Skip main navigation

Association Between a 20‐Year Cardiovascular Disease Risk Score Based on Modifiable Lifestyles and Total and Cause‐Specific Mortality Among US Men and Women

Originally publishedhttps://doi.org/10.1161/JAHA.118.010052Journal of the American Heart Association. 2018;7:e010052

    Abstract

    Background

    The previously validated Healthy Heart Score effectively predicted the 20‐year risk of cardiovascular disease (CVD). We examine whether the Healthy Heart Score may extend to an association with total and cause‐specific mortality.

    Methods and results

    The prospective cohort study investigated 58 319 women (mean age 50.2 years) in the Nurses’ Health Study (1984–2010) and 29 854 in men (mean age 52.7 years) in the Health Professionals’ Follow‐up Study (1986–2010) free of cancer and CVD at baseline. The Healthy Heart Score included baseline current smoking; high body mass index; low physical activity; no or excessive alcohol intake; low intake of fruits and vegetables, cereal fiber, or nuts; and high intake of sugar‐sweetened beverages or red/processed meats. There were 19 122 total deaths. Compared with participants in the first quintile of the Healthy Heart Score (lowest CVD risk), participants in the fifth quintile (highest CVD risk) had a pooled hazard ratio of 2.26 (95% confidence interval [CI], 1.53–3.33) for total mortality; 2.85 (95 % CI, 1.92–4.23) for CVD mortality, and 2.14 (95% CI, 1.56–2.95) for cancer mortality. Participants in the fifth versus the first quintile also had significantly greater risk of death due to coronary heart disease (3.37; 95% CI, 2.16–5.25), stroke (1.75; 95% CI, 1.02–2.99), lung cancer (6.04; 95% CI, 2.78–13.13), breast cancer (1.45; 95% CI, 1.14–1.86), and colon cancer (1.51; 95% CI, 1.18–1.93).

    Conclusions

    The Healthy Heart Score, composed of 9 self‐reported, modifiable lifestyle predictors of CVD, is a potentially useful tool for the counseling of healthy lifestyles that was strongly associated with greater risk of all‐cause, CVD, and cancer mortality.

    Clinical Perspective

    What Is New?

    • This is a large prospective cohort that extends the association of the Healthy Heart Score, which is composed of 9 self‐reported, modifiable lifestyle predictors of cardiovascular disease, to mortality risk.

    • Participants in the fifth quintile with a higher predictive cardiovascular disease risk based on the Healthy Heart Score had a 2.2‐fold higher risk of total mortality, 2.9‐fold higher risk of cardiovascular disease mortality, and 2.1‐fold higher risk of cancer mortality over 26 years (women) or 24 years (men).

    What Are the Clinical Implications?

    • The Healthy Heart Score is a potentially useful tool for the counseling of healthy lifestyles that was strongly associated with greater risk of all‐cause, cardiovascular disease, and cancer mortality.

    • A lifestyle‐only risk score could be used to assess and motivate a larger audience in clinical and population‐wide settings.

    Introduction

    Despite the decline in cardiovascular disease (CVD) mortality in the United States, it remains the leading cause of death.1 Several well‐established clinical risk factors for CVD, including high blood pressure, diabetes mellitus, and hypercholesterolemia, are viewed as major risk factors for management and control of subsequent CVD risk.2 The primary prevention of CVD has largely focused on pharmacological treatment plus lifestyle counseling, mostly addressing high‐risk adults identified by risk prediction tools that include in their assessment clinical risk factors (eg, high blood pressure or high cholesterol). Additionally, in the 1990s, dietary guidelines focused on low‐fat diets for prevention of CVD based on little evidence. However, evidence from controlled feeding trials with risk factors as outcome, long‐term epidemiologic studies, and older small randomized trials indicated that the health effects of dietary fats are heavily dependent on the replacement macronutrien.3 Another strategy for CVD prevention is through the primordial prevention of CVD risk factors through lifestyle modification, rather than the treatment or modification of risk factors once they become elevated. Data from epidemiological studies have shown that healthy dietary choices, physical activity, weight maintenance, and not smoking each play an important role in primordial prevention5 and the maintenance of cardiovascular health.7

    A primordial prevention strategy may also extend to a lower risk in major cause‐specific deaths and greater longevity. For example, following a healthy lifestyle pattern may prevent more than 50% of deaths due to ischemic strokes,8 80% of sudden cardiac deaths,9 and 75% of all deaths due to CVD.10 Recent data suggest that a 60% lower risk of premature mortality was found in individuals with a body mass index (BMI) <22.4 kg/m2 and with a high score on the Alternate Healthy Eating Index, high level of physical activity, and nonsmoking.11

    The previously validated Healthy Heart Score predicted the 20‐year risk of CVD in mid‐adulthood based on modifiable health behaviors (diet, physical activity, alcohol intake, smoking, and body weight) to address both primordial and primary prevention.12 Previously, women in the fifth quintile, with higher predicted CVD risk based on the Healthy Heart Score, had 18, 5, and 3‐fold higher risk of diabetes mellitus, hypertension, and hypercholesterolemia, respectively.13 In addition, the Healthy Heart Score recently showed moderately good performance (C statistic, 0.71; 95% confidence interval [CI], 0.66–0.76) in younger people.14

    It remains unclear how well the Healthy Heart Score, a tool that can be potentially translated to a clinical setting, may extend to lower mortality risk. The support that the Healthy Heart Score may be associated with a broad range of outcomes is important clinically because an individual can adopt a set of behaviors to prevent different outcomes. Thus, we assessed the association between the Healthy Heart Score and total and cause‐specific mortality in NHS (Nurses’ Health Study) and HPFS (Health Professionals’ Follow‐up Study).

