • 1. US Census Bureau population estimates. http://www.census.gov/popest/data/historical/2000s/index.html. Accessed October 29, 2012.Google Scholar
  • 2. National Center for Health Statistics. Health, United States, 2009, With Special Feature on Medical Technology.Hyattsville, MD: National Center for Health Statistics;2010. http://www.cdc.gov/nchs/data/hus/hus09.pdf. Accessed October 29, 2012.Google Scholar
  • 3. National Center for Health Statistics, Centers for Medicare and Medicaid Services. ICD-9-CM Official Guidelines for Coding and Reporting, 2011. http://www.cdc.gov/nchs/data/icd9/icd9cm_guidelines_2011.pdf. Accessed October 29,2012.Google Scholar
  • 4. Anderson RN, Rosenberg HM. Age standardization of death rates: implementation of the year 2000 standard.Natl Vital Stat Rep. 1998; 127:e11–e13.Google Scholar
  • 5. World Health Organization. World Health Statistics Annual. Geneva, Switzerland: World Health Organization; 1998.Google Scholar

2. American Heart Association’s 2020 Impact Goals

See Tables 2-1 through 2-8 and Charts 2-1 through 2-12.

After achieving its major Impact Goals for 2010, the AHA created a new set of Impact Goals for the current decade.1 Specifically, the AHA committed to the following central organizational goals:

By 2020, to improve the cardiovascular health of all Americans by 20%, while reducing deaths from CVDs and stroke by 20%.1

These goals introduce a new concept, cardiovascular health, which is characterized by 7 different metrics, including 4 health behaviors and 3 health factors. Ideal cardiovascular health is defined by the absence of clinically manifest CVD together with the simultaneous presence of optimal levels of all 7 metrics, including 4 health behaviors (not smoking and having sufficient physical activity [PA], a healthy diet pattern, and appropriate energy balance as represented by normal body weight) and 3 health factors (optimal total cholesterol, BP, and fasting blood glucose, in the absence of drug treatment; Table 2-1). Because a spectrum of cardiovascular health can also be envisioned and the ideal cardiovascular health profile is known to be rare in the US population, a broader spectrum of cardiovascular health can also be represented as being “ideal,” “intermediate,” or “poor” for each of the health behaviors and health factors.1Table 2-1 provides the specific definitions for ideal, intermediate, and poor cardiovascular health for each of the 7 metrics, both for adults (≥20 years of age) and for children (age ranges for each metric depending on data availability).

This concept of cardiovascular health represents a new focus for the AHA. Three novel emphases are central to the AHA 2020 Impact Goals:

  • An expanded focus on CVD prevention and promotion of positive “cardiovascular health,” rather than primarily the treatment of established CVD.

  • Efforts to promote healthy behaviors (healthy diet pattern, appropriate energy intake, PA, and nonsmoking) and healthy biomarker levels (optimal blood lipids, BP, glucose levels) throughout the lifespan.

  • A population-level health promotion strategy that shifts the majority of the public towards greater cardiovascular health in addition to targeting those individuals at greatest CVD risk, since healthy lifestyles in all domains are uncommon throughout the US population.

Beginning in 2011, and recognizing the time lag in the nationally representative US data sets, this chapter in the annual Statistical Update evaluates and publishes metrics and information to provide insights into both progress toward meeting the 2020 AHA goals and areas that require greater attention to meet these goals.

Cardiovascular Health: Current Prevalence

  • The most up-to-date data on national prevalence of ideal, intermediate, and poor levels of each of the 7 cardiovascular health metrics are shown for children (adolescents and teens 12–19 years of age) in Chart 2-1 and for adults (≥20 years of age) in Chart 2-2.

  • For most metrics, the prevalence of ideal levels of health behaviors and health factors is much higher in US children than in US adults. The major exceptions are diet and PA, for which the prevalence of ideal levels in children is similar (for PA) or worse (for diet) than in adults.

  • Among children (Chart 2-1), the prevalence (unadjusted) of ideal levels of cardiovascular health behaviors and factors currently varies from 0% for the healthy diet pattern (ie, essentially no children meet at least 4 of the 5 dietary components) to >80% for the smoking, BP, and fasting glucose metrics. More than 90% of US children meet 0 or only 1 of the 5 healthy dietary components.

  • Among US adults (Chart 2-2), the age-standardized prevalence of ideal levels of cardiovascular health behaviors and factors currently varies from 0.3% for having at least 4 of 5 components of the healthy diet pattern to up to 76% for never having smoked or being a former smoker who has quit for >12 months.

  • Age-standardized and age-specific prevalence estimates for ideal cardiovascular health and for ideal levels of each of its components for 2007 to 2008 (baseline) and 2009 to 2010 are shown in Table 2-2.

—In 2009 to 2010, the prevalence of ideal levels across 6 available health factors and health behaviors (ie, excluding diet, for which 2009–2010 data were not available at the time of this analysis) decreases dramatically from younger to older age groups. The same trend was seen in 2007-2008.

  • Chart 2-3 displays the prevalence estimates for the population of US children (12–19 years of age) meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2007 to 2008.

—Very few US children (<5%) meet only 1 or 2 criteria for ideal cardiovascular health.

—About half of US children meet 3 or 4 fewer criteria for ideal cardiovascular health, and about half meet 5 or 6 criteria.

—Virtually no children meet all 7 criteria for ideal cardiovascular health.

—Overall distributions were similar in boys and girls.

  • Charts 2-4 and 2-5 display the age-standardized prevalence estimates of US adults meeting different numbers of criteria for ideal cardiovascular health (out of 7 possible) in 2007 to 2008, overall and stratified by age, sex, and race.

—Approximately 2.5% of US adults have 0 of the 7 criteria at ideal levels, and another 15% meet only 1 of 7 criteria. This is much worse than among children.

—Most US adults (≈60%) have 2, 3, or 4 criteria at ideal cardiovascular health, with approximately 1 in 5 adults within each of these categories.

—Approximately 12% meet 5 metrics, 4% meet 6 metrics, and 0% meet 7 metrics at ideal levels.

—Presence of ideal cardiovascular health is strongly age related (Chart 2-4). Younger adults are more likely to meet greater numbers of ideal metrics, and older adults are much less likely. More than 60% of those >60 years of age have only 2 or fewer metrics at ideal levels (Chart 2-4).

—Women tend to have more metrics at ideal levels than do men (Chart 2-4).

—Blacks and Mexican Americans tend to have fewer metrics at ideal levels than whites or other races (Chart 2-5). Approximately 6 in 10 white adults and 7 in 10 black or Mexican American adults have no more than 3 of 7 metrics at ideal levels (Chart 2-5).

  • Chart 2-6 displays the age-standardized percentages of US adults and percentages of children who have 5 or more of the metrics (out of 7 possible) at ideal levels.

—Approximately 40% of US children 12 to 19 years of age have 5 or more metrics at ideal levels, with lower levels in boys (36%) than in girls (45%).

—In comparison, only 16% of US adults have 5 or more metrics with ideal levels, with lower prevalence in men (12%) than in women (21%).

—Whites had nearly twice the percentage of adults with 5 or more metrics with ideal levels (18%) as Mexican Americans (9.5%) or blacks (11%).

  • Chart 2-7 displays the age-standardized percentages of US adults meeting different numbers of criteria for both poor and ideal cardiovascular health. Meeting the AHA 2020 Strategic Impact Goals is predicated on reducing the relative percentage of those with poor levels while increasing the relative percentage of those with ideal levels for each of the 7 metrics.

—Approximately 94% of US adults have at least 1 metric at poor levels.

—Approximately 38% of US adults have at least 3 metrics at poor levels.

—Few US adults (<3%) have 5 or more metrics at poor levels.

—More US adults have 4 to 6 ideal metrics than 4 to 6 poor metrics.

  • The prevalence of poor health behaviors and health factors and their awareness, treatment, and control are displayed in Table 2-3 separately for those with and without self-reported CVD.

—Americans with CVD are much more likely to be current or former smokers than Americans without CVD.

—Approximately 20% of US adults are current smokers or have quit recently (<12 months ago).

—As measured by self-reported data, Americans with CVD are very likely to have intermediate or poor levels of PA (74.1%), whereas Americans without CVD still commonly have such levels (58.4%). Furthermore, 64.5% of those with CVD and 47.3% of those without CVD report engaging in no moderate or vigorous activity at all.

—79% of US adults meet 0 or only 1 of the 5 healthy diet metrics.

—Two thirds of US adults are overweight, with little difference by prevalent CVD. Half of all US adults with CVD and one third without CVD are obese.

—Hypertension is present in 28.5% of US adults without CVD and 51.0% of US adults with CVD. Of these, nearly all with CVD are aware of their hypertension (98.6%) and are receiving treatment (97.4%), but a much smaller proportion of those without CVD are aware (70.6%) or receiving treatment (61.4%).

—Both presence of hypercholesterolemia (total cholesterol ≥240 mg/dL or receiving medication) and DM (fasting glucose ≥125 mg/dL or receiving medications) and awareness and treatment of these conditions are similarly higher among those with CVD than among those without CVD.

Cardiovascular Health: Trends Over Time

  • The trends over the last decade in each of the 7 cardiovascular health metrics (for diet, trends from 2005–2006 to 2007–2008) are shown in Chart 2-8 (for children 12–19 years of age) and Chart 2-9 (for adults ≥20 years of age).

—Among children, there appears to be a negative trend for meeting the body mass index (BMI) metric and positive trends for meeting the smoking and total cholesterol metrics. Other metrics do not show consistent trends.

—Among adults, there appears to be a positive trend for meeting the smoking metric and negative trends for meeting the BMI and glucose metrics. Trends for other metrics appear fairly flat.

  • Huffman et al2 made projections in cardiovascular health metrics to 2020 based on NHANES data from 1988–2008 (Chart 2-10). If current trends continue, estimated cardiovascular health will improve by 6%, short of the AHA’s goal of 20%. On the basis of current trends among individual metrics, anticipated declines in prevalence of smoking, high cholesterol, and high BP (in men) would be offset by substantial increases in the prevalence of obesity and DM and small expected changes in ideal dietary patterns or PA.2

Cardiovascular Disease

  • In 2009, the age-standardized death rate attributable to all CVDs was 237.1 per 100 000 (Chart 2-11), down 6% from 252.4 per 100 000 in 2007 (baseline data for the 2020 Impact Goals on CVD and stroke mortality).

—Death rates in 2009 attributable to stroke, HDs, and other cardiovascular causes were 38.9, 116.1, and 81.0 per 100 000, respectively.

  • Data from NHANES 2009–2010 reveal that overall, 7.2% of Americans self-reported having some type of CVD (Table 2-3), including 3.2% with coronary heart disease (CHD), 2.7% with stroke, and 2.0% with congestive heart failure (CHF) (some individuals reported more than 1 condition).

Relevance of Ideal Cardiovascular Health

Since the AHA announced its 2020 Impact Goals, several investigations have confirmed the importance of these metrics of cardiovascular health. Overall, these data demonstrate the relevance of the concept of cardiovascular health to risk of future risk factors, disease, and mortality, with strong inverse, stepwise association with all–cause, CVD, and ischemic HD mortality.

  • Bambs et al3 and Folsom et al4 both described the low prevalence (<1%) of ideal cardiovascular health, defined as being in the ideal category of all 7 AHA metrics in the Heart Strategies Concentrating on Risk Evaluation and ARIC cohorts, respectively.

  • In ARIC, a stepwise, inverse association was present between the number of ideal health metrics and incident CVD events (including CHD death, nonfatal MI, stroke, and HF) during 20 years of follow-up. For participants with 0, 1, 2, 3, 4, 5, 6, and 7 metrics at ideal levels, the age-, sex-, and race-adjusted rates of incident CVD incidence were 3.21, 2.19, 1.60, 1.20, 0.86, 0.64, 0.39, and 0 per 100 person-years, respectively.4

  • Importantly, both ideal health behaviors and ideal health factors were independently associated with lower CVD risk in a stepwise fashion (Chart 2-12). Thus, across any levels of health behaviors, health factors were still associated with incident CVD; and across any levels of health factors, health behaviors were still associated with incident CVD.

  • Dong et al5 demonstrated a similar strong graded, inverse relationship between the number of ideal cardiovascular health metrics and risk of CVD in white, black, and Hispanic participants of the Northern Manhattan Study (NOMAS) after 11 years of follow-up.

  • On the basis of data from NHANES (1988–2010), a similarly low prevalence of ideal cardiovascular health is present across the United States: 2.0% (95% confidence interval [CI], 1.5%–2.5%) in 1988–1994 and 1.2% (95% CI, 0.8%–1.9%) in 2005–2010.6

  • Furthermore, a stepwise association is present between the number of ideal cardiovascular health metrics and risk of all-cause mortality, CVD mortality, and ischemic HD mortality after 14.5 years of follow-up in the United States.6 The adjusted hazard ratios (HRs) for individuals with 6 or 7 ideal health metrics compared with individuals with 0 ideal health metrics were 0.49 (95% CI, 0.33–0.74) for all-cause mortality, 0.24 (95% CI, 0.13–0.47) for CVD mortality, and 0.30 (95% CI, 0.13–0.68) for ischemic HD mortality.6 Ford et al7 demonstrated similar relationships.

  • Adjusted population attributable fractions for CVD mortality were as follows6:

—40.6% (95% CI, 24.5%–54.6%) for HBP

—13.7% (95% CI, 4.8%–22.3%) for smoking

—13.2% (95% CI, 3.5%–29.2%) for poor diet

—11.9% (95% CI, 1.3%–22.3%) for insufficient PA

—8.8% (95% CI, 2.1%–15.4%) for abnormal glucose levels

  • Adjusted population attributable fractions for ischemic HD mortality were as follows6:

—34.7% (95% CI, 6.6%–57.7%) for HBP

—16.7% (95% CI, 6.4%–26.6%) for smoking

—20.6% (95% CI, 1.2%–38.6%) for poor diet

—7.8% (95% CI, 0%–22.2%) for insufficient PA

—7.5% (95% CI, 3.0%–14.7%) for abnormal glucose levels

  • Interestingly, based on NHANES 1999–2002, only modest intercorrelations are present between different cardiovascular health metrics. For example, these ranged from a correlation of −0.12 between PA and hemoglobin A1c (HbA1c) to a correlation of 0.29 between BMI and HbA1c. Thus, the 7 AHA cardiovascular health metrics appear interrelated, but only modestly, with substantial independent variation in each.7

  • The AHA cardiovascular health metrics predict future cardiometabolic risk when assessed in youth. In the Young Finns Study, a stepwise, inverse association was found between the number of ideal cardiovascular health metrics in adolescence (12–18 years of age) and risk of developing hypertension, dyslipidemia, or high carotid intima-media thickness (IMT) in adulthood after 21 years of follow-up (1986–2007).8 For every 1 additional ideal cardiovascular health metric present in adolescence, compared with either poor or intermediate levels, the age- and sex-adjusted odds ratios (ORs) for adult cardiometabolic outcomes were

—Hypertension: OR=0.66 (95% CI, 0.54–0.80)

—High low-density lipoprotein (LDL) cholesterol (>160 mg/dL): OR=0.66 (95% CI, 0.52–0.85)

—High-risk IMT (90th percentile or plaque present): OR=0.75 (95% CI, 0.60–0.94)

Achieving the 2020 Impact Goals

  • Taken together, these data continue to demonstrate both the tremendous relevance of the AHA 2020 Impact Goals for cardiovascular health and the substantial progress that will be needed to achieve these goals over the next decade.

  • A range of complementary strategies and approaches can lead to improvements in cardiovascular health. These include each of the following:

— Individual-focused approaches, which target lifestyle and treatments at the individual level (Table 2-4).

—Health-care systems approaches, which encourage, facilitate, and reward efforts by providers to improve health behaviors and health factors (Table 2-5).

—Population approaches, which target lifestyle and treatments in schools or workplaces, local communities, and states, as well as throughout the nation (Table 2-6).

  • Such approaches can focus on both (1) improving cardiovascular health among those who currently have less than optimal levels and (2) preserving cardiovascular health among those who currently have ideal levels (in particular, children, adolescents, and young adults) as they age.

  • The metrics with the greatest potential for improvement are health behaviors, including diet quality, PA, and body weight. However, each of the cardiovascular health metrics can be improved and deserves major focus, including smoking, which remains the leading cause of preventable death in the United States.

  • Continued emphasis is also needed on the treatment of acute CVD events and secondary prevention through treatment and control of health behaviors and risk factors.

  • For each cardiovascular health metric, modest shifts in the population distribution toward improved health would produce relatively large increases in the proportion of Americans in both ideal and intermediate categories. For example, on the basis of NHANES 2009–2010, the current prevalence of ideal levels of BP among US adults is 44.3%. To achieve the 2020 goals, a 20% relative improvement would require an increase in this proportion to 53.1% by 2020 (44.3% × 1.20). On the basis of NHANES data, a reduction in population mean BP of just 2 mm Hg would result in 56.1% of US adults having ideal levels of BP, which represents a 26.8% relative improvement in this metric (Table 2-7). Larger population reductions in BP would lead to even larger numbers of people with ideal levels. Such small reductions in population BP could result from small health behavior changes at a population level, such as increased PA, increased fruit and vegetable consumption, decreased sodium intake, decreased adiposity, or some combination of these and other lifestyle changes, with resulting substantial projected decreases in CVD rates in US adults.9

  • The AHA has a broad range of policy initiatives to improve cardiovascular health and meet the 2020 Strategic Impact Goals (Table 2-8). Future Statistical Updates will update these initiatives and track progress toward the 2020 Strategic Impact Goals.

Table 2-1. Definitions of Poor, Intermediate, and Ideal Cardiovascular Health for Each Metric in the AHA 2020 Goals

Level of Cardiovascular Health for Each Metric
PoorIntermediateIdeal
Current smoking
Adults ≥20 y of ageYesFormer ≤12 moNever or quit >12 mo
Children 12–19 y of ageTried during the prior 30 dNever tried; never smoked whole cigarette
BMI*
Adults ≥20 y of age≥30 kg/m225–29.9 kg/m2<25 kg/m2
Children 2–19 y of age>95th percentile85th-95th percentile<85th percentile
PA
Adults ≥20 y of ageNone1–149 min/wk moderate or1–74 min/wk vigorous or1–149 min/wk moderate + 2×vigorous≥150 min/wk moderate or ≥75 min/wk vigorous or ≥150 min/wk moderate + 2×vigorous
Children 12–19 y of ageNone>0 and <60 min of moderate or vigorous every day≥60 min of moderate or vigorous every day
Healthy diet pattern, No. of components†
Adults ≥20 y of age0–12–34–5
Children 5–19 y of age0–12–34–5
Total cholesterol, mg/dL
Adults ≥20 y of age≥240200–239 or treated to goal<200
Children 6–19 y of age≥200170–199<170
Blood pressure
Adults ≥20 y of ageSBP ≥140 mm Hg or DBP ≥90 mm HgSBP120–139 mm Hg or DBP 80–89 mm Hg or treated to goal<120 mm Hg/<80 mm Hg
Children 8–19 y of age>95th percentile90th–95th percentile or SBP ≥120 mm Hg or DBP ≥80 mm Hg<90th percentile
Fasting plasma glucose, mg/dL
Adults ≥20 y of age≥126100–125 or treated to goal<100
Children 12–19 y of age≥126100–125<100

AHA indicates American Heart Association; BMI, body mass index; PA, physical activity; SBP, systolic blood pressure; and DBP, diastolic blood pressure.

*Represents appropriate energy balance, ie, appropriate dietary quantity and PA to maintain normal body weight.

†In the context of a healthy dietary pattern that is consistent with a Dietary Approaches to Stop Hypertension [DASH]–type eating pattern, to consume ≥4.5 cups/d of fruits and vegetables, ≥2 servings/wk of fish, and ≥3 servings/d of whole grains and no more than 36 oz/wk of sugar-sweetened beverages and 1500 mg/d of sodium.

Table 2-2. Prevalence of US Population With Ideal Cardiovascular Health and With Components of Ideal Cardiovascular Health, Overall and in Selected Age Strata From NHANES 2007–2008 and 2009–2010

Prevalence, %
Ages 12–19 yAges ≥20 y*Ages 20–39 yAges 40–59 yAges ≥60 y
2007–2008 (baseline)
Ideal CV health profile (composite–all 7)0.00.00.00.00.0
≥6 Ideal CV health composite score8.23.67.12.10.1
≥5 Ideal CV health composite score39.815.829.79.72.9
Ideal health factors index (composite–all 4)35.513.927.77.31.0
Individual components
Total cholesterol <200 mg/dL (untreated)69.646.364.137.129.9
SBP <120 mm Hg and DBP <80 mm Hg (untreated)82.343.863.836.914.6
Not current smoker (never or quit ≥12 mo)83.772.966.472.986.1
Fasting blood glucose <100 mg/dL76.252.067.445.631.9
Ideal health behaviors index (composite–all 4)0.00.10.10.00.0
Individual components
PA at goal39.039.545.636.433.7
Not current smoker (never or quit ≥12 mo)83.772.966.472.986.1
BMI <25 kg/m262.531.939.128.025.3
4–5 Diet goals met†0.00.30.30.10.5
Fruits and vegetables ≥4.5 cups/d7.912.311.711.415.8
Fish ≥2 3.5-oz servings/wk (preferably oily fish)9.218.316.819.719.4
Sodium <1500 mg/d0.00.60.60.80.3
Sugar-sweetened beverages ≤450 kcal/wk32.051.941.054.671.2
Whole grains (1.1 g fiber/10 g carb) ≥3 1-oz equivalents/d3.27.37.07.18.4
Other dietary measures
Nuts, legumes, seeds ≥4 servings/wk8.721.719.622.524.7
Processed meats ≤2 servings/wk56.357.654.059.761.1
Saturated fat <7% of total energy intake (kcal)4.58.79.38.09.0
2009–2010
Ideal CV health profile (composite–all 6)‡17.44.48.12.60.6
≥5 Ideal CV health composite score47.217.229.810.75.8
Ideal health factors index (composite–all 4)48.415.929.79.22.4
Individual components
Total cholesterol <200 mg/dL (untreated)69.147.367.836.829.4
SBP <120 mm Hg and DBP <80 mm Hg (untreated)85.844.364.340.715.8
Not current smoker (never or quit ≥12 mo)85.276.269.575.588.3
Fasting blood glucose <100 mg/dL88.257.473.554.035.7
Ideal health behaviors index (composite–all 3)‡42.117.821.415.814.9
Individual components
PA at goal36.141.146.540.833.3
Not current smoker (never or quit ≥12 mo)85.276.269.575.588.3
BMI <25 kg/m264.231.338.627.525.4

NHANES indicates National Health and Nutrition Examination Survey; CV, cardiovascular; SBP, systolic blood pressure; DBP, diastolic blood pressure; PA, physical activity; and BMI, body mass index.

*Standardized to the age distribution of the 2000 US Standard population.

†Scaled for 2000 kcal/d and in the context of intake with appropriate energy balance and a DASH (Dietary Approaches to Stop Hypertension)–like eating plan.

‡Dietary data for the 2009–2010 cycle of NHANES were not available at the time of this analysis.

Table 2-3. Selected Secondary Metrics for Monitoring CVD, NHANES 2009–2010*

In the Presence of CVDIn the Absence of CVD
N†% (SE)‡N†% (SE)‡
Total16 209 4747.2 (0.4)199 590 59692.8 (0.4)
CHD6 916 0123.2 (0.3)
Stroke5 717 7592.7 (0.2)
CHF4 320 2272.0 (0.3)
Acute MI6 929 9053.2 (0.3)
Health behaviors
Smoking
Current smoker or smokers who quit <12 mo ago3 127 27337.2 (4.9)40 760 06620.1 (0.9)
PA
PA: intermediate or poor§11 813 01174.1 (5.1)115 561 98858.4 (1.5)
PA: none10 598 90864.5 (5.5)93 459 55647.3 (1.2)
Diet, no. of metrics*
Total diet score 0–3 of 512 665 860100.00 (0.00)161 370 15499.71 (0.11)
Total diet score 0–1 of 59 540 53270.06 (4.69)127 156 29378.84 (1.42)
Overweight/obesity
Overweight or obese (BMI ≥25.0 kg/m2)12 621 70169.4 (4.1)134 879 71368.1 (1.3)
Obese (BMI ≥30.0 kg/m2)7 763 61149.0 (5.2)68 655 70234.7 (1.0)
Health factors
Hypertension
Prevalence of BP ≥140/90 mm Hg or taking medications10 591 17051.0 (5.0)53 523 89528.5 (0.9)
Awareness among those with hypertension10 071 34398.6 (0.3)42 436 78270.6 (3.2)
Treatment those with hypertension9 819 24497.4 (0.4)39 194 94861.4 (2.9)
BP control to <140/<90 mm Hg among treated6 886 17664.2 (9.5)27 323 64972.4 (2.4)
BP control to <140/<90 mm Hg among hypertensive6 886 17662.3 (9.4)27 323 64943.3 (2.8)
Hypercholesterolemia
Prevalence of total cholesterol ≥240 mg/dL or taking medications8 201 82937.1 (4.2)48 701 19825.7 (0.7)
Awareness among those with hypercholesterolemia7 742 12784.6 (8.0)35 174 93159.9 (2.6)
Treatment among those with hypercholesterolemia7 219 07879.3 (8.5)25 405 33438.7 (2.4)
Cholesterol control to <200 mg/dL among treated6 659 73295.0 (1.4)22 804 72490.4 (1.2)
Cholesterol control to <200 mg/dL among hypercholesterolemia6 659 73275.0 (8.9)22 804 72434.7 (2.4)
Diabetes mellitus
Prevalence of fasting glucose ≥125 mg/dL or taking medications4 769 75915.2 (2.2)21 078 44310.3 (1.1)
Awareness among diabetics4 006 15390.4 (2.3)14 242 76064.3 (4.6)
Treatment among diabetics3 935 44687.1 (3.2)13 391 29158.4 (5.3)
Blood glucose control among treated1 527 15132.6 (9.9)5 878 67645.0 (8.0)
Blood glucose control among diabetics1 527 15127.2 (8.7)5 878 67625.5 (5.9)

CVD indicates cardiovascular disease; NHANES, National Health and Nutrition Examination Survey; SE, standard error; CHD, coronary heart disease; CHF, congestive heart failure; MI, myocardial infarction; PA, physical activity; BMI, body mass index; and BP, blood pressure.

*Dietary data are based on 2007–2008, the most recent data available at the time of this analysis.

†Weighted sample size.

‡Standardized to the age distribution of the 2000 US Standard population.

§Moderate <150 min/wk AND Vigorous <75 min/wk AND Combined <150 min/wk.

Table 2-4. Evidence-Based Individual Approaches for Improving Health Behaviors and Health Factors in the Clinic Setting

• Set specific goals (Class IA). Set specific, proximal goals with the patient, including a personalized plan to achieve the goals (eg, over the next 3 mo, increase fish by 1 serving/wk, reduce smoking by half a pack per day, or walk 30 min 3 times per week).• Establish self-monitoring (Class IA). Develop a strategy for self-monitoring, such as a dietary or physical activity diary or Web-based or mobile applications.• Schedule follow-up (Class IA). Schedule regular follow-up (in-person, telephone, written, and/or electronic), with clear frequency and duration of contacts, to assess success, reinforce progress, and set new goals as necessary.• Provide feedback (Class IA). Provide feedback on progress toward goals, including using in person, telephone, and/or electronic feedback.• Increase self-efficacy (Class IA). Increase the patient’s perception that they can successfully change their behavior.*• Use motivational interviewing† (Class IA). Use motivational interviewing when patients are resistant or ambivalent about behavior change.• Provide long-term support (Class IB). Arrange long-term support from family, friends, or peers for behavior change, such as in other workplace, school, or community-based programs.• Use a multicomponent approach (Class IA). Combine 2 or more of the above strategies into the behavior change efforts.

*Examples of approaches include mastery experiences (set a reasonable, proximal goal that the person can successfully achieve); vicarious experiences (have the person see someone with similar capabilities performing the behavior, such as walking on a treadmill or preparing a healthy meal); physiological feedback (explain to the patient when a change in their symptoms is related to worse or improved behaviors); and verbal persuasion (persuade the person that you believe in their capability to perform the behavior).

†Motivational interviewing represents use of individual counseling to explore and resolve ambivalence toward changing behavior. Major principles include fostering the person’s own awareness and resolution of their ambivalence, as well as their own self-motivation to change, in a partnership with the counselor or provider.

Table adapted from Artinian et al.10

Table 2-5. Evidence-Based Healthcare Systems Approaches to Support and Facilitate Improvements in Health Behaviors and Health Factors11-15

• Electronic systems for scheduling and tracking initial visits and regular follow-up contacts for behavior change and treatments.• Electronic medical records systems to help assess, track, and report on specific health behaviors (diet, PA, tobacco, body weight) and health factors (BP, cholesterol, glucose), as well as to provide feedback and the latest guidelines to providers.• Practical paper or electronic toolkits for assessment of key health behaviors and health factors, including during, before, and after provider visits.• Electronic systems to facilitate provision of feedback to patients on their progress during behavior change and other treatment efforts.• Education and ongoing training for providers on evidence-based behavior change strategies, as well as the most relevant behavioral targets, including training on relevant ethnic and cultural issues.• Integrated systems to provide coordinated care by multidisciplinary teams of providers, including physicians, nurse practitioners, dieticians, PA specialists, and social workers.• Reimbursement guidelines and incentives that reward efforts to change health behaviors and health factors.Restructuring of practice goals and quality benchmarks to incorporate health behavior (diet, PA, tobacco, body weight) and health factor (BP, cholesterol, glucose) interventions and targets for both primary and secondary prevention.

BP indicates blood pressure; PA, physical activity.

Table 2-6. Summary of Evidence-Based Population Approaches for Improving Diet, Increasing Physical Activity, and ReducingTobacco Use*

Diet Media and educationSustained, focused media and educational campaigns, using multiple modes, for increasing consumption of specific healthful foods or reducing consumption of specific less healthful foods or beverages, either alone (IIa B) or as part of multicomponent strategies (I B)†‡§On-site supermarket and grocery store educational programs to support the purchase of healthier foods (IIa B)† Labeling and informationMandated nutrition facts panels or front-of-pack labels/icons as a means to influence industry behavior and product formulations (IIa B)† Economic incentivesSubsidy strategies to lower prices of more healthful foods and beverages (I A)†Tax strategies to increase prices of less healthful foods and beverages (IIa B)†Changes in both agricultural subsidies and other related policies to create an infrastructure that facilitates production, transportation, and marketing of healthier foods, sustained over several decades (IIa B)† SchoolsMulticomponent interventions focused on improving both diet and physical activity, including specialized educational curricula, trained teachers, supportive school policies, a formal PE program, healthy food and beverage options, and a parental/family component (I A)†School garden programs, including nutrition and gardening education and hands-on gardening experiences (IIa A)† Fresh fruit and vegetable programs that provide free fruits and vegetables to students during the school day (IIa A)† WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa A)†Increased availability of healthier food/beverage options and/or strong nutrition standards for foods and beverages served, in combination with vending machine prompts, labels, or icons to make healthier choices (IIa B)† Local environmentIncreased availability of supermarkets near homes (IIa B)†‡ Restrictions and mandatesRestrictions on television advertisements for less healthful foods or beverages advertised to children (I B)†Restrictions on advertising and marketing of less healthful foods or beverages near schools and public places frequented by youths (IIa B)†General nutrition standards for foods and beverages marketed and advertised to children in any fashion, including on-package promotion (IIa B)†Regulatory policies to reduce specific nutrients in foods (eg, trans fats, salt, certain fats) (I B)†§Physical activity Labeling and informationPoint-of-decision prompts to encourage use of stairs (IIa A)† Economic incentivesIncreased gasoline taxes to increase active transport/commuting (IIa B)† SchoolsMulticomponent interventions focused on improving both diet and physical activity, including specialized educational curricula, trained teachers, supportive school policies, a formal PE program, serving of healthy food and beverage options, and a parental/family component (IIa A)†Increased availability and types of school playground spaces and equipment (I B)†Increased number of PE classes, revised PE curricula to increase time in at least moderate activity, and trained PE teachers at schools (IIa A/IIb A∥)†Regular classroom physical activity breaks during academic lessons (IIa A)†§ WorkplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa A)†Structured worksite programs that encourage activity and also provide a set time for physical activity during work hours (IIa B)†Improving stairway access and appeal, potentially in combination with “skip-stop” elevators that skip some floors (IIa B)†Adding new or updating worksite fitness centers (IIa B)† Local environmentImproved accessibility of recreation and exercise spaces and facilities (eg, building of parks and playgrounds, increasing operating hours, use of school facilities during nonschool hours) (IIa B)†Improved land-use design (eg, integration and interrelationships of residential, school, work, retail, and public spaces) (IIa B)†Improved sidewalk and street design to increase active commuting (walking or bicycling) to school by children (IIa B)†Improved traffic safety (IIa B)†Improved neighborhood aesthetics (to increase activity in adults) (IIa B)†Improved walkability, a composite indicator that incorporates aspects of land-use mix, street connectivity, pedestrian infrastructure, aesthetics, traffic safety, and/or crime safety (IIa B)†Smoking Media and educationSustained, focused media and educational campaigns to reduce smoking, either alone (IIa B) or as part of larger multicomponent population-level strategies (I A)† Labeling and informationCigarette package warnings, especially those that are graphic and health related (I B)†‡§ Economic incentivesHigher taxes on tobacco products to reduce use and fund tobacco control programs (I A)†‡§ Schools and workplacesComprehensive worksite wellness programs with nutrition, physical activity, and tobacco cessation/prevention components (IIa A)† Local environmentReduced density of retail tobacco outlets around homes and schools (I B)†Development of community telephone lines for cessation counseling and support services (I A)† Restrictions and mandatesCommunity (city, state, or federal) restrictions on smoking in public places (I A)†Local workplace-specific restrictions on smoking (I A)†‡§Stronger enforcement of local school-specific restrictions on smoking (IIa B)†Local residence-specific restrictions on smoking (IIa B)†§Partial or complete restrictions on advertising and promotion of tobacco products (I B)†

PE indicates physical education.