    Methods

    Because of the sensitive nature of the data collected for this study, requests to access the data set from qualified researchers trained in human subject confidentiality protocols may be sent to the Channing Division of Network Medicine at .

    Study Participants

    We conducted analyses in NHS, a prospective cohort of 121 700 female nurses aged 30–55 years at baseline in 197615 and in HPFS, a cohort of 51 529 US male health professionals, aged 40 to 75 years, in 1986.16 Participants in both cohorts provided information on medical history, lifestyle factors, and newly diagnosed diseases on self‐reported questionnaires throughout follow‐up every 2 to 4 years. In the current investigation, 1984 was used as baseline for NHS and 1986 for HPFS, when we first obtained detailed information on diet and lifestyle, to calculate the 20‐year CVD risk score. We excluded participants with a history of CVD (myocardial infarction, angina, stroke, transient ischemic attack, and coronary revascularization) or cancer, or who were missing information on alcohol, physical activity, BMI, or smoking at baseline, and those who were outside of the predefined limits of energy intake levels (<800 or >4200 kcal/d for men and <500 or >3500 kcal/d for women) at baseline. The final study population consisted of 58 319 women in NHS (1984–2010) (mean age 50.2 years in 1984) and 29 854 in men in HPFS (1986–2010) (mean age 52.7 years in 1986). The institutional review boards at the Harvard T.H. Chan School of Public Health and Brigham and Women's Hospital approved the study protocols and return of the questionnaire implied informed consent.

    Assessment of Healthy Lifestyle

    The Healthy Heart Score is a CVD risk prediction model that estimates the 20‐year risk of CVD (nonfatal MI, fatal coronary heart disease [CHD], and ischemic stroke) based on lifestyle factors and was developed among a random two thirds of participants separately within 2 cohorts (HPFS and NHS)15 free of CVD, diabetes mellitus, and cancer at baseline.12 The sex‐specific risk scores were validated in the remaining one third of participants in each cohort and demonstrated good discrimination (Harrell C Index: 0.72; 95% CI, 0.71–0.74 [women]; 0.77; 95% CI, 0.76–0.79 [men]), fit, and calibration. While numerous lifestyle predictors of CVD were considered, the final parsimonious model included the 9 factors that best estimated CVD risk: current smoking; higher BMI; low physical activity; no or excessive alcohol consumption; low intake of fruits, vegetables, cereal fiber, or nuts; and high intake of sugar‐sweetened beverages or red/processed meats (Figure S1). A higher Healthy Heart Score reflected a higher risk of CVD.12 In addition, we set age as a constant (age=50) in the prediction model for this analysis because we were interested in the modifiable components of the Healthy Heart Score. Additionally, age is predictive of all disease, and specifically mortality. Because it is the strongest component of the score, it would have driven any observed association with mortality. We also adjusted for age separately by including it as a covariate in our Cox models. In sensitivity analysis we also conducted the analysis with the original equation.

    Smoking status was self‐reported and categorized as “never,” “past,” or “current.” BMI (kg/m2) was calculated from self‐reported height and weight, which was highly correlated with previously directly measured weight (r=0.96).17 For physical activity, we used a previously validated physical activity questionnaire18 to estimate the average hours per week spent in moderate‐ or vigorous‐intensity activity (≥3 metabolic equivalent task). For each food item, participants were asked how often on average a specified portion was consumed during the past year.20 Cereal fiber and alcohol intake was calculated by multiplying the nutrient content of each food item (from the Harvard University Food Composition Database) by the frequency of intake and summed across all food items. We used the residual method to adjust cereal fiber for total energy.21 We calculated average grams per day of alcohol intake, assuming 12.8 g of alcohol in 12 oz of beer, 11.0 g of alcohol in 4 oz of wine, and 14.0 g of alcohol in 1.5 oz of liquor.

    Data were obtained on family history of myocardial infarction, cancer, or diabetes mellitus; aspirin use; vitamin supplement use; new physician‐diagnosed hypertension, hypercholesterolemia, or diabetes mellitus; and menopausal status and postmenopausal hormone therapy and oral contraceptives use (in women).

    Ascertainment of Mortality

    Deaths through 2010 from any cause were the primary outcome of this analysis. Deaths were identified from the state vital statistics records and the National Death Index, or reported by families and the postal system.22 Using these methods, 98% of deaths in each cohort were able to be ascertained.22 For all deaths, we sought death certificates and, when appropriate, requested permission from the next of kin to review medical records. The underlying cause of death was assigned by a physician who was unaware of the data on diet quality after reviewing death certificates and medical records according to the International Classification of Diseases, Eighth Revision (ICD‐8).

    Statistical Analysis

    Person‐years were calculated from the date of return of the baseline questionnaire to the date of diagnosis of death or the end of follow‐up (January 31, 2010, for HPFS and June 30, 2010, for NHS), whichever occurred first. We categorized the Healthy Heart Score into quintiles based on the distribution in each study population.

    The hazard ratios (HRs) and 95% CIs for all‐cause and cause‐specific mortality according to quintiles of the Healthy Heart Score were estimated using Cox proportional hazards models using calendar year as the underlying time scale, and adjusting for age, race (white versus others), family history of myocardial infarction, aspirin use, multivitamin use, menopausal status and hormone use in women, total energy, and last time of physical checkup examination (first multivariable model). Since diagnoses of some conditions may encourage a participant to change their diet or other lifestyles, we additionally adjusted for history of hypertension, hypercholesterolemia, or type 2 diabetes mellitus (yes versus no) in a separate model. As the inclusion of these covariates did not change the HRs, we reported only the age‐adjusted and first multivariable model above.