*The specific population interventions listed here are either a Class I or IIa recommendation with an evidence grade of either A or B. The American Heart Association evidence grading system for class of recommendation and level of evidence is summarized in Table 2. Because implementation of population-level strategies does not require perfect evidence but rather consideration of risks versus benefits, associated costs, and alternate approaches, the absence of any specific strategy herein does not mean it should not also be considered for implementation. See the more detailed tables and text below for further information on the evidence for each of these interventions, as well as other strategies that were reviewed.

†At least some evidence from studies conducted in high-income Western regions and countries (eg, North America, Europe, Australia, New Zealand).

‡At least some evidence from studies conducted in high-income non-Western regions and countries (eg, Japan, Hong Kong, South Korea, Singapore).

§At least some evidence from studies conducted in low- or middle-income regions and countries (eg, Africa, China, Pakistan, India).

‖Based on cross-sectional studies only; only 2 longitudinal studies have been performed, with no significant relations seen.

∥Evidence IIa A for improving physical activity; evidence IIb B for reducing adiposity.

Reprinted from Mozaffarian et al11 with permission. © 2012 American Heart Association, Inc.

Table 2-7. Reduction in BP Required to Increase Prevalence of Ideal BP Among Adults ≥20 y of Age; NHANES 2009–2010

%
Percent BP ideal among adults, 2009–201044.26
20% Relative increase53.11
Percent of US adults whose BP would be ideal if population mean BP were lowered by*
2 mm Hg56.13
3 mm Hg59.49
4 mm Hg61.59
5 mm Hg65.31

Standardized to the age distribution of the 2000 US standard population.

BP indicates blood pressure; NHANES, National Health and Nutrition Examination Survey.

*Reduction in BP=(observed average systolic−X mm Hg) AND (observed average diastolic−X mm Hg).

Table 2-8. AHA Advocacy and Policy Strategies Related to the 2020 Impact Goals for Ideal Cardiovascular Health

Measure of Cardiovascular HealthAdvocacy/Policy Solutions
Smoking StatusIdeal for cardiovascular health:Adults: Never smoked or quit more than a year agoChildren: Never tried or never smoked a whole cigaretteFederal• Support the full, authorized funding level for the FDA’s Center for Tobacco Products and advocate for comprehensive implementation of FDA regulation of tobacco.• Implement clinical guidance and monitor health claims concerning smokeless tobacco and other “harm reduction” products.• Support the Tobacco Tax Equity Act that closes tax loopholes to ensure that all tobacco products are taxed at levels similar to the current tax rate for cigarettes.• Continue to advocate for ratification of WHO’s Framework Convention of Tobacco Control as part of the UN Political Declaration on Non-Communicable Diseases for implementation by all countries who are a party to the treaty.
State• Establish, strengthen, and protect smoke-free air laws in compliance with the Fundamentals of Smokefree Workplace Laws guidelines.• Support tobacco-free secondary school, college, university, and hospital campuses.• Support significant increases in tobacco excise taxes on all tobacco products.• Establish and protect sustainable funding for tobacco prevention and cessation programs to levels that meet or exceed the CDC recommendations.• Provide comprehensive tobacco cessation benefits in Medicaid, Medicare, and private health insurance plans.• Eliminate tobacco sales in pharmacies and other health-related institutions.
Physical ActivityIdeal for cardiovascular health:Adults: At least 150 min of moderate or 75 min of vigorous PA each weekChildren: >60 min of moderate-vigorous PA per dayFederal• Preserve funding for Safe Routes to School and Complete Streets in Transportation Reauthorization.• Include PA in nutrition education funding for the Farm Bill Supplemental Nutrition Assistance Program.• Incorporate PA into electronic medical records.• Support implementation of the National Physical Activity Plan.• Increase the quality of physical education in schools and advocate for Physical Education for Progress grants to increase funding to schools to improve their PE programs.• Advocate for regular revision and update of the Physical Activity Guidelines for Americans.
State• Implement shared use of school facilities within the community and support the construction of school fitness facilities.• Increase sports, recreational opportunities, parks, and green spaces in the community.• Support efforts to design workplaces, communities, and schools around active living and integrate PA opportunities throughout the day.• Provide safe routes to schools and school sites that offer walking/biking options for more students.• Support the creation of complete streets.• Support the use of zoning policy to increase access to safe places for recreation.• Create and maintain comprehensive worksite wellness programs.• Support the creation and implementation, through legislation and regulation (including licensing), of PA standards for preschool, day care, and other out-of-school care programs.• Require quality, more frequent physical education in schools.• Promote efforts within the school environment that will lead to increased PA.
BMIIdeal for cardiovascular health:Adults: between 18.5 and 25 kg/m2Children: between the 15th and 85th percentileGo to www.americanheart.org/obesitypolicy for additional policy resourcesFederal• Provide obesity counseling and treatment coverage in the healthcare environment.• Provide robust surveillance and monitoring of obesity, diet, PA, and tobacco use.
State• Provide robust coverage for guidelines-based prevention, diagnosis, and treatment of overweight and obesity in the healthcare environment.• Implement and monitor strong local wellness policies in all schools.• Ensure adequate funding and implementation of coordinated school health programs.• Establish comprehensive obesity prevention strategies in early childhood and day care programs.• Advocate for continued funding for obesity prevention research and work to ensure a strong evaluation component is a part of implementation of new laws and programs.
Healthy DietIdeal for cardiovascular health: In the context of a DASH-type dietary pattern, adults and children should achieve at least 4 of the 5 following key components of a healthy diet:• Fruits and vegetables: >4.5 cups/d• Fish: More than two 3.5-oz servings/wk (preferably oily fish)• Fiber-rich whole grains (>1.1 g of fiber per 10 g of carbohydrates): three 1-oz-equivalent servings per dayFederal• Work to eliminate food deserts and improve access and affordability of healthy foods.• Strengthen nutrition standards in schools for meals and competitive foods and in all government nutrition assistance or feeding programs.• Improve food labeling to make the labels easier to read and convey more accurately the content of added sugars, trans fats, sodium, and whole grains in foods.• Implement menu labeling in restaurants.• Continue to support and monitor the removal of industrially produced trans fats from the food supply and ensure the use of healthy replacement oils.• Restrict the marketing and advertising of unhealthy food to children.• Support robust implementation of nutrition education and promotion in schools.• Reduce added sugar and sodium in the food supply.• Support the implementation and dissemination of procurement standards across federal agencies.• Ensure that diet counseling is a covered benefit in Medicare.
• Sodium: <1500 mg/d• Sugar-sweetened beverages: <450 kcal (36 oz) per weekGo to www.americanheart.org/obesitypolicy for more specific policy resourcesState• Support the implementation of the reauthorization of the Federal Childhood Nutrition Act and new regulations concerning competitive foods and beverages and use all available techniques, including legislation, to encourage schools to take advantage of opportunities to provide even healthier options for children.• Support improvements in the school food environment just outside of school property, including corner stores and food trucks• Support the creation and implementation of nutrition standards, through legislation and regulation (including licensing), for preschool and day care and other out-of-school care program meals.• Support opportunities for greater nutrition education in schools. Support opportunities to expand the availability of fruits, vegetables, and water, including policies that support expansion of school gardens and farm-to-school programs.• Support strategies that reduce sodium in the food supply.• Reduce trans fats in packaged foods, baked goods, restaurant meals, and school meal programs.• Support the elimination of food deserts through policies such as Healthy Food Financing that increase the availability of fruits, vegetables, and water in underserved neighborhoods.• Support the establishment of food procurement policies that meet the AHA or federal guidelines for government offices.• Support policies identified to reduce children’s exposure to marketing and advertising for unhealthy food.• Support policies that change relative prices of healthy versus unhealthy food items.• Support on a pilot basis the taxation of sugar-sweetened beverages to assess impact on health and consumer behavior, including 6 minimum criteria (at least a portion of the money is dedicated for HD and stroke prevention and/or obesity prevention, the tax is structured so as to result in an increase in price for sugar-sweetened beverages, tax is at least 1 cent/oz, there is money dedicated for evaluation with guidance that ensures rigorous evaluation including health outcomes, there is a standard definition of “sugar-sweetened beverage,” and there is no sunset).• Support policies designed to encourage retailers to increase access to healthy foods while decreasing access to unhealthy foods.• Expand state participation in the Department of Defense Fresh Fruit and Vegetable program.
Total CholesterolIdeal for cardiovascular health:Adults: Total cholesterol <200 mg/dLChildren: <170 mg/dLFederal and State• Partner with Department of Health and Human Services to promote the Million Hearts Campaign through increased public awareness and partnership engagement, science and evaluation, clinical care improvement, patient outreach, and public policy.• Ensure adequate healthcare coverage for prevention and treatment of dyslipidemia.• Secure and protect dedicated state appropriations aligned with HD and stroke priorities and work to support appropriate program implementation. Support other public health initiatives and evaluation targeted at HD, stroke, and related risk factors, as well as the disparities that exist in these areas.
Blood PressureIdeal for cardiovascular health:Adults: <120/80 mm HgChildren: <90th percentileFederal• Partner with the Department of Health and Human Services to promote the Million Hearts Campaign, as above.• Implement the Institute of Medicine’s recommendations to reduce sodium in the food supply.• Improve food labeling to increase consumer understanding of sodium levels in packaged foods.• Advocate for robust sodium limits in procurement standards, nutrition standards in schools, and other government feeding programs.
State• Promote public funding for Heart Disease and Stroke Prevention Programs.• Ensure the availability of essential CVD preventive benefits in private insurance and public health programs.
Fasting Plasma GlucoseIdeal for cardiovascular health:Children and Adults: Fasting blood glucose <100 mg/dLFederal and State• Ensure adequate healthcare coverage for early treatment and prevention of diabetes mellitus.

For AHA advocacy resources, including fact sheets, policy briefs, published papers, and position statements, go to http://www.heart.org/HEARTORG/Advocate/PolicyResources/Policy-Resources_UCM_001135_SubHomePage.jsp.

AHA indicates American Heart Association; FDA, Food and Drug Administration; WHO, World Health Organization; UN, United Nations; CDC, Centers for Disease Control and Prevention; PA, physical activity; PE, physical education; DASH, Dietary Approaches to Stop Hypertension; HD, heart disease; and CVD, cardiovascular disease.

Chart 2-1.

Chart 2-1. Prevalence (unadjusted) estimates for poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, US children aged 12 to 19 years, National Health and Nutrition Examination Survey (NHANES) 2009-2010* (available data as of June 1, 2012). *Healthy Diet Score reflects 2007-2008 NHANES data.

Chart 2-2.

Chart 2-2. Age-standardized prevalence estimates for poor, intermediate, and ideal cardiovascular health for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US adults aged ≥20 years, National Health and Nutrition Examination Survey (NHANES) 2009-2010* (available data as of June 1, 2012). *Healthy Diet Score reflects 2007-2008 NHANES data

Chart 2-3.

Chart 2-3. Proportion (unadjusted) of US children aged 12 to 19 years meeting different numbers of criteria for Ideal Cardiovascular Health, overall and by sex, National Health and Nutrition Examination Survey (NHANES) 2007-2008 (available data as of June 1, 2012).

Chart 2-4.

Chart 2-4. Age-standardized prevalence estimates of US adults aged ≥20 years meeting different numbers of criteria for Ideal Cardiovascular Health, overall and by age and sex subgroups, National Health and Nutrition Examination Survey (NHANES) 2007-2008 (available data as of June 1, 2012).

Chart 2-5.

Chart 2-5. Age-standardized prevalence estimates of US adults aged ≥20 years meeting different numbers of criteria for Ideal Cardiovascular Health, overall and in selected race subgroups from National Health and Nutrition Examination Survey (NHANES) 2007-2008 (available data as of June 1, 2012).

Chart 2-6.

Chart 2-6. Prevalence estimates of meeting at least 5 criteria for Ideal Cardiovascular Health, US adults aged ≥20 years (age-standardized), overall and by sex and race, and US children aged 12 to 19 years (unadjusted), by sex, National Health and Nutrition Examination Survey (NHANES) 2007-2008 (available data as of June 1, 2012).

Chart 2-7.

Chart 2-7. Age-standardized prevalence estimates of US adults meeting different numbers of criteria for Ideal and Poor Cardiovascular Health, for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US adults aged ≥20 years, National Health and Nutrition Examination Survey (NHANES) 2007-2008 (available data as of June 1, 2012).

Chart 2-8.

Chart 2-8. Trends in prevalence (unadjusted) of meeting criteria for Ideal Cardiovascular Health, for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US children aged 12 to 19 years, National Health and Nutrition Examination Survey (NHANES) 1999-2000 through 2009-2010*† (available data as of June 1, 2012). *Due to changes in the physical activity questionnaire between different cycles of the NHANES survey, trends over time for this indicator should be interpreted with caution and statistical comparisons should not be attempted. †Data for the Healthy Diet Score, based on a 2-day average intake, was only available for the 2003-2004, 2005-2006, and 2007-2008 NHANES cycles at the time of this analysis.

Chart 2-9.

Chart 2-9. Age-standardized trends in prevalence of meeting criteria for Ideal Cardiovascular Health, for each of the 7 metrics of cardiovascular health in the American Heart Association 2020 goals, among US adults aged ≥20 years, National Health and Nutrition Examination Survey (NHANES) 1999-2000 through 2009-2010 (available data as of June 1, 2012). *Due to changes in the physical activity questionnaire between different cycles of the NHANES survey, trends over time for this indicator should be interpreted with caution and statistical comparisons should not be attempted. †Data for the Healthy Diet Score, based on a 2-day average intake, was only available for the 2003-2004, 2005-2006, and 2007-2008 NHANES cycles at the time of this analysis.

Chart 2-10.

Chart 2-10. Prevalence of ideal, intermediate, and poor CV health metrics in 2006 (AHA 2020 Impact Goals baseline year) and 2020 projections assuming current trends continue. 2020 targets for each CV health metric, assuming a 20% relative increase in ideal CV health prevalence metrics and a 20% relative decrease in poor CV health prevalence metrics for men and women. Reprinted from Huffman et al2 with permission. Copyright © 2012, American Heart Association.

Chart 2-11.

Chart 2-11. US age-standardized death rates* from cardiovascular diseases, 2000-2009. *Directly standardized to the age distribution of the 2000 US standard population. †Total CVD (Cardiovascular Disease): ICD-10 I00-I99, Q20-Q28. §Stroke (All cerebrovascular disease): ICD-10 I60-I69. ∥CHD (Coronary Heart Disease): ICD-10 I20-I25. **Other CVD: ICD-10 I00 –I15, I26 –I51, I70 –I78, I80 –I89, I95–I99. Source: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2009 on CDC WONDER Online Database, released 2012. Data for year 2009 are compiled from the Multiple Cause of Death File 2009, Series 20 No. 2O, 2012, data for year 2008 are compiled from the Multiple Cause of Death File 2008, Series 20 No. 2N, 2011, data for year 2007 are compiled from the Multiple Cause of Death File 2007, Series 20 No. 2M, 2010, data for years 2005-2006 data are compiled from Multiple Cause of Death File 2005-2006, Series 20, No. 2L, 2009, and data for years 1999-2004 are compiled from the Multiple Cause of Death File 1999-2004, Series 20, No. 2J, 2007. http://wonder.cdc.gov/mcd-icd10.html. Accessed October 9, 2012.

Chart 2-12.

Chart 2-12. Incidence of cardiovascular disease according to the number of ideal health behaviors and health factors. Reprinted with permission from Folsom et al.4

Abbreviations Used in Chapter 2

AHA

American Heart Association

ARIC

Atherosclerosis Risk in Communities Study

BMI

body mass index

BP

blood pressure

CDC

Centers for Disease Control and Prevention

CHD

coronary heart disease

CHF

congestive heart failure

CI

confidence interval

CVD

cardiovascular disease

DASH

Dietary Approaches to Stop Hypertension

DBP

diastolic blood pressure

DM

diabetes mellitus

FDA

Food and Drug Administration

HbA1c

hemoglobin A1c

HBP

high blood pressure

HD

heart disease

HF

heart failure

HR

hazard ratio

IMT

intima-media thickness

LDL

low-density lipoprotein

MI

myocardial infarction

NHANES

National Health and Nutrition Examination Survey

NOMAS

Northern Manhattan Study

OR

odds ratio

PA

physical activity

PE

physical education

SBP

systolic blood pressure

SE

standard error

UN

United Nations

WHO

World Health Organization

References

References

  • 1. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 127:e14–e31.Google Scholar
  • 2. Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM. Cardiovascular health behavior and health factor changes (1988-2008) and projections to 2020: results from the National Health and Nutrition Examination Surveys.Circulation. 2012; 125:2595–2602.LinkGoogle Scholar
  • 3. Bambs C, Kip KE, Dinga A, Mulukutla SR, Aiyer AN, Reis SE. Low prevalence of “ideal cardiovascular health” in a community-based population: the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) study.Circulation. 2011; 123:850–857.LinkGoogle Scholar
  • 4. Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD; ARIC Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence.J Am Coll Cardiol. 2011; 57:1690–1696.CrossrefMedlineGoogle Scholar
  • 5. Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and Hispanics: the Northern Manhattan Study.Circulation. 2012; 125:2975–2984.LinkGoogle Scholar
  • 6. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults.JAMA. 2012; 307:1273–1283.CrossrefMedlineGoogle Scholar
  • 7. Ford ES, Greenlund KJ, Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States.Circulation. 2012; 125:987–995.LinkGoogle Scholar
  • 8. Laitinen TT, Pahkala K, Magnussen CG, Viikari JS, Oikonen M, Taittonen L, Mikkilä V, Jokinen E, Hutri-Kähönen N, Laitinen T, Kähönen M, Lehtimäki T, Raitakari OT, Juonala M. Ideal cardiovascular health in childhood and cardiometabolic outcomes in adulthood: the Cardiovascular Risk in Young Finns Study.Circulation. 2012; 125:1971–1978.LinkGoogle Scholar
  • 9. Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease.N Engl J Med. 2010; 362:590–599.CrossrefMedlineGoogle Scholar
  • 10. Artinian NT, Fletcher GF, Mozaffarian D, Kris-Etherton P, Van Horn L, Lichtenstein AH, Kumanyika S, Kraus WE, Fleg JL, Redeker NS, Meininger JC, Banks J, Stuart-Shor EM, Fletcher BJ, Miller TD, Hughes S, Braun LT, Kopin LA, Berra K, Hayman LL, Ewing LJ, Ades PA, Durstine JL, Houston-Miller N, Burke LE; on behalf of the American Heart Association Prevention Committee of the Council on Cardiovascular Nursing. Interventions to promote physical activity and dietary lifestyle changes for cardiovascular risk factor reduction in adults: a scientific statement from the American Heart Association.Circulation. 2010; 122:406–441.LinkGoogle Scholar
  • 11. Mozaffarian D, Afshin A, Benowitz NL, Bittner V, Daniels SR, Franch HA, Jacobs DR, Kraus WE, Kris-Etherton PM, Krummel DA, Popkin BM, Whitsel LP, Zakai NA; on behalf of the American Heart Association Council on Epidemiology and Prevention, Council on Nutrition, Physical Activity and Metabolism, Council on Clinical Cardiology, Council on Cardiovascular Disease in the Young, Council on the Kidney in Cardiovascular Disease, Council on Peripheral Vascular Disease, and the Advocacy Coordinating Committee. Population approaches to improve diet, physical activity, and smoking habits: a scientific statement from the American Heart Association. Circulation.2012; 126:1514–1563.Google Scholar
  • 12. Bodenheimer T. Helping patients improve their health-related behaviors: what system changes do we need?Dis Manag. 2005; 8:319–330.CrossrefMedlineGoogle Scholar
  • 13. Simpson LA, Cooper J. Paying for obesity: a changing landscape.Pediatrics. 2009; 123(suppl 5):S301–S307.CrossrefMedlineGoogle Scholar
  • 14. Quist-Paulsen P. Cessation in the use of tobacco: pharmacologic and non-pharmacologic routines in patients.Clin Respir J. 2008; 2:4–10.CrossrefMedlineGoogle Scholar
  • 15. Davis D, Galbraith R; American College of Chest Physicians Health and Science Policy Committee. Continuing medical education effect on practice performance: effectiveness of continuing medical education: American College of Chest Physicians Evidence-Based Educational Guidelines.Chest. 2009; 135(s1):42S–48S.MedlineGoogle Scholar

3. Smoking/Tobacco Use

See Table 3-1 and Charts 3-1 and 3-2.

Prevalence

Youth

(See Chart 3-1.)

  • In 2011, in grades 9 through 12:

—18.1% of students reported current cigarette use (on at least 1 day during the 30 days before the survey), 13.1% of students reported current cigar use, and 7.7% of students reported current smokeless tobacco use. Overall, 23.4% of students reported any current tobacco use (Youth Risk Behavior Survey [YRBS]; Chart 3-1).1

—Male students were more likely than female students to report current cigarette use (19.9% compared with 16.1%). Male students were also more likely than female students to report current cigar use (17.8% compared with 8.0%) and current smokeless tobacco use (12.8% compared with 2.2%; YRBS).1

—Non-Hispanic white students were more likely than Hispanic or non-Hispanic black students to report any current tobacco use, which includes cigarettes, cigars, or smokeless tobacco (26.5% compared with 20.5% for Hispanic students and 15.4% for non-Hispanic black students; YRBS).1

  • Among youths 12 to 17 years of age in 2010, 2.6 million (10.7%) used a tobacco product (cigarettes, cigars, or smokeless tobacco) in the past month, and 2.0 million (8.3%) used cigarettes. Cigarette use in the past month in this age group declined significantly from 13.0% in 2002 to 8.3% in 2010 (National Survey on Drug Use and Health [NSDUH]).2

  • Data from the YRBS3 for students in grades 9 to 12 indicated the following:

—The percentage of students who reported ever trying cigarettes remained stable from 1991 to 1999 and then declined from 70.4% in 1999 to 44.7% in 2011.

—The percentage who reported current cigarette use (on at least 1 day in the 30 days before the survey) increased between 1991 and 1997 and then declined from 36.4% in 1997 to 18.1% in 2011.

—The percentage who reported current frequent cigarette use (smoked on ≥20 of the 30 days before the survey) increased from 1991 to 1999 and then declined from 16.8% in 1999 to 6.4% in 2011.

  • In 2011, 49.9% of students in grades 9 to 12 who currently smoked cigarettes had tried to quit smoking cigarettes during the previous 12 months. The prevalence of this behavior was higher among female student smokers (53.9%) than among male student smokers (47.0%) and among white females (54.0%) and Hispanic females (55.9%) than among white males (46.3%) and Hispanic males (44.7%; YRBS).1

Adults

(See Table 3-1 and Chart 3-2.)

  • In 2011, among adults ≥18 years of age:

— 21.3% of men and 16.7% of women were current cigarette smokers (NHIS).5

— The percentage of current cigarette smokers (19.0%) declined 21% since 1998 (24.1%).5–7

— The states with the highest percentage of current cigarette smokers were Kentucky (29.0%), West Virginia (28.6%), and Arkansas (27.0%). Utah has the lowest percentage of smokers (11.8%) (BRFSS).4

  • In 2010, an estimated 69.6 million Americans ≥12 years of age were current (past month) users of a tobacco product (cigarettes, cigars, smokeless tobacco, or tobacco in pipes). The rate of current use of any tobacco product in this age range declined from 2007 to 2010 (from 28.6% to 27.4%; NSDUH).2

  • From 1998 to 2007, cigarette smoking prevalence among adults ≥18 years of age decreased in 44 states and the District of Columbia. Six states had no substantial changes in prevalence after controlling for age, sex, and race/ethnicity (BRFSS).8

  • In 2008 to 2010, among people ≥65 years of age, 9.4% of men and 9.1% of women were current smokers. In this age group, men were more likely than women to be former smokers (54.0% compared with 30.0%) on the basis of age-adjusted estimates (NHIS).9

  • In 2008 to 2010, among adults ≥18 years of age, Asian men (15.2%) and Hispanic men (17.3%) were less likely to be current cigarette smokers than non-Hispanic black men (23.7%), non-Hispanic white men (23.9%), and American Indian or Alaska Native men (24.6%) on the basis of age-adjusted estimates (NHIS). Similarly, in 2008 to 2010, Asian women (5.5%) and Hispanic women (9.6%) were less likely to be current cigarette smokers than non-Hispanic black women (17.6%), non-Hispanic white women (20.9%), and American Indian or Alaska Native women (20.7%; NHIS).9

  • In 2004 to 2006 data, adult cigarette smoking varied among Asian subgroups. Most Asian adults had never smoked, with rates ranging from 65% of Korean adults to 84% of Chinese adults. Korean adults (22%) were approximately 2 to 3 times as likely to be current smokers as Japanese (12%), Asian Indian (7%), or Chinese (7%) adults on the basis of age-adjusted estimates (NHIS).10

  • In 2009 to 2010, among women 15 to 44 years of age, past-month cigarette use was lower for those who were pregnant (16.3%) than among those who were not pregnant (26.7%). This pattern was found for women 18 to 25 years of age (22.7% versus 31.2% for pregnant and nonpregnant women, respectively) and for women 26 to 44 years of age (11.8% versus 27.0%, respectively). Among adolescents 15 to 17 years of age, past-month cigarette use was higher for those who were pregnant (22.7%) than for those who were not pregnant (13.4%; NSDUH).2

Incidence

  • In 2010:

—≈2.4 million people ≥12 years of age smoked cigarettes for the first time within the past 12 months, which was similar to the estimate in 2009 (2.5 million). The 2010 estimate averages out to ≈6500 new cigarette smokers every day. Most new smokers (58.8%) in 2010 were <18 years of age when they first smoked cigarettes (NSDUH). 2

—The number of new smokers <18 years of age (1.4 million) is similar to that in 2002 (1.3 million); however, new smokers ≥18 years of age increased from ≈600 000 in 2002 to 1 million in 2010 (NSDUH).2

—Among people ages 12 to 49 years of age who had started smoking within the past 12 months, the average age of first cigarette use was 17.3 years, similar to the average in 2009 (17.5 years).2

  • Data from 2002 to 2004 suggest that ≈1 in 5 nonsmokers 12 to 17 years of age is likely to start smoking. Youths in the Mexican subpopulations were significantly more susceptible (28.8%) to start smoking than those in non-Hispanic white (20.8%), non-Hispanic black (23.0%), Cuban (16.4%), Asian Indian (15.4%), Chinese (15.3%), and Vietnamese (13.8%) subpopulations. There was no significant difference in susceptibility to start smoking between boys and girls in any of the major populations or subpopulations (NSDUH).11

Morbidity

A 2010 report of the US Surgeon General on how tobacco causes disease summarizes an extensive body of literature on smoking and CVD and the mechanisms through which smoking is thought to cause CVD. Among its conclusions are the following:

  • There is a sharp increase in CVD risk with low levels of exposure to cigarette smoke, including secondhand smoke, and a less rapid further increase in risk as the number of cigarettes per day increases.

  • A meta-analysis comparing pooled data of ≈2.4 million smokers and nonsmokers found the relative risk (RR) ratio of smokers to nonsmokers for developing CHD was 25% higher in women than in men (95% CI, 1.12–1.39).12

  • Current smokers have a 2 to 4 times increased risk of stroke compared with nonsmokers or those who have quit for >10 years.13,14

Mortality

  • In 2005, tobacco smoking was the cause of ≈467 000 adult deaths (19.1%) in the United States. Approximately one third of these deaths were related to CVD.15

  • During 2000 to 2004, ≈49 000 (11.1%) of cigarette smoking–related deaths were attributable to secondhand smoke.16

  • Each year from 2000 to 2004, smoking caused 3.1 million years of potential life lost for males and 2.0 million years for females, excluding deaths attributable to smoking-attributable residential fires and adult deaths attributable to secondhand smoke.16

  • From 2000 to 2004, smoking during pregnancy resulted in an estimated 776 infant deaths annually.16

  • During 2000 to 2004, cigarette smoking resulted in an estimated 269 655 deaths annually among males and 173 940 deaths annually among females.16

  • On average, male smokers die 13.2 years earlier than male nonsmokers, and female smokers die 14.5 years earlier than female nonsmokers.17

Smoking Cessation

  • Smoking cessation reduces the risk of cardiovascular morbidity and mortality for smokers with and without CHD.

—There is no evidence to date that reducing the amount smoked by smoking fewer cigarettes per day reduces the risk of CVD.18

  • In 2008, 64.5% of adult current smokers ≥18 years of age with a checkup during the preceding year reported that they had been advised to quit, which was not significantly different from 2002 (63.1%). Smokers between 18 and 44 years of age were less likely to be advised to quit than those at older ages. Women were more likely than men to receive advice to quit smoking (MEPS).19

Secondhand Smoke

  • Data from a 2006 report of the US Surgeon General on the consequences of involuntary exposure to tobacco smoke20 indicate the following:

—Nonsmokers who are exposed to secondhand smoke at home or at work increase their risk of developing CHD by 25% to 30%.

—Short exposures to secondhand smoke can cause blood platelets to become stickier, damage the lining of blood vessels, and decrease coronary flow velocity reserves, potentially increasing the risk of an acute MI (AMI).

  • In 2008, data from 11 states showed that the majority of people surveyed in each state reported having smoke-free home rules, ranging from 68.8% in West Virginia to 85.6% in Arizona (BRFSS).21

  • As of December 31, 2010, 25 states and the District of Columbia had laws that prohibited smoking in indoor areas of worksites, restaurants, and bars; no states had such laws in 2000. As of December 31, 2010, an additional 10 states had laws that prohibited smoking in 1 or 2 but not all 3 venues.22

  • Pooled data from 17 studies in North America, Europe, and Australasia suggest that smoke-free legislation can reduce the incidence of acute coronary events by 10%.23

  • The percentage of the US nonsmoking population with detectable serum cotinine declined from 52.5% in 1999 to 2000 to 40.1% in 2007 to 2008, with declines occurring for children and adults. During 2007 to 2008, the percentage of nonsmokers with detectable serum cotinine was higher for those 3 to 11 years of age (53.6%) and those 12 to 19 years of age (46.5%) than for those ≥20 years of age (36.7%); the percentage was also higher for non-Hispanic blacks (55.9%) than for non-Hispanic whites (40.1%) and Mexican Americans (28.5%; NHANES).24

Cost

  • Direct medical costs ($96 billion) and lost productivity costs ($97 billion) associated with smoking totaled an estimated $193 billion per year between 2000 and 2004.16

  • In 2008, $9.94 billion was spent on marketing cigarettes in the United States.25

  • Cigarette prices have increased 283% between the early 1980s and 2011, resulting in decreased sales from ≈30 million packs sold in 1982 to ≈14 million packs sold in 2011.25

Chart 3-1.

Chart 3-1. Prevalence (%) of students in grades 9 to 12 reporting current cigarette use by sex and race/ethnicity (Youth Risk Behavior Surveillance System, 2011). NH indicates non-Hispanic. Data derived from MMWR: Morbidity and Mortality Weekly Report.1

Chart 3-2.

Chart 3-2. Prevalence (%) of current smoking for adults >18 years of age by race/ethnicity and sex (National Health Interview Survey: 2008–2010). All percentages are age-adjusted. NH indicates non-Hispanic. *Includes both Hispanics and non-Hispanics. Data derived from Centers for Disease Control and Prevention/National Center for Health Statistics, Health Data Interactive.9

Table 3-1. Cigarette Smoking

Population GroupPrevalence, 2011 Age ≥18 y*5Cost16
Both sexes43 821 000 (19.0%)$193 Billion per year
Males24 138 000 (21.3%)
Females19 683 000 (16.7%)
NH white males22.8%
NH white females19.7%
NH black males23.3%
NH black females15.1%
Hispanic or Latino males16.2%
Hispanic or Latino females8.3%
Asian only (both sexes)9.6%
American Indian/Alaska Native only (both sexes)26.7%

Percentages are age adjusted. Estimates for Asian only and American Indian/Alaska Native only include non-Hispanic and Hispanic persons.Ellipses (…) indicate data not available; NH, non-Hispanic.

*Rounded to the nearest thousand.

Abbreviations Used in Chapter 3

AMI

acute myocardial infarction

BRFSS

Behavioral Risk Factor Surveillance System

CHD

coronary heart disease

CI

confidence interval

CVD

cardiovascular disease

MEPS

Medical Expenditure Panel Survey

NH

non-Hispanic

NHANES

National Health and Nutrition Examination Survey

NHIS

National Health Interview Survey

NSDUH

National Survey on Drug Use and Health

RR

relative risk

YRBS

Youth Risk Behavior Survey

References

References

  • 1. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H; Centers for Disease Control and Prevention. Youth risk behavior surveillance: United States, 2011.MMWR Surveill Summ. 2012; 127:e32–e36.Google Scholar
  • 2. Substance Abuse and Mental Health Services Administration.Results from the 2010 National Survey on Drug Use and Health: National Findings, Detailed Tables.http://oas.samhsa.gov/NSDUH/2k10NSDUH/tabs/Cover.pdf. Accessed May 30, 2012.Google Scholar
  • 3. Centers for Disease Control and Prevention.Behavioral Risk Factor Surveillance System: 2011 prevalence and trends data.http://apps.nccd.cdc.gov/brfss/index.asp. Accessed November 30, 2012.Google Scholar
  • 4. Centers for Disease Control and Prevention.Behavioral Risk Factor Surveillance System: 2011 prevalence and trends data.http://apps.nccd.cdc.gov/brfss/index.asp. Accessed November 30, 2012.Google Scholar
  • 5. Schiller J, Lucas J, Peregoy J. Summary health statistics for U.S. adults: National Health Interview Survey, 2011. Vital Health Stat 10. In press.Google Scholar
  • 6. Centers for Disease Control and Prevention (CDC).Cigarette smoking among adults and trends in smoking cessation: United States, 2008.MMWR Morb Mortal Wkly Rep. 2009; 58:1227–1232.MedlineGoogle Scholar
  • 7. Centers for Disease Control and Prevention (CDC).Vital signs: current cigarette smoking among adults aged ≥18 years: United States, 2005–2010.MMWR Morb Mortal Wkly Rep. 2011; 60:1207–1212.MedlineGoogle Scholar
  • 8. Centers for Disease Control and Prevention (CDC).State-specific prevalence and trends in adult cigarette smoking: United States, 1998–2007.MMWR Morb Mortal Wkly Rep. 2009; 58:221–226.MedlineGoogle Scholar
  • 9. Centers for Disease Control and Prevention, National Center for Health Statistics.Health Data Interactive.http://www.cdc.gov/nchs/hdi.htm. Accessed July 19, 2011.Google Scholar
  • 10. Barnes PM, Adams PF, Powell-Griner E. Health Characteristics of the Asian Adult Population: United States, 2004–2006. Advance Data From Vital and Health Statistics; No. 394.Hyattsville, Md: National Center for Health Statistics; 2008.Google Scholar
  • 11. Centers for Disease Control and Prevention (CDC).Racial/ethnic differences among youths in cigarette smoking and susceptibility to start smoking: United States, 2002–2004.MMWR Morb Mortal Wkly Rep. 2006; 55:1275–1277.MedlineGoogle Scholar
  • 12. Huxley RR, Woodward M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and –meta-analysis of prospective cohort studies.Lancet. 2011; 378:1297–1305.CrossrefMedlineGoogle Scholar
  • 13. Goldstein LB, Bushnell CD, Adams RJ, Appel LJ, Braun LT, Chaturvedi S, Creager MA, Culebras A, Eckel RH, Hart RG, Hinchey JA, Howard VJ, Jauch EC, Levine SR, Meschia JF, Moore WS, Nixon JV, Pearson TA. Guidelines for the primary prevention of stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published correction appears in Stroke. 2011;42:e26].Stroke. 2011; 42:517–584.LinkGoogle Scholar
  • 14. Shah RS, Cole JW. Smoking and stroke: the more you smoke the more you stroke.Expert Rev Cardiovasc Ther. 2010; 8:917–932.CrossrefMedlineGoogle Scholar
  • 15. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, Ezzati M. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors [published correction appears in PLoS Med. 2011;8(1). doi:10.1371/annotation/0ef47acd-9dcc-4296-a897-872d182cde57].PLoS Med. 2009; 6:e1000058.CrossrefMedlineGoogle Scholar
  • 16. Centers for Disease Control and Prevention (CDC). Smoking-attributable mortality, years of potential life lost, and productivity losses: United States, 2000–2004.MMWR Morb Mortal Wkly Rep. 2008; 57:1226–1228.MedlineGoogle Scholar
  • 17. The 2004 United States Surgeon General’s Report: The Health Consequences of Smoking.N S W Public Health Bull. 2004; 15:107.MedlineGoogle Scholar
  • 18. US Department of Health and Human Services.How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General.Atlanta, Ga: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2010.Google Scholar
  • 19. Agency for Healthcare Research and Quality.2011 National Healthcare Quality & Disparities Reports.Rockville, Md: US Department of Health and Human Services, Agency for Healthcare Research and Quality; 2012. http://www.ahrq.gov/qual/qrdr11/9_lifestylemodification/T9_1_1_1.htm. Accessed May 30, 2012.Google Scholar
  • 20. US Department of Health and Human Services.The Health Consequences of Involuntary Exposure to Tobacco Smoke: A Report of the Surgeon General.Atlanta, Ga: US Department of Health and Human Services, Centers for Disease Control and Prevention, Coordinating Center for Health Promotion, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health;2006.Google Scholar
  • 21. Centers for Disease Control and Prevention (CDC). State-specific secondhand smoke exposure and current cigarette smoking among adults: United States, 2008.MMWR Morb Mortal Wkly Rep. 2009; 58:1232–1235.MedlineGoogle Scholar
  • 22. Centers for Disease Control and Prevention (CDC). State smoke-free laws for worksites, restaurants, and bars: United States, 2000–2010.MMWR Morb Mortal Wkly Rep. 2011; 60:472–475.MedlineGoogle Scholar
  • 23. Mackay DF, Irfan MO, Haw S, Pell JP. Meta-analysis of the effect of comprehensive smoke-free legislation on acute coronary events.Heart. 2010; 96:1525–1530.CrossrefMedlineGoogle Scholar
  • 24. Centers for Disease Control and Prevention (CDC).Vital signs: nonsmokers’ exposure to secondhand smoke: United States, 1999–2008.MMWR Morb Mortal Wkly Rep. 2010; 59:1141–1146.MedlineGoogle Scholar
  • 25. US Department of Health and Human Services.Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General.Atlanta, GA:US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health;2012. http://www.surgeongeneral.gov/library/reports/preventing-youth-tobacco-use/prevent_youth_by_section.html. Accessed May 30, 2012.Google Scholar

4. Physical Inactivity

See Table 4-1 and Charts 4-1 through 4-5.

Chart 4-1.

Chart 4-1. Prevalence of students in grades 9 to 12 who did not participate in at least 60 minutes of physical activity on any day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.1

Prevalence

Youth
Inactivity

(See Chart 4-1.)

  • The proportion of adolescents (grades 9–12) who report engaging in no regular PA is high and varies by sex and race.1

  • Nationwide, 13.8% of adolescents were inactive during the previous 7 days, as indicated by their response that they did not participate in ≥60 minutes of any kind of PA that increased their heart rate and made them breathe hard on any 1 of the previous 7 days.1

  • Girls were more likely than boys to report inactivity (17.7% versus 10.0%).1

  • The prevalence of inactivity was highest among black (26.7%) and Hispanic (21.3%) girls, followed by white girls (13.7%), black boys (12.3%), Hispanic boys (10.7%), and white boys (8.5%).1

  • A study of 3068 youths between 14 and 24 years of age from 1999 to 2006 found that the prevalence of inactivity went up with age for both boys and girls.2 Across ages, girls had a higher prevalence of physical inactivity than boys.2

  • In a study of 12 812 youth 9 to 18 years of age, the PA level in boys and girls declined starting at the age of 13, with a significantly greater decline in activity among girls.3

Television/Video/Computers

(See Chart 4-2.)

Chart 4-2.

Chart 4-2. Percentage of students in grades 9 to 12 who used a computer for ≥3 hours a day by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.1

In 2011:

  • Nationwide, 31.1% of adolescents used a computer for activities other than school work (eg, videogames or other computer games) for ≥3 hours per day on an average school day.1

  • The prevalence of using computers or watching television ≥3 hours per day was highest among black (41.1%) and Hispanic boys (36.3%), followed by white boys (33.3%), black girls (35.2%), Hispanic girls (28.3%), and white girls (22.6%).1

  • 32.4% of adolescents watched television for ≥3 hours per day.1

  • The prevalence of watching television ≥3 hours per day was highest among black girls (54.9%) and boys (54.4%), followed by Hispanic boys (38.4%) and girls (37.2%) and white boys (27.3%) and girls (23.9%).1

  • Increased television time has significant nutritional associations with weight gain (refer to Chapter 5, Nutrition).

Activity Recommendations

(See Charts 4-3 and 4-4.)

  • In 2011, the proportion of students who met activity recommendations of at least 60 minutes of PA on 7 days of the week was 28.7% nationwide and declined from 9th (30.7%) to 12th (25.1%) grades. At each grade level, the proportion was higher in boys than in girls.1

  • In 2011, more high school boys (38.3%) than girls (18.5%) self-reported having been physically active at least 60 minutes per day on all 7 days; self-reported rates of activity were higher in white (30.4%) than in black (26.0%) or Hispanic (26.5%) adolescents.1

  • The 2010 National Youth Physical Activity and Nutrition Study showed that a total of 15.3% of high school students met the recommendations for aerobic activity, 51.0% met the recommendations for muscle-strengthening activity, and 12.2% met the recommendations for both aerobic and muscle-strengthening activities.4

  • There was a marked discrepancy between the proportion of youth (ages 6–11 years) who reported engaging in ≥60 minutes of moderate-to-vigorous PA on most days of the week and those who actually engaged in moderate-to-vigorous PA for ≥60 minutes when activity was measured objectively with accelerometers (ie, portable motion sensors that record and quantify the duration and intensity of movements) in the NHANES 2003–2004 survey.5

  • On the basis of accelerometer counts per minute >2020, 42% of 6- to 11-year-olds accumulated ≥260 minutes of moderate-to-vigorous PA on ≥5 days per week, whereas only 8% of 12-to 15-year-olds and 7.6% of 16- to 19-year-olds achieved similar counts.5

  • More boys than girls met PA recommendations (≥60 minutes of moderate-to-vigorous activity on most days of the week) as measured by accelerometry.5

Structured Activity Participation
  • Despite recommendations from the National Association for Sport and Physical Education that schools should require daily physical education for students in kindergarten through 12th grade,6 only 51.8% of students attended physical education classes in school daily (56.7% of boys and 46.7% of girls).1

  • Physical education class participation declined from the 9th through the 12th grades among boys and girls.1

  • Little more than half (58.4%) of high school students played on at least 1 school or community sports team in the previous year; however, the prevalence declined with increasing grade level, from 61.4% in the 9th grade to 52.5% in the 12th grade.1

Adults
Inactivity

According to 2011 data from the NHIS, in adults ≥18 years of age:

  • Thirty-two percent do not engage in leisure-time PA (“no leisure-time PA/inactivity” refers to no sessions of light/moderate or vigorous PA of at least 10 minutes duration per day).7

  • Inactivity was higher among women than men (33.2% versus 29.9%, age-adjusted) and increased with age from 26.1% to 33.4%, 40.0%, and 52.4% among adults 18 to 44, 45 to 64, 65 to 74, and ≥75 years of age, respectively.7

  • Non-Hispanic black and Hispanic adults were more likely to be inactive (41.1% and 42.2%, respectively) than were non-Hispanic white adults (27.7%) on the basis of age-adjusted estimates.7

Activity Recommendations

(See Table 4-1 and Chart 4-5.)

According to 2011 data from the NHIS, in adults ≥18 years of age:

  • 21.0% of adults met the 2008 federal PA guidelines for both aerobic and strengthening activity, an important component of overall physical fitness.7

  • The age-adjusted proportion who reported engaging in moderate or vigorous PA that met the 2008 aerobic Physical Activity Guidelines for Americans (at least 150 minutes of moderate PA or 75 minutes of vigorous PA or an equivalent combination each week) was 48.9%; 52.7% of men and 45.5% of women met the recommendations. Age-adjusted Prevalence was 52.5% for non-Hispanic whites, 41.2% for non-Hispanic blacks, and 40.1% for Hispanics.7

  • The proportion of respondents who did not meet the federal PA guidelines increased with age from 44.1% of 18- to 44-year-olds to 72.6% of adults ≥75 years of age.7

  • Hispanic/Latino adults (59.8%) and non-Hispanic black adults (58.8%) were more likely to not meet the federal PA guidelines than non-Hispanic white (47.4%) adults, according to age-adjusted estimates.7

  • The percentage of adults ≥25 years of age not meeting the full (aerobic and muscle-strengthening) federal PA guidelines was inversely associated with education; participants with no high school diploma (68.4%), a high school diploma (59.0%), some college (48.2%), or a bachelor's degree or higher (34.0%), respectively, did not meet the full federal PA guidelines.7

  • The proportion of adults ≥25 years of age who met the 2008 federal PA guidelines for aerobic activity was positively associated with education level: 62.5% of those with a college degree or higher met the PA guidelines compared with 28.6% of adults with less than a high school diploma.7

  • The proportion of adults reporting levels of PA consistent with the 2008 Physical Activity Guidelines for Americans remains low and decreases with age.8,9 Thirty-three percent of respondents in a study examining awareness of current US PA guidelines had direct knowledge of the recommended dosage of PA (ie, frequency/duration).10

  • The percentage of men who engaged in both leisure-time aerobic and strengthening activities decreased with age, from 39.8% at age 18 to 24 years to 11.1% at ≥75 years of age. The percentage of women who engaged in both leisure-time aerobic and strengthening activities also decreased with age, from 20.7% at age 18 to 24 years to 5.3% at ≥75 years of age, on the basis of the 2011 NHIS.9

  • Using PA recommendations that existed at the time of the survey, adherence to PA recommendations was much lower when based on PA measured by accelerometer in NHANES 2003–20045:

—Among adults 20 to 59 years of age, 3.8% of men and 3.2% of women met recommendations to engage in moderate-to-vigorous PA (accelerometer counts >2020/min) for 30 minutes (in sessions of ≥10 minutes) on ≥5 of 7 days.

—Among those ≥60 years of age, adherence was 2.5% in men and 2.3% in women.

  • Accelerometry data from NHANES 2003–2006 showed that men engaged in 35 minutes of moderate activity per day, whereas for women, it was 21 minutes. More than 75% of moderate activity was accumulated in 1-minute bouts. Levels of activity declined sharply after the age of 50 years in all groups.11

  • In a review examining self-reported versus actual measured PA (eg, accelerometers, pedometers, indirect calorimetry, double-labeled water, heart rate monitor), 60% of respondents self-reported higher values of activity than what was measured by use of direct methods.12

  • Among men, self-reported PA was 44% greater than actual measured values; among women, self-reported activity was 138% greater than actual measured PA.12

Trends
Youth

In 2011:

  • Among adolescents, there was a significant decrease in the prevalence of watching television ≥3 hours per day, from 42.8% in 1999 to 32.4%, although there was no significant decrease from the 2009 prevalence of 32.8%.1

  • Among students nationwide, there was a significant increase in the prevalence of having participated in muscle-strengthening activities on ≥3 days per week, from 47.8% in 1991 to 55.6%.1

  • Nationwide, the prevalence of adolescents using computers ≥3 hours per day increased from 21.1% in 2005 to 24.9% in 2009 and 31.1%.1

  • Among adolescents nationwide, the prevalence of attending physical education classes at least once per week did not increase significantly, from 48.9% in 1991 to 51.8%.1

  • The prevalence of adolescents playing at least 1 team sport in the past year increased from 55.1% in 1999 to 58.4%.1

Adults
  • Between NHANES III (1988–1994) and NHANES 2001–2006, the non–age-adjusted proportion of adults who engaged in >12 bouts of PA per month declined from 57.0% to 43.3% in men and from 49.0% to 43.3% in women.13

  • The proportion of US adults who meet criteria for muscle strength has improved between 1998 and 2011. Annual estimates of the percentage of US adults who met the muscle-strengthening criteria increased from 17.7% in 1998 to 24.5% in 2011, and estimates of the percentage who met both the muscle-strengthening and aerobic criteria increased from 14.4% in 1998 to 21.0% in 2011.7,8

  • A 2.3% decline in physical inactivity between 1980 and 2000 prevented or postponed ≈17 445 deaths (≈5%) attributable to CHD in the United States.14

CVD and Metabolic Risk Factors

Youth
  • More girls (67.9%) than boys (55.7%) reported having exercised to lose weight or to keep from gaining weight.1

  • White girls (72.2%) were more likely than black (54.2%) and Hispanic (66.3%) girls to report exercising to lose weight or to keep from gaining weight.1

  • Total and vigorous PA are inversely correlated with body fat and the prevalence of obesity.15

  • Among children 4 to 18 years of age, increased time in moderate-to-vigorous PA was associated with improvements in waist circumference, systolic BP (SBP), fasting triglycerides, high-density lipoprotein (HDL) cholesterol, and insulin. These findings were significant regardless of the amount of the children’s sedentary time.16

Adults
  • Participants in the Diabetes Prevention Project randomized trial who met the PA goal of 150 minutes of PA per week were 44% less likely to develop DM after 3.2 years of follow-up, even if they did not meet the weight-loss target.17

  • Exercise for weight loss, without dietary interventions, was associated with significant reductions in diastolic BP (DBP; –2 mm Hg; 95% CI, –4 to –1 mm Hg), triglycerides (–0.2 mmol/L; 95% CI, –0.3 to –0.1 mmol/L), and fasting glucose (–0.2 mmol/L; 95% CI, –0.3 to –0.1 mmol/L).18

  • A total of 120 to 150 minutes per week of moderate-intensity activity, compared with none, can reduce the risk of developing metabolic syndrome.19

  • In Coronary Artery Risk Development in Young Adults (CARDIA), women who maintained high activity through young adulthood gained 6.1 fewer kilograms of weight and 3.8 fewer centimeters in waist circumference in middle age than those with lower activity. Highly active men gained 2.6 fewer kilograms and 3.1 fewer centimeters than their lower-activity counterparts.20

  • Self-reported low lifetime recreational activity has been associated with increased PAD.21

  • In 3 US cohort studies, men and women who increased their PA over time gained less weight in the long-term, whereas those who decreased their PA over time gained more weight and those who maintained their current PA had intermediate weight gain.22

  • Among US men and women, every hour per day of increased television watching was associated with 0.3 pounds of greater weight gain every 4 years, whereas every hour per day of decreased television watching was associated with a similar amount of relative weight loss.22

Morbidity and Mortality
  • Physical inactivity is responsible for 12.2% of the global burden of MI after accounting for other CVD risk factors such as cigarette smoking, DM, hypertension, abdominal obesity, lipid profile, no alcohol intake, and psychosocial factors.23

  • In a meta-analysis of longitudinal studies among women, RRs of incident CHD were 0.83 (95% CI, 0.69–0.99), 0.77 (95% CI, 0.64–0.92), 0.72 (95% CI, 0.59–0.87), and 0.57 (95% CI, 0.41–0.79) across increasing quintiles of PA compared with the lowest quintile.28

  • A 2003 meta-analysis of 23 studies on the association of PA with stroke indicated that compared with low levels of activity, high (RR, 0.79; 95% CI, 0.69–0.91) and moderate (RR, 0.91; 95% CI, 0.80–1.05) levels of activity were inversely associated with the likelihood of developing total stroke (ischemic and hemorrhagic).24

  • With television watching as a sedentary activity, 2 hours of television per day is associated with an RR for type 2 DM of 1.20 (95% CI, 1.14–1.27), an RR for fatal or nonfatal CVD of 1.15 (95% CI, 1.06–1.23), and an RR for all-cause mortality of 1.13 (95% CI, 1.07–1.18). The risk for all-cause mortality further increases with >3 hours of television daily.25

  • Longitudinal studies commonly report a graded, inverse association of PA amount and duration (ie, dose) with incident CHD and stroke.26

  • The PA guidelines for adults cite evidence that ≈150 minutes per week of moderate-intensity aerobic activity, compared with none, can reduce the risk of CVD.27

  • Adherence to PA guidelines for both aerobic and muscle-strengthening activities is associated with 27% lower all-cause mortality among adults without existing chronic conditions such as DM, cancer, MI, angina, CVD, stroke, or respiratory diseases and with 46% lower mortality among people with chronic comorbidities.28

  • In the Health Professionals Follow-Up Study, for every 3-hour-per-week increase in vigorous-intensity activity, the multivariate RR of MI was 0.78 (95% CI, 0.61–0.98) for men. This 22% reduction of risk can be explained in part by beneficial effects of PA on HDL cholesterol, vitamin D, apolipoprotein B, and HbA1c.29

  • In a 20-year study of older male veterans, an inverse, graded, and independent association between impaired exercise capacity and all-cause mortality risk was found. For each increase of 1 metabolic equivalent tasks in exercise capacity, mortality risk was 12% lower (HR 0.88; 95% CI, 0.86–0.90). Unfit individuals who improved their fitness status had a 35% lower mortality risk (HR 0.65; 95% CI, 0.46–0.93) than those who remained unfit.30

Secondary Prevention
  • PA improves inflammatory markers in people with existing stable CHD. After a 6-week training session, C-reactive protein levels declined by 23.7% (P<0.001), and plasma vascular cell adhesion molecule-1 levels declined by 10.23% (P<0.05); there was no difference in leukocyte count or levels of intercellular adhesion molecule-1.31

  • In a randomized trial of patients with PAD, supervised treadmill exercise training and lower-extremity resistance training were each associated with significant improvements in functional performance and quality of life compared with a usual-care control group. Exercise training was additionally associated with improved brachial artery flow-mediated dilation (FMD), whereas resistance training was associated with better stair-climbing ability versus control.32

  • On the basis of a meta-analysis of 34 randomized controlled trials, exercise-based cardiac rehabilitation after MI was associated with lower rates of reinfarction, cardiac mortality, and overall mortality.33

  • The benefit of intense exercise training for cardiac rehabilitation in people with HF was tested in a trial of 27 patients with stable, medically treated HF. Intense activity (an aerobic interval-training program 3 times per week for 12 weeks) was associated with a significant 35% improvement in left ventricular ejection fraction (EF) and decreases in pro-brain natriuretic peptide (40%), left ventricular end-diastolic volume (18%), and left ventricular end-systolic volume (25%) compared with control and endurance-training groups.34

Costs
  • The economic consequences of physical inactivity are substantial. In a summary of WHO data sources, the economic costs of physical inactivity were estimated to account for 1.5% to 3.0% of total direct healthcare expenditures in developed countries such as the United States.35

  • The 1996 MEPS was linked to self-reported activity in the 1995 NHIS. On the basis of a self-reported prevalence of inactivity of 47.5% and a prevalence of CVD of 21.5%, direct expenditures for CVD associated with inactivity were estimated to be $23.7 billion in 2001.36

Chart 4-3.

Chart 4-3. Prevalence of students in grades 9 to 12 who met currently recommended levels of physical activity during the past 7 days by race/ethnicity and sex (Youth Risk Behavior Surveillance: 2011). “Currently recommended levels” was defined as activity that increased their heart rate and made them breathe hard some of the time for a total of at least 60 minutes per day on 5 of the 7 days preceding the survey. NH indicates non-Hispanic. Data derived from MMWR Surveillance Summaries.1

Chart 4-4.

Chart 4-4. Prevalence of children 6 to 19 years of age who attained sufficient moderate-to-vigorous physical activity to meet public health recommendations (≥60 minutes per day on 5 or more of the 7 days preceding the survey), by sex and age (National Health and Nutrition Examination Survey: 2003–2004). Source: Troiano et al.5

Chart 4-5.

Chart 4-5. Prevalence of meeting the 2008 Federal Physical Activity Guidelines among adults ≥18 years of age by race/ethnicity and sex (National Health Interview Survey: 2010). NH indicates non-Hispanic. Percentages are age-adjusted. Meeting the 2008 Federal Physical Activity Guidelines is defined as engaging in moderate leisure-time physical activity for at least 150 minutes per week or vigorous activity at least 75 minutes per week or an equivalent combination. Source: Schiller et al.7

Table 4-1. Met 2008 Federal PA Guidelines for Adults

Population GroupPrevalence, 2011 (Age ≥18 y), %
Both sexes21.0
Males24.9
Females17.1
NH white only23.0
NH black only18.0
Hispanic or Latino15.4
American Indian/Alaska Native only17.0
Asian only16.7

“Met 2008 Federal PA Guidelines for Adults” is defined as engaging in at least 150 minutes of moderate or 75 minutes of vigorous aerobic leisure-time PA per week (or an equivalent combination) and engaging in leisure-time strengthening PA at least twice a week. Data are age-adjusted for adults ≥18 y of age. PA indicates physical activity; NH, non-Hispanic.

Source: National Health Interview Survey 2011 (National Center for Health Statistics).7

Abbreviations Used in Chapter 4

BP

blood pressure

CARDIA

Coronary Artery Risk Development in Young Adults

CHD

coronary heart disease

CI

confidence interval

CVD

cardiovascular disease

DBP

diastolic blood pressure

DM

diabetes mellitus

EF

ejection fraction

FMD

flow-mediated dilation

HbA1c

hemoglobin A1c

HBP

high blood pressure

HDL

high-density lipoprotein

HF

heart failure

HR

hazard ratio

MEPS

Medical Expenditure Panel Survey

MI

myocardial infarction

NH

non-Hispanic

NHANES

National Health and Nutrition Examination Survey

NHIS

National Health Interview Survey

PA

physical activity

PAD

peripheral artery disease

RR

relative risk

SBP

systolic blood pressure

WHO

World Health Organization

References

References

  • 1. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H; Centers for Disease Control and Prevention (CDC). Youth risk behavior surveillance: United States, 2011.MMWR Surveill Summ. 2012; 127:e37–e44.Google Scholar
  • 2. Zimmermann-Sloutskis D, Wanner M, Zimmermann E, Martin BW. Physical activity levels and determinants of change in young adults: a longitudinal panel study.Int J Behav Nutr Phys Act. 2010; 7:2.CrossrefMedlineGoogle Scholar
  • 3. Kahn JA, Huang B, Gillman MW, Field AE, Austin SB, Colditz GA, Frazier AL. Patterns and determinants of physical activity in U.S. adolescents.J Adolesc Health. 2008; 42:369–377.CrossrefMedlineGoogle Scholar
  • 4. Centers for Disease Control and Prevention (CDC).Physical activity levels of high school students: United States, 2010.MMWR Morb Mortal Wkly Rep. 2011; 60:773–777.MedlineGoogle Scholar
  • 5. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer.Med Sci Sports Exerc. 2008; 40:181–188.CrossrefMedlineGoogle Scholar
  • 6. National Association for Sport and Physical Education. Moving Into the Future: National Standards for Physical Education. 2nd ed.Reston, VA: National Association for Sport and Physical Education; 2004.Google Scholar
  • 7. Schiller J, Lucas J, Peregoy J. Summary health statistics for U.S. adults: National Health Interview Survey, 2011. Vital Health Stat 10. In press.Google Scholar
  • 8. Carlson SA, Fulton JE, Schoenborn CA, Loustalot F. Trend and prevalence estimates based on the 2008 Physical Activity Guidelines for Americans.Am J Prev Med. 2010; 39:305–313.CrossrefMedlineGoogle Scholar
  • 9. Ward B, Barnes P, Freeman G, Schiller J. Early release of selected estimates based on data from the 2011 National Health Interview Survey.National Center for Health Statistics.June 2012. http://www.cdc.gov/nchs/nhis/released201206.htm. Accessed July 20, 2012.Google Scholar
  • 10. Bennett GG, Wolin KY, Puleo EM, Mâsse LC, Atienza AA. Awareness of national physical activity recommendations for health promotion among US adults.Med Sci Sports Exerc. 2009; 41:1849–1855.CrossrefMedlineGoogle Scholar
  • 11. Luke A, Dugas LR, Durazo-Arvizu RA, Cao G, Cooper RS. Assessing physical activity and its relationship to cardiovascular risk factors: NHANES 2003-2006.BMC Public Health. 2011; 11:387.CrossrefMedlineGoogle Scholar
  • 12. Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review.Int J Behav Nutr Phys Act. 2008; 5:56.CrossrefMedlineGoogle Scholar
  • 13. King DE, Mainous AG, Carnemolla M, Everett CJ. Adherence to healthy lifestyle habits in US adults, 1988-2006.Am J Med. 2009; 122:528–534.CrossrefMedlineGoogle Scholar
  • 14. Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, Giles WH, Capewell S. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000.N Engl J Med. 2007; 356:2388–2398.CrossrefMedlineGoogle Scholar
  • 15. Kim Y, Lee S. Physical activity and abdominal obesity in youth.Appl Physiol Nutr Metab. 2009; 34:571–581.CrossrefMedlineGoogle Scholar
  • 16. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A; International Children’s Accelerometry Database (ICAD) Collaborators. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents [published correction appears in JAMA. 2012;307:1915].JAMA. 2012; 307:704–712.CrossrefMedlineGoogle Scholar
  • 17. Hamman RF, Wing RR, Edelstein SL, Lachin JM, Bray GA, Delahanty L, Hoskin M, Kriska AM, Mayer-Davis EJ, Pi-Sunyer X, Regensteiner J, Venditti B, Wylie-Rosett J. Effect of weight loss with lifestyle intervention on risk of diabetes.Diabetes Care. 2006; 29:2102–2107.CrossrefMedlineGoogle Scholar
  • 18. Shaw K, Gennat H, O’Rourke P, Del Mar C. Exercise for overweight or obesity. Cochrane Database Syst Rev. 2006; (4):CD003817.Google Scholar
  • 19. Department of Health and Human Services, Centers for Disease Control and Prevention.Physical activity for everyone: physical activity and health: the benefits of physical activity.http://www.cdc.gov/physicalactivity/everyone/health/index.html#ReduceCardiovascularDisease. Accessed August 1, 2011.Google Scholar
  • 20. Hankinson AL, Daviglus ML, Bouchard C, Carnethon M, Lewis CE, Schreiner PJ, Liu K, Sidney S. Maintaining a high physical activity level over 20 years and weight gain [published correction appears in JAMA. 2011;305:150].JAMA. 2010; 304:2603–2610.CrossrefMedlineGoogle Scholar
  • 21. Wilson AM, Sadrzadeh-Rafie AH, Myers J, Assimes T, Nead KT, Higgins M, Gabriel A, Olin J, Cooke JP. Low lifetime recreational activity is a risk factor for peripheral arterial disease.J Vasc Surg. 2011; 54:427–432, 432.e1.CrossrefMedlineGoogle Scholar
  • 22. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men.N Engl J Med. 2011; 364:2392–2404.CrossrefMedlineGoogle Scholar
  • 23. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L; INTERHEART Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.Lancet. 2004; 364:937–952.CrossrefMedlineGoogle Scholar
  • 24. Lee CD, Folsom AR, Blair SN. Physical activity and stroke risk: a meta-analysis.Stroke. 2003; 34:2475–2481.LinkGoogle Scholar
  • 25. Grøntved A, Hu FB. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta-analysis.JAMA. 2011; 305:2448–2455.CrossrefMedlineGoogle Scholar
  • 26. Carnethon MR. Physical activity and cardiovascular disease: how much is enough?Am J Lifestyle Med. 2009; 3(1):44S–49S.CrossrefMedlineGoogle Scholar
  • 27. Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association.Circulation. 2007; 116:1081–1093.LinkGoogle Scholar
  • 28. Schoenborn CA, Stommel M. Adherence to the 2008 adult physical activity guidelines and mortality risk.Am J Prev Med. 2011; 40:514–521.CrossrefMedlineGoogle Scholar
  • 29. Chomistek AK, Chiuve SE, Jensen MK, Cook NR, Rimm EB. Vigorous physical activity, mediating biomarkers, and risk of myocardial infarction.Med Sci Sports Exerc. 2011; 43:1884–1890.CrossrefMedlineGoogle Scholar
  • 30. Kokkinos P, Myers J, Faselis C, Panagiotakos DB, Doumas M, Pittaras A, Manolis A, Kokkinos JP, Karasik P, Greenberg M, Papademetriou V, Fletcher R. Exercise capacity and mortality in older men: a 20-year follow-up study.Circulation. 2010; 122:790–797.LinkGoogle Scholar
  • 31. Rankovic G, Milicic B, Savic T, Dindic B, Mancev Z, Pesic G. Effects of physical exercise on inflammatory parameters and risk for repeated acute coronary syndrome in patients with ischemic heart disease.Vojnosanit Pregl. 2009; 66:44–48.CrossrefMedlineGoogle Scholar
  • 32. McDermott MM, Ades P, Guralnik JM, Dyer A, Ferrucci L, Liu K, Nelson M, Lloyd-Jones D, Van Horn L, Garside D, Kibbe M, Domanchuk K, Stein JH, Liao Y, Tao H, Green D, Pearce WH, Schneider JR, McPherson D, Laing ST, McCarthy WJ, Shroff A, Criqui MH. Treadmill exercise and resistance training in patients with peripheral arterial disease with and without intermittent claudication: a randomized controlled trial [published correction appears in JAMA. 2012;307:1694].JAMA. 2009; 301:165–174.CrossrefMedlineGoogle Scholar
  • 33. Lawler PR, Filion KB, Eisenberg MJ. Efficacy of exercise-based cardiac rehabilitation post-myocardial infarction: a systematic review and meta-analysis of randomized controlled trials.Am Heart J. 2011; 162:571–584.e2.CrossrefMedlineGoogle Scholar
  • 34. Wisløff U, Støylen A, Loennechen JP, Bruvold M, Rognmo Ø, Haram PM, Tjønna AE, Helgerud J, Slørdahl SA, Lee SJ, Videm V, Bye A, Smith GL, Najjar SM, Ellingsen Ø, Skjaerpe T. Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study.Circulation. 2007; 115:3086–3094.LinkGoogle Scholar
  • 35. Oldridge NB. Economic burden of physical inactivity: healthcare costs associated with cardiovascular disease.Eur J Cardiovasc Prev Rehabil. 2008; 15:130–139.CrossrefMedlineGoogle Scholar
  • 36. Wang G, Pratt M, Macera CA, Zheng ZJ, Heath G. Physical activity, cardiovascular disease, and medical expenditures in U.S. adults.Ann Behav Med. 2004; 28:88–94.CrossrefMedlineGoogle Scholar

5. Nutrition

See Tables 5-1 and 5-2 and Charts 5-1 through 5-3.

This chapter of the update highlights national dietary consumption data, focusing on key foods, nutrients, dietary patterns, and other dietary factors related to cardiometabolic health. It is intended to examine current intakes, trends and changes in intakes, and estimated effects on disease to support and further stimulate efforts to monitor and improve dietary habits in relation to cardiovascular health.

Prevalence

Foods and Nutrients: Adults

See Table 5–1; NHANES 2005–2008).

The dietary consumption by US adults of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-1 according to sex and race or ethnic subgroups:

  • Average consumption of whole grains by white and black men and women was between 0.5 and 0.8 servings per day, with only between 3% and 5% of white and black adults meeting guidelines of ≥3 servings per day. Average whole grain consumption by Mexican Americans was ≈2 servings per day, with 21% to 27% consuming ≥3 servings per day.

  • Average fruit consumption ranged from 1.1 to 1.8 servings per day in these sex and race or ethnic subgroups: 9% to 11% of whites, 6% to 7% of blacks, and 8% to 10% of Mexican Americans met guidelines of ≥2 cups per day. When 100% fruit juices were included, the number of servings consumed and the proportions of adults consuming ≥2 cups per day approximately doubled.

  • Average vegetable consumption ranged from 1.3 to 2.2 servings per day; 6% to 7% of whites, 3% of blacks, and 3% of Mexican Americans consumed ≥2.5 cups per day. The inclusion of vegetable juices and sauces generally produced little change in these consumption patterns.

  • Average consumption of fish and shellfish was lowest among white women (1.2 servings per week) and highest among black men and women (1.7 servings per week); ≈75% to 80% of all adults in each sex and race or ethnic subgroup consumed <2 servings per week. Approximately 10% to 13% of whites, 14% to 15% of blacks, and 12% of Mexican Americans consumed ≥250 mg of eicosapentaenoic acid and docosahexaenoic acid per day.