    We conducted a test for linear trend across quintiles of the Healthy Heart Score by assigning the median value to each quintile and modeling this as a continuous variable.23

    We tested for interaction by age (<median versus ≥median), BMI status (<25 versus ≥25 kg/m2, ie, normal versus overweight/obesity), smoking status (never smoker versus ever smoker), alcohol intake (nondrinker, moderate drinker, heavy drinker), physical activity (<150 minutes versus ≥150 minutes), and diet score (above and below the median). For each potential modifier, we created a cross‐product term between the modifier and quintiles of the Healthy Heart Score. We used the quintile cut points established in the combined NHS and HPFS population to maintain consistency in the distribution of the Healthy Heart Score across categories of each modifier. We used likelihood ratio tests to compare models with and without the cross‐product terms to test formally for an interaction. All analyses were performed separately in each cohort and then pooled using an inverse, variance‐weighted meta‐analysis with fixed‐effects model. All analyses were performed using SAS statistical software, version 9.3 (SAS Institute Inc).

    Results

    During 7 789 315 participant‐years of follow‐up, we documented 19 122 total deaths, including 11 403 in women and 7719 in men.

    Baseline and lifestyle characteristics by quintiles of the Healthy Heart Score are shown in Table 1. In both cohorts, participants with the higher predictive CVD risk (top quintile) were more likely to have a higher BMI, be a current smoker, have higher energy intake, have a higher prevalence of diabetes mellitus or hypertension, and have a family history of diabetes mellitus. In addition, participants with the highest quintile had lower diet score, physical activity, and multivitamin use, and were less likely to undergo physical checkup examinations.

    Table 1 Age‐Adjusted Baseline Characteristics According to Quintiles of the Healthy Heart Score

    Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5
    Nurses’ Health Study
    Healthy Heart Score (20‐y risk)a2.1 (0.2)2.6 (0.1)3.1 (0.2)4.2 (0.6)7.3 (3.0)
    Healthy Heart Score components
    Age, y50.0 (7.3)50.1 (7.3)50.4 (7.2)50.7 (7.0)50.0 (6.9)
    BMI, kg/m221.5 (1.8)23.3 (2.2)25.3 (2.6)27.4 (5.1)27.0 (6.4)
    Current smoker, %000.127.186.0
    Fruits and vegetables, servings per d5.9 (2.6)5.5 (2.5)5.2 (2.6)5.2 (2.6)4.5 (2.5)
    Sugar‐sweetened beverages, servings per d0.2 (0.3)0.2 (0.4)0.3 (0.5)0.3 (0.6)0.4 (0.8)
    Red and processed meats, servings per d0.9 (0.5)1.1 (0.6)1.2 (0.7)1.3 (0.7)1.3 (0.8)
    Cereal fiber, g/d5.2 (2.8)4.5 (2.4)4.0 (2.0)3.8 (2.0)3.4 (1.8)
    Nuts, servings per d0.2 (0.3)0.1 (0.3)0.1 (0.2)0.1 (0.3)0.1 (0.2)
    Alcohol intake, g/d8.6 (9.3)6.0 (8.8)4.7 (8.6)6.7 (11.4)8.5 (15.7)
    Physical activity, MET‐h/wk4.3 (2.2)3.2 (2.1)2.7 (1.9)2.9 (2.1)2.6 (1.9)
    Energy intake, kcal1736 (489)1735 (511)1747 (531)1765 (544)1754 (554)
    Baseline diabetes mellitus, %1.21.62.54.53.9
    Baseline hypertension, %13.115.921.627.925.3
    Baseline hypercholesterolemia, %6.37.38.49.58.6
    Family history of MI, %17.017.719.720.720.5
    Family history of cancer, %15.915.415.315.514.1
    Family history of diabetes mellitus, %24.727.431.732.831.9
    Aspirin use (yes), %71.572.671.671.070.1
    Multivitamin use,%44.239.836.635.331.7
    Underwent physical examination for screening purposes63.361.559.556.953.9
    Health Professionals’ Follow‐up Study
    Healthy Heart Score (20‐y risk)†2.7 (0.3)3.3 (0.1)3.8 (0.2)4.5 (0.2)6.6 (3.1)
    Healthy Heart Score components
    Age, y53.1 (9.8)52.5 (9.6)52.7 (9.5)52.8 (9.3)52.2 (8.9)
    BMI, kg/m222.5 (1.6)24.0 (1.4)25.1 (1.5)26.4 (1.9)29.2 (4.1)
    Current smoker, %0.40.92.89.632.7
    Fruits and vegetables, servings per d5.4 (2.8)4.9 (2.5)4.6 (2.4)4.4 (2.4)4.2 (2.4)
    Sugar‐sweetened beverages, servings per d0.2 (0.3)0.3 (0.4)0.3 (0.5)0.4 (0.6)0.6 (0.8)
    Red and processed meats, servings per d0.8 (0.6)1.0 (0.7)1.2 (0.7)1.3 (0.8)1.6 (0.9)
    Cereal fiber, g/d7.4 (3.6)6.2 (3.2)5.6 (2.9)5.1 (2.6)4.5 (2.3)
    Nuts, servings per d0.8 (0.9)0.6 (0.7)0.6 (0.7)0.6 (0.7)0.5 (0.6)
    Alcohol intake, g/d14.0 (14.7)12.1 (14.1)11.0 (14.1)10.4 (14.6)10.0 (15.2)
    Physical activity, MET‐h/wk5.7 (6.1)3.2 (3.7)2.4 (3.0)1.8 (2.6)1.3 (2.2)
    Energy intake, kcal2076 (587)2037 (593)2044 (617)2065 (628)2145 (668)
    Baseline diabetes mellitus, %2.02.22.22.33.2
    Baseline hypertension, %13.916.317.821.026.0
    Baseline hypercholesterolemia, %11.29.610.09.711.0
    Family history of MI, %32.531.531.731.632.0
    Family history of cancer, %36.334.134.934.432.8
    Family history of diabetes mellitus, %17.817.619.020.121.6
    Aspirin use (yes), %25.425.627.127.029.7
    Currently uses multivitamins, %50.245.541.439.436.9
    Underwent physical examination for screening purposes53.351.950.949.842.6

    MET indicates metabolic equivalent; MI, myocardial infarction.