  • Average consumption of nuts, legumes, and seeds was ≈2 to 3 servings per week among white and black men and women and 6 servings per week among Mexican American men and women. Approximately 20% of whites, 15% of blacks, and 40% of Mexican Americans met guidelines of ≥4 servings per week.

  • Average consumption of processed meats was lowest among Mexican American women (1.8 servings per week) and highest among black men (3.6 servings per week). Between 36% (Mexican American women) and 66% (black men) of adults consumed ≥1 serving per week.

  • Average consumption of sugar-sweetened beverages ranged from ≈7 servings per week among white women to 16 servings per week among Mexican American men. Approximately 50% and 33% of white men and women, 73% and 65% of black men and women, and 76% and 62% of Mexican American men and women, respectively, consumed >36 oz (4.5 8-oz servings) per week.

  • Average consumption of sweets and bakery desserts ranged from ≈4 servings per day (Mexican American men) to 7 servings per day (white men). Approximately two thirds of white and black men and women and half of all Mexican American men and women consumed >2.5 servings per week.

  • Between 33% and 50% of adults in each sex and race or ethnic subgroup consumed <10% of total calories from saturated fat, and between 58% and 70% consumed <300 mg of dietary cholesterol per day.

  • Only 4% to 7% of whites, 2% to 4% of blacks, and 9% to 11% of Mexican Americans consumed ≥28 g of dietary fiber per day.

  • Only 8% to 11% of whites, 9% to 11% of blacks, and 13% to 19% of Mexican Americans consumed <2.3 g of sodium per day. In 2005, the US Department of Health and Human Services and US Department of Agriculture recommended that adults in specific groups, including people with hypertension, all middle-aged and older adults, and all blacks, should consume ≤1.5 g of sodium per day. In 2005 to 2006, the majority (69.2%) of US adults belonged to ≥1 of these specific groups in whom sodium consumption should be <1.5 g/d.1

Foods and Nutrients: Children and Teenagers

See Table 5-2; NHANES 2005–2008).

The dietary consumption by US children and teenagers of selected foods and nutrients related to cardiometabolic health is detailed in Table 5-2:

  • Average whole grain consumption was low, ranging from 0.4 to 0.6 servings per day, with <4% of all children in different age and sex subgroups meeting guidelines of ≥3 servings per day.

  • Average fruit consumption was low and decreased with age: ≈1.5 servings per day in younger boys and girls (5–9 years of age), 1.3 servings per day in adolescent boys and girls (10–14 years of age), and 0.9 servings per day in teenage boys and girls (15–19 years of age). The proportion meeting guidelines of ≥2 cups per day was also low and decreased with age: ≈8% in those 5 to 9 years of age, 7% to 8% in those 10 to 14 years of age, and 4% in those 15 to 19 years of age. When 100% fruit juices were included, the number of servings consumed approximately doubled or tripled, and proportions consuming ≥2 cups per day were 29% to 36% of those 5 to 9 years of age, 22% to 26% of those 10 to 14 years of age, and 21% to 22% of those 15 to 19 years of age.

  • Average vegetable consumption was low, ranging from 0.9 to 1.1 servings per day, with <2% of children in different age and sex subgroups meeting guidelines of ≥2.5 cups per day.

  • Average consumption of fish and shellfish was low, ranging between 0.5 and 0.7 servings per week in all age and sex groups. Among all ages, only 10% to 13% of children and teenagers consumed ≥2 servings per week.

  • Average consumption of nuts, legumes, and seeds ranged from 1.3 to 1.4 servings per week among 5- to 9-year-olds, 1.4 to 2.1 servings per week among 10- to 14-year-olds, and 0.8 to 1.1 servings per week among 15- to 19-year-olds. Only between 7% and 14% of children in different age and sex subgroups consumed ≥4 servings per week.

  • Average consumption of processed meats ranged from 2.1 to 3.2 servings per week; was uniformly higher than the average consumption of nuts, legumes, and seeds; and was up to 6 times higher than the average consumption of fish and shellfish. Between 40% and 54% of children consumed ≥2 servings per week.

  • Average consumption of sugar-sweetened beverages was higher in boys than in girls and was ≈8 servings per week in 5- to 9-year-olds, 11 to 13 servings per week in 10- to 14-year-olds, and 14 to 18 servings per week in 15- to 19-year-olds. This was generally considerably higher than the average consumption of whole grains, fruits, vegetables, fish and shellfish, or nuts, legumes, and seeds. Only between 17% (boys 15–19 years of age) and 42% (boys and girls 5–9 years of age) of children consumed <4.5 servings per week.

  • Average consumption of sweets and bakery desserts was ≈8 to 10 servings per week in 5- to 9-year-olds and 10- to 14-year-olds and 6 to 8 servings per week in 15- to 19-year-olds. From 82% (girls 5–9 years of age) to 58% (boys 15–19 years of age) of youths consumed >2.5 servings per week.

  • Average consumption of eicosapentaenoic acid and docosahexaenoic acid was low, ranging from ≈45 to 75 mg/d in boys and girls at all ages. Only between 3% and 7% of children and teenagers at all ages consumed ≥250 mg/d.

  • Average consumption of saturated fat was between 11% and 12% of calories, and average consumption of dietary cholesterol was ≈230 mg/d. Approximately one fifth to one third of children consumed <10% energy from saturated fat, and ≈80% consumed <300 mg of dietary cholesterol per day.

  • Average consumption of dietary fiber ranged from 12 to 14 g/d. Less than 2% of children in all different age and sex subgroups consumed ≥28 g/d.

  • Average consumption of sodium ranged from 3.1 to 3.4 g/d. Between 7% and 12% of children in different age and sex subgroups consumed <2.3 g/d.

Energy Balance

Energy balance, or consumption of total calories appropriate for needs, is determined by the balance of average calories consumed versus expended, with this balance depending on multiple factors, including calories consumed, PA, body size, age, sex, and underlying basal metabolic rate. Thus, one individual may consume relatively high calories but have negative energy balance (as a result of even greater calories expended), whereas another individual may consume relatively few calories but have positive energy balance (because of low calories expended). Given such variation, the most practical and reasonable method to assess energy balance in populations is to assess changes in weight over time (Trends section).

  • Average daily caloric intake in the United States is ≈2500 calories in adult men and 1800 calories in adult women (Table 5-1). In children and teenagers, average caloric intake is higher in boys than in girls and increases with age in boys (Table 5-2). Trends in energy balance are described below. The average US adult gains ≈1 pound per year. In an analysis of >120 000 US men and women in 3 separate US cohorts followed up for up to 20 years, changes in intakes of different foods and beverages were linked to long-term weight gain in different ways.2 Foods and beverages most positively linked to weight gain included poor-quality carbohydrates, such as potatoes, refined grains (eg, white bread, white rice, low-fiber breakfast cereals), sweets/desserts, sugar-sweetened beverages, and red and processed meats. In contrast, increased consumption of several other foods, including nuts, whole grains, fruits, vegetables, and yogurt, was linked to less weight gain over time.2

  • Other nutritional determinants of positive energy balance (more calories consumed than expended), as determined by adiposity or weight gain, include larger portion sizes3,4 and greater consumption of fast food and commercially prepared meals.5–9

  • Preferences for portion size are associated with BMI, socioeconomic status, eating in fast-food restaurants, and television watching.10,11 Most portion sizes are larger at fast-food restaurants than at home or at other restaurants.12

  • Between 1999 and 2004, 53% of Americans consumed an average of 1 to 3 restaurant meals per week, and 23% consumed ≥4 restaurant meals per week.13 Spending on food away from home, including restaurant meals, catered foods, and food eaten during out-of-town trips, increased from 26% of average annual food expenditures in 1970 to 42% in 2004.13 Macronutrient composition of the overall diet or of specific foods, such as percent calories from total fat, does not appear to be strongly associated with energy balance as ascertained by weight gain or loss.2,14–16 In contrast, dietary quality as characterized by higher or lower intakes of specific foods and beverages is associated with weight gain (see above).2

  • Preliminary evidence suggests that consumption of trans fat may be associated with energy imbalance as assessed by changes in adiposity or weight, as well as more specific adverse effects on visceral adiposity, but such data are still emerging.17–19

  • Other individual factors associated with positive energy balance (weight gain) include greater television watching (with evidence that effects are mediated by diet, rather than physical inactivity, including greater snacking in front of the television and the influence of advertising on poor food choices)2,20–24 and lower average sleep duration.2,25

  • Randomized controlled trials of weight loss in obese individuals generally show modestly greater weight loss with low-carbohydrate (high-fat) diets than with low-fat diets at 6 months, but at 1 year, such differences diminish, and a diet that focuses on dietary quality and whole foods may be most successful in the long-term.26–29

  • On the basis of BRFSS data from 2003, among all American adults who are overweight or obese, a higher proportion are trying to lose weight if also diagnosed with hypertension (58% trying to lose weight), DM (60%), or both diseases (72%) than adults with neither condition (50%).31

  • A 2007–2008 national survey of 1082 retail stores in 19 US cities found that energy-dense snack foods/beverages were present in 96% of pharmacies, 94% of gas stations, 22% of furniture stores, 16% of apparel stores, and 29% to 65% of other types of stores.32

  • Societal and environmental factors independently associated with energy imbalance (weight gain), via either increased caloric consumption or decreased expenditure, include education, income, race/ethnicity, and local conditions such as availability of grocery stores, types of restaurants, safety, parks and open spaces, and walking or biking paths.33–35 PA is covered in Chapter 4 of this update.

Dietary Patterns

In addition to individual foods and nutrients, overall dietary patterns can be used to assess more global dietary quality. Different dietary patterns have been defined, including the Healthy Eating Index (HEI), Alternative HEI, Western versus prudent dietary patterns, Mediterranean dietary pattern, and DASH (Dietary Approaches to Stop Hypertension)-type diet. The higher-monounsaturated-fat DASH–type diet is generally similar to a traditional Mediterranean dietary pattern.36

Table 5-1. Dietary Consumption in 2005–2008 Among US Adults ≥20 Years of Age of Selected Foods and Nutrients Related to Cardiometabolic Health96–99

NH White MenNH White WomenNH Black MenNH Black WomenMexican American MenMexican American Women
Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*
Foods
Whole grains, servings/d0.7±0.74.60.8±0.75.30.5±0.53.10.6±0.64.12.1±1.627.41.7±1.421.0
Fruits, servings/d1.3±1.38.91.6±1.410.71.1±1.46.21.2±1.37.01.4±1.38.01.8±1.510.4
Fruits including 100% juices, servings/d2.4±2.323.32.5±2.124.92.8±0.926.62.9±1.226.72.8±2.323.93.2±2.628.7
Vegetables including starch, servings/d1.9±1.16.32.2±1.17.21.6±0.93.21.8±1.03.61.3±0.72.61.6±0.72.5
Vegetables including starch and juices/sauces, servings/d2.2±1.28.82.5±1.39.61.7±0.93.91.9±1.14.91.7±0.83.91.9±0.74.8
Fish and shellfish, servings/wk1.5±1.422.01.2±0.619.11.7±1.323.01.7±0.925.21.6±1.320.01.4±1.319.7
Nuts, legumes, and seeds, servings/wk2.7±2.020.32.4±1.919.92.3±1.515.91.8±0.414.56.3±6.841.25.8±3.639.9
Processed meats, servings/wk3.2±1.946.12.0±1.060.13.6±2.243.82.7±2.252.12.1±2.260.81.8±2.264.2
Sugar-sweetened beverages, servings/wk9.9±11.650.06.6±10.966.713.8±9.027.211.8±8.934.815.6±10.324.210.0±8.837.9
Sweets and bakery desserts, servings/wk6.5±4.835.47.4±4.532.26.0±4.142.06.8±3.538.63.7±2.955.15.5±0.948.4
Nutrients
Total calories, kcal/d2520±659NA1757±455NA2371±722NA1749±568NA2400±703NA1798±528NA
EPA/DHA, g/d0.129±0.13813.00.109±0.13810.20.146±0.13115.20.146±0.10213.80.146±0.10212.10.119±0.10212.0
ALA, g/d1.35±0.3325.51.52±0.5072.21.32±0.3823.41.43±0.3368.01.21±0.2316.51.34±0.2764.1
n-6 PUFA, % energy7.1±1.2NA7.5±1.6NA7.3±1.6NA7.6±1.5NA6.7±0.9NA6.9±1.4NA
Saturated fat, % energy11.4±2.233.311.4±2.136.010.8±1.739.810.6±2.043.310.1±2.050.410.4±1.748.5
Dietary cholesterol, mg/d277±9066.9274±8369.5303±12361.8317±10657.7323±14258.8310±12061.2
Total fat, % energy34.1±5.154.233.9±4.753.633.8±4.751.133.5±4.654.231.6±5.165.731.8±4.965.4
Carbohydrate, % energy47.3±7.2NA49.7±6.6NA48.6±6.2NA50.7±6.3NA50.3±6.7NA52.3±6.5NA
Dietary fiber, g/d15.0±5.04.217.2±5.87.013.1±4.62.414.2±5.03.817.7±6.19.318.9±4.711.2
Sodium, g/d3.3±0.610.53.5±0.68.33.2±0.511.33.4±0.58.93.0±0.719.43.2±0.612.7

Based on data from National Health and Nutrition Examination Survey 2005–2006 and 2007–2008, derived from two 24-hour dietary recalls per person, with population SDs adjusted for within-person vs between-person variation. All values are energy-adjusted using individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d.

SD indicates standard deviation; NH, non-Hispanic; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid; ALA, α-linoleic acid; n-6-PUFA, ω-6-polyunsaturatedfatty acid; and NA, not available.

*Guidelines adjusted to a 2000-kcal/d diet. Whole grains (characterized as minimum 1.1 g of fiber per 10 g of carbohydrate), 3 or more 1-oz equivalent (1 oz of bread; 1 cup of dry cereal; 1/2 cup of cooked rice, pasta, or cereal) servings per day (Dietary Guidelines for Americans)58a; fish or shellfish, 2 or more 100-g (3.5-oz) servings per week58a; fruits, 2 cups per day116; vegetables, 2 1/2 cups per day, including up to 3 cups per week of starchy vegetables116; nuts, legumes, and seeds, 4 or more 50-g servings per week58a; processed meats (bacon, hot dogs, sausage, processed deli meats), 2 or fewer 100-g (3.5-oz) servings per week (1/4 of discretionary calories)116; sugar-sweetened beverages (defined as ≥50 cal/8 oz, excluding whole juices), ≤36 oz per week (≈1/4 of discretionary calories)58a,116; sweets and bakery desserts, 2.5 or fewer 50-g servings per week (≈1/4 of discretionary calories)58a,116; EPA/DHA, ≥0.250 g/d120; ALA, ≥1.6/1.1 g/d (men/women)117; saturated fat, <10% energy; dietary cholesterol, <300 mg/d116; total fat, 20% to 35% energy116; dietary fiber, ≥28/d116; and sodium, <2.3 g/d.116

Table 5-2. Dietary Consumption in 2005–2008 Among US Children and Teenagers of Selected Foods and Nutrients Related to Cardiometabolic Health

Boys (5–9 y)Girls (5–9 y)Boys (10 –14 y)Girls (10 –14 y)Boys (15–19 y)Girls (15–19 y)
Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*Average Consumption (Mean±SD)% Meeting Guidelines*
Foods
Whole grains, servings/d0.5±0.52.70.5±0.31.10.6±0.64.00.5±0.41.80.4±0.41.60.5±0.52.6
Fruits, servings/d1.5±1.18.31.5±0.88.51.2±1.06.91.4±1.18.40.9±0.83.90.9±0.84.6
Fruits including 100% juices, servings/d3.3±1.735.73.1±1.428.52.4±1.721.72.8±1.926.02.2±1.721.12.4±1.721.7
Vegetables including starch, servings/d0.9±0.40.51.0±0.50.91.0±0.61.11.1±0.61.61.0±0.11.11.1±0.21.1
Vegetables including starch and juices/sauces, servings/d1.1±0.40.81.1±0.61.31.1±0.61.21.3±0.61.91.3±0.91.51.3±0.42.4
Fish and shellfish, servings/wk0.5±0.79.90.7±0.711.70.9±0.713.50.6±0.710.30.7±0.911.00.7±0.912.2
Nuts, legumes, and seeds, servings/wk1.4±0.412.51.3±2.59.62.1±2.814.11.4±1.010.11.1±1.09.40.8±1.06.8
Processed meats, servings/wk2.3±1.155.52.1±1.060.02.6±1.054.92.3±1.052.13.2±1.545.62.4±1.055.8
Sugar-sweetened beverages, servings/wk8.5±5.938.68.3±5.137.913.3±7.023.510.9±7.331.618.2±11.116.713.9±10.132.4
Sweets and bakery desserts, servings/wk10.1±2.119.59.3±2.118.39.0±2.123.58.1±2.030.26.0±5.241.98.2±5.231.5
Nutrients
Total calories, kcal/d1946±328NA1743±330NA2139±403NA1849±432NA2670±903NA1845±453NA
EPA/DHA, g/d0.045±0.0253.10.056±0.0255.90.074±0.0307.30.052±0.0304.70.071±0.0225.20.065±0.0215.7
ALA, g/d1.12±0.159.51.15±0.2046.31.11±0.209.71.19±0.2849.11.14±0.1813.21.34±0.1859.2
n-6 PUFA, % energy6.4±1.1NA6.5±1.0NA6.5±1.0NA6.7±0.9NA6.4±0.6NA7.1±1.3NA
Saturated fat, % energy11.7±1.424.911.8±0.821.311.6±0.727.811.5±1.828.611.8±1.424.811.3±1.734.1
Dietary cholesterol, mg/d225±6981.6239±5778.4245±5776.6226±11481.6244±11476.9240±11477.7
Total fat, % energy33.0±3.367.633.3±3.066.633.1±2.665.633.1±4.261.633.6±3.158.933.3±4.957.0
Carbohydrate, % energy54.5±4.1NA53.8±3.7NA53.1±3.6NA53.8±4.9NA51.4±4.1NA52.9±6.3NA
Dietary fiber, g/d13.6±2.30.213.9±2.20.713.3±3.41.113.9±3.31.811.9±2.40.613.3±2.91.9
Sodium, g/d3.1±0.38.23.2±0.48.73.2±0.29.83.3±0.27.43.2±0.411.93.4±0.59.1

Based on data from National Health and Nutrition Examination Survey 2005–2006 and 2007–2008, derived from two 24-hour dietary recalls per person, with population SDs adjusted for within-person vs between-person variation. All values are energy-adjusted using individual regressions or percent energy, and for comparability, means and proportions are reported for a 2000-kcal/d diet. To obtain actual mean consumption levels, the group means for each food or nutrient can be multiplied by the group-specific total calories (kcal/d) divided by 2000 kcal/d.

SD indicates standard deviation; NA, not available; EPA, eicosapentaenoic acid; DHA, docosahexaenoic acid; ALA, α-linoleic acid; and n-6-PUFA, ω-6-polyunsaturated fatty acid.

*See Table 5-1 for food group, serving size, and guideline definitions. For different age and sex subgroups here, the guideline cutpoints are standardized to a 2000-kcal/d diet to account for differences in caloric intake in these groups.

  • In 1999 to 2004, only 19.4% of hypertensive US adults were following a DASH-type diet (based on intake of fiber, magnesium, calcium, sodium, potassium, protein, total fat, saturated fat, and cholesterol). This represented a decrease from 26.7% of hypertensive US adults in 1988 to 1994.37

  • Among older US adults (≥60 years of age) in 1999 to 2002, 72% met guidelines for dietary cholesterol intake, but only between 18% and 32% met guidelines for the HEI food groups (meats, dairy, fruits, vegetables, and grains). On the basis of the HEI score, only 17% of older US adults consumed a good-quality diet. Higher HEI scores were seen in white adults and individuals with greater education; lower HEI scores were seen in black adults and smokers.38

Dietary Supplements

Use of dietary supplements is common in the United States among both adults and children:

  • More than half (53%) of US adults in 2003 to 2006 used dietary supplements, with the most common supplement being multivitamins and multiminerals (40% of men and women reporting use).39 It has been shown that most supplements are taken daily and for at least 2 years. Supplement use was associated with older age, higher education, greater PA, wine intake, lower BMI, and white race.40

  • One third (32%) of US children (birth to 18 years of age) used dietary supplements in 1999 to 2002, with the highest use (48.5%) occurring among 4- to 8-year-olds. The most common supplements were multivitamins and multiminerals (58% of supplement users). The primary nutrients supplemented (either by multivitamins and/or individual vitamins) included vitamin C (29% of US children), vitamin A (26%), vitamin D (26%), calcium (21%), and iron (19%). Supplement use was associated with higher family income, a smoke-free home environment, lower child BMI, and less screen time (television, video games, or computers).41

  • In a 2005 to 2006 telephone survey of US adults, 41.3% were making or had made in the past a serious weight-loss attempt. Of these, one third (33.9%) had used a dietary supplement for weight loss, with such use being more common in women (44.9%) than in men (19.8%) and in blacks (48.7%) or Hispanics (41.6%) than in whites (31.2%); in those with high school education or less (38.4%) than in those with some college or more (31.1%); and in those with household income less than $40 000 per year (41.8%) than in those with higher incomes (30.3%).42

  • Multiple trials of most dietary supplements, including folate, vitamin C, and vitamin E, have generally shown no significant benefits for CVD risk, and even potential for harm.36 For example, a multicenter randomized trial in patients with diabetic nephropathy found that B vitamin supplementation (folic acid 2.5 mg/d, vitamin B6 25 mg/d, and vitamin B12 1 mg/d) decreased glomerular filtration rate (GFR) and increased risk of MI and stroke compared with placebo.43 Fish oil supplements at doses of 1 to 2 g/d have shown CVD benefits in 2 large randomized, open-label trials and 1 large randomized, placebo-controlled trial (Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico [GISSI]-Prevenzione, Japan Eicosapentaenoic Acid Lipid Intervention Study, and GISSI-HF),44–46 but other smaller trials of fish oil have not shown significant effects on CVD risk.47 A meta-analysis of placebo-controlled trials (ie, excluding the 2 large open-label trials) showed a modest benefit for CVD mortality but no statistically significant effects on other CVD end points.48

Trends

Energy Balance

(See Chart 5-1)

Energy balance, or consumption of total calories appropriate for needs, has been steadily worsening in the United States over the past several decades, as evidenced by the dramatic increases in overweight and obesity among both children and adults across broad cross sections of sex, race/ethnicity, geographic residence, and socioeconomic status.49,50,50a

  • Although trends in total calories consumed are difficult to quantify exactly because of differing methods of serial national dietary surveys over time, multiple lines of evidence indicate that average total energy consumption has increased by at least 200 kcal/d per person in the past 3 decades.

  • Data from NHANES indicate that between 1971 and 2004, average total energy consumption among US adults increased by 22% in women (from 1542 to 1886 kcal/d) and by 10% in men (from 2450 to 2693 kcal/d). These increases are supported by data from the Nationwide Food Consumption Survey (1977–1978) and the Continuing Surveys of Food Intake (1989–1998).12

  • The increases in calories consumed during this time period are attributable primarily to greater average carbohydrate intake, particularly of starches, refined grains, and sugars (Foods and Nutrients section). Other specific changes related to increased caloric intake in the United States include larger portion sizes, greater food quantity and calories per meal, and increased consumption of sugar-sweetened beverages, snacks, commercially prepared (especially fast-food) meals, and higher-energy-density foods.6,12,51–55

  • Between 1977 and 1978 and 1994 and 1996, the average portion sizes for nearly all foods increased at fast-food outlets, other restaurants, and home. These included a 33% increase in the average portion of Mexican food (from 408 to 541 calories), a 34% increase in the average portion of cheeseburgers (from 397 to 533 calories), a 36% increase in the average portion of French fries (from 188 to 256 calories), and a 70% increase in the average portion of salty snacks such as crackers, potato chips, pretzels, puffed rice cakes, and popcorn (from 132 to 225 calories).12

  • Among US children 2 to 7 years of age, an estimated energy imbalance of only 110 to 165 kcal/d (the equivalent of one 12- to 16-oz bottle of soda/cola) was sufficient to account for the excess weight gain between 1988 and 1994 and 1999 and 2002.56

  • In a quantitative analysis using various US surveys between 1977 and 2006, the relations of changes in energy density, portion sizes, and number of daily eating/drinking occasions to changes in total energy intake were assessed.57 Decreases in energy density were actually linked to lower total energy intake over time, whereas increases in both portion size and number of eating occasions were linked to greater energy intake.

  • Among US children 2 to 18 years of age, increases in energy intake between 1977 and 2006 (179 kcal/d) were entirely attributable to substantial increases in energy eaten away from home (255 kcal/d).58 The percentage of energy eaten away from home increased from 23.4% to 33.9% during this time, with a shift toward energy from fast food as the largest contributor to foods away from home for all age groups.

Foods and Nutrients

Several changes in foods and nutrients have occurred over time. Selected changes are highlighted:

Macronutrients

(See Chart 5-1)

  • Starting in 1977 and continuing until the most recent dietary guidelines revision in 2010, a major focus of US dietary guidelines was reduction of dietary fats.58a During this time, average total fat consumption declined as a percent of calories from 36.9% to 33.4% in men and from 36.1% to 33.8% in women.13

  • Dietary guidelines during this time also emphasized carbohydrate consumption as the base of one’s dietary pattern,59 and more recently specifies the importance of complex rather than refined carbohydrates58a (eg, as the base of the Food Guide Pyramid).59 Total carbohydrate intake increased from 42.4% to 48.2% of calories in men and from 45.4% to 50.6% of calories in women.13 Evaluated as absolute intakes, the increase in total calories consumed during this period was attributable primarily to the greater consumption of carbohydrates, both as foods (starches and grains) and as beverages.60,61

Sugar-Sweetened Beverages

(See Chart 5-2)

  • Between 1965 and 2002, the average percentage of total calories consumed from beverages in the United States increased from 11.8% to 21.0% of energy, which represents an overall absolute increase of 222 cal/d per person.54 This increase was largely caused by increased consumption of sugar-sweetened beverages and alcohol: Average consumption of fruit juices went from 20 to 39 kcal/d; of milk, from 125 to 94 kcal/d; of alcohol, from 26 to 99 kcal/d; of sweetened fruit drinks, from 13 to 38 kcal/d; and of soda/cola, from 35 to 143 kcal/d.57

  • In addition to increased overall consumption, the average portion size of a single sugar-sweetened beverage increased by >50% between 1977 and 1996, from 13.1 to 19.9 fl oz.12

  • Among children and teenagers (2–19 years of age), the largest increases in consumption of sugar-sweetened beverages between 1988 to 1994 and 1999 to 2004 were seen among black and Mexican American youths compared with white youths.55

Fruits and Vegetables
  • Between 1994 and 2005, the average consumption of fruits and vegetables declined slightly, from a total of 3.4 to 3.2 servings per day. The proportions of men and women consuming combined fruits and vegetables ≥5 times per day were low (≈20% and 29%, respectively) and did not change during this period.62

Morbidity and Mortality

Effects on Cardiovascular Risk Factors

Dietary habits affect multiple cardiovascular risk factors, including both established risk factors (SBP, DBP, LDL cholesterol levels, HDL cholesterol levels, glucose levels, and obesity/weight gain) and novel risk factors (eg, inflammation, cardiac arrhythmias, endothelial cell function, triglyceride levels, lipoprotein[a] levels, and heart rate):

  • A DASH dietary pattern with low sodium reduced SBP by 7.1 mm Hg in adults without hypertension and by 11.5 mm Hg in adults with hypertension.63

  • Compared with the low-fat DASH diet, DASH-type diets that increased consumption of either protein or unsaturated fat had similar or greater beneficial effects on CVD risk factors. Compared with a baseline usual diet, each of the DASH-type diets, which included various percentages (27%–37%) of total fat and focused on whole foods such as fruits, vegetables, whole grains, and fish, as well as potassium and other minerals and low sodium, reduced SBP by 8 to 10 mm Hg, DBP by 4 to 5 mm Hg, and LDL cholesterol by 12 to 14 mg/dL. The diets that had higher levels of protein and unsaturated fat also lowered triglyceride levels by 16 and 9 mg/dL, respectively.64 The DASH-type diet higher in unsaturated fat also improved glucose-insulin homeostasis compared with the low-fat/high-carbohydrate DASH diet.65

  • In a meta-analysis of randomized controlled trials, consumption of 1% of calories from trans fat in place of saturated fat, monounsaturated fat, or polyunsaturated fat increased the ratio of total to HDL cholesterol by 0.031, 0.054, and 0.67; increased apolipoprotein B levels by 3, 10, and 11 mg/L; decreased apolipoprotein A-1 levels by 7, 5, and 3 mg/L; and increased lipoprotein(a) levels by 3.8, 1.4, and 1.1 mg/L, respectively.66

  • In meta-analyses of randomized controlled trials, consumption of eicosapentaenoic acid and docosahexaenoic acid for 212 weeks lowered SBP by 2.1 mm Hg67 and lowered resting heart rate by 2.5 beats per minute.68

  • In a pooled analysis of 25 randomized trials totaling 583 men and women both with and without hypercholesterolemia, nut consumption significantly improved blood lipid levels.69 For a mean consumption of 67 g of nuts per day, total cholesterol was reduced by 10.9 mg/dL (5.1%), LDL cholesterol by 10.2 mg/dL (7.4%), and the ratio of total cholesterol to HDL cholesterol by 0.24 (5.6% change; P<0.001 for each). Triglyceride levels were also reduced by 20.6 mg/dL (10.2%) in subjects with high triglycerides (2150 mg/dL). Different types of nuts had similar effects.69 A review of cross-sectional and prospective cohort studies suggests that higher intake of sugar-sweetened beverages is associated with greater visceral fat and higher risk of type 2 DM.70 In the Prospective Registry Evaluating Myocardial Infarction: Events and Recovery (PREMIER) study, a prospective analysis of the 810 participants indicated that a reduction in sugar-sweetened beverages of 1 serving per day was associated with a reduction in SBP of 1.8 mm Hg (95% CI, 1.2–2.4 mm Hg) and a reduction in DBP of 1.1 mm Hg (95% CI, 0.7–1.4 mm Hg).71

  • In a cross-sectional analysis among nearly 3000 adults from the United States and the United Kingdom, higher consumption of sugar-sweetened beverages was linked to higher BP (1.6 and 0.8 mm Hg higher SBP and DBP per serving per day, respectively, and 1.1 and 0.4 mm Hg higher SBP and DBP per serving per day after adjustment for weight and weight).72

  • In a randomized controlled trial, compared with a low-fat diet, 2 Mediterranean dietary patterns that included either virgin olive oil or mixed nuts lowered SBP by 5.9 and 7.1 mm Hg, plasma glucose by 7.0 and 5.4 mg/dL, fasting insulin by 16.7 and 20.4 pmol/L, the homeostasis model assessment index by 0.9 and 1.1, and the ratio of total to HDL cholesterol by 0.38 and 0.26 and raised HDL cholesterol by 2.9 and 1.6 mg/dL, respectively. The Mediterranean dietary patterns also lowered levels of C-reactive protein, interleukin-6, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1.73

Effects on Cardiovascular Outcomes

Because dietary habits affect a broad range of established and novel risk factors, estimation of the impact of nutritional factors on cardiovascular health by considering only a limited number of pathways (eg, only effects on lipids, BP, and obesity) will systematically underestimate or even misconstrue the actual total impact on cardiovascular health. Randomized controlled trials and prospective observational studies have been used to quantify the total effects of dietary habits on clinical outcomes:

Fats and Carbohydrates
  • In the Women’s Health Initiative (WHI) randomized clinical trial (n=48 835), reduction of total fat consumption from 37.8% energy (baseline) to 24.3% energy (at 1 year) and 28.8% energy (at 6 years) had no effect on incidence of CHD (RR, 0.98; 95% CI, 0.88–1.09), stroke (RR, 1.02; 95% CI, 0.90–1.15), or total CVD (RR, 0.98; 95% CI, 0.92–1.05) over a mean of 8.1 years.74 This was consistent with null results of 4 prior randomized clinical trials and multiple large prospective cohort studies that indicated little effect of total fat consumption on CVD risk.75

  • In 3 separate meta-analyses of prospective cohort studies, the largest of which included 21 studies with up to 2 decades of follow-up, saturated fat consumption overall had no significant association with incidence of CHD, stroke, or total CVD.76–78 In comparison, in a pooled individual-level analysis of 11 prospective cohort studies, the specific exchange of polyunsaturated fat consumption in place of saturated fat was associated with lower CHD risk, with 13% lower risk for each 5% energy exchange (RR, 0.87; 95% CI, 0.70–0.97).79 These findings are consistent with a meta-analysis of randomized controlled trials in which increased polyunsaturated fat consumption in place of saturated fat reduced CHD events, with 10% lower risk for each 5% energy exchange (RR, 0.90; 95% CI, 0.83–0.97).80

  • In a pooled analysis of individual-level data from 11 prospective cohort studies in the United States, Europe, and Israel that included 344 696 participants, each 5% higher energy consumption of carbohydrate in place of saturated fat was associated with a 7% higher risk of CHD (RR, 1.07; 95% CI, 1.01–1.14).79 Each 5% higher energy consumption of monounsaturated fat in place of saturated fat was not significantly associated with CHD risk.79

  • Together these findings suggest that reducing saturated fat without specifying the replacement may have minimal effects on CHD risk, whereas increasing polyunsaturated fats from vegetable oils will reduce CHD.36

  • In a meta-analysis of prospective cohort studies, each 2% of calories from trans fat was associated with a 23% higher risk of CHD (RR, 1.23; 95% CI, 1.11–1.37).81

  • In meta-analyses of prospective cohort studies, greater consumption of refined complex carbohydrates, starches, and sugars, as assessed by glycemic index or load, was associated with significantly higher risk of CHD and DM. When the highest category was compared with the lowest category, risk of CHD was 36% greater (glycemic load: RR, 1.36; 95% CI, 1.13–1.63), and risk of DM was 40% greater (glycemic index: RR, 1.40; 95% CI, 1.23–1.59).82,83

Foods and Beverages
  • In meta-analyses of prospective cohort studies, each daily serving of fruits or vegetables was associated with a 4% lower risk of CHD (RR, 0.96; 95% CI, 0.93–0.99) and a 5% lower risk of stroke (RR, 0.95; 95% CI, 0.92–0.97).84,85

  • In a meta-analysis of prospective cohort studies, greater whole grain intake (2.5 compared with 0.2 servings per day) was associated with a 21% lower risk of CVD events (RR, 0.79; 95% CI, 0.73–0.85), with similar estimates in men and women and for various outcomes (CHD, stroke, and fatal CVD). In contrast, refined grain intake was not associated with lower risk of CVD (RR, 1.07; 95% CI, 0.94–1.22).86

  • In a meta-analysis of 16 prospective cohort studies that included 326 572 generally healthy individuals in Europe, the United States, China, and Japan, fish consumption was associated with significantly lower risk of CHD mortality.87 Compared with no consumption, an estimated 250 mg of long-chain omega-3 fatty acids per day was associated with 35% lower risk of CHD death (P<0.001).