    Continuous variables are presented as means (SDs) and categorical values as percentages.

    aThe formula to estimate the 20‐year risk of cardiovascular disease (percentage) based on lifestyle predictors derived includes age as a constant (50 years) and includes smoking, body mass index (BMI), physical activity, alcohol, and a composite diet score (fruits and vegetables, sugar‐sweetened beverages, red/processed meats, cereal fiber, nuts) (Figure S1).

    Both age‐ and multivariable‐adjusted analyses showed a significant association across quintiles of the Healthy Heart Score and total mortality, as well as cause‐specific mortality for CVD or cancer in both men and women (all P trend<0.05), across quintiles, and per 5% increase in the 20‐year risk of CVD (Table 2). The pooled HR comparing participants in the highest quintile (median, 6.6 in Healthy Heart Score) versus participants in the lowest quintile (median, 2.1) was 2.26 (95% CI, 1.53–3.33) for total mortality, 2.85 (95% CI, 1.92–4.23) for CVD mortality, and 2.14 (95% CI, 1.56–2.95) for cancer mortality. Those results were stronger for women than men especially in the fourth and fifth quintiles (Table 2).

    Table 2 Total, Cardiovascular, and Cancer Mortality According to Quintiles of the Healthy Heart Scorea

    Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5P TrendPer 5% Increase in the Score
    Total mortality
    NHS
    Baseline median2.12.63.04.16.6
    Cases14941604182127523732
    Person‐years284 421284 321284 031283 160282 278
    Age‐adjusted model1 [Reference]1.08 (1.00–1.15)1.21 (1.13–1.30)1.92 (1.80–2.04)2.99 (2.81–3.17)<0.00013.42 (3.25–3.60)
    MV‐adjusted model1 [Reference]1.06 (0.99–1.14)1.15 (1.08–1.23)1.74 (1.63–1.85)2.75 (2.59–2.92)<0.00013.17 (3.01–3.34)
    HPFS
    Baseline median2.83.33.84.55.9
    Cases13621350145315761978
    Person‐years132 506132 475132 282132 063131 594
    Age‐adjusted model1 [Reference]1.07 (0.99–1.15)1.17 (1.08–1.26)1.32 (1.3–1.42)1.99 (1.86–2.13)<0.00013.07 (2.77–3.39)
    MV‐adjusted model1 [Reference]1.06 (0.98–1.14)1.14 (1.06–1.23)1.28 (1.19–1.37)1.86 (1.73–2.00)<0.00012.75 (2.48–3.05)
    Pooled
    Age‐adjusted model1 [Reference]1.08 (1.02–1.14)1.19 (1.13–1.25)1.59 (1.11–2.29)2.43 (1.63–3.64)<0.00013.27 (2.94–3.63)
    MV‐adjusted model1 [Reference]1.06 (1.01–1.12)1.15 (1.09–1.21)1.48 (1.09–2.02)2.26 (1.53–3.33)<0.00012.97 (2.59–3.41)
    Cardiovascular mortality
    NHS
    Cases238314393611821
    Age‐adjusted model1 [Reference]1.32 (1.11–1.56)1.63 (1.39–1.92)2.67 (2.30–3.11)4.23 (3.66–4.89)<0.00014.23 (3.78–4.73)
    MV‐adjusted model1 [Reference]1.25 (1.06–1.48)1.42 (1.20–1.66)2.09 (1.79–2.43)3.49 (3.01–4.04)<0.00013.63 (3.23–4.08)
    HPFS
    Cases374390410503683
    Age‐adjusted model1 [Reference]1.14 (0.99–1.32)1.22 (1.06–1.40)1.58 (1.38–1.81)2.65 (2.33–3.01)<0.00014.95 (4.13–5.94)
    MV‐adjusted model1 [Reference]1.11 (0.97–1.28)1.17 (1.01–1.34)1.47 (1.28–1.68)2.33 (2.05–2.65)<0.00014.05 (3.37–4.87)
    Pooled
    Age‐adjusted model1 [Reference]1.23 (1.09–1.38)1.41 (1.06–1.87)2.06 (1.23–3.43)3.34 (2.11–5.29)<0.00014.50 (3.87–5.24)
    MV‐adjusted model1 [Reference]1.18 (1.06–1.32)1.28 (1.06–1.55)1.75 (1.25–2.46)2.85 (1.92–4.23)<0.00013.75 (3.39–4.13)
    Cancer mortality
    NHS
    Cases65864976110871563
    Age‐adjusted model1 [Reference]0.99 (0.89–1.10)1.14 (1.03–1.27)1.67 (1.51–1.84)2.62 (2.39–2.87)<0.00013.08 (2.84–3.34)
    MV‐adjusted model1 [Reference]0.99 (0.88–1.10)1.13 (1.01–1.25)1.62 (1.47–1.79)2.52 (2.29–2.76)<0.00012.95 (2.72–3.20)
    HPFS
    Cases444467479565668
    Age‐adjusted model1 [Reference]1.11 (0.98–1.27)1.15 (1.01–1.31)1.38 (1.21–1.56)1.87 (1.65–2.10)<0.00012.74 (2.30–3.25)
    MV‐adjusted model1 [Reference]1.11 (0.97–1.26)1.14 (1.00–1.30)1.37 (1.21–1.55)1.82 (1.61–2.06)<0.00012.63 (2.20–3.12)
    Pooled
    Age‐adjusted model1 [Reference]1.05 (0.93–1.18)1.14 (1.06–1.24)1.51 (1.24–1.85)2.21 (1.59–3.09)<0.00012.98 (2.68–3.31)
    MV‐adjusted model1 [Reference]1.04 (0.93–1.17)1.13 (1.04–1.23)1.49 (1.25–1.77)2.14 (1.56–2.95)<0.00012.86 (2.58–3.17)

    Values are expressed as hazard ratios (95% confidence intervals).