  • In a meta-analysis of prospective cohort and case-control studies from multiple countries, consumption of unprocessed red meat was not significantly associated with incidence of CHD. In contrast, each 50-g serving per day of processed meats (eg, sausage, bacon, hot dogs, deli meats) was associated with a higher incidence of CHD (RR, 1.42; 95% CI, 1.07–1.89).88

  • In a meta-analysis of prospective cohort studies that included 442 101 participants and 28 228 DM cases, unprocessed red meat consumption was associated with a higher risk of DM (RR, 1.19; 95% CI, 1.04–1.37, per 100 g/d). On a per g/d basis, risk of DM was nearly 7-fold higher for processed meat consumption (RR, 1.51; 95% CI, 1.25–1.83, per 50 g/d).89

  • In a meta-analysis of 6 prospective observational studies, nut consumption was associated with significantly lower incidence of CHD (comparing higher to low intake: RR, 0.70; 95% CI, 0.57–0.82).77

  • Higher consumption of dairy or milk products is associated with lower incidence of DM and trends toward lower risk of stroke.77,90,91 Some limited evidence suggests that these associations are stronger for low-fat dairy or milk than for other dairy products. Dairy consumption is not significantly associated with higher or lower risk of CHD.77,91

  • Among 88 520 generally healthy women in the Nurses’ Health Study who were 34 to 59 years of age in 1980 and were followed up from 1980–2004, regular consumption of sugar-sweetened beverages was independently associated with higher incidence of CHD, with 23% and 35% higher risk with 1 and ≥2 servings per day, respectively, compared with <1 per month.92 Among the 15 745 participants in the ARIC study, the OR for developing CHD was 2.59 for participants who had a serum uric acid level >9.0 mg/dL and who drank >1 sugar-sweetened soda per day.93

Sodium and Potassium
  • Lower estimated consumption of dietary sodium was not associated with lower CVD mortality in NHANES,94 although such findings may be limited by changes in behaviors that result from underlying risk (reverse causation). In a post hoc analysis of the Trials of Hypertension Prevention, participants randomized to low-sodium interventions had a 25% lower risk of CVD (RR, 0.75; 95% CI, 0.57–0.99) after 10 to 15 years of follow-up after the original trials.95

  • In a meta-analysis of small randomized trials of sodium reduction of ≥6 months’ duration, nonsignificant trends were seen toward fewer CVD events in subjects with normal BP (RR, 0.71; 95% CI, 0.42–1.20; n=200 events) or hypertension (RR, 0.84; 95% CI, 0.57–1.23; n=93 events), but the findings were not statistically significant, with relatively low statistical power because of the small numbers of events. Sodium restriction increased total mortality in trials of patients with CHF (RR, 2.59; 95% CI, 1.04–6.44), but these data were based on very few events (n=21 deaths).96

  • In a meta-analysis of 13 prospective cohorts that included 177 025 participants and >11 000 vascular events, higher sodium consumption was associated with greater risk of stroke (pooled RR, 1.23; 95% CI, 1.06–1.43; P=0.007) and a trend toward higher risk of CVD (1.14, 0.99–1.32; P=0.07). These associations were greater with larger differences in sodium intake and longer follow-up.97

  • In a meta-analysis of 15 prospective cohort samples that included 247 510 participants and 7066 strokes, 3058 CHD events, and 2497 total CVD events, each 1.64-g/d (42 mmol/d) higher potassium intake was associated with a 21% lower risk of stroke (RR, 0.79; 95% CI, 0.68–0.90) and trends toward lower risk of CHD and total CVD.98

Dietary Patterns
  • In a cohort of 380 296 US men and women, greater versus lower adherence to a Mediterranean dietary pattern, characterized by higher intakes of vegetables, legumes, nuts, fruits, whole grains, fish, and unsaturated fat and lower intakes of red and processed meat, was associated with a 22% lower cardiovascular mortality (RR, 0.78; 95% CI, 0.69–0.87).99 Similar findings have been seen for the Mediterranean dietary pattern and risk of incident CHD and stroke100 and for the DASH-type dietary pattern.101

  • In a cohort of 72 113 US female nurses, a dietary pattern characterized by higher intakes of vegetables, fruits, legumes, fish, poultry, and whole grains was associated with a 28% lower cardiovascular mortality (RR, 0.72; 95% CI, 0.60–0.87), whereas a dietary pattern characterized by higher intakes of processed meat, red meat, refined grains, French fries, and sweets/desserts was associated with a 22% higher cardiovascular mortality (RR, 1.22; 95% CI, 1.01–1.48).102 Similar findings have been seen in other cohorts and for other outcomes, including development of DM and metabolic syndrome.103–109

Impact on US Mortality
  • In one report that used consistent and comparable risk assessment methods and nationally representative data, the mortality effects in the United States of 12 modifiable dietary, lifestyle, and metabolic risk factors were assessed. High dietary salt consumption was estimated to be responsible for 102 000 annual deaths, low dietary omega-3 fatty acids for 84 000 annual deaths, high dietary trans fatty acids for 82 000 annual deaths, and low consumption of fruits and vegetables for 55 000 annual deaths.110

Cost

(See Chart 5-3)

The US Department of Agriculture forecast that the Consumer Price Index for all food would increase 3.0% to 4.0% in 2013 as retailers continued to pass on higher commodity and energy costs to consumers in the form of higher retail prices. The Consumer Price Index for food increased 3.7% in 2011. Prices for foods eaten at home increased 4.8% in 2011, whereas prices for foods eaten away from home increased by 1.9%.111

  • The proportion of total US food expenditures for meals outside the home, as a share of total food dollars, increased from 27% in 1961 to 40% in 1981 to 49% in 2011.59

  • The proportion of sales of meals and snacks from fast-food restaurants compared with total meals and snacks away from home increased from 5% in 1958 to 29% in 1982 to 36% in 2011.111

  • As a proportion of income, food has become less expensive over time in the United States. As a share of personal disposable income, average (mean) total food expenditures by families and individuals have decreased from 22.3% (1949) to 18.1% (1961) to 14.9% (1981) to 11.3% (2011). For any given year, the share of disposable income spent on food is inversely proportional to absolute income. The share increases as absolute income levels decline.111

  • Among 153 forms of fruits and vegetables priced with 2008 Nielsen Homescan data, price and calorie per portion of 20 fruits and vegetables were compared with 20 common snack foods such as cookies, chips, pastries, and crackers. Average price per portion of fruits and vegetables was 31 cents with an average of 57 calories per portion, compared with 33 cents and 183 calories per portion for snack foods.111

  • An overview of the costs of various strategies for primary prevention of CVD determined that the estimated costs per year of life gained were between $9800 and $18 000 for statin therapy, ≥$1500 for nurse screening and lifestyle advice, $500 to $1250 for smoking cessation, and $20 to $900 for population-based healthy eating.112

  • Each year, >$33 billion in medical costs and $9 billion in lost productivity resulting from HD, cancer, stroke, and DM are attributed to poor nutrition.113–117

  • Two separate cost-effectiveness analyses estimated that population reductions in dietary salt would not only be cost-effective but actually cost-saving.118,119 In 1 analysis, a 1.2 g/d reduction in dietary sodium was projected to reduce US annual cases of incident CHD by 60 000 to 120 000, stroke by 32 000 to 66 000, and total mortality by 44 000 to 92 000.119 If accomplished through a regulatory intervention, estimated savings in healthcare costs would be $10 to $24 billion annually.119 Such an intervention would be more cost-effective than using medications to lower BP in all people with hypertension.

Chart 5-1.

Chart 5-1. Age-adjusted trends in macronutrients and total calories consumed by US adults (20–74 years of age), 1971–2004. Data derived from National Center for Health Statistics. Health, United States 2007, With Chartbook on Trends in the Health of Americans.13

Chart 5-2.

Chart 5-2. Per-capita calories consumed from different beverages by US adults (≥19 years of age), 1965–2002. Data derived from Nationwide Food Consumption Surveys (1965, 1977–1978), National Health and Nutrition Examination Survey (1988–1994, 1999–2002), and Duffey and Popkin.57

Chart 5-3.

Chart 5-3. Total US food expenditures away from home and at home, 1977 and 2007. Data derived from Davis et al.59

Abbreviations Used in Chapter 5

ALA

α-linoleic acid

ARIC

Atherosclerosis Risk in Communities Study

BMI

body mass index

BP

blood pressure

BRFSS

Behavioral Risk Factor Surveillance System

CHD

coronary heart disease

CHF

congestive heart failure

CI

confidence interval

CVD

cardiovascular disease

DASH

Dietary Approaches to Stop Hypertension

DBP

diastolic blood pressure

DHA

docosahexaenoic acid

DM

diabetes mellitus

EPA

eicosapentaenoic acid

GFR

glomerular filtration rate

GISSI

Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico

HD

heart disease

HDL

high-density lipoprotein

HEI

Healthy Eating Index

LDL

low-density lipoprotein

MI

myocardial infarction

n-6-PUFA

ω-6-polyunsaturated fatty acid

NA

not available

NH

non-Hispanic

NHANES

National Health and Nutrition Examination Survey

OR

odds ratio

PA

physical activity

PREMIER

Prospective Registry Evaluating Myocardial Infarction: Events and Recovery

RR

relative risk

SBP

systolic blood pressure

SD

standard deviation

WHI

Women’s Health Initiative

References

References

  • 1. Ayala C, Kuklina E, Peralez J, Keenan N, Labarthe D; Centers for Disease Control and Prevention (CDC).Application of lower sodium intake recommendations to adults: United States, 1999–2006.MMWR Morb Mortal Wkly Rep. 2009; 127:e45–e58.Google Scholar
  • 2. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men.N Engl J Med. 2011; 364:2392–2404.CrossrefMedlineGoogle Scholar
  • 3. Ello-Martin JA, Ledikwe JH, Rolls BJ. The influence of food portion size and energy density on energy intake: implications for weight management.Am J Clin Nutr. 2005; 82(suppl):236S–241S.CrossrefMedlineGoogle Scholar
  • 4. Fisher JO, Kral TV. Super-size me: portion size effects on young children’s eating.Physiol Behav. 2008; 94:39–47.CrossrefMedlineGoogle Scholar
  • 5. Bowman SA, Vinyard BT. Fast food consumption of U.S. adults: impact on energy and nutrient intakes and overweight status.J Am Coll Nutr. 2004; 23:163–168.CrossrefMedlineGoogle Scholar
  • 6. Kant AK, Graubard BI. Eating out in America, 1987–2000: trends and nutritional correlates.Prev Med. 2004; 38:243–249.CrossrefMedlineGoogle Scholar
  • 7. Duerksen SC, Elder JP, Arredondo EM, Ayala GX, Slymen DJ, Campbell NR, Baquero B. Family restaurant choices are associated with child and adult overweight status in Mexican-American families.J Am Diet Assoc. 2007; 107:849–853.CrossrefMedlineGoogle Scholar
  • 8. Duffey KJ, Gordon-Larsen P, Jacobs DR, Williams OD, Popkin BM. Differential associations of fast food and restaurant food consumption with 3-y change in body mass index: the Coronary Artery Risk Development in Young Adults Study.Am J Clin Nutr. 2007; 85:201–208.CrossrefMedlineGoogle Scholar
  • 9. Rosenheck R. Fast food consumption and increased caloric intake: a systematic review of a trajectory towards weight gain and obesity risk.Obes Rev. 2008; 9:535–547.CrossrefMedlineGoogle Scholar
  • 10. Burger KS, Kern M, Coleman KJ. Characteristics of self-selected portion size in young adults.J Am Diet Assoc. 2007; 107:611–618.CrossrefMedlineGoogle Scholar
  • 11. Colapinto CK, Fitzgerald A, Taper LJ, Veugelers PJ. Children’s preference for large portions: prevalence, determinants, and consequences.J Am Diet Assoc. 2007; 107:1183–1190.CrossrefMedlineGoogle Scholar
  • 12. Nielsen SJ, Popkin BM. Patterns and trends in food portion sizes, 1977-1998.JAMA. 2003; 289:450–453.CrossrefMedlineGoogle Scholar
  • 13. National Center for Health Statistics.Health, United States, 2007: With Chartbook on Trends in the Health of Americans.Hyattsville, MD:National Center for Health Statistics;2007.http://www.cdc.gov/nchs/data/hus/hus07.pdf. Accessed July 20, 2011.Google Scholar
  • 14. Willett WC, Leibel RL. Dietary fat is not a major determinant of body fat.Am J Med. 2002; 113(suppl 9B):47S–59S.CrossrefMedlineGoogle Scholar
  • 15. Brehm BJ, D’Alessio DA. Weight loss and metabolic benefits with diets of varying fat and carbohydrate content: separating the wheat from the chaff.Nat Clin Pract Endocrinol Metab. 2008; 4:140–146.CrossrefMedlineGoogle Scholar
  • 16. van Dam RM, Seidell JC. Carbohydrate intake and obesity.Eur J Clin Nutr. 2007; 61(suppl 1):S75–S99.CrossrefMedlineGoogle Scholar
  • 17. Koh-Banerjee P, Chu NF, Spiegelman D, Rosner B, Colditz G, Willett W, Rimm E. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men.Am J Clin Nutr. 2003; 78:719–727.CrossrefMedlineGoogle Scholar
  • 18. Field AE, Willett WC, Lissner L, Colditz GA. Dietary fat and weight gain among women in the Nurses’ Health Study.Obesity (Silver Spring). 2007; 15:967–976.CrossrefMedlineGoogle Scholar
  • 19. Kavanagh K, Jones KL, Sawyer J, Kelley K, Carr JJ, Wagner JD, Rudel LL. Trans fat diet induces abdominal obesity and changes in insulin sensitivity in monkeys.Obesity (Silver Spring). 2007; 15:1675–1684.CrossrefMedlineGoogle Scholar
  • 20. Robinson TN. Reducing children’s television viewing to prevent obesity: a randomized controlled trial.JAMA. 1999; 282:1561–1567.CrossrefMedlineGoogle Scholar
  • 21. Gable S, Chang Y, Krull JL. Television watching and frequency of family meals are predictive of overweight onset and persistence in a national sample of school-aged children.J Am Diet Assoc. 2007; 107:53–61.CrossrefMedlineGoogle Scholar
  • 22. Temple JL, Giacomelli AM, Kent KM, Roemmich JN, Epstein LH. Television watching increases motivated responding for food and energy intake in children.Am J Clin Nutr. 2007; 85:355–361.CrossrefMedlineGoogle Scholar
  • 23. Dubois L, Farmer A, Girard M, Peterson K. Social factors and television use during meals and snacks is associated with higher BMI among pre-school children.Public Health Nutr. 2008; 11:1267–1279.CrossrefMedlineGoogle Scholar
  • 24. Epstein LH, Roemmich JN, Robinson JL, Paluch RA, Winiewicz DD, Fuerch JH, Robinson TN. A randomized trial of the effects of reducing television viewing and computer use on body mass index in young children.Arch Pediatr Adolesc Med. 2008; 162:239–245.CrossrefMedlineGoogle Scholar
  • 25. Patel SR, Hu FB. Short sleep duration and weight gain: a systematic review.Obesity (Silver Spring). 2008; 16:643–653.CrossrefMedlineGoogle Scholar
  • 26. Nordmann AJ, Nordmann A, Briel M, Keller U, Yancy WS, Brehm BJ, Bucher HC. Effects of low-carbohydrate vs low-fat diets on weight loss and cardiovascular risk factors: a meta-analysis of randomized controlled trials.Arch Intern Med. 2006; 166:285–293.CrossrefMedlineGoogle Scholar
  • 27. Gardner CD, Kiazand A, Alhassan S, Kim S, Stafford RS, Balise RR, Kraemer HC, King AC. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial [published correction appears in JAMA. 2007;298:178].JAMA. 2007; 297:969–977.CrossrefMedlineGoogle Scholar
  • 28. Shai I, Schwarzfuchs D, Henkin Y, Shahar DR, Witkow S, Greenberg I, Golan R, Fraser D, Bolotin A, Vardi H, Tangi-Rozental O, Zuk-Ramot R, Sarusi B, Brickner D, Schwartz Z, Sheiner E, Marko R, Katorza E, Thiery J, Fiedler GM, Bluher M, Stumvoll M, Stampfer MJ. Dietary Intervention Randomized Controlled Trial G. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet.N Engl J Med. 2008; 359:229–241.CrossrefMedlineGoogle Scholar
  • 29. Sacks FM, Bray GA, Carey VJ, Smith SR, Ryan DH, Anton SD, McManus K, Champagne CM, Bishop LM, Laranjo N, Leboff MS, Rood JC, de Jonge L, Greenway FL, Loria CM, Obarzanek E, Williamson DA. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates.N Engl J Med. 2009; 360:859–873.CrossrefMedlineGoogle Scholar
  • 30. Deleted in proof.Google Scholar
  • 31. Zhao G, Ford ES, Li C, Mokdad AH. Weight control behaviors in overweight/obese U.S. adults with diagnosed hypertension and diabetes.Cardiovasc Diabetol. 2009; 8:13.CrossrefMedlineGoogle Scholar
  • 32. Farley TA, Baker ET, Futrell L, Rice JC. The ubiquity of energy-dense snack foods: a national multicity study.Am J Public Health. 2010; 100:306–311.CrossrefMedlineGoogle Scholar
  • 33. Kumanyika S, Grier S. Targeting interventions for ethnic minority and low-income populations.Future Child. 2006; 16:187–207.CrossrefMedlineGoogle Scholar
  • 34. Sallis JF, Glanz K. The role of built environments in physical activity, eating, and obesity in childhood.Future Child. 2006; 16:89–108.CrossrefMedlineGoogle Scholar
  • 35. Li F, Harmer PA, Cardinal BJ, Bosworth M, Acock A, Johnson-Shelton D, Moore JM. Built environment, adiposity, and physical activity in adults aged 50-75.Am J Prev Med. 2008; 35:38–46.CrossrefMedlineGoogle Scholar
  • 36. Mozaffarian D, Appel LJ, Van Horn L. Components of a cardioprotective diet: new insights.Circulation. 2011; 123:2870–2891.LinkGoogle Scholar
  • 37. Mellen PB, Gao SK, Vitolins MZ, Goff DC. Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988–1994 and 1999–2004.Arch Intern Med. 2008; 168:308–314.CrossrefMedlineGoogle Scholar
  • 38. Ervin RB. Healthy Eating Index scores among adults, 60 years of age and over, by sociodemographic and health characteristics: United States, 1999–2002.Adv Data. 2008;1:1–16.MedlineGoogle Scholar
  • 39. Gahche J, Bailey R, Burt V, Hughes J, Yetley EA, Dwyer JT, Picciano MF, McDowell M, Sempos C. Dietary supplement use among U.S. adults has increased since NHANES III (1988–1994).NCHS Data Brief.2011;(61):1–8.MedlineGoogle Scholar
  • 40. Radimer K, Bindewald B, Hughes J, Ervin B, Swanson C, Picciano MF. Dietary supplement use by US adults: data from the National Health and Nutrition Examination Survey, 1999-2000.Am J Epidemiol. 2004; 160:339–349.CrossrefMedlineGoogle Scholar
  • 41. Picciano MF, Dwyer JT, Radimer KL, Wilson DH, Fisher KD, Thomas PR, Yetley EA, Moshfegh AJ, Levy PS, Nielsen SJ. Dietary supplement use among infants, children, and adolescents in the United States, 1999–2002.Arch Pediatr Adolesc Med. 2007; 161:978–985.CrossrefMedlineGoogle Scholar
  • 42. Pillitteri JL, Shiffman S, Rohay JM, Harkins AM, Burton SL, Wadden TA. Use of dietary supplements for weight loss in the United States: results of a national survey.Obesity (Silver Spring). 2008; 16:790–796.CrossrefMedlineGoogle Scholar
  • 43. House AA, Eliasziw M, Cattran DC, Churchill DN, Oliver MJ, Fine A, Dresser GK, Spence JD. Effect of B-vitamin therapy on progression of diabetic nephropathy: a randomized controlled trial.JAMA. 2010; 303:1603–1609.CrossrefMedlineGoogle Scholar
  • 44. Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico.Dietary supplementation with n-3 polyunsaturated fatty acids and vitamin E after myocardial infarction: results of the GISSI-Prevenzione trial [published corrections appear in Lancet. 2001;357:642 and Lancet. 2007;369:106]. Lancet. 1999; 354:447–455.CrossrefMedlineGoogle Scholar
  • 45. Yokoyama M, Origasa H, Matsuzaki M, Matsuzawa Y, Saito Y, Ishikawa Y, Oikawa S, Sasaki J, Hishida H, Itakura H, Kita T, Kitabatake A, Nakaya N, Sakata T, Shimada K, Shirato K; Japan EPA Lipid Intervention Study (JELIS) Investigators. Effects of eicosapentaenoic acid on major coronary events in hypercholesterolaemic patients (JELIS): a randomised open-label, blinded endpoint analysis [published correction appears in Lancet. 2007;370:220].Lancet. 2007; 369:1090–1098.CrossrefMedlineGoogle Scholar
  • 46. Tavazzi L, Maggioni AP, Marchioli R, Barlera S, Franzosi MG, Latini R, Lucci D, Nicolosi GL, Porcu M, Tognoni G; GISSI-HF Investigators. Effect of n-3 polyunsaturated fatty acids in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial.Lancet. 2008; 372:1223–1230.CrossrefMedlineGoogle Scholar
  • 47. Mozaffarian D, Wu JH. Omega-3 fatty acids and cardiovascular disease: effects on risk factors, molecular pathways, and clinical events.J Am Coll Cardiol. 2011; 58:2047–2067.CrossrefMedlineGoogle Scholar
  • 48. Kwak SM, Myung SK, Lee YJ, Seo HG; Korean Meta-analysis Study Group. Efficacy of omega-3 fatty acid supplements (eicosapentaenoic acid and docosahexaenoic acid) in the secondary prevention of cardiovascular disease: a meta-analysis of randomized, double-blind, placebo-controlled trials.Arch Intern Med. 2012; 172:686–694.CrossrefMedlineGoogle Scholar
  • 49. Ford ES, Li C, Zhao G, Tsai J. Trends in obesity and abdominal obesity among adults in the United States from 1999–2008.Int J Obes (Lond). 2011; 35:736–743.CrossrefMedlineGoogle Scholar
  • 50. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.JAMA. 2012; 307:491–497.CrossrefMedlineGoogle Scholar
  • 50a. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010.JAMA. 2012; 307:483–490.CrossrefMedlineGoogle Scholar
  • 51. Briefel RR, Johnson CL. Secular trends in dietary intake in the United States.Annu Rev Nutr. 2004; 24:401–431.CrossrefMedlineGoogle Scholar
  • 52. Kant AK, Graubard BI. Secular trends in patterns of self-reported food consumption of adult Americans: NHANES 1971–1975 to NHANES 1999–2002.Am J Clin Nutr. 2006; 84:1215–1223.CrossrefMedlineGoogle Scholar
  • 53. Popkin BM, Armstrong LE, Bray GM, Caballero B, Frei B, Willett WC. A new proposed guidance system for beverage consumption in the United States [published correction appears in Am J Clin Nutr. 2007;86:525].Am J Clin Nutr. 2006; 83:529.CrossrefMedlineGoogle Scholar
  • 54. Duffey KJ, Popkin BM. Shifts in patterns and consumption of beverages between 1965 and 2002.Obesity (Silver Spring). 2007; 15:2739–2747.CrossrefMedlineGoogle Scholar
  • 55. Wang YC, Bleich SN, Gortmaker SL. Increasing caloric contribution from sugar-sweetened beverages and 100% fruit juices among US children and adolescents, 1988–2004.Pediatrics. 2008; 121:e1604–e1614.CrossrefMedlineGoogle Scholar
  • 56. Wang YC, Gortmaker SL, Sobol AM, Kuntz KM. Estimating the energy gap among US children: a counterfactual approach.Pediatrics. 2006; 118:e1721–e1733.CrossrefMedlineGoogle Scholar
  • 57. Duffey KJ, Popkin BM. Energy density, portion size, and eating occasions: contributions to increased energy intake in the United States, 1977–2006.PLoS Med. 2011; 8:e1001050.CrossrefMedlineGoogle Scholar
  • 58. Poti JM, Popkin BM. Trends in energy intake among US children by eating location and food source, 1977–2006.J Am Diet Assoc. 2011; 111:1156–1164.CrossrefMedlineGoogle Scholar
  • 58a. US Department of Health & Human Services.Dietary Guidelines for Americans, 2010.http://health.gov/dietaryguidelines/2010.asp. Accessed November 29, 2012.Google Scholar
  • 59. Davis C, Saltos E. Dietary recommendations and how they have changed over time. In: , Frazao E, ed. America’s Eating Habits: Changes and Consequences. Washington, DC: US Department of Agriculture; 1999:33–50. Agriculture Information Bulletin No. 750.Google Scholar
  • 60. Centers for Disease Control and Prevention (CDC). Trends in intake of energy and macronutrients–United States, 1971–2000.MMWR Morb Mortal Wkly Rep. 2004; 53:80–82.MedlineGoogle Scholar
  • 61. Egan SK, Bolger PM, Carrington CD. Update of US FDA’s Total Diet Study food list and diets.J Expo Sci Environ Epidemiol. 2007; 17:573–582.CrossrefMedlineGoogle Scholar
  • 62. Blanck HM, Gillespie C, Kimmons JE, Seymour JD, Serdula MK. Trends in fruit and vegetable consumption among U.S. men and women, 1994-2005.Prev Chronic Dis. 2008; 5:A35.MedlineGoogle Scholar
  • 63. Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D, Obarzanek E, Conlin PR, Miller ER, Simons-Morton DG, Karanja N, Lin PH; DASH-Sodium Collaborative Research Group. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet.N Engl J Med. 2001; 344:3–10.CrossrefMedlineGoogle Scholar
  • 64. Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, Conlin PR, Erlinger TP, Rosner BA, Laranjo NM, Charleston J, McCarron P, Bishop LM; OmniHeart Collaborative Research Group. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial.JAMA. 2005; 294:2455–2464.CrossrefMedlineGoogle Scholar
  • 65. Gadgil M, Anderson C, Sacks F, Yeung E, Appel L, Miller E. Effect of carbohydrate, unsaturated fats and protein intake on measures of insulin sensitivity: results from the OmniHeart Trial.Circulation2011; 124:A15253. Abstract.LinkGoogle Scholar
  • 66. Mozaffarian D, Aro A, Willett WC. Health effects of trans-fatty acids: experimental and observational evidence.Eur J Clin Nutr. 2009; 63(suppl 2):S5–S21.CrossrefMedlineGoogle Scholar
  • 67. Geleijnse JM, Giltay EJ, Grobbee DE, Donders AR, Kok FJ. Blood pressure response to fish oil supplementation: metaregression analysis of randomized trials.J Hypertens. 2002; 20:1493–1499.CrossrefMedlineGoogle Scholar
  • 68. Mozaffarian D, Geelen A, Brouwer IA, Geleijnse JM, Zock PL, Katan MB. Effect of fish oil on heart rate in humans: a meta-analysis of randomized controlled trials.Circulation. 2005; 112:1945–1952.LinkGoogle Scholar
  • 69. Sabaté J, Oda K, Ros E. Nut consumption and blood lipid levels: a pooled analysis of 25 intervention trials.Arch Intern Med. 2010; 170:821–827.CrossrefMedlineGoogle Scholar
  • 70. Hu FB, Malik VS. Sugar-sweetened beverages and risk of obesity and type 2 diabetes: epidemiologic evidence.Physiol Behav. 2010; 100:47–54.CrossrefMedlineGoogle Scholar
  • 71. Chen L, Caballero B, Mitchell DC, Loria C, Lin PH, Champagne CM, Elmer PJ, Ard JD, Batch BC, Anderson CA, Appel LJ. Reducing consumption of sugar-sweetened beverages is associated with reduced blood pressure: a prospective study among United States adults [published correction appears in Circulation. 2010;122:e408].Circulation. 2010; 121:2398–2406.LinkGoogle Scholar
  • 72. Brown IJ, Stamler J, Van Horn L, Robertson CE, Chan Q, Dyer AR, Huang CC, Rodriguez BL, Zhao L, Daviglus ML, Ueshima H, Elliott P; International Study of Macro/Micronutrients and Blood Pressure Research Group. Sugar-sweetened beverage, sugar intake of individuals, and their blood pressure: international study of macro/micronutrients and blood pressure.Hypertension. 2011; 57:695–701.LinkGoogle Scholar
  • 73. Estruch R, Martínez-González MA, Corella D, Salas-Salvadó J, Ruiz-Gutiérrez V, Covas MI, Fiol M, Gómez-Gracia E, López-Sabater MC, Vinyoles E, Arós F, Conde M, Lahoz C, Lapetra J, Sáez G, Ros E; PREDIMED Study Investigators. Effects of a Mediterranean-style diet on cardiovascular risk factors: a randomized trial.Ann Intern Med. 2006; 145:1–11.CrossrefMedlineGoogle Scholar
  • 74. Howard BV, Van Horn L, Hsia J, Manson JE, Stefanick ML, Wassertheil-Smoller S, Kuller LH, LaCroix AZ, Langer RD, Lasser NL, Lewis CE, Limacher MC, Margolis KL, Mysiw WJ, Ockene JK, Parker LM, Perri MG, Phillips L, Prentice RL, Robbins J, Rossouw JE, Sarto GE, Schatz IJ, Snetselaar LG, Stevens VJ, Tinker LF, Trevisan M, Vitolins MZ, Anderson GL, Assaf AR, Bassford T, Beresford SA, Black HR, Brunner RL, Brzyski RG, Caan B, Chlebowski RT, Gass M, Granek I, Greenland P, Hays J, Heber D, Heiss G, Hendrix SL, Hubbell FA, Johnson KC, Kotchen JM. Low-fat dietary pattern and risk of cardiovascular disease: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial.JAMA. 2006; 295:655–666.CrossrefMedlineGoogle Scholar
  • 75. World Health Organization, Food and Agriculture Organization of the United Nations. Diet, Nutrition and the Prevention of Chronic Diseases: Report of a Joint WHO/FAO Expert Consultation (WHO Technical Report Series 916).Geneva, Switzerland:World Health Organization;2003.Google Scholar
  • 76. Skeaff CM, Miller J. Dietary fat and coronary heart disease: summary of evidence from prospective cohort and randomised controlled trials.Ann Nutr Metab. 2009; 55:173–201.CrossrefMedlineGoogle Scholar
  • 77. Mente A, de Koning L, Shannon HS, Anand SS. A systematic review of the evidence supporting a causal link between dietary factors and coronary heart disease.Arch Intern Med. 2009; 169:659–669.CrossrefMedlineGoogle Scholar
  • 78. Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease.Am J Clin Nutr. 2010; 91:535–546.CrossrefMedlineGoogle Scholar
  • 79. Jakobsen MU, O’Reilly EJ, Heitmann BL, Pereira MA, Bälter K, Fraser GE, Goldbourt U, Hallmans G, Knekt P, Liu S, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A. Major types of dietary fat and risk of coronary heart disease: a pooled analysis of 11 cohort studies.Am J Clin Nutr. 2009; 89:1425–1432.CrossrefMedlineGoogle Scholar
  • 80. Mozaffarian D, Micha R, Wallace S. Effects on coronary heart disease of increasing polyunsaturated fat in place of saturated fat: a systematic review and meta-analysis of randomized controlled trials.PLoS Med. 2010; 7:e1000252.CrossrefMedlineGoogle Scholar
  • 81. Mozaffarian D, Katan MB, Ascherio A, Stampfer MJ, Willett WC. Trans fatty acids and cardiovascular disease.N Engl J Med. 2006; 354:1601–1613.CrossrefMedlineGoogle Scholar
  • 82. Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, Mitchell P, Brand-Miller JC. Glycemic index, glycemic load, and chronic disease risk: a meta-analysis of observational studies.Am J Clin Nutr. 2008; 87:627–637.CrossrefMedlineGoogle Scholar
  • 83. Dong JY, Zhang YH, Wang P, Qin LQ. Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease.Am J Cardiol. 2012; 109:1608–1613.CrossrefMedlineGoogle Scholar
  • 84. Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies.J Nutr. 2006; 136:2588–2593.CrossrefMedlineGoogle Scholar
  • 85. Dauchet L, Amouyel P, Dallongeville J. Fruit and vegetable consumption and risk of stroke: a meta-analysis of cohort studies.Neurology. 2005; 65:1193–1197.CrossrefMedlineGoogle Scholar
  • 86. Mellen PB, Walsh TF, Herrington DM. Whole grain intake and cardiovascular disease: a meta-analysis.Nutr Metab Cardiovasc Dis. 2008; 18:283–290.CrossrefMedlineGoogle Scholar
  • 87. Harris WS, Mozaffarian D, Lefevre M, Toner CD, Colombo J, Cunnane SC, Holden JM, Klurfeld DM, Morris MC, Whelan J. Towards establishing dietary reference intakes for eicosapentaenoic and docosahexaenoic acids.J Nutr. 2009; 139:804S–819S.CrossrefMedlineGoogle Scholar
  • 88. Micha R, Wallace SK, Mozaffarian D. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis.Circulation. 2010; 121:2271–2283.LinkGoogle Scholar
  • 89. Pan A, Sun Q, Bernstein AM, Schulze MB, Manson JE, Willett WC, Hu FB. Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis.Am J Clin Nutr. 2011; 94:1088–1096.CrossrefMedlineGoogle Scholar
  • 90. Tong X, Dong JY, Wu ZW, Li W, Qin LQ. Dairy consumption and risk of type 2 diabetes mellitus: a meta-analysis of cohort studies.Eur J Clin Nutr. 2011; 65:1027–1031.CrossrefMedlineGoogle Scholar
  • 91. Soedamah-Muthu SS, Ding EL, Al-Delaimy WK, Hu FB, Engberink MF, Willett WC, Geleijnse JM. Milk and dairy consumption and incidence of cardiovascular diseases and all-cause mortality: dose-response meta-analysis of prospective cohort studies.Am J Clin Nutr. 2011; 93:158–171.CrossrefMedlineGoogle Scholar
  • 92. Fung TT, Malik V, Rexrode KM, Manson JE, Willett WC, Hu FB. Sweetened beverage consumption and risk of coronary heart disease in women.Am J Clin Nutr. 2009; 89:1037–1042.CrossrefMedlineGoogle Scholar
  • 93. Bomback AS, Derebail VK, Shoham DA, Anderson CA, Steffen LM, Rosamond WD, Kshirsagar AV. Sugar-sweetened soda consumption, hyperuricemia, and kidney disease.Kidney Int. 2010; 77:609–616.CrossrefMedlineGoogle Scholar
  • 94. Cohen HW, Hailpern SM, Alderman MH. Sodium intake and mortality follow-up in the Third National Health and Nutrition Examination Survey (NHANES III).J Gen Intern Med. 2008; 23:1297–1302.CrossrefMedlineGoogle Scholar
  • 95. Cook NR, Cutler JA, Obarzanek E, Buring JE, Rexrode KM, Kumanyika SK, Appel LJ, Whelton PK. Long term effects of dietary sodium reduction on cardiovascular disease outcomes: observational follow-up of the Trials Of Hypertension Prevention (TOHP).BMJ. 2007; 334:885–888.CrossrefMedlineGoogle Scholar
  • 96. Taylor RS, Ashton KE, Moxham T, Hooper L, Ebrahim S. Reduced dietary salt for the prevention of cardiovascular disease: a meta-analysis of randomized controlled trials (Cochrane review).Am J Hypertens. 2011; 24:843–853.CrossrefMedlineGoogle Scholar
  • 97. Strazzullo P, D’Elia L, Kandala NB, Cappuccio FP. Salt intake, stroke, and cardiovascular disease: meta-analysis of prospective studies.BMJ. 2009; 339:b4567.CrossrefMedlineGoogle Scholar
  • 98. D’Elia L, Barba G, Cappuccio FP, Strazzullo P. Potassium intake, stroke, and cardiovascular disease: a meta-analysis of prospective studies.J Am Coll Cardiol. 2011; 57:1210–1219.CrossrefMedlineGoogle Scholar
  • 99. Mitrou PN, Kipnis V, Thiébaut AC, Reedy J, Subar AF, Wirfält E, Flood A, Mouw T, Hollenbeck AR, Leitzmann MF, Schatzkin A. Mediterranean dietary pattern and prediction of all-cause mortality in a US population: results from the NIH-AARP Diet and Health Study.Arch Intern Med. 2007; 167:2461–2468.CrossrefMedlineGoogle Scholar
  • 100. Fung TT, Rexrode KM, Mantzoros CS, Manson JE, Willett WC, Hu FB. Mediterranean diet and incidence of and mortality from coronary heart disease and stroke in women [published correction appears in Circulation. 2009;119:e379].Circulation. 2009; 119:1093–1100.LinkGoogle Scholar
  • 101. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women [published correction appears in Arch Intern Med. 2008;168:1276].Arch Intern Med. 2008; 168:713–720.CrossrefMedlineGoogle Scholar
  • 102. Heidemann C, Schulze MB, Franco OH, van Dam RM, Mantzoros CS, Hu FB. Dietary patterns and risk of mortality from cardiovascular disease, cancer, and all causes in a prospective cohort of women.Circulation. 2008; 118:230–237.LinkGoogle Scholar
  • 103. Osler M, Heitmann BL, Gerdes LU, Jørgensen LM, Schroll M. Dietary patterns and mortality in Danish men and women: a prospective observational study.Br J Nutr. 2001; 85:219–225.CrossrefMedlineGoogle Scholar
  • 104. van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in U.S. men.Ann Intern Med. 2002; 136:201–209.CrossrefMedlineGoogle Scholar
  • 105. Heidemann C, Hoffmann K, Spranger J, Klipstein-Grobusch K, Möhlig M, Pfeiffer AF, Boeing H; European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study Cohort. A dietary pattern protective against type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam Study cohort.Diabetologia. 2005; 48:1126–1134.CrossrefMedlineGoogle Scholar
  • 106. Brunner EJ, Mosdøl A, Witte DR, Martikainen P, Stafford M, Shipley MJ, Marmot MG. Dietary patterns and 15-y risks of major coronary events, diabetes, and mortality.Am J Clin Nutr. 2008; 87:1414–1421.CrossrefMedlineGoogle Scholar
  • 107. Lutsey PL, Steffen LM, Stevens J. Dietary intake and the development of the metabolic syndrome: the Atherosclerosis Risk in Communities study.Circulation. 2008; 117:754–761.LinkGoogle Scholar
  • 108. Fitzgerald KC, Chiuve SE, Buring JE, Ridker PM, Glynn RJ. Comparison of associations of adherence to a Dietary Approaches to Stop Hypertension (DASH)-style diet with risks of cardiovascular disease and venous thromboembolism.J Thromb Haemost. 2012; 10:189–198.CrossrefMedlineGoogle Scholar
  • 109. Joosten MM, Grobbee DE, van der A DL, Verschuren WM, Hendriks HF, Beulens JW. Combined effect of alcohol consumption and lifestyle behaviors on risk of type 2 diabetes.Am J Clin Nutr. 2010; 91:1777–1783.CrossrefMedlineGoogle Scholar
  • 110. Danaei G, Ding EL, Mozaffarian D, Taylor B, Rehm J, Murray CJ, Ezzati M. The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors [published correction appears in PLoS Med. 2011;8(1)].PLoS Med. 2009; 6:e1000058.CrossrefMedlineGoogle Scholar
  • 111. US Department of Agriculture. Food expenditures.http://www.ers.usda.gov/Briefing/CPIFoodAndExpenditures/Data/. Accessed October 31, 2012.Google Scholar
  • 112. Brunner E, Cohen D, Toon L. Cost effectiveness of cardiovascular disease prevention strategies: a perspective on EU food based dietary guidelines.Public Health Nutr. 2001; 4(2B):711–715.MedlineGoogle Scholar
  • 113. Centers for Disease Control and Prevention.Preventing Chronic Diseases: Investing Wisely in Health: Preventing Obesity and Chronic Diseases Through Good Nutrition and Physical Activity.Atlanta, Ga: Centers for Disease Control and Prevention; Revised 2008. http://www.cdc.gov/nccdphp/publications/factsheets/prevention/pdf/obesity.pdf. Accessed July 21, 2011.Google Scholar
  • 114. Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, Franklin B, Kris-Etherton P, Harris WS, Howard B, Karanja N, Lefevre M, Rudel L, Sacks F, Van Horn L, Winston M, Wylie-Rosett J.Diet and lifestyle recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee [published corrections appear in Circulation. 2006;114:e27 and Circulation. 2006;114:e629].Circulation. 2006; 114:82–96.LinkGoogle Scholar
  • 115. Deleted in proof.Google Scholar
  • 116. International Society for the Study of Fatty Acids and Lipids. Recommendations for Intake of Polyunsaturated Fatty Acids in Healthy Adults.Devon, UK: International Society for the Study of Fatty Acids and Lipids; 2004.Google Scholar
  • 117. Food and Nutrition Board, Institute of Medicine.Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: National Academies Press; 2005.Google Scholar
  • 118. Smith-Spangler CM, Juusola JL, Enns EA, Owens DK, Garber AM. Population strategies to decrease sodium intake and the burden of cardiovascular disease: a cost-effectiveness analysis.Ann Intern Med. 2010; 152:481–487, W170–W173.CrossrefMedlineGoogle Scholar
  • 119. Bibbins-Domingo K, Chertow GM, Coxson PG, Moran A, Lightwood JM, Pletcher MJ, Goldman L. Projected effect of dietary salt reductions on future cardiovascular disease.N Engl J Med. 2010; 362:590–599.CrossrefMedlineGoogle Scholar
  • 120. Joint FAO/WHO Expert Consultation on Fats and Fatty Acids in Human Nutrition, 2008.http://www.who.int/nutrition/topics/FFA_summary_rec_conclusion.pdf. Accessed November 17, 2010.Google Scholar