    Multivariable model, adjusted for age, race, marital status, baseline postmenopausal hormone use(women only), family history of diabetes mellitus, myocardial infarction and cancer, and baseline history of diabetes mellitus, hypertension, hypercholesterolemia, multivitamin use, aspirin use, energy intake, and physical examination.

    HPFS indicates Health Professionals’ Follow‐up Study; MV, multivariable; NHS, Nurses’ Health Study.

    aThe formula to estimate the 20‐year risk of cardiovascular disease (percentage) based on lifestyle predictors derived include age as a constant (50 years) and includes smoking, body mass index, physical activity, alcohol, and a composite diet score (fruits and vegetables, sugar‐sweetened beverages, red/processed meats, cereal fiber, and nuts) (Figure S1).

    In addition, we evaluated specific causes of CVD, cancer, and non‐CVD and noncancer deaths. Participants in the top quintile versus the first quintile of the Healthy Heart Score had significantly higher risk of death due to CHD (pooled HR, 3.37; 95% CI, 2.16–5.25), stroke (pooled HR, 1.75; 95% CI, 1.02–2.99), lung cancer (pooled HR, 6.04; 95% CI, 2.78–13.13), breast cancer (pooled HR, 1.45; 95% CI, 1.14–1.86), and colon cancer (pooled HR, 1.51; 95% CI, 1.18–1.93) (Table 3).

    Table 3 Cause‐Specific Mortality Based on Quintiles of the Healthy Heart Scorea

    Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5P Trend
    Coronary heart disease
    Cases4134855728481200
    MV‐adjusted model1 [Reference]1.20 (1.06–1.37)1.35 (1.19–1.53)2.00 (1.37–2.91)3.37 (2.16–5.25)<0.0001
    Stroke
    Cases197222230265305
    MV‐adjusted model1 [Reference]1.13 (0.93–1.37)1.06 (0.55–2.04)1.26 (0.80–1.97)1.75 (1.02–2.99)<0.0001
    Lung cancer
    Cases132158174359792
    MV‐adjusted model1 [Reference]1.26 (0.89–1.79)1.38 (1.10–1.73)2.84 (2.16–3.75)6.04 (2.78–13.13)<0.0001
    Breast cancer (women)
    Cases7998133182219
    MV‐adjusted model1 [Reference]1.14 (0.89–1.47)1.13 (0.88–1.46)1.53 (1.21–1.95)1.45 (1.14–1.86)0.0009
    Prostate cancer (men)
    Cases8482807269
    MV‐adjusted model1 [Reference]1.10 (0.81–1.49)1.08 (0.79–1.47)0.99 (0.72–1.37)1.13 (0.82–1.57)0.64
    Colon cancer
    Cases109104143168160
    MV‐adjusted model1 [Reference]0.95 (0.72–1.24)1.26 (0.98–1.63)1.48 (1.16–1.89)1.51 (1.18–1.93)0.005
    Other deaths
    Cases70689792315582543
    MV‐adjusted model1 [Reference]1.01 (0.91–1.13)1.09 (1.00–1.18)1.29 (0.75–2.22)1.92 (1.02–3.60)0.006

    Values are expressed as pooled hazard ratios (95% confidence intervals).

    Multivariable model, adjusted for age, race, marital status, baseline postmenopausal hormone use (women only), family history of diabetes mellitus, myocardial infarction and cancer, and baseline history of diabetes mellitus, hypertension, hypercholesterolemia, multivitamin use, aspirin use, energy intake, and physical examination. Pooled data from the Nurses’ Health Study and Health Professionals’ Follow‐up Study.

    aThe formula to estimate the 20‐year risk of cardiovascular disease (percentage) based on lifestyle predictors derived include age as a constant (50 years) and includes smoking, body mass index, physical activity, alcohol, and a composite diet score (fruits and vegetables, sugar‐sweetened beverages, red/processed meats, cereal fiber, and nuts) (Figure S1).

    MV indicates multivariable.

    In analyses stratified by dichotomous categories of risk factors of the Healthy Heart Score, the association between quintiles of the Healthy Heart Score and total mortality was significantly higher for participants in the top quintile for each category studied, for both cohorts (Table 4).