6. Overweight and Obesity

See Table 6-1 and Charts 6-1 through 6-3

Prevalence

Youth

(See Table 6-1 and Chart 6-1)

  • The prevalence of overweight and obesity in children 2 to 5 years of age, based on a BMI-for-age value ≥85th percentile of the 2000 CDC growth charts, was 26% for non-Hispanic white boys and 21% for non-Hispanic white girls, 31% for non-Hispanic black boys and 27% for non-Hispanic black girls, and 32% for Mexican American boys and 33% for Mexican American girls according to 2009–2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 30% for non-Hispanic white boys and 25% for non-Hispanic white girls, 41% for non-Hispanic black boys and 44% for non-Hispanic black girls, and 39% for Mexican American boys and 40% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 32% for non-Hispanic white boys and 28% for non-Hispanic white girls, 37% for non-Hispanic black boys and 45% for non-Hispanic black girls, and 46% for Mexican American boys and 41% for Mexican American girls.2

  • The prevalence of obesity in children 2 to 5 years of age, based on BMI-for-age values ≥95th percentile of the 2000 CDC growth charts, was 12% for non-Hispanic white boys and 6% for non-Hispanic white girls, 21% for non-Hispanic black boys and 17% for non-Hispanic black girls, and 19% for Mexican American boys and 12% for Mexican American girls according to 2009–2010 data from NHANES (NCHS). In children 6 to 11 years of age, the prevalence was 17% for non-Hispanic white boys and 11% for non-Hispanic white girls, 30% for non-Hispanic black boys and 28% for non-Hispanic black girls, and 22% for Mexican American boys and 22% for Mexican American girls. In children 12 to 19 years of age, the prevalence was 18% for non-Hispanic white boys and 15% for non-Hispanic white girls, 23% for non-Hispanic black boys and 25% for non-Hispanic black girls, and 29% for Mexican American boys and 19% for Mexican American girls.2

  • Overall, 18% of US children and adolescents 6 to 19 years of age have BMI-for-age values ≥95th percentile of the 2000 CDC growth charts for the United States (NHANES [2009–2010], NCHS).2

  • NHANES 2009–2010 found that 16.9% (15.4%–18.4%) of youth aged 2 to 19 years were obese, which was unchanged from 2007–2008. Rates of overweight and obesity (≥85th BMI percentile) were 39.1% for Hispanics, 39.4% for Mexican Americans, 27.9% for non-Hispanic whites, and 39.1% for non-Hispanic blacks.2

  • A study of >8500 4-year-olds in the Early Childhood Longitudinal Study, Birth Cohort (National Center for Education Statistics) found that 1 in 5 were obese. Almost 13% of Asian children, 16% of white children, nearly 21% of black children, 22% of Hispanic children, and 31% of American Indian children were obese. Children were considered obese if their BMI was ≥95th percentile on the basis of CDC BMI growth charts. For 4-year-olds, that would be a BMI of ≈18 kg/m2.3

  • Childhood sociodemographic factors may contribute to sex disparities in obesity prevalence. A study of data from the National Longitudinal Study of Adolescent Health found that parental education consistently modified sex disparity in blacks. The sex gap was largest in those with low parental education (16.7% of men compared with 45.4% of women were obese) and smallest in those with high parental education (28.5% of men compared with 31.4% of women were obese). In whites, there was little overall sex difference in obesity prevalence.4

  • The obesity epidemic is disproportionally more rampant among children living in low-income, low-education, and higher-unemployment households, according to data from the National Survey of Children’s Health.5

  • Data from 2011 show that American Indian/Alaskan Native youth have an obesity rate of 17.7%, whereas rates are 14.7% for Hispanics, 10.6% for non-Hispanic blacks, 10.3% for non-Hispanic whites, and 9.3% for Asian/Pacific Islanders.6

  • According to 1999–2008 NHANES survey data, lowest-income girls had an obesity prevalence of 17.9% compared with 13.1% among those with higher income; similar observations were observed for boys (20.6% versus 15.6%, respectively).7

  • According to the US National Longitudinal Study of Adolescent Health, 1.0% of adolescents were severely obese in 1996 (defined as age <20 years and BMI ≥95th sex-specific BMI-for-age growth chart or BMI ≥30 kg/m2); the majority (70.5%) maintained this weight status into adulthood. Obese adolescents had a 16-fold increased risk of becoming severely obese adults compared with those with normal weight or those who were overweight.8

  • NHANES 2003–2004 and 2005–2006 data were used to determine overweight and obesity prevalence in rural versus urban youth; the results showed that 39% of rural versus 32% of urban children had BMI >85th percentile.9

Adults

(See Table 6-1 and Chart 6-2)

  • According to NHANES 2007-2010 (unpublished NHLBI tabulations):

—Overall, 68% of US adults were overweight or obese (73% of men and 64% of women).

—Among men, Mexican-Americans (81%) and non-Hispanic whites (73%) were more likely to be overweight or obese than non-Hispanic blacks (69%).

—Among women, non-Hispanic blacks (80%) and Mexican-Americans (78%) were more likely to be overweight or obese than non-Hispanic whites (60%).

—Among US adults, 35% were obese (35% of men and 36% of women).

—Among men, non-Hispanic blacks (38%) and Mexican-Americans (36%) were more likely to be obese than non-Hispanic whites (34%).

—Among women, non-Hispanic blacks (54%) and Mexican-Americans (45%) were more likely to be obese than non-Hispanic whites (33%).

  • When estimates were based on self-reported height and weight in the BRFSS/CDC survey in 2011, the prevalence of obesity ranged from 20.7% in Colorado to 34.9% in Mississippi. The median percentage by state was 27.8%.10 Additionally, no state met the Healthy People 2010 goal of reducing obesity to 15% of adults.11

  • On the basis of 2011 data on self-reported weights and heights from the 2011 NHIS12:

—Blacks ≥18 years of age (26.4%), American Indians or Alaska Natives (27.6%), and whites (36.6%) were less likely than Asians (56.7%) to be at a healthy weight.12

— Blacks ≥18 years of age (38.9%) and American Indians or Alaska Natives (40.8%) were more likely to be obese than were whites (27.2%) and Asians (9.3%).12

  • Most adults in Asian subgroups were in the healthy weight range, with rates ranging from 51% for Filipino adults to 68% for Chinese adults. Although the prevalence of obesity is low within the Asian adult population, Filipino adults (14%) were more than twice as likely to be obese (BMI ≥30 kg/m2) as Asian Indian (6%), Vietnamese (5%), or Chinese (4%) adults.13

  • According to the 2008 National Healthcare Disparities Report (on the basis of NHANES 2003–2006)14:

—Approximately 64.8% of obese adults were told by a doctor or health professional that they were overweight.

—The proportion of obese adults told that they were overweight was significantly lower for non-Hispanic blacks (60.5%) and Mexican Americans (57.1%) than for non-Hispanic whites (66.4%), for middle-income people than for high-income people (62.4% versus 70.6%), and for adults with less than a high school education than for those with any college education (59.2% versus 70.3%).14

  • As judged by an analysis of data from the Multi-Ethnic Study of Atherosclerosis (MESA), a large proportion of white, black, and Hispanic participants were overweight (60%–85%) or obese (30%–50%), whereas fewer Chinese American participants were overweight (33%) or obese (5%).15

  • In a cross-sectional study of 7000 elderly subjects at high cardiovascular risk, adherence to a Mediterranean diet, moderate alcohol consumption, the expenditure of ≥200 kcal/d in PA, and nonsmoking were associated with a 1.3-kg/m2 (0.9–1.7) lower BMI and a 4.3-cm (3.1–5.4) lower waist circumference than having ≤1 healthy lifestyle factor.16

  • Among severely obese (class II or III) patients, a 1-year intensive lifestyle intervention of diet and PA achieved clinically significant weight loss and improvement in cardiometabolic factors in addition to reduction in visceral and hepatic fat content.17

Trends

Youth

(See Chart 6-3)

  • Among infants and children between 6 and 23 months of age, the prevalence of high weight for recumbent length was 7% in 1976–1980 and 12% in 2003–2006 (NHANES, NCHS).18

  • The obesity epidemic in children continues to grow on the basis of recent data from the Bogalusa Heart Study. Compared with 1973 to 1974, the proportion of children 5 to 17 years of age who were obese was 5 times higher in 2008 to 2009.19

  • A comparison of NHANES 2009–2010 data with 1999–2000 data demonstrates an increase in obesity prevalence in male youth of 5% (OR, 1.05; 95% CI, 1.01–1.10) but not in female youth (OR, 1.02; 95% CI, 0.98–1.07).2

Adults
  • On the basis of 2009 self-reported BRFSS data, overall obesity prevalence was 26.7% in the United States, with rates of 27.4% in men and 26.0% in women. By race/ethnicity, the prevalence of obesity among non-Hispanic whites was 25.2%, whereas it was 36.8% among non-Hispanic blacks and 30.7% among Hispanics. There was an inverse association by education level: College graduates had a 20.8% rate of obesity, whereas those who attained less than a high school education had an obesity prevalence of 32.9%.20

  • The prevalence of obesity increased by 5.6% or ≈2.7 million people from 1997 to 2002 among Medicare beneficiaries. By 2002, 21.4% of beneficiaries and 39.3% of disabled beneficiaries were obese compared with 16.4% and 32.5%, respectively, in 1997.21

  • The population attributable fraction for CHD associated with reducing current population mean BMI to 21 kg/m2 in the Asia-Pacific region ranged from 2% in India to 58% in American Samoa; the population attributable fraction for ischemic stroke ranged from 3% in India to 64% in American Samoa. These data from 15 countries show the proportion of CV that would be prevented if the population mean BMI were reduced below the current overweight cut point.22a

  • The CARDIA study showed that young adults who were overweight or obese had lower health-related quality of life than normal-weight participants 20 years later. On the basis of data from the Medical Outcomes Study 12-item short-form health survey, overweight and obese participants had lower multivariable-adjusted scores on the physical component summary score but not on the mental component summary score.22

  • Forecasts through 2030 using the BRFSS 1990–2008 data set suggest that by 2030, 51% of the population will be obese, with 11% with severe obesity, an increase of 33% for obesity and 130% for severe obesity.23

Morbidity

  • Overweight children and adolescents are at increased risk for future adverse health effects, including the following24:

—Increased prevalence of traditional cardiovascular risk factors such as hypertension, hyperlipidemia, and DM.

—Poor school performance, tobacco use, alcohol use, premature sexual behavior, and poor diet.

—Other associated health conditions, such as asthma, hepatic steatosis, sleep apnea, stroke, some cancers (breast, colon, and kidney), musculoskeletal disorders, and gallbladder disease.

  • According to data from the Bogalusa Heart Study and the Young Finns study, adolescents with high BMI in the overweight or obese range are at a 2.5-fold increased risk of developing metabolic syndrome, a 2.2-fold increased risk of high carotid IMT, and a 3.4-fold increased risk of DM in adulthood.25

  • The increasing prevalence of obesity is driving an increased incidence of type 2 DM. Data from the FHS indicate a doubling in the incidence of DM over the past 30 years, most dramatically during the 1990s and primarily among individuals with a BMI >30 kg/m2.26

  • Obesity was the most powerful predictor of DM in the Nurses’ Health Study. Women with a BMI of ≥35 kg/m2 had an RR for DM of 38.8 compared with women with a BMI of <23 kg/m2.27

  • Obesity is also a strong predictor of sleep-disordered breathing, itself strongly associated with the development of CVD, as well as with myriad other health conditions, including numerous cancers, nonalcoholic fatty liver disease, gallbladder disease, musculoskeletal disorders, and reproductive abnormalities.28

  • A recent meta-analysis of 15 prospective studies demonstrated the increased risk for Alzheimer disease or vascular dementia and any dementia was 1.35 and 1.26 for overweight, respectively, and 2.04 and 1.64 for obesity, respectively.29

  • A randomized clinical trial of 130 severely obese adult individuals randomized to either 12 months of diet and PA or only 6 months of PA resulted in 12.1 and 9.9 kg, respectively, of weight loss at 1 year, with improvements in waist circumference, visceral fat, BP, and insulin resistance.17

  • Data from 4 Finnish cohort studies examining childhood and adult BMI with a mean follow-up of 23 years found that overweight or obese children who remained obese in adulthood had increased risks of type 2 DM, hypertension, dyslipidemia, and carotid atherosclerosis. However, those who became normal weight by adulthood had risks comparable to individuals who were never obese.30

  • In a meta-analysis from 58 cohorts, representing 221 934 people in 17 developed countries with 14 297 incident CVD outcomes, BMI, waist circumference, and waist-to-hip ratio were only minimally associated with cardiovascular outcomes after controlling for baseline SBP, DM, and total and HDL cholesterol in addition to age, sex, and smoking status. Measures of adiposity also did not improve risk discrimination or reclassification when risk factor data were included.31

  • Ten-year follow-up data from the Swedish Obese Subjects intervention study indicated that to maintain a favorable effect on cardiovascular risk factors, more than the short-term goal of 5% weight loss is needed to overcome secular trends and aging effects.32

  • An obesity paradox has been reported, with obese patients demonstrating favorable outcomes in CHF, hypertension, peripheral vascular disease, and coronary artery disease (CAD). In the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) study, a multicenter trial of atrial fibrillation (AF), obese patients had lower all-cause mortality (HR=0.77, P=0.01) than normal-weight patients after multivariable adjustment over a 3-year follow-up period.33

  • A systematic review of prospective studies examining overweight and obesity as predictors of major stroke subtypes in >2 million participants over ≥4 years found an adjusted RR for ischemic stroke of 1.22 (1.05–1.41) in overweight individuals and an RR of 1.64 (1.36–1.99) for obese individuals relative to normal-weight individuals. RRs for hemorrhagic stroke were 1.01 (0.88–1.17) and 1.24 (0.99–1.54) for overweight and obese individuals, respectively. These risks were graded with increasing BMI and were independent of age, lifestyle, and other cardiovascular risk factors.34

Mortality

  • Elevated childhood BMIs in the highest quartile were associated with premature death as an adult in a cohort of 4857 American Indian children during a median follow-up of 23.9 years.35

  • Among adults, obesity was associated with nearly 112 000 excess deaths (95% CI, 53 754–170 064) relative to normal weight in 2000. Grade I obesity (BMI 30 to <35 kg/m2) was associated with almost 30 000 of these excess deaths (95% CI, 8534–68 220) and grade II to III obesity (BMI ≥35 kg/m2) with >82 000 (95% CI, 44 843–119 289). Underweight was associated with nearly 34 000 excess deaths (95% CI, 15 726–51 766). As other studies have found,36 overweight (BMI 25 to <30 kg/m2) was not associated with excess deaths.37

  • Overweight was associated with significantly increased mortality resulting from DM or kidney disease and was not associated with increased mortality resulting from cancer or CVD in an analysis of 2004 data from NHANES. Obesity was associated with significantly increased mortality caused by CVD, some cancers, and DM or kidney disease. Obesity was associated with 13% of CVD deaths in 2004.38

  • In a collaborative analysis of data from almost 900 000 adults in 57 prospective studies, mostly in western Europe and North America, overall mortality was lowest at a BMI of ≈22.5 to 25 kg/m2 in both sexes and at all ages, after exclusion of early follow-up and adjustment for smoking status. Above this range, each 5-kg/m2-higher BMI was associated with ≈30% higher all-cause mortality, and no specific cause of death was inversely associated with BMI. Below 22.5 to 25 kg/m2, the overall inverse association with BMI was predominantly related to strong inverse associations for smoking-related respiratory disease, and the only clearly positive association was for ischemic heart disease.39

  • In a meta-analysis of 1.46 million white adults, over a mean follow-up period of 10 years, all-cause mortality was lowest at BMI levels of 20.0 to 24.9 kg/m2. Among women, compared with a BMI of 22.5 to 24.9 kg/m2, the HRs for death were as follows: BMI 15.0 to 18.4 kg/m2, 1.47; 18.5 to 19.9 kg/m2, 1.14; 20.0 to 22.4 kg/m2, 1.0; 25.0 to 29.9 kg/m2, 1.13; 30.0 to 34.9 kg/m2, 1.44; 35.0 to 39.9 kg/m2, 1.88; and 40.0 to 49.9 kg/m2, 2.51. Similar estimates were observed in men.40

  • Calculations based on NHANES data from 1978–2006 suggest that the gains in life expectancy from smoking cessation are beginning to be outweighed by the loss of life expectancy related to obesity.41

  • Because of the increasing prevalence of obesity, the number of quality-adjusted life-years lost as a result of obesity is similar to or greater than that lost as a result of smoking, according to data from the BRFSS.42

  • Recent estimates suggest that reductions in smoking, cholesterol, BP, and PA levels resulted in a gain of 2 770 500 life-years; however, these gains were reduced by a loss of 715 000 life-years caused by the increased prevalence of obesity and DM.43

  • Using self-reported NHIS data with linked mortality files through 2006, the HR for all-cause mortality was 1.07 (95% CI, 0.91–1.26) for overweight individuals, 1.41 (95% CI, 1.16–1.73) for obese individuals, and 2.46 for extremely obese individuals (95% CI, 1.91–3.16).44

  • In a comparison of 5 different anthropometric variables (BMI, waist circumference, hip circumference, waist-to-hip ratio, and waist-to-height ratio) in 62 223 individuals from Norway with 12 years of follow-up from the HUNT 2 study (Nord-Trøndelag Health Study), the risk of death per SD increase in each measure was 1.02 (95% CI, 0.99–1.06) for BMI, 1.10 (95% CI, 1.06–1.14) for waist circumference, 1.01 (95% CI, 0.97–1.05) for hip circumference, 1.15 (95% CI, 1.11–1.19) for waist-to-hip ratio, and 1.12 (95% CI, 1.08–1.16) for waist-to-height ratio. For CVD mortality, the risk of death per SD increase was 1.12 (95% 1.06–1.20) for BMI, 1.19 (1.12–1.26) for waist circumference, 1.06 (1.00–1.13) for hip circumference, 1.23 (95% CI, 1.16–1.30) for waist-to-hip ratio, and 1.24 (95% CI, 1.16–1.31) for waist-to-height ratio.45

  • According to data from the National Cardiovascular Data Registry, among patients presenting with ST-segment–elevation myocardial infarction (STEMI) and a BMI of at least 40 kg/m2, in-hospital mortality rates were higher for patients with class III obesity (OR, 1.64; 95% CI, 1.32–2.03) when class I obesity was used as the referent.46

  • The Canadian Heart Health Surveys from 1986 to 1995 demonstrated an increased risk of cancer across BMI categories, with an HR of 1.34 (95% CI, 1.01–1.78) for those with BMI 30 to 34.9 kg/m2 and an HR of 1.82 (95% CI, 1.22–2.71) for those with BMI ≥35 kg/m2.47

  • In a study of 22 203 women and men from England and Scotland, metabolically unhealthy obese individuals were at an increased risk of all-cause mortality compared with metabolically healthy obese individuals (HR, 1.72; 95% CI, 1.23–2.41).48

Cost

  • Among children and adolescents, annual hospital costs related to obesity were $127 million between 1997 and 1999.49

  • According to 1 study, overall estimates show that the annual medical burden of obesity has increased to almost 10% of all medical spending and could amount to $147 billion per year in 2008 (in 2008 dollars).50

  • If current trends in the growth of obesity continue, total healthcare costs attributable to obesity could reach $861 to $957 billion by 2030, which would account for 16% to 18% of US health expenditures.51

  • According to NHANES I data linked to Medicare and mortality records, obese 45-year-olds had lifetime Medicare costs of $163 000 compared with $117 000 among those with normal weight by the time they reached 65 years of age.52

  • The total excess cost related to the current prevalence of adolescent overweight and obesity is estimated to be $254 billion ($208 billion in lost productivity secondary to premature morbidity and mortality and $46 billion in direct medical costs).53

  • According to 2006 MEPS and 2006 BRFSS data, annual medical expenditures would be 6.7% to 10.7% lower in the absence of obesity.54

  • According to data from the Medicare Current Beneficiary Survey from 1997–2006, in 1997, expenditures for a Part A and Part B services beneficiary were $6832 for a normal-weight individual, which was more than for overweight ($5473) or obese ($5790) individuals. However, over time, expenses increased more rapidly for overweight and obese individuals.55

  • The costs of obesity are high: Obese people pay on average $1429 (42%) more for healthcare costs than normal-weight individuals. For obese beneficiaries, Medicare pays $1723 more, Medicaid pays $1021 more, and private insurances pay $1140 more than for beneficiaries who are at normal weight. Similarly, obese people have 46% higher inpatient costs and 27% more outpatient visits and spend 80% more on prescription drugs.50

Bariatric Surgery

  • Patients with BMI >40 kg/m2 or >35 kg/m2 with an obesity-related comorbidity are eligible for gastric bypass surgery, which is typically performed as either a Roux-en-Y gastric bypass or a biliopancreatic diversion.

  • According to the 2006 NHDS, the incidence of bariatric surgery was estimated at 113 000 cases per year, with costs of ≈1.5 billion dollars annually.56

  • Among obese Swedish patients undergoing bariatric surgery and followed up for up to 15 years, maximum weight loss was 32%. The risk of death was 0.76 among those who underwent bariatric surgery compared with matched control subjects.57 More recent data examining MI and stroke showed that bariatric surgery was associated with fewer CVD deaths (HR, 0.47; 95% CI, 0.29–0.76) and fewer strokes (HR, 0.67; 95% CI, 0.54–0.83) than in the control group.58

  • Among 641 patients followed up for 10 years compared with 627 matched control subjects, after 2 years of follow-up, 72% of the surgically treated patients versus 21% of the control patients had remission of their DM; at 10 years of follow-up, results were 36% and 13%, respectively. Similar results have been observed for hypertension, elevated triglycerides, and low HDL cholesterol.59

  • According to retrospective data from the United States, among 9949 patients who underwent gastric bypass surgery, after a mean of 7 years, long-term mortality was 40% lower among the surgically treated patients than among obese control subjects. Specifically, cancer mortality was reduced by 60%, DM mortality by 92%, and CAD mortality by 56%. Non-disease death rates (eg, accidents, suicide) were 58% higher in the surgery group.60

  • A recent retrospective cohort from the Veterans Affairs medical system showed that in a propensity-matched analysis, bariatric surgery was not associated with reduced mortality compared with obese control subjects (time-adjusted HR, 0.94; 95% CI, 0.64–1.39).61

  • Two recent randomized controlled trials were performed that randomized bariatric surgery compared with intensive medical treatment among patients with type 2 DM. The first study randomized 150 patients and conducted 12-month follow-up; this study showed that glycemic control improved (6.4%) and weight loss was greater (29.4 versus 5.4 kg) in the surgical arm.62 The second trial randomized 60 patients to bariatric surgery versus medical therapy and conducted follow-up for 24 months. The results showed that DM remission occurred in 75% of the group that underwent gastric bypass surgery compared with 0% of those in the medical treatment arm, with HbA1c values of 6.35% in the surgical arm compared with 7.69% in the medical treatment arm.63

  • A recent cost-effectiveness study of laparoscopic adjustable gastric banding showed that after 5 years, $4970 was saved in medical expenses; if indirect costs were included (absenteeism and presenteeism), savings increased to $6180 and $10 960, respectively.64

Table 6-1. Overweight and Obesity

Prevalence of Overweight and Obesity, 2007–2010, Age >20 yPrevalence of Obesity in Adults, 2007–2010, Age >20 yPrevalence of Overweight and Obesity in Children, 2009–2010, Ages 2–19 yPrevalence of Obesity in Children, 2009–2010, Ages 2–19 yCost, 2008*
Both sexes, n (%)154 700 000 (68.2)78 400 000 (34.6)23 900 000 (31.8)12 700 000 (16.9)$147 Billion
Males79 900 000 (72.9)36 800 000 (33.6)12,700 000 (33.0)7 200 000 (18.6)
Females74 800 000 (63.7)41 600 000 (35.6)11 200 000 (30.4)5 500,000 (15.0)
NH white males, %73.133.830.116.1
NH white females, %60.232.525.611.7
NH black males, %68.737.936.924.3
NH black females, %79.953.941.324.3
Mexican American males, %81.336.040.524.0
Mexican American females, %78.244.838.218.2

Overweight and obesity in adults is defined as body mass index (BMI) ≥25 kg/m2. Obesity in adults is defined as BMI ≥30 kg/m2. In children, overweight and obesity are based on BMI-for-age values at or above the 85th percentile of the 2000 Centers for Disease Control and Prevention (CDC) growth charts. In children, obesity is based on BMI-for-age values at or above the 95th percentile of the CDC growth charts. In January 2007, the American Medical Association’s Expert Task Force on Childhood Obesity recommended new definitions for overweight and obesity in children and adolescents65; however, statistics based on this new definition are not yet available.

NH indicates non-Hispanic; ellipses (…), data not available.

*Data from Health Affairs.50

Sources: National Health and Nutrition Examination Survey (NHANES) 2007–2010 (adults), unpublished National Heart, Lung, and Blood Institute (NHLBI) tabulation; NHANES 2009–2010 (ages 2–19 years) from Ogden et al.2

Extrapolation for ages 2 to 19 years from NHLBI tabulation of US Census resident population on April 1, 2010.

Chart 6-1.

Chart 6-1. Prevalence of overweight and obesity among students in grades 9 through 12 by sex and race/ethnicity. NH indicates non-Hispanic. Data derived from Youth Risk Behavior Surveillance—United States, 2011, Table 101.66

Chart 6-2.

Chart 6-2. Age-adjusted prevalence of obesity in adults 20 to 74 years of age by sex and survey year (National Health Examination Survey: 1960–1962; National Health and Nutrition Examination Survey: 1971–1974, 1976–1980, 1988–1994, 1999–2002, and 2007–2010). Obesity is defined as body mass index of 30.0 kg/m2. Data derived from Health, United States, 2011 (National Center for Health Statistics).67

Chart 6–3.

Chart 6–3. Trends in the prevalence of obesity among US children and adolescents by age and survey year (National Health and Nutrition Examination Survey: 1971–1974, 1976–1980, 1988–1994, 1999–2002, 2003–2006 and 2007–2010). Data derived from Health, United States, 2011 (National Center for Health Statistics).67

Abbreviations Used in Chapter 6

AF

atrial fibrillation

AFFIRM

Atrial Fibrillation Follow-up Investigation of Rhythm Management

BMI

body mass index

BP

blood pressure

BRFSS

Behavioral Risk Factor Surveillance System

CAD

coronary artery disease

CARDIA

Coronary Artery Risk Development in Young Adults

CDC

Centers for Disease Control and Prevention

CHF

congestive heart failure

CI

confidence interval

CVD

cardiovascular disease

DM

diabetes mellitus

FHS

Framingham Heart Study

HbA1c

hemoglobin A1c

HDL

high-density lipoprotein

HR

hazard ratio

HUNT 2

Nord-Trøndelag Health Study

IMT

intima-media thickness

MEPS

Medical Expenditure Panel Survey

MESA

Multi-Ethnic Study of Atherosclerosis

MI

myocardial infarction

NCHS

National Center for Health Statistics

NH

non-Hispanic

NHANES

National Health and Nutrition Examination Survey

NHDS

National Hospital Discharge Survey

NHIS

National Health Interview Survey

NHLBI

National Heart, Lung, and Blood Institute

OR

odds ratio

PA

physical activity

RR

relative risk

SBP

systolic blood pressure

SD

standard deviation

STEMI

ST-segment–elevation myocardial infarction

WHO

World Health Organization

References

References

  • 1. Deleted in proofGoogle Scholar
  • 2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010.JAMA. 2012; 127:e59–e67.Google Scholar
  • 3. Anderson SE, Whitaker RC. Prevalence of obesity among US preschool children in different racial and ethnic groups.Arch Pediatr Adolesc Med. 2009; 163:344–348.CrossrefMedlineGoogle Scholar
  • 4. Robinson WR, Gordon-Larsen P, Kaufman JS, Suchindran CM, Stevens J. The female-male disparity in obesity prevalence among black American young adults: contributions of sociodemographic characteristics of the childhood family.Am J Clin Nutr. 2009; 89:1204–1212.CrossrefMedlineGoogle Scholar
  • 5. Singh GK, Siahpush M, Kogan MD. Rising social inequalities in US childhood obesity, 2003-2007 [published correction appears in Ann Epidemiol. 2010;20:250].Ann Epidemiol. 2010; 20:40–52.CrossrefMedlineGoogle Scholar
  • 6. Centers for Disease Control and Prevention.Pediatric and Pregnancy Surveillance System.2011Pediatric Data Tables, Table 16. http://www.cdc.gov/pednss/pednss_tables/tables_analysis.htm. Accessed November 30, 2012.Google Scholar
  • 7. Ali MK, Bullard KM, Beckles GL, Stevens MR, Barker L, Narayan KM, Imperatore G. Household income and cardiovascular disease risks in U.S. children and young adults: analyses from NHANES 1999-2008.Diabetes Care. 2011; 34:1998–2004.CrossrefMedlineGoogle Scholar
  • 8. The NS, Suchindran C, North KE, Popkin BM, Gordon-Larsen P. Association of adolescent obesity with risk of severe obesity in adulthood.JAMA. 2010; 304:2042–2047.CrossrefMedlineGoogle Scholar
  • 9. Davis AM, Bennett KJ, Befort C, Nollen N. Obesity and related health behaviors among urban and rural children in the United States: data from the National Health And Nutrition Examination Survey 2003-2004 and 2005-2006.J Pediatr Psychol. 2011; 36:669–676.CrossrefMedlineGoogle Scholar
  • 10. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System: 2011 prevalence and trends data.Centers for Disease Control and Prevention Web site.http://apps.nccd.cdc.gov/brfss/index.asp. Accessed November 30, 2012.Google Scholar
  • 11. Centers for Disease Control and Prevention (CDC). State-specific cholesterol screening trends: United States, 1991–1999.MMWR Morb Mortal Wkly Rep. 2000; 49:750–755.MedlineGoogle Scholar
  • 12. Schiller J, Lucas J, Peregoy J. Summary health statistics for U.S. adults: National Health Interview Survey, 2011. Vital Health Stat 10. In press.Google Scholar
  • 13. Barnes PM, Adams PF, Powell-Griner E. Health Characteristics of the Asian Adult Population: United States, 2004–2006. Advance Data From Vital and Health Statistics; No. 394. Hyattsville, MD: National Center for Health Statistics; 2008.Google Scholar
  • 14. Agency for Healthcare Research and Quality. 2008 National Healthcare Disparities Report.Rockville, MD: US Department of Health and Human Services, Agency for Healthcare Research and Quality; 2009. AHRQ publication No. 09-0002.Google Scholar
  • 14. Burke GL, Bertoni AG, Shea S, Tracy R, Watson KE, Blumenthal RS, Chung H, Carnethon MR. The impact of obesity on cardiovascular disease risk factors and subclinical vascular disease: the Multi-Ethnic Study of Atherosclerosis.Arch Intern Med. 2008; 168:928–935.CrossrefMedlineGoogle Scholar
  • 16. Bulló M, Garcia-Aloy M, Martínez-González MA, Corella D, Fernández-Ballart JD, Fiol M, Gómez-Gracia E, Estruch R, Ortega-Calvo M, Francisco S, Flores-Mateo G, Serra-Majem L, Pintó X, Covas MI, Ros E, Lamuela-Raventós R, Salas-Salvadó J. Association between a healthy lifestyle and general obesity and abdominal obesity in an elderly population at high cardiovascular risk.Prev Med. 2011; 53:155–161.CrossrefMedlineGoogle Scholar
  • 17. Goodpaster BH, Delany JP, Otto AD, Kuller L, Vockley J, South-Paul JE, Thomas SB, Brown J, McTigue K, Hames KC, Lang W, Jakicic JM. Effects of diet and physical activity interventions on weight loss and cardiometabolic risk factors in severely obese adults: a randomized trial.JAMA. 2010; 304:1795–1802.CrossrefMedlineGoogle Scholar
  • 18. NCHS Health E-Stat.Prevalence of overweight, infants and children less than 2 years of age: United States, 2003–2004.Centers for Disease Control and Prevention Web site.http://www.cdc.gov/nchs/data/hestat/overweight/overweight_child_under02.htm. Accessed September 28, 2010.Google Scholar
  • 19. Broyles S, Katzmarzyk PT, Srinivasan SR, Chen W, Bouchard C, Freedman DS, Berenson GS. The pediatric obesity epidemic continues unabated in Bogalusa, Louisiana.Pediatrics. 2010; 125:900–905.CrossrefMedlineGoogle Scholar
  • 20. Centers for Disease Control and Prevention (CDC). Vital signs: state-specific obesity prevalence among adults: United States, 2009.MMWR Morb Mortal Wkly Rep. 2010; 59:951–955.MedlineGoogle Scholar
  • 21. Doshi JA, Polsky D, Chang VW. Prevalence and trends in obesity among aged and disabled U.S. Medicare beneficiaries, 1997-2002.Health Aff (Millwood). 2007; 26:1111–1117.CrossrefMedlineGoogle Scholar
  • 22. Lee CM, Colagiuri S, Ezzati M, Woodward M. The burden of cardiovascular disease associated with high body mass index in the Asia-Pacific region.Obes Rev. 2011; 12:e454–e459.CrossrefMedlineGoogle Scholar
  • 23. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, Dietz W. Obesity and severe obesity forecasts through 2030.Am J Prev Med. 2012; 42:563–570.CrossrefMedlineGoogle Scholar
  • 24. Daniels SR, Jacobson MS, McCrindle BW, Eckel RH, Sanner BM. American Heart Association Childhood Obesity Research Summit: executive summary.Circulation. 2009; 119:2114–2123.LinkGoogle Scholar
  • 25. Magnussen CG, Koskinen J, Chen W, Thomson R, Schmidt MD, Srinivasan SR, Kivimäki M, Mattsson N, Kähönen M, Laitinen T, Taittonen L, Rönnemaa T, Viikari JS, Berenson GS, Juonala M, Raitakari OT. Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone: the Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study.Circulation. 2010; 122:1604–1611.LinkGoogle Scholar
  • 26. Fox CS, Pencina MJ, Meigs JB, Vasan RS, Levitzky YS, D’Agostino RB. Trends in the incidence of type 2 diabetes mellitus from the 1970s to the 1990s: the Framingham Heart Study.Circulation. 2006; 113:2914–2918.LinkGoogle Scholar
  • 27. Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, Willett WC. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women.N Engl J Med. 2001; 345:790–797.CrossrefMedlineGoogle Scholar
  • 28. Brown WV, Fujioka K, Wilson PW, Woodworth KA. Obesity: why be concerned?Am J Med. 2009; 122(s1):S4–S11.MedlineGoogle Scholar
  • 29. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies.Obes Rev. 2011; 12:e426–e437.CrossrefMedlineGoogle Scholar
  • 30. Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, Srinivasan SR, Daniels SR, Davis PH, Chen W, Sun C, Cheung M, Viikari JS, Dwyer T, Raitakari OT. Childhood adiposity, adult adiposity, and cardiovascular risk factors.N Engl J Med. 2011; 365:1876–1885.CrossrefMedlineGoogle Scholar
  • 31. Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR, Lawlor DA, Nordestgaard BG, Ridker P, Salomaa V, Stevens J, Woodward M, Sattar N, Collins R, Thompson SG, Whitlock G, Danesh J; Emerging Risk Factors Collaboration. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.Lancet. 2011; 377:1085–1095.CrossrefMedlineGoogle Scholar
  • 32. Sjöström CD, Lystig T, Lindroos AK. Impact of weight change, secular trends and ageing on cardiovascular risk factors: 10-year experiences from the SOS study.Int J Obes (Lond). 2011; 35:1413–1420.CrossrefMedlineGoogle Scholar
  • 33. Badheka AO, Rathod A, Kizilbash MA, Garg N, Mohamad T, Afonso L, Jacob S. Influence of obesity on outcomes in atrial fibrillation: yet another obesity paradox.Am J Med. 2010; 123:646–651.CrossrefMedlineGoogle Scholar
  • 34. Czernichow S, Ninomiya T, Huxley R, Kengne AP, Batty GD, Grobbee DE, Woodward M, Neal B, Chalmers J. Impact of blood pressure lowering on cardiovascular outcomes in normal weight, overweight, and obese individuals: the Perindopril Protection Against Recurrent Stroke Study trial.Hypertension. 2010; 55:1193–1198.LinkGoogle Scholar
  • 35. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. Childhood obesity, other cardiovascular risk factors, and premature death.N Engl J Med. 2010; 362:485–493.CrossrefMedlineGoogle Scholar
  • 36. McGee DL; Diverse Populations Collaboration. Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies.Ann Epidemiol. 2005; 15:87–97.CrossrefMedlineGoogle Scholar
  • 37. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity.JAMA. 2005; 293:1861–1867.CrossrefMedlineGoogle Scholar
  • 38. Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths associated with underweight, overweight, and obesity.JAMA. 2007; 298:2028–2037.CrossrefMedlineGoogle Scholar
  • 39. Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, Qizilbash N, Collins R, Peto R; Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies.Lancet. 2009; 373:1083–1096.CrossrefMedlineGoogle Scholar
  • 40. Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, Moore SC, Tobias GS, Anton-Culver H, Freeman LB, Beeson WL, Clipp SL, English DR, Folsom AR, Freedman DM, Giles G, Hakansson N, Henderson KD, Hoffman-Bolton J, Hoppin JA, Koenig KL, Lee IM, Linet MS, Park Y, Pocobelli G, Schatzkin A, Sesso HD, Weiderpass E, Willcox BJ, Wolk A, Zeleniuch-Jacquotte A, Willett WC, Thun MJ. Body-mass index and mortality among 1.46 million white adults [published correction appears in N Engl J Med. 2011;365:869].N Engl J Med. 2010; 363:2211–2219.CrossrefMedlineGoogle Scholar
  • 41. Stewart ST, Cutler DM, Rosen AB. Forecasting the effects of obesity and smoking on U.S. life expectancy.N Engl J Med. 2009; 361:2252–2260.CrossrefMedlineGoogle Scholar
  • 42. Jia H, Lubetkin EI. Trends in quality-adjusted life-years lost contributed by smoking and obesity.Am J Prev Med. 2010; 38:138–144.CrossrefMedlineGoogle Scholar
  • 43. Capewell S, Hayes DK, Ford ES, Critchley JA, Croft JB, Greenlund KJ, Labarthe DR. Life-years gained among US adults from modern treatments and changes in the prevalence of 6 coronary heart disease risk factors between 1980 and 2000.Am J Epidemiol. 2009; 170:229–236.CrossrefMedlineGoogle Scholar
  • 44. Ma J, Flanders WD, Ward EM, Jemal A. Body mass index in young adulthood and premature death: analyses of the US National Health Interview Survey linked mortality files.Am J Epidemiol. 2011; 174:934–944.CrossrefMedlineGoogle Scholar
  • 45. Petursson H, Sigurdsson JA, Bengtsson C, Nilsen TI, Getz L. Body configuration as a predictor of mortality: comparison of five anthropometric measures in a 12 year follow-up of the Norwegian HUNT 2 study.PLoS ONE. 2011; 6:e26621.CrossrefMedlineGoogle Scholar
  • 46. Das SR, Alexander KP, Chen AY, Powell-Wiley TM, Diercks DB, Peterson ED, Roe MT, de Lemos JA. Impact of body weight and extreme obesity on the presentation, treatment, and in-hospital outcomes of 50,149 patients with ST-Segment elevation myocardial infarction results from the NCDR (National Cardiovascular Data Registry).J Am Coll Cardiol. 2011; 58:2642–2650.CrossrefMedlineGoogle Scholar
  • 47. Katzmarzyk PT, Reeder BA, Elliott S, Joffres MR, Pahwa P, Raine KD, Kirkland SA, Paradis G. Body mass index and risk of cardiovascular disease, cancer and all-cause mortality.Can J Public Health. 2012; 103:147–151.MedlineGoogle Scholar
  • 48. Hamer M, Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality.J Clin Endocrinol Metab. 2012; 97:2482–2488.CrossrefMedlineGoogle Scholar
  • 49. Centers for Disease Control and Prevention. Preventing Chronic Diseases: Investing Wisely in Health: Preventing Obesity and Chronic Diseases Through Good Nutrition and Physical Activity.Atlanta, GA:Centers for Disease Control and Prevention;2008.http://www.cdc.gov/nccdphp/publications/factsheets/prevention/pdf/obesity.pdf. Accessed July 21, 2011.Google Scholar
  • 50. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer- and service-specific estimates.Health Aff (Millwood). 2009; 28:w822–w831.CrossrefMedlineGoogle Scholar
  • 51. Wang Y, Beydoun MA, Liang L, Caballero B, Kumanyika SK. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic.Obesity (Silver Spring). 2008; 16:2323–2330.CrossrefMedlineGoogle Scholar
  • 52. Cai L, Lubitz J, Flegal KM, Pamuk ER. The predicted effects of chronic obesity in middle age on Medicare costs and mortality.Med Care. 2010; 48:510–517.CrossrefMedlineGoogle Scholar
  • 53. Lightwood J, Bibbins-Domingo K, Coxson P, Wang YC, Williams L, Goldman L. Forecasting the future economic burden of current adolescent overweight: an estimate of the coronary heart disease policy model.Am J Public Health. 2009; 99:2230–2237.CrossrefMedlineGoogle Scholar
  • 54. Trogdon JG, Finkelstein EA, Feagan CW, Cohen JW. State- and payer-specific estimates of annual medical expenditures attributable to obesity.Obesity (Silver Spring). 2012; 20:214–220.CrossrefMedlineGoogle Scholar
  • 55. Alley D, Lloyd J, Shaffer T, Stuart B. Changes in the association between body mass index and Medicare costs, 1997-2006.Arch Intern Med. 2012; 172:277–278.CrossrefMedlineGoogle Scholar
  • 56. Livingston EH. The incidence of bariatric surgery has plateaued in the U.S.Am J Surg. 2010; 200:378–385.CrossrefMedlineGoogle Scholar
  • 57. Sjöström L, Narbro K, Sjöström CD, Karason K, Larsson B, Wedel H, Lystig T, Sullivan M, Bouchard C, Carlsson B, Bengtsson C, Dahlgren S, Gummesson A, Jacobson P, Karlsson J, Lindroos AK, Lönroth H, Näslund I, Olbers T, Stenlöf K, Torgerson J, Agren G, Carlsson LM; Swedish Obese Subjects Study. Effects of bariatric surgery on mortality in Swedish obese subjects.N Engl J Med. 2007; 357:741–752.CrossrefMedlineGoogle Scholar
  • 58. Sjöström L, Peltonen M, Jacobson P, Sjöström CD, Karason K, Wedel H, Ahlin S, Anveden Å, Bengtsson C, Bergmark G, Bouchard C, Carlsson B, Dahlgren S, Karlsson J, Lindroos AK, Lönroth H, Narbro K, Näslund I, Olbers T, Svensson PA, Carlsson LM. Bariatric surgery and long-term cardiovascular events.JAMA. 2012; 307:56–65.CrossrefMedlineGoogle Scholar
  • 59. Sjöström L, Lindroos AK, Peltonen M, Torgerson J, Bouchard C, Carlsson B, Dahlgren S, Larsson B, Narbro K, Sjöström CD, Sullivan M, Wedel H; Swedish Obese Subjects Study Scientific Group. Lifestyle, diabetes, and cardiovascular risk factors 10 years after bariatric surgery.N Engl J Med. 2004; 351:2683–2693.CrossrefMedlineGoogle Scholar
  • 60. Adams TD, Gress RE, Smith SC, Halverson RC, Simper SC, Rosamond WD, Lamonte MJ, Stroup AM, Hunt SC. Long-term mortality after gastric bypass surgery.N Engl J Med. 2007; 357:753–761.CrossrefMedlineGoogle Scholar
  • 61. Maciejewski ML, Livingston EH, Smith VA, Kavee AL, Kahwati LC, Henderson WG, Arterburn DE. Survival among high-risk patients after bariatric surgery.JAMA. 2011; 305:2419–2426.CrossrefMedlineGoogle Scholar
  • 62. Schauer PR, Kashyap SR, Wolski K, Brethauer SA, Kirwan JP, Pothier CE, Thomas S, Abood B, Nissen SE, Bhatt DL. Bariatric surgery versus intensive medical therapy in obese patients with diabetes.N Engl J Med. 2012; 366:1567–1576.CrossrefMedlineGoogle Scholar
  • 63. Mingrone G, Panunzi S, De Gaetano A, Guidone C, Iaconelli A, Leccesi L, Nanni G, Pomp A, Castagneto M, Ghirlanda G, Rubino F. Bariatric surgery versus conventional medical therapy for type 2 diabetes.N Engl J Med. 2012; 366:1577–1585.CrossrefMedlineGoogle Scholar
  • 64. Finkelstein EA, Allaire BT, Dibonaventura MD, Burgess SM. Incorporating indirect costs into a cost-benefit analysis of laparoscopic adjustable gastric banding.Value Health. 2012; 15:299–304.CrossrefMedlineGoogle Scholar
  • 65. American Medical Association Expert Task Force on Childhood Obesity. Expert Committee recommendations on the assessment, prevention, and treatment of child and adolescent overweight and obesity.http://www.ama-assn.org/ama1/pub/upload/mm/433/ped_obesity_recs.pdf. Accessed September 20, 2012.Google Scholar
  • 66. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, Wechsler H; Centers for Disease Control and Prevention (CDC). Youth risk behavior surveillance: United States, 2011.MMWR Surveill Summ. 2012; 61:1–162.MedlineGoogle Scholar
  • 67. National Center for Health Statistics. Health, United States, 2011: With Special Feature on Socioeconomic Status and Health.Hyattsville, MD:National Center for Health Statistics;2011.http://www.cdc.gov/nchs/data/hus/hus11.pdf. Accessed August 1, 2012.Google Scholar

7. Family History and Genetics

See Tables 7-1 through 7-3.

Biologically related first-degree relatives (siblings, offspring and parents) share roughly 50% of their genetic variation with one another. This constitutes much greater sharing of genetic variation than with a randomly selected person from the population, and thus, when a trait aggregates within a family, this lends evidence for a genetic risk factor for the trait. Similarly, racial/ethnic minorities are more likely to share their genetic variation within their demographic than with other demographics. Familial aggregation of CVD may be related to aggregation of specific behaviors (eg, smoking, alcohol use) or risk factors (eg, hypertension, DM, obesity) that may themselves have environmental and genetic contributors. Unlike classic mendelian genetic risk factors, whereby usually 1 mutation directly causes 1 disease, a complex trait’s genetic contributors may increase risk without necessarily always causing the condition. The effect size of any specific contributor to risk may be small but widespread throughout a population, or may be large but affect only a small population, or may have an enhanced risk when an environmental contributor is present. Although the breadth of all genetic research into CVD is beyond the scope of this chapter, we present a summary of evidence that a genetic risk for CVD is likely, as well as a summary of evidence on the most consistently replicated genetic markers for HD and stroke identified to date.

Family History

Prevalence
  • Among adults ≥20 years of age, 12.6% reported having a parent or sibling with a heart attack or angina before the age of 50 years. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—For non-Hispanic whites, 12.4% for men, 14.9% for women

—For non-Hispanic blacks, 8.1% for men, 13.0% for women

—For Mexican Americans, 8.1% for men, 10.0% for women

—For other Hispanics, 8.8% for men, 12.0% for women

—For other races, 8.7% for men, 10.7% for women

  • HD occurs as people age, and those without a family history of HD may survive longer, so the prevalence of family history will vary depending on the age at which it is assessed. The breakdown of reported family history of heart attack by age in the US population as measured by NHANES is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Age 20 to 39 years, 8.4% for men, 10.3% for women

—Age 40 to 59 years, 12.8% for men, 15.3% for women

—Age 60 to 79 years, 13.7% for men, 17.5% for women

—Age ≥80 years, 9.8% for men, 13.7% for women

  • In the multigenerational FHS, only 75% of participants with a documented parental history of a heart attack before age 55 years reported that history when asked.1

Impact of Family History
  • Premature paternal history of a heart attack has been shown to approximately double the risk of a heart attack in men and increase the risk in women by ≈70%.2,3

  • History of a heart attack in both parents increases the risk of heart attack, especially when 1 parent had a premature heart attack4 (Table 7-1).

  • Sibling history of HD has been shown to increase the odds of HD in men and women by ≈50%.5

  • Premature family history of angina, MI, angioplasty, or bypass surgery increased the lifetime risk by ≈50% for both HD (from 8.9% to 13.7%) and CVD mortality (from 14.1% to 21%).6

  • Similarly, parental history of AF is associated with ≈80% increased odds of AF in men and women,7 and a history of stroke in a first-degree relative increased the odds of stroke in men and women by ≈50%.8

Genetics

Heart Disease
  • Genome-wide association is a robust technique to identify associations between genotypes and phenotypes. Table 7-2 presents results from the CARDIoGRAM (Coronary ARteryDIsease Genome-wide Replication And Meta-analysis) consortium, which represents the largest genetic study of MI to date, with 22 233 MI case subjects and 64 762 control subjects and with independent validation in an additional 56 682 individuals.9 Altogether, there are 23 well-replicated loci for MI. The ORs are modest, ranging from 1.06 to 1.51 per copy of the risk allele (individuals may harbor up to 2 copies of a risk allele). However, these are common alleles, which suggests that the attributable risk may be substantial.

  • Genetic markers discovered thus far have not been shown to add to cardiovascular risk prediction tools beyond current models that incorporate family history.10 Genetic markers have also not been shown to improve prediction of subclinical atherosclerosis beyond traditional risk factors.11

  • The most consistently replicated genetic marker for HD in European-derived populations is located at 9p21.3. At this single-nucleotide polymorphism, ≈27% of the white population is estimated to have 0 risk alleles, 50% is estimated to have 1 risk allele, and the remaining 23% is estimated to have 2 risk alleles.12

  • The 10-year HD risk for a 65-year-old man with 2 risk alleles at 9p21.3 and no other traditional risk factors is ≈13.2%, whereas a similar man with 0 alleles would have a 10-year risk of ≈9.2%. The 10-year HD risk for a 40-year-old woman with 2 alleles and no other traditional risk factors is ≈2.4%, whereas a similar woman with 0 alleles would have a 10-year risk of ≈1.7%.12

  • Variation at the 9p21.3 region also increases the risk of HF13 and sudden death.14 Associations have also been observed between the 9p21.3 region and coronary artery calcification (CAC).15,16 Additionally, stronger associations have been found between variation at 9p21.3 and earlier17,18 and more severe19 heart attacks. The biological mechanism underpinning the association of genetic variation in the 9p21 region with disease outcomes is still under investigation.

Stroke
  • The same 9p21.3 region has also been associated with intracranial aneurysm, abdominal aortic aneurysm (AAA),20 and ischemic stroke.21

  • For large-vessel ischemic stroke, a new association for large-vessel stroke with histone deacetylase 9 on chromosome 7p21.1 has been identified (>9000 subjects) and replicated (>12 000 subjects).21

CVD Risk Factors
  • Heritability is the ratio of genetically caused variation to the total variation of a trait or measure. Table 7-3 presents heritability estimates for standard CVD risk factors using data generated from the FHS. These data suggest that most CVD risk factors have at least moderate heritability.

Table 7-1. Odds Ratio for Combinations of Parental Heart Attack History

OR (95% CI)
No family history1.00
One parent with heart attack ≥50 y of age1.67 (1.55–1.81)
One parent with heart attack <50 y of age2.36 (1.89–2.95)
Both parents with heart attack ≥50 y of age2.90 (2.30–3.66)
Both parents with heart attack, one <50 y of age3.26 (1.72–6.18)
Both parents with heart attack, both <50 y of age6.56 (1.39–30.95)

OR indicates odds ratio; CI, confidence interval.

Data derived from Chow et al.4

Table 7-2. Validated SNPs for MI, the Nearest Gene, and the OR From the CARDIoGRAM Consortium

SNPChromosomal RegionGeneEffect Size (OR)Minor Allele Frequency
rs5998391p13.3SORT11.110.22
rs174656371q41MIA31.140.26
rs171140361p32.2PPAP2B1.170.09
rs112065101p32.3PCSK91.080.18
rs67258872q33WDI121.140.15
rs23063743q22.3MRAS1.120.18
rs176099406p21.31ANKS1A1.070.25
rs125264536p24.1PHACTI11.100.33
rs121902876q23.2TCF211.080.38
rs7982206q25LPA1.510.02
rs115569247q32.2ZC3HC11.090.38
rs49775749p21.3CDKN2A, CDKN2B1.290.46
rs5794599q34.2ABO1.100.21
rs174604810q11CXCL121.090.13
rs1241340910q24.32CYP17A1-CNNM2-NT5C21.120.11
rs96418411q23.3ZNF259-APOA5-A4-C3-A11.130.13
rs318450412q24Sh2b31.070.44
rs477314413q34COL4A1-COL4A21.070.44
rs289581114q32.2HHIPL11.080.43
rs382580715q25.1ADAMTS71.070.43
rs21617217p13.3SMG6-SRR1.070.37
rs1293658717p11.2RASD1-SMCI3-PEMT1.070.44
rs4652217q21.32UBE2Z-GIP-ATP5G1-SNF81.060.47
rs112260819q13.2LDLR1.140.23
rs998260121q22.11MRPS61.180.15

SNPs indicates single-nucleotide polymorphisms; MI, myocardial infarction; OR, odds ratio; and CARDIoGRAM, Coronary Artery DIsease Genome-wide Replication And Meta-analysis Consortium.

Data derived from Schunkert et al.9

Table 7-3. Heritability of CVD Risk Factors From the FHS

TraitHeritability
ABI0.2122
SBP0.4223
DBP0.3923
Left ventricular mass0.24 to 0.3224
BMI0.37 (mean age 40 y) to 0.52 (mean age 60 y)25
Waist circumference0.4126
Visceral abdominal fat0.3627
Subcutaneous abdominal fat0.5727
Fasting glucose0.3428
HbA1c0.2728
Triglycerides0.4829
HDL cholesterol0.5229
Total cholesterol0.5729
LDL cholesterol0.5929
Estimated GFR0.3330

CVD indicates cardiovascular disease; FHS, Framingham Heart Study; ABI, ankle-brachial index; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HbA1c, glycosylated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein; and GFR, glomerular filtration rate.