    Table 4 Total Mortality Based on Each Behavioral Component of the Healthy Heart Score by Quintiles of the Healthy Heart Score

    Quintile 1Quintile 2Quintile 3Quintile 4Quintile 5P TrendP for interaction
    Age younger than the mediana
    Cases4434445367041341
    Person‐years, NHS140 303140 671134 535131 542145 491
    Person‐years, HPFS6597968 95567 78566 21471 837
    MV‐adjusted model, NHS1 [Reference]1.01 (0.85–1.19)1.16 (0.98–1.36)1.65 (1.42–1.92)2.84 (2.48–3.26)<0.00010.083
    MV‐adjusted model, HPFS0.91 (0.73–1.13)1.20 (0.98–1.47)1.31 (1.07–1.60)2.19 (1.83–2.62)<0.00010.002
    Age older than or equal to the median
    Cases24102515273536204374
    Person‐years, NHS144 073143 730149 461151 576136 749
    Person‐years, HPFS66 52763 52064 49765 84959 757
    MV‐adjusted model, NHS1 [Reference]1.07 (0.99–1.16)1.15 (1.07–1.24)1.76 (1.64–1.88)2.73 (2.55–2.92)<0.0001
    MV‐adjusted model, HPFS1.09 (1.01–1.18)1.13 (1.05–1.23)1.26 (1.17–1.37)1.78 (1.64–1.92)<0.0001
    BMI <25 kg/m2
    Cases20671751113814552007
    Person‐years, NHS
    Person‐years, HPFS92 74466 14339 41319 721104 412
    MV‐adjusted model, NHS1 [Reference]1.05 (0.97–1.14)1.12 (1.03–1.23)2.56 (2.37–2.77)2.80 (2.61–3.01)<0.0001<0.001
    MV‐adjusted model, HPFS1 [Reference]1.21 (1.08–1.36)1.40 (1.24–1.58)2.10 (1.84–2.39)3.34 (2.88–3.86)<0.00010.07
    BMI ≥25 kg/m2
    Cases162584146621492806
    Person‐years, NHS274 811227 442132 59092 408141 142
    Person‐years, HPFS18 48144 12970 49787 75893 902
    MV‐adjusted model, NHS1 [Reference]1.02 (0.79–1.30)1.08 (0.85–1.37)1.31 (1.03–1.66)2.51 (1.98–3.18)<0.0001
    MV‐adjusted model, HPFS1 [Reference]0.87 (0.67–1.12)1.15 (0.91–1.44)1.26 (1.00–1.58)2.06 (1.66–2.57)<0.0001
    Never smoker
    Cases1777155415461349670
    Person‐years, NHS203 965171 480149 07498 89417 689
    Person‐years, HPFS88 89878 60469 68159 84942 990
    MV‐adjusted model, NHS1 [Reference]1.05 (0.96–1.14)1.16 (1.06–1.27)1.43 (1.30–1.58)2.37 (2.03–2.76)<0.00010.007
    MV‐adjusted model, HPFS1 [Reference]1.05 (0.94–1.17)1.12 (1.01–1.25)1.17 (1.05–1.31)1.56 (1.39–1.76)<0.00010.11
    Ever smoker
    Cases10761405172529755045
    Person‐years, NHS80 411112 821134 981184 225264 551
    Person‐years, HPFS43 60853 87162 60172 21488 605
    MV‐adjusted model, NHS1 [Reference]1.01 (0.90–1.13)1.04 (0.93–1.16)1.68 (1.51–1.85)2.41 (2.19–2.65)<0.0001
    MV‐adjusted model, HPFS1 [Reference]1.04 (0.93–1.16)1.08 (0.98–1.20)1.23 (1.11–1.37)1.77 (1.60–1.95)<0.0001
    Nondrinkerb
    Cases14671836224027993840
    Person‐years, NHS134 666185 892213 637193 055188 171
    Person‐years, HPFS66 95774 69481 56187 98692 204
    MV‐adjusted model, NHS1 [Reference]1.05 (0.95–1.15)1.12 (1.02–1.23)1.59 (1.45–1.74)2.79 (2.56–3.04)<0.0001<0.001
    MV‐adjusted model, HPFS1 [Reference]1.05 (0.96–1.16)1.05 (0.96–1.16)1.15 (1.05–1.27)1.68 (1.54–1.84)<0.00010.04
    Moderate drinker
    Cases9207486758791009
    Person‐years, NHS94 48065 90447 77250 24951 844
    Person‐years, HPFS47 83342 89338 19132 63827 917
    MV‐adjusted model, NHS1 [Reference]1.11 (0.97–1.27)1.19 (1.03–1.38)2.10 (1.85–2.39)2.58 (2.28–2.92)<0.0001
    MV‐adjusted model, HPFS1 [Reference]1.00 (0.87–1.15)1.17 (1.01–1.34)1.34 (1.17–1.55)1.97 (1.71–2.26)<0.0001
    Heavy drinker
    Cases519423331544649
    Person‐years, NHS44 46022 06812 46122 10714 900
    Person‐years, HPFS71465988471438802877
    MV‐adjusted model, NHS1 [Reference]1.12 (0.91–1.36)1.60 (1.29–1.99)1.95 (1.63–2.32)2.76 (2.29–3.32)<0.0001
    MV‐adjusted model, HPFS1 [Reference]1.46 (0.96–2.22)1.28 (0.81–2.02)1.37 (0.84–2.25)2.80 (1.69–4.64)0.0004
    Physical activity >150 min
    Cases19891513128317611885
    Person‐years, NHS217 459163 657130 670137 792121 665
    Person‐years, HPFS90 54365 99550 61939 10527 926
    MV‐adjusted model, NHS1 [Reference]1.04 (0.96–1.14)1.10 (1.01–1.21)1.88 (1.74–2.04)2.74 (2.53–2.97)<0.00010.008
    MV‐adjusted model, HPFS1 [Reference]1.02 (0.91–1.13)1.15 (1.02–1.28)1.28 (1.13–1.44)1.72 (1.51–1.95)<0.00010.07
    Physical activity ≤150 min
    Cases8641446198825633830
    Person‐years, NHS66 917120 644153 326145 327160 575
    Person‐years, HPFS41 96366 48081 66292 958103 669
    MV‐adjusted model, NHS1 [Reference]1.08 (0.95–1.22)1.20 (1.06–1.35)1.61 (1.43–1.80)2.78 (2.49–3.10)<0.0001
    MV‐adjusted model, HPFS1 [Reference]1.06 (0.94–1.18)1.07 (0.96–1.19)1.17 (1.05–1.30)1.73 (1.56–1.91)<0.0001
    Diet score above the medianc
    Cases11311632137916781411
    Person‐years, NHS114 001164 323122 100115 886115 886
    Person‐years, HPFS90 54365 99550 61939 10527 926
    MV‐adjusted model, NHS1 [Reference]1.05 (0.97–1.14)1.14 (1.05–1.24)1.81 (1.68–1.96)2.34 (2.15–2.54)<0.0001<0.001
    MV‐adjusted model, HPFS1.09 (0.99–1.19)1.08 (0.98–1.19)1.23 (1.12–1.36)1.78 (1.61–1.98)<0.00010.008
    Diet score below the median
    Cases7221327189226464304
    Person‐years, NHS59 375119 978161 895167 232202 255
    Person‐years, HPFS41 96366 48081 66292 958103 669
    MV‐adjusted model, NHS1 [Reference]1.07 (0.92–1.25)1.18 (1.02–1.36)1.70 (1.48–1.95)3.04 (2.66–3.47)<0.0001
    MV‐adjusted model, HPFS1 [Reference]0.99 (0.85–1.16)1.13 (0.97–1.30)1.18 (1.03–1.36)1.71 (1.49–1.96)<0.0001