Abbreviations Used in Chapter 7

AAA

abdominal aortic aneurysm

ABI

ankle-brachial index

AF

atrial fibrillation

BMI

body mass index

CAC

coronary artery calcification

CARDIoGRAM

Coronary Artery Disease Genome-wide Replication and Meta-Analysis Consortium

CI

confidence interval

CVD

cardiovascular disease

DBP

diastolic blood pressure

DM

diabetes mellitus

FHS

Framingham Heart Study

GFR

glomerular filtration rate

HbA1c

glycosylated hemoglobin

HD

heart disease

HDL

high-density lipoprotein

HF

heart failure

LDL

low-density lipoprotein

MI

myocardial infarction

NHANES

National Health and Nutrition Examination Survey

OR

odds ratio

SBP

systolic blood pressure

SNP

single-nucleotide polymorphism

References

References

  • 1. Murabito JM, Nam BH, D’Agostino RB, Lloyd-Jones DM, O’Donnell CJ, Wilson PW. Accuracy of offspring reports of parental cardiovascular disease history: the Framingham Offspring Study.Ann Intern Med. 2004; 127:e68–e71.Google Scholar
  • 2. Lloyd-Jones DM, Nam BH, D’Agostino RB, Levy D, Murabito JM, Wang TJ, Wilson PW, O’Donnell CJ. Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring.JAMA. 2004; 291:2204–2211.CrossrefMedlineGoogle Scholar
  • 3. Sesso HD, Lee IM, Gaziano JM, Rexrode KM, Glynn RJ, Buring JE. Maternal and paternal history of myocardial infarction and risk of cardiovascular disease in men and women.Circulation. 2001; 104:393–398.CrossrefMedlineGoogle Scholar
  • 4. Chow CK, Islam S, Bautista L, Rumboldt Z, Yusufali A, Xie C, Anand SS, Engert JC, Rangarajan S, Yusuf S. Parental history and myocardial infarction risk across the world: the INTERHEART Study.J Am Coll Cardiol. 2011; 57:619–627.CrossrefMedlineGoogle Scholar
  • 5. Murabito JM, Pencina MJ, Nam BH, D’Agostino RB, Wang TJ, Lloyd-Jones D, Wilson PW, O’Donnell CJ. Sibling cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults.JAMA. 2005; 294:3117–3123.CrossrefMedlineGoogle Scholar
  • 6. Bachmann JM, Willis BL, Ayers CR, Khera A, Berry JD. Association between family history and coronary heart disease death across long-term follow-up in men: the Cooper Center Longitudinal Study.Circulation. 2012; 125:3092–3098.LinkGoogle Scholar
  • 7. Fox CS, Parise H, D’Agostino RB, Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ. Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring.JAMA. 2004; 291:2851–2855.CrossrefMedlineGoogle Scholar
  • 8. Liao D, Myers R, Hunt S, Shahar E, Paton C, Burke G, Province M, Heiss G. Familial history of stroke and stroke risk: the Family Heart Study.Stroke. 1997; 28:1908–1912.CrossrefMedlineGoogle Scholar
  • 9. Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF, Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K, Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K, Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, Buysschaert I, Carlquist JF, Chen L, Cichon S, Codd V, Davies RW, Dedoussis G, Dehghan A, Demissie S, Devaney JM, Diemert P, Do R, Doering A, Eifert S, Mokhtari NE, Ellis SG, Elosua R, Engert JC, Epstein SE, de Faire U, Fischer M, Folsom AR, Freyer J, Gigante B, Girelli D, Gretarsdottir S, Gudnason V, Gulcher JR, Halperin E, Hammond N, Hazen SL, Hofman A, Horne BD, Illig T, Iribarren C, Jones GT, Jukema JW, Kaiser MA, Kaplan LM, Kastelein JJ, Khaw KT, Knowles JW, Kolovou G, Kong A, Laaksonen R, Lambrechts D, Leander K, Lettre G, Li M, Lieb W, Loley C, Lotery AJ, Mannucci PM, Maouche S, Martinelli N, McKeown PP, Meisinger C, Meitinger T, Melander O, Merlini PA, Mooser V, Morgan T, Mühleisen TW, Muhlestein JB, Münzel T, Musunuru K, Nahrstaedt J, Nelson CP, Nöthen MM, Olivieri O, Patel RS, Patterson CC, Peters A, Peyvandi F, Qu L, Quyyumi AA, Rader DJ, Rallidis LS, Rice C, Rosendaal FR, Rubin D, Salomaa V, Sampietro ML, Sandhu MS, Schadt E, Schäfer A, Schillert A, Schreiber S, Schrezenmeir J, Schwartz SM, Siscovick DS, Sivananthan M, Sivapalaratnam S, Smith A, Smith TB, Snoep JD, Soranzo N, Spertus JA, Stark K, Stirrups K, Stoll M, Tang WH, Tennstedt S, Thorgeirsson G, Thorleifsson G, Tomaszewski M, Uitterlinden AG, van Rij AM, Voight BF, Wareham NJ, Wells GA, Wichmann HE, Wild PS, Willenborg C, Witteman JC, Wright BJ, Ye S, Zeller T, Ziegler A, Cambien F, Goodall AH, Cupples LA, Quertermous T, März W, Hengstenberg C, Blankenberg S, Ouwehand WH, Hall AS, Deloukas P, Thompson JR, Stefansson K, Roberts R, Thorsteinsdottir U, O’Donnell CJ, McPherson R, Erdmann J, Samani NJ; Cardiogenics; CARDIoGRAM Consortium. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease.Nat Genet. 2011; 43:333–338.CrossrefMedlineGoogle Scholar
  • 10. Holmes MV, Harrison S, Talmud PJ, Hingorani AD, Humphries SE. Utility of genetic determinants of lipids and cardiovascular events in assessing risk.Nat Rev Cardiol. 2011; 8:207–221.CrossrefMedlineGoogle Scholar
  • 11. Hernesniemi JA, Seppälä I, Lyytikäinen LP, Mononen N, Oksala N, Hutri-Kähönen N, Juonala M, Taittonen L, Smith EN, Schork NJ, Chen W, Srinivasan SR, Berenson GS, Murray SS, Laitinen T, Jula A, Kettunen J, Ripatti S, Laaksonen R, Viikari J, Kähönen M, Raitakari OT, Lehtimäki T. Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey: a meta-analysis of three independent studies.PLoS ONE. 2012; 7:e28931.MedlineGoogle Scholar
  • 12. Palomaki GE, Melillo S, Bradley LA. Association between 9p21 genomic markers and heart disease: a meta-analysis.JAMA. 2010; 303:648–656.CrossrefMedlineGoogle Scholar
  • 13. Yamagishi K, Folsom AR, Rosamond WD, Boerwinkle E; ARIC Investigators. A genetic variant on chromosome 9p21 and incident heart failure in the ARIC study.Eur Heart J. 2009; 30:1222–1228.CrossrefMedlineGoogle Scholar
  • 14. Newton-Cheh C, Cook NR, VanDenburgh M, Rimm EB, Ridker PM, Albert CM. A common variant at 9p21 is associated with sudden and arrhythmic cardiac death.Circulation. 2009; 120:2062–2068.LinkGoogle Scholar
  • 15. Assimes TL, Knowles JW, Basu A, Iribarren C, Southwick A, Tang H, Absher D, Li J, Fair JM, Rubin GD, Sidney S, Fortmann SP, Go AS, Hlatky MA, Myers RM, Risch N, Quertermous T. Susceptibility locus for clinical and subclinical coronary artery disease at chromosome 9p21 in the multi-ethnic ADVANCE study.Hum Mol Genet. 2008; 17:2320–2328.CrossrefMedlineGoogle Scholar
  • 16. O’Donnell CJ, Cupples LA, D’Agostino RB, Fox CS, Hoffmann U, Hwang SJ, Ingellson E, Liu C, Murabito JM, Polak JF, Wolf PA, Demissie S. Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.BMC Med Genet. 2007; 8(suppl 1):S4.CrossrefMedlineGoogle Scholar
  • 17. Abdullah KG, Li L, Shen GQ, Hu Y, Yang Y, MacKinlay KG, Topol EJ, Wang QK. Four SNPS on chromosome 9p21 confer risk to premature, familial CAD and MI in an American Caucasian population (GeneQuest).Ann Hum Genet. 2008; 72(1):654–657.CrossrefMedlineGoogle Scholar
  • 18. Ellis KL, Pilbrow AP, Frampton CM, Doughty RN, Whalley GA, Ellis CJ, Palmer BR, Skelton L, Yandle TG, Palmer SC, Troughton RW, Richards AM, Cameron VA. A common variant at chromosome 9P21.3 is associated with age of onset of coronary disease but not subsequent mortality.Circ Cardiovasc Genet. 2010; 3:286–293.LinkGoogle Scholar
  • 19. Dandona S, Stewart AF, Chen L, Williams K, So D, O’Brien E, Glover C, Lemay M, Assogba O, Vo L, Wang YQ, Labinaz M, Wells GA, McPherson R, Roberts R. Gene dosage of the common variant 9p21 predicts severity of coronary artery disease.J Am Coll Cardiol. 2010; 56:479–486.CrossrefMedlineGoogle Scholar
  • 20. Helgadottir A, Thorleifsson G, Magnusson KP, Grétarsdottir S, Steinthorsdottir V, Manolescu A, Jones GT, Rinkel GJ, Blankensteijn JD, Ronkainen A, Jääskeläinen JE, Kyo Y, Lenk GM, Sakalihasan N, Kostulas K, Gottsäter A, Flex A, Stefansson H, Hansen T, Andersen G, Weinsheimer S, Borch-Johnsen K, Jorgensen T, Shah SH, Quyyumi AA, Granger CB, Reilly MP, Austin H, Levey AI, Vaccarino V, Palsdottir E, Walters GB, Jonsdottir T, Snorradottir S, Magnusdottir D, Gudmundsson G, Ferrell RE, Sveinbjornsdottir S, Hernesniemi J, Niemelä M, Limet R, Andersen K, Sigurdsson G, Benediktsson R, Verhoeven EL, Teijink JA, Grobbee DE, Rader DJ, Collier DA, Pedersen O, Pola R, Hillert J, Lindblad B, Valdimarsson EM, Magnadottir HB, Wijmenga C, Tromp G, Baas AF, Ruigrok YM, van Rij AM, Kuivaniemi H, Powell JT, Matthiasson SE, Gulcher JR, Thorgeirsson G, Kong A, Thorsteinsdottir U, Stefansson K. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm.Nat Genet. 2008; 40:217–224.CrossrefMedlineGoogle Scholar
  • 21. Bellenguez C, Bevan S, Gschwendtner A, Spencer CC, Burgess AI, Pirinen M, Jackson CA, Traylor M, Strange A, Su Z, Band G, Syme PD, Malik R, Pera J, Norrving B, Lemmens R, Freeman C, Schanz R, James T, Poole D, Murphy L, Segal H, Cortellini L, Cheng YC, Woo D, Nalls MA, Muller-Myhsok B, Meisinger C, Seedorf U, Ross-Adams H, Boonen S, Wloch-Kopec D, Valant V, Slark J, Furie K, Delavaran H, Langford C, Deloukas P, Edkins S, Hunt S, Gray E, Dronov S, Peltonen L, Gretarsdottir S, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Boncoraglio GB, Parati EA, Attia J, Holliday E, Levi C, Franzosi MG, Goel A, Helgadottir A, Blackwell JM, Bramon E, Brown MA, Casas JP, Corvin A, Duncanson A, Jankowski J, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Viswanathan AC, Wood NW, Worrall BB, Kittner SJ, Mitchell BD, Kissela B, Meschia JF, Thijs V, Lindgren A, Macleod MJ, Slowik A, Walters M, Rosand J, Sharma P, Farrall M, Sudlow CL, Rothwell PM, Dichgans M, Donnelly P, Markus HS; llab International Stroke Genetics Consortium, Wellcome Trust Case Control Consortium 2 (WTCCC2). Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke.Nat Genet. 2012; 44:328–333.CrossrefMedlineGoogle Scholar
  • 22. Murabito JM, Guo CY, Fox CS, D’Agostino RB. Heritability of the ankle-brachial index: the Framingham Offspring study.Am J Epidemiol. 2006; 164:963–968.CrossrefMedlineGoogle Scholar
  • 23. Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17: genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study.Hypertension. 2000; 36:477–483.CrossrefMedlineGoogle Scholar
  • 24. Post WS, Larson MG, Myers RH, Galderisi M, Levy D. Heritability of left ventricular mass: the Framingham Heart Study.Hypertension. 1997; 30:1025–1028.CrossrefMedlineGoogle Scholar
  • 25. Atwood LD, Heard-Costa NL, Cupples LA, Jaquish CE, Wilson PW, D’Agostino RB. Genomewide linkage analysis of body mass index across 28 years of the Framingham Heart Study.Am J Hum Genet. 2002; 71:1044–1050.CrossrefMedlineGoogle Scholar
  • 26. Fox CS, Heard-Costa NL, Wilson PW, Levy D, D’Agostino RB, Atwood LD. Genome-wide linkage to chromosome 6 for waist circumference in the Framingham Heart Study.Diabetes. 2004; 53:1399–1402.CrossrefMedlineGoogle Scholar
  • 27. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB, O’Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study.Circulation. 2007; 116:39–48.LinkGoogle Scholar
  • 28. Meigs JB, Panhuysen CI, Myers RH, Wilson PW, Cupples LA. A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study.Diabetes. 2002; 51:833–840.CrossrefMedlineGoogle Scholar
  • 29. Kathiresan S, Manning AK, Demissie S, D’Agostino RB, Surti A, Guiducci C, Gianniny L, Burtt NP, Melander O, Orho-Melander M, Arnett DK, Peloso GM, Ordovas JM, Cupples LA. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study.BMC Med Genet. 2007; 8(suppl 1):S17.CrossrefMedlineGoogle Scholar
  • 30. Fox CS, Yang Q, Cupples LA, Guo CY, Larson MG, Leip EP, Wilson PW, Levy D. Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study.J Am Soc Nephrol. 2004; 15:2457–2461.CrossrefMedlineGoogle Scholar

8. High Blood Cholesterol and Other Lipids

See Table 8-1 and Charts 8-1 through 8-3

Prevalence

For information on dietary cholesterol, total fat, saturated fat, and other factors that affect blood cholesterol levels, see Chapter 5 (Nutrition).

Youth

(See Chart 8-1.)

  • Among children 4 to 11 years of age, the mean total blood cholesterol level is 161.9 mg/dL. For boys, it is 162.3 mg/dL; for girls, it is 161.5 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—For non-Hispanic whites, 160.9 mg/dL for boys and 161.6 mg/dL for girls

—For non-Hispanic blacks, 165.2 mg/dL for boys and 157.9 mg/dL for girls

—For Mexican Americans, 159.6 mg/dL for boys and 160.7 mg/dL for girls

  • Among adolescents 12 to 19 years of age, the mean total blood cholesterol level is 158.2 mg/dL. For boys, it is 156.1 mg/dL; for girls, it is 160.3 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—For non-Hispanic whites, 156.8 mg/dL for boys and 161.1 mg/dL for girls

—For non-Hispanic blacks, 154.1 mg/dL for boys and 160.6 mg/dL for girls

—For Mexican Americans, 157.8 mg/dL for boys and 158.0 mg/dL for girls

  • The prevalence of abnormal lipid levels among youths 12 to 19 years of age is 20.3%; 14.2% of normal-weight youths, 22.3% of overweight youths, and 42.9% of obese youths have at least 1 abnormal lipid level (NHANES 1999–2006, NCHS).1

  • Approximately 7.8% of adolescents 12 to 19 years of age have total cholesterol levels ≥200 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).

  • Fewer than 1% of adolescents are potentially eligible for pharmacological treatment on the basis of guidelines from the American Academy of Pediatrics.1,2

Adults

(See Table 8-1 and Charts 8-2 and 8-3.)

  • An estimated 31.9 million adults ≥20 years of age have total serum cholesterol levels ≥240 mg/dL (extrapolated to 2010 by use of NCHS/NHANES 2007–2010 data), with a prevalence of 13.8% (Table 8-1; unpublished NHLBI tabulation).

  • Approximately 5.6% of adults ≥20 years of age have undiagnosed hypercholesterolemia3 (NHANES 2007–2010, unpublished NHLBI tabulation).

  • Between the periods 1988–1994 and 1999–2002 (NHANES/NCHS), the age-adjusted mean total serum cholesterol level of adults ≥20 years of age decreased from 206 to 203 mg/dL, and LDL cholesterol levels decreased from 129 to 123 mg/dL.4

  • Data from NHANES 2003–2008 (NCHS) showed the serum total crude mean cholesterol level in adults ≥20 years of age was 195 mg/dL for men and 201 mg/dL for women.5

  • Data from the Minnesota Heart Survey (1980–1982 to 2000–2002) showed a decline in age-adjusted mean total cholesterol concentrations from 5.49 and 5.38 mmol/L for men and women, respectively, in 1980–1982 to 5.16 and 5.09 mmol/L, respectively, in 2000–2002; however, the decline was not uniform across all age groups. Middle-aged to older people have shown substantial decreases, but younger people have shown little overall change and recently had increased total cholesterol values. Lipid-lowering drug use rose significantly for both sexes among those 35 to 74 years of age. Awareness, treatment, and control of hypercholesterolemia have increased; however, more than half of those at borderline-high risk remain unaware of their condition.6

  • According to data from NHANES 2005–2006, between the periods 1999 to 2000 and 2005 to 2006, mean serum total cholesterol levels in adults ≥20 years of age declined from 204 to 199 mg/dL. This decline was observed for men ≥40 years of age and for women ≥60 years of age. There was little change over this time period for other sex/age groups. In 2005 to 2006, ≈65% of men and 70% of women had been screened for high cholesterol in the past 5 years, and 16% of adults had serum total cholesterol levels ≥240 mg/dL.7

  • According to data from NHANES, from 1999 to 2006, the prevalence of elevated LDL cholesterol levels (as defined by levels higher than the specified Adult Treatment Panel III risk category) in adults ≥20 years of age has decreased by ≈33%.8

  • During the period from 1999 to 2006, 26.0% of adults had hypercholesterolemia, 9% of adults had both hypercholesterolemia and hypertension, 1.5% of adults had DM and hypercholesterolemia, and 3% of adults had all 3 conditions.3

Screening
  • Data from the BRFSS study of the CDC in 2011 showed that the percentage of adults who had been screened for high blood cholesterol in the preceding 5 years ranged from 66.3% in Utah to 83.7% in Massachusetts. The median percentage among all 50 states was 75.5%.9

  • The percentage of adults who reported having had a cholesterol check increased from 68.6% during 1999 to 2000 to 74.8% during 2005 to 2006.10

Awareness
  • Data from the BRFSS (CDC) survey in 2011 showed that among adults screened for high blood cholesterol, the percentage who had been told that they had high blood cholesterol ranged from 33.5% in Colorado to 42.3% in Mississippi. The median percentage among states was 38.4%.9

  • Among adults with hypercholesterolemia, the percentage who had been told that they had high cholesterol increased from 42.0% during 1999 to 2000 to 50.4% during 2005 to 2006.10

Treatment
  • NHANES data on the treatment of high LDL cholesterol showed an increase from 28.4% of individuals during 1999 to 2002 to 48.1% during 2005 to 2008.11

  • Self-reported use of cholesterol-lowering medications increased from 8.2% during 1999 to 2000 to 14.0% during 2005 to 2006.10

Adherence

Youth

The American Academy of Pediatrics recommends screening for dyslipidemia in children and adolescents who have a family history of dyslipidemia or premature CVD, those whose family history is unknown, and those youths with risk factors for CVD, such as being overweight or obese, having hypertension or DM, or being a smoker.1

Analysis of data from NHANES 1999–2006 showed that the overall prevalence of abnormal lipid levels among youths 12 to 19 years of age was 20.3%.1

Table 8-1. High Total and LDL Cholesterol and Low HDL Cholesterol

Population GroupPrevalence of Total Cholesterol ≥200 mg/dL, 2010 Age ≥20 yPrevalence of Total Cholesterol ≥240 mg/dL, 2010 Age ≥20 yPrevalence of LDL Cholesterol ≥130 mg/dL, 2010 Age ≥20 yPrevalence of HDL Cholesterol <40 mg/dL, 2010 Age ≥20 y
Both sexes*98 900 000 (43.4%)31 900 000 (13.8%)71 000 000 (31.1%)48 700 000 (21.8%)
Males*45 300 000 (41.3%)14 000 000 (12.7%)35 200 000 (31.9%)34 600 000 (31.8%)
Females*53 600 000 (44.9%)17 900 000 (14.7%)35 800 000 (30.0%)14 100 000 (12.3%)
NH white males, %40.512.330.133.1
NH white females, %45.815.629.312.4
NH black males, %38.610.833.120.3
NH black females, %40.711.731.210.2
Mexican-American males, %48.115.239.934.2
Mexican-American females, %44.713.530.415.1

Prevalence of total cholesterol ≥200 mg/dL includes people with total cholesterol ≥240 mg/dL. In adults, levels of 200 to 239 mg/dL are considered borderline high. Levels of ≥240 mg/dL are considered high.

LDL indicates low-density lipoprotein; HDL, high-density lipoprotein; and NH, non-Hispanic.

*Total data for total cholesterol are for Americans ≥20 y of age. Data for LDL cholesterol, HDL cholesterol, and all racial/ethnic groups are age adjusted for age ≥20 y.

Source for total cholesterol ≥200 mg/dL, ≥240 mg/dL, LDL, and HDL: National Health and Nutrition Examination Survey (2007–2010), National Center for Health Statistics, and National Heart, Lung, and Blood Institute. Estimates from National Health and Nutrition Examination Survey 2007–2010 (National Center for Health Statistics) applied to 2010 population estimates.

Adults

  • On the basis of data from the Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults12:

—Fewer than half of all people who qualify for any kind of lipid-modifying treatment for CHD risk reduction are receiving it.

—Fewer than half of even the highest-risk people (those with symptomatic CHD) are receiving lipid-lowering treatment.

—Only approximately one third of treated patients are achieving their LDL goal; <20% of patients with CHD are at their LDL goal.

  • Data from NHANES 2005–2006 indicate that among those with elevated LDL cholesterol levels, 35.5% had not been screened previously, 24.9% were screened but not told they had elevated cholesterol, and 39.6% were treated inadequately.8

  • There were 33.2% of adults overall during 2005–2008 in NHANES who achieved LDL cholesterol goals. Among adults without health insurance, only 22.6% achieved LDL cholesterol goals; however, 82.8% of those adults with uncontrolled LDL cholesterol did have some form of health insurance.11

Lipid Levels

LDL (Bad) Cholesterol
Youth
  • There are limited data available on LDL cholesterol for children 4 to 11 years of age.

  • Among adolescents 12 to 19 years of age, the mean LDL cholesterol level is 89.5 mg/dL. For boys, it is 88.6 mg/dL, and for girls, it is 90.5 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Among non-Hispanic whites, 90.4 mg/dL for boys and 90.9 mg/dL for girls

—Among non-Hispanic blacks, 85.8 mg/dL for boys and 91.8 mg/dL for girls

—Among Mexican Americans, 90.6 mg/dL for boys and 87.1 mg/dL for girls

  • High levels of LDL cholesterol occurred in 7.3% of male adolescents and 7.6% of female adolescents during 2007 to 2010.1

Adults
  • The mean level of LDL cholesterol for American adults ≥20 years of age was 115.8 mg/dL in 2007 to 2010.8 Levels of 130 to 159 mg/dL are considered borderline high, levels of 160 to 189 mg/dL are classified as high, and levels of ≥190 mg/dL are considered very high according to Adult Treatment Panel III.

  • According to NHANES 2007–2010 (unpublished NHLBI tabulation):

—Among non-Hispanic whites, mean LDL cholesterol levels were 115.1 mg/dL for men and 115.7 mg/dL for women.

—Among non-Hispanic blacks, mean LDL cholesterol levels were 115.9 mg/dL for men and 114.2 mg/dL for women.

—Among Mexican Americans, mean LDL cholesterol levels were 119.7 mg/dL for men and 115.0 mg/dL for women.

  • The age-adjusted prevalence of high LDL cholesterol in US adults was 26.6% in 1988 to 1994 and 25.3% in 1999 to 2004 (NHANES/NCHS). Between 1988 to 1994 and 1999 to 2004, awareness increased from 39.2% to 63.0%, and use of pharmacological lipid-lowering treatment increased from 11.7% to 40.8%. LDL cholesterol control increased from 4.0% to 25.1% among those with high LDL cholesterol. In 1999 to 2004, rates of LDL cholesterol control were lower among adults 20 to 49 years of age than among those ≥65 years of age (13.9% versus 30.3%, respectively), among non-Hispanic blacks and Mexican Americans than among non-Hispanic whites (17.2% and 16.5% versus 26.9%, respectively), and among men than among women (22.6% versus 26.9%, respectively).13

  • Mean levels of LDL cholesterol decreased from 126.1 mg/dL during 1999 to 2000 to 116.1 mg/dL during 2009 to 2010. The prevalence of high LDL cholesterol decreased from 31.5% during 1999 to 2000 to 28.2% during 2009 to 20108 (unpublished NHLBI tabulation).

HDL (Good) Cholesterol
Youth
  • Among children 4 to 11 years of age, the mean HDL cholesterol level is 53.6 mg/dL. For boys, it is 55.1 mg/dL, and for girls, it is 51.9 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Among non-Hispanic whites, 53.9 mg/dL for boys and 51.4 mg/dL for girls

—Among non-Hispanic blacks, 59.9 mg/dL for boys and 55.3 mg/dL for girls

—Among Mexican Americans, 53.5 mg/dL for boys and 50.5 mg/dL for girls

  • Among adolescents 12 to 19 years of age, the mean HDL cholesterol level is 51.4 mg/dL. For boys, it is 49.2 mg/dL, and for girls, it is 53.6 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Among non-Hispanic whites, 48.4 mg/dL for boys and 53.0 mg/dL for girls

—Among non-Hispanic blacks, 53.9 mg/dL for boys and 55.4 mg/dL for girls

—Among Mexican Americans, 47.5 mg/dL for boys and 53.3 mg/dL for girls

  • Low levels of HDL cholesterol occurred in 21.7% of male adolescents and 10.7% of female adolescents during 2007 to 20101 (NHANES 2007–2010, unpublished NHLBI tabulation).

Adults
  • An HDL cholesterol level <40 mg/dL in adult males and <50 mg/dL in adult females is considered low and is a risk factor for HD and stroke. The mean level of HDL cholesterol for American adults ≥20 years of age is 52.5 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).

  • According to NHANES 2007–2010 (unpublished NHLBI tabulation):

—Among non-Hispanic whites, mean HDL cholesterol levels were 46.7 mg/dL for men and 58.1 mg/dL for women

—Among non-Hispanic blacks, mean HDL cholesterol levels were 52.6 mg/dL for men and 58.7 mg/dL for women

—Among Mexican Americans, mean HDL cholesterol levels were 45.4 mg/dL for men and 53.7 mg/dL for women

Triglycerides
Youth
  • There are limited data available on triglycerides for children 4 to 11 years of age.

  • Among adolescents 12 to 19 years of age, the mean triglyceride level is 82.9 mg/dL. For boys, it is 85.6 mg/dL, and for girls, it is 80.1 mg/dL. The racial/ethnic breakdown is as follows (NHANES 2007–2010, unpublished NHLBI tabulation):

—Among non-Hispanic whites, 89.6 mg/dL for boys and 83.5 mg/dL for girls

—Among non-Hispanic blacks, 66.7 mg/dL for boys and 58.6 mg/dL for girls

—Among Mexican Americans, 97.1 mg/dL for boys and 83.5 mg/dL for girls

  • High levels of triglycerides occurred in 9.4% of male adolescents and 6.7% of female adolescents during 2007 to 2010.1

Adults
  • A fasting triglyceride level ≥150 mg/dL in adults is considered elevated and is a risk factor for HD and stroke. The mean level of triglycerides for American adults ≥20 years of age is 130.3 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation).

—Among men, the mean triglyceride level is 141.7 mg/dL (NHANES 2007–2010, unpublished NHLBI tabulation). The racial/ethnic breakdown is as follows:

○ 140.0 mg/dL for non-Hispanic white men

○ 111.3 mg/dL for non-Hispanic black men

○ 161.4 mg/dL for Mexican American men

—Among women, the mean triglyceride level is 119.1 mg/dL, with the following racial/ethnic breakdown:

○ 121.5 mg/dL for non-Hispanic white women

○ 94.4 mg/dL for non-Hispanic black women

○ 134.1 mg/dL for Mexican American women

  • Approximately 27% of adults ≥20 years of age had a triglyceride level ≥150 mg/dL during 2007 to 201014 (NHANES 2007–2010, unpublished NHLBI tabulation).

  • Fewer than 3% of adults with a triglyceride level ≥150 mg/dL received pharmacological treatment during 1999 to 2004.14

Chart 8-1.

Chart 8-1. Trends in mean total serum cholesterol among adolescents 12 to 17 years of age by race, sex, and survey year (National Health and Nutrition Examination Survey: 1976–1980,* 1988–1994,* 1999–2004, and 2005–2010). Values are in mg/dL. NH indicates non-Hispanic; Mex. Am., Mexican American. *Data for Mexican Americans not available. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.

Chart 8-2.

Chart 8-2. Trends in mean total serum cholesterol among adults ages ≥20 years old by race and survey year (National Health and Nutrition Examination Survey: 1988–1994, 1999–2002, 2003-2006, and 2007–2010). Values are in mg/dL. NH indicates non-Hispanic. Source: National Center for Health Statistics and National Heart, Lung, and Blood Institute.

Chart 8-3.

Chart 8-3. Age-adjusted trends in the prevalence of total serum cholesterol ≥200 mg/dL in adults ≥20 years of age by sex, race/ethnicity, and survey year (National Health and Nutrition Examination Survey 2005–2006, 2007–2008, and 2009–2010). NH indicates non-Hispanic; Mex. Am., Mexican American.

Abbreviations Used in Chapter 8

BRFSS

Behavioral Risk Factor Surveillance System

CDC

Centers for Disease Control and Prevention

CHD

coronary heart disease

CVD

cardiovascular diseases

DM

diabetes mellitus

HD

heart disease

HDL

high-density lipoprotein

LDL

low-density lipoprotein

Mex. Am.

Mexican American

NCHS

National Center for Health Statistics

NH

non-Hispanic

NHANES

National Health and Nutrition Examination Survey

NHLBI

National Heart, Lung, and Blood Institute

References

References

  • 1. Centers for Disease Control and Prevention (CDC). Prevalence of abnormal lipid levels among youths: United States, 1999–2006 [published correction appears in MMWR Morb Mortal Wkly Rep. 2010;59:78].MMWR Morb Mortal Wkly Rep. 2010; 127:e72–e76.Google Scholar
  • 2. Ford ES, Li C, Zhao G, Mokdad AH. Concentrations of low-density lipoprotein cholesterol and total cholesterol among children and adolescents in the United States.Circulation. 2009; 119:1108–1115.LinkGoogle Scholar
  • 3. Fryar CD, Hirsch R, Eberhardt MS, Yoon SS, Wright JD. Hypertension, high serum total cholesterol, and diabetes: racial and ethnic prevalence differences in U.S. adults, 1999–2006.NCHS Data Brief. 2010; (36):1–8.Google Scholar
  • 4. Carroll MD, Lacher DA, Sorlie PD, Cleeman JI, Gordon DJ, Wolz M, Grundy SM, Johnson CL. Trends in serum lipids and lipoproteins of adults, 1960-2002.JAMA. 2005; 294:1773–1781.CrossrefMedlineGoogle Scholar
  • 5. National Center for Health Statistics. Health, United States, 2010: With Special Feature on Death and Dying.Hyattsville, MD: National Center for Health Statistics; 2011. http://www.cdc.gov/nchs/data/hus/hus10.pdf. Accessed July 5, 2011.Google Scholar
  • 6. Arnett DK, Jacobs DR, Luepker RV, Blackburn H, Armstrong C, Claas SA. Twenty-year trends in serum cholesterol, hypercholesterolemia, and cholesterol medication use: the Minnesota Heart Survey, 1980-1982 to 2000-2002.Circulation. 2005; 112:3884–3891.LinkGoogle Scholar
  • 7. Schober S, Carroll M, Lacher D, Hirsch R. High serum total cholesterol: an indicator for monitoring cholesterol lowering efforts: US adults, 2005–2006.NCHS Data Brief. 2007; (2):1–8.Google Scholar
  • 8. Kuklina EV, Yoon PW, Keenan NL. Trends in high levels of low-density lipoprotein cholesterol in the United States, 1999-2006.JAMA. 2009; 302:2104–2110.CrossrefMedlineGoogle Scholar
  • 9. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System: 2011 Prevalence and Trends Data.http://apps.nccd.cdc.gov/brfss/index.asp. Accessed November 30, 2012.Google Scholar
  • 10. Ford ES, Li C, Pearson WS, Zhao G, Mokdad AH. Trends in hypercholesterolemia, treatment and control among United States adults.Int J Cardiol. 2010; 140:226–235.CrossrefMedlineGoogle Scholar
  • 11. Centers for Disease Control and Prevention (CDC). Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol: United States, 1999–2002 and 2005–2008.MMWR Morb Mortal Wkly Rep. 2011; 60:109–114.MedlineGoogle Scholar
  • 12. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report.Circulation. 2002; 106:3143-3421.Google Scholar
  • 13. Hyre AD, Muntner P, Menke A, Raggi P, He J. Trends in ATP-III-defined high blood cholesterol prevalence, awareness, treatment and control among U.S. adults.Ann Epidemiol. 2007; 17:548–555.CrossrefMedlineGoogle Scholar
  • 14. Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Hypertriglyceridemia and its pharmacologic treatment among US adults.Arch Intern Med. 2009; 169:572–578.CrossrefMedlineGoogle Scholar

9. High Blood Pressure

ICD-9 401 to 404, ICD-10 I10 to I15. See Tables 9-1 and 9-2 and Charts 9-1 through 9-5.

Prevalence

(See Table 9-1 and Chart 9-1.)

  • HBP is defined as:

—SBP ≥140 mm Hg or DBP ≥90 mm Hg or taking antihypertensive medicine, or

—Having been told at least twice by a physician or other health professional that one has HBP.

  • One in 3 US adults has HBP (unpublished NHLBI tabulation).

  • Data from NHANES 2007–2010 found that ≈6% of US adults have undiagnosed hypertension. Data from the 2007–2008 BRFSS, NHIS, and NHANES surveys found 27.8%, 28.5%, and 30.7% US adults were told they had hypertension, respectively.1

  • An estimated 77.9 million adults ≥20 years of age have HBP, extrapolated to 2010 with NHANES 2007–2010 data (Table 9-1).

  • NHANES data show that a higher percentage of men than women have hypertension until 45 years of age. From 45 to 54 and from 55 to 64 years of age, the percentages of men and women with hypertension are similar. After that, a higher percentage of women have hypertension than men (Chart 9-1).

  • HBP is 2 to 3 times more common in women taking oral contraceptives, especially among obese and older women, than in women not taking them.2

  • Data from NHANES 2005–2006 found that 29% of US adults ≥18 years of age were hypertensive. The prevalence of hypertension was nearly equal between men and women; 7% of adults had HBP but had never been told that they had hypertension. Among hypertensive adults, 78% were aware of their condition, 68% were using antihypertensive medication, and 64% of those treated had their hypertensioncontrolled.3

  • Data from the 2011 BRFSS/CDC indicate that the percentage of adults ≥18 years of age who had been told that they had HBP ranged from 22.9% in Utah to 40.1% in Alabama. The median percentage was 30.8%.4

—According to NHANES data 2003–2008, among US adults with hypertension, 8.9% met the criteria for resistant hypertension (BP was ≥140/90 mm Hg, and they reported using antihypertensive medications from 3 different drug classes or drugs from ≥4 antihypertensive drug classes regardless of BP). This represents 12.8% of the population taking antihypertensive medication.5

  • According to data from NHANES 1988–1994 and 2007–2008, HBP control rates improved from 27.3% to 50.1%, treatment improved from 54.0% to 73.5%, and the control/treated rates improved from 50.6% to 72.3%.6

  • Projections show that by 2030, prevalence of hypertension will increase 7.2% from 2013 estimates (unpublished AHA computation, based on methodology described by Heidenreich et al).7

Older Adults
  • In 2009 to 2010, hypertension was among the diagnosed chronic conditions that were more prevalent among older (≥65 years of age) women than older men (57% for women, 54% for men). Ever-diagnosed conditions that were more prevalent among older men than older women included HD (37% for men, 26% for women) and DM (24% for men, 18% for women), on the basis of data from NHIS/NCHS.8

  • The age-adjusted prevalence of hypertension (both diagnosed and undiagnosed) in 2003 to 2006 was 75% for older women and 65% for older men on the basis of data from NHANES/NCHS.9

  • Data from the 2004 NNHS revealed the most frequent chronic medical condition among this nationally representative sample of long-term stay residents aged ≥65 years was hypertension (53% of men and 56% of women). In men, prevalence of hypertension decreased with increasing age.10

  • Among US adults ≥65 years of age (NHANES 1999–2004), prevalence of hypertension was 70.8%, awareness of hypertension was 75.9%, treatment for hypertension was 69.3%, and control of hypertension was 48.8%. Women had a slightly higher prevalence than men and a significantly lower rate of hypertension control.11

Children and Adolescents
  • Analysis of the National Health Examination Survey (NHES), the Hispanic Health and Nutrition Examination Survey, and the NHANES/NCHS surveys of the NCHS (1963–2002) found that the BP, pre-HBP, and HBP trends in children and adolescents 8 to 17 years of age moved downward from 1963 to 1988 and upward thereafter. Pre-HBP and HBP increased 2.3% and 1%, respectively, between 1988 and 1999. Increased obesity (abdominal obesity more so than general obesity) partially explained the HBP and pre-HBP rise from 1988 to 1999. BP and HBP reversed their downward trends 10 years after the increase in the prevalence of obesity. In addition, an ethnic and sex gap appeared in 1988 for pre-HBP and in 1999 for HBP: Non-Hispanic blacks and Mexican Americans had a greater prevalence of HBP and pre-HBP than non-Hispanic whites, and the prevalence was greater in boys than in girls. In that study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.12

  • A study in Ohio of >14 000 children and adolescents 3 to 18 years of age who were observed at least 3 times between 1999 and 2006 found that 3.6% had hypertension.13 Of these, 26% had been diagnosed and 74% were undiagnosed. In addition, 3% of those with hypertension had stage 2 hypertension, and 41% of those with stage 2 hypertension were undiagnosed. Criteria for prehypertension were met by 485 children. Of these, 11% were diagnosed. In this study, HBP in children and adolescents was defined as SBP or DBP that was, on repeated measurement, ≥95th percentile.13

  • A study from 1988–1994 through 1999–2000 of children and adolescents 8 to 17 years of age showed that among non-Hispanic blacks, mean SBP levels increased by 1.6 mm Hg among girls and by 2.9 mm Hg among boys compared with non-Hispanic whites. Among Mexican Americans, girls’ SBP increased 1.0 mm Hg and boys’ SBP increased 2.7 mm Hg compared with non-Hispanic whites.14

  • Analysis of data from the Search for Diabetes in Youth Study (SEARCH), which included children 3 to 17 years of age with type 1 and type 2 DM, found the prevalence of elevated BP to be 5.9% among those with type 1 DM and 23.7% among those with type 2 DM.15

  • A study of high school students in Houston, TX (mean age 15.4 years; 45.2% male, 49.3% Hispanic, 25.2% Caucasian, and 16.1% African American) found ≈30% of the students