    Values are expressed as hazard ratios (95% confidence intervals).

    Multivariable model, adjusted for age, race, marital status, baseline postmenopausal hormone use (women only), family history of diabetes mellitus, myocardial infarction and cancer, and baseline history of diabetes mellitus, hypertension, hypercholesterolemia, multivitamin use, aspirin use, energy intake, physical examination.

    BMI indicates body mass index; HPFS, Health Professionals’ Follow‐up Study; MV, multivariable; NHS, Nurses’ Health Study.

    aThe median age was 50 years for women and 52 years for men.

    bNondrinker: 0 to 5 g/alcohol (women) and 0 to 10 g alcohol (men); moderate: 5 to 14.9 g/alcohol (women) and 10 to 25 g/alcohol (men); heavy drinker: >15 g/alcohol (women) and >25 g/alcohol (men).

    cThe median of diet score was 1.83 for women and 0.72 for men.

    However, the association was greatest among participants who were younger (below the median [50 years for women and 52 years for men]), were ever smokers, had an alcohol consumption higher than the recommendations (5–14 g for women, 10–25 g for men), and had a diet score below the median (1.83 for women and 0.71 for men) (Table 4).

    Discussion

    In this large prospective cohort of women and men, participants in higher quintiles of the Healthy Heart Score, composed of 9 self‐reported, modifiable lifestyle predictors of CVD (higher BMI, current smoking, low physical activity, lack of moderate alcohol consumption, low composite diet score), had a significantly increased risk of total and cause‐specific mortality. Specifically, participants in the fifth quintile with a higher predictive CVD risk based on the Healthy Heart Score had a 2.2‐fold higher risk of total mortality, 2.9‐fold higher risk of CVD mortality, and 2.1‐fold higher risk of cancer mortality over 26 years (women) or 24 years (men). Further, a higher predictive CVD risk was associated with greater risk of death due to specific types of CVD (CHD, stroke) and several site‐specific cancers (lung, breast, and colon). The association appeared to be more pronounced among participants who were younger, had optimal weight, were ever smokers, had alcohol consumption higher than recommended, or had a diet score below the median.

    The findings in the current study are consistent with the scientific literature that an excess of adiposity,11 insufficient physical activity,25 cigarette smoking,26 and poor diet27 are independently associated with a greater risk of mortality. Studies analyzing a set of different lifestyles are difficult to compare because the estimates vary according to the lifestyle definition and the lifestyle factors selected.28 For example, prior studies in the NHS and the HPFS cohorts compared 5 versus 0 lifestyle risk factors (cigarette smoking, lack of physical activity, low diet quality, alcohol intake of 0 or ≥15 g/d, and overweight) and reported significant relative risks of 3.26, 8.17, and 4.31 for cancer, cardiovascular, and total mortality, respectively.10 In a Dutch study, the relative risks of total mortality for the least versus the most healthy lifestyle score were 4.07 for women and 2.61 for men.30

    In our study that comprised the 9 most predictive modifiable lifestyle factors for CVD risk, the observed estimates were higher when we included age in the risk equation, as expected. A recent study that compared the contribution of changes in modifiable risk factors versus aging in a cohort of black participants showed that aging alone accounted for 60% of the development of 10‐year predicted atherosclerotic CVD risk.31 In our study, by observing significant associations exclusive of age, we showed that modifiable risk factors have an important contribution to CVD risk and, thus, these factors should be targeted for primary and primordial prevention strategies. Although our results were stronger in women than in men in the categorical analysis, that difference was less in the continuous analysis (per 5% increment in the risk score). That might be because the range of the score was greater among women than men.

    This set of lifestyle behaviors may decrease CVD risk,12 the development of clinical risk factors,13 and, based on the present study, total, CVD, and cancer mortality. Based on data from the National Health and Nutrition Examination Survey 1988 to 2006, individuals with 6 or 7 ideal metrics, compared with individuals with 0 metrics, had significant relative risks of 0.49 for all‐cause mortality and 0.24 for CVD mortality over 14.5 years of follow‐up. The Healthy Heart Score extends upon this range as a 20‐year risk assessment, which uniquely focuses on risk factors for the primordial prevention of CVD. In addition, in our study, a higher predictive CVD risk was associated with cause‐specific mortality, due to CHD, stroke, lung cancer, breast cancer, or colon cancer. However, no effect was found for the Healthy Heart Score on prostate cancer mortality. Prior studies in the NHS and the HPFS cohorts showed that 82% of CHD,32 47% of total stroke,8 54% of ischemic stroke,8 and 81% of sudden cardiac death9 could be attributed to poor adherence to a low‐risk lifestyle pattern (defined as not smoking, healthy weight, daily exercise at moderate intensity, moderate alcohol intake, and prudent diet). Among men in the HPFS, 62% of CHD (79% among men younger than 65 years),33 35% of total stroke8 and 52% of ischemic stroke8 deaths may have been prevented with adherence to a low‐risk lifestyle. With regard to prostate cancer incidence, a previous meta‐analysis found no association with a healthy dietary pattern; however, an association may be observed with advanced cancer rather than total prostate cancer.34 More studies are needed to clarify these results. In our study, the associations were slightly more pronounced among participants who were younger (<50 years), had optimal weight, were ever smokers, had alcohol consumption higher than the recommendations, and had a diet score below the median. A recent study showed that adults with a BMI <22.4 kg/m2 and unhealthy lifestyles had a significantly higher risk of mortality than overweight individuals.11 The authors found that the lowest risk of premature mortality was in people with a BMI <22.4 kg/m2 with a healthy diet, physical activity, moderate alcohol consumption, and who did not smoke.

    Study Strengths and Limitations

    Our study includes a large sample size, a long and high follow‐up rate, large number of deaths, and the inclusion of overall as well as cause‐specific mortality. We studied a combination of 9 key modifiable lifestyle factors previously determined to predict CVD risk,12 which may have a stronger additive impact in behavioral lifestyle strategies and outcomes. In addition, recognizing that physicians now have less time to assess or advise patients on healthy lifestyle behaviors,35 this evidence‐based tool (web/online calculator: https://healthyheartscore.sph.harvard.edu/) can simplify the incorporation of health behavior assessment and counseling during clinical visits. Additionally, a lifestyle‐only risk score could be used to assess and motivate a larger audience in clinical and population‐wide settings, who may not have laboratory‐based measures available because of irregular checkups or lack of healthcare resources.

    Some limitations need to be considered. The study may not be generalizable to the broader population as it included mostly white, well‐educated male and female health professionals, although the resulting homogeneity by socioeconomic status, education, or healthcare access helps reduce confounding. Measurement error in self‐reported lifestyle variables is inevitable; however, the data were collected prospectively and this error may be independent of study outcome ascertainment and, therefore, are more likely to attenuate associations towards the null.

    Conclusions

    The Healthy Heart Score, composed of 9 self‐reported, modifiable lifestyle predictors of CVD, is a potentially useful tool for the counseling of healthy lifestyles that was strongly associated with greater risk of all‐cause, CVD, and cancer mortality.

    Sources of Funding

    The study was supported by research grants UM1 CA186107, UM1 CA167552, HL60712, P01 CA87969, P01 CA55075, R01 HL034594, R01 HL088521, and R01 HL35464 from the National Institutes of Health.

    Disclosures

    None.

    Acknowledgments

    Sotos‐Prieto formulated the study question and design, performed the statistical analyses, interpreted the results, and drafted the article. Cook and Chiuve contributed to the statistical modeling and interpretation of the results. Mattei and Sesso contributed to drafting of the article. Chiuve, Hu, Rimm, Willett, and Sesso contributed to the conception and design of the study and acquisition of the data. All authors contributed to the interpretation of data and critical revision of the article and approved the final version.

    Footnotes

    *Correspondence to: Mercedes Sotos‐Prieto and Howard D. Sesso, Department of Food and Nutrition Sciences, Ohio University, Athens (OH) and Departments of Nutrition and Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, 02115. Emails: / and

    References

    • 1 Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jimenez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P; American Heart Association Statistics Committee and Stroke Statistics Subcommittee . Heart disease and stroke statistics––2017 update: a report from the American Heart Association. Circulation. 2017; 135:e146–e603.LinkGoogle Scholar
    • 2 Miura K, Daviglus ML, Dyer AR, Liu K, Garside DB, Stamler J, Greenland P. Relationship of blood pressure to 25‐year mortality due to coronary heart disease, cardiovascular diseases, and all causes in young adult men: the Chicago Heart Association Detection Project in Industry. Arch Intern Med. 2001; 161:1501–1508.CrossrefMedlineGoogle Scholar
    • 3 Zong G, Li Y, Wanders AJ, Alssema M, Zock PL, Willett WC, Hu FB, Sun Q. Intake of individual saturated fatty acids and risk of coronary heart disease in US men and women: two prospective longitudinal cohort studies. BMJ. 2016; 355:i5796.CrossrefMedlineGoogle Scholar
    • 4 Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris‐Etherton PM, Miller M, Rimm EB, Rudel LL, Robinson JG, Stone NJ, Van Horn LV; American Heart Association . Dietary fats and cardiovascular disease: a presidential advisory from the American Heart Association. Circulation. 2017; 136:e1–e23.LinkGoogle Scholar
    • 5 Chomistek AK, Chiuve SE, Eliassen AH, Mukamal KJ, Willett WC, Rimm EB. Healthy lifestyle in the primordial prevention of cardiovascular disease among young women. J Am Coll Cardiol. 2015; 65:43–51.CrossrefMedlineGoogle Scholar
    • 6 Liu K, Daviglus ML, Loria CM, Colangelo LA, Spring B, Moller AC, Lloyd‐Jones DM. Healthy lifestyle through young adulthood and the presence of low cardiovascular disease risk profile in middle age: the Coronary Artery Risk Development in (Young) Adults (CARDIA) study. Circulation. 2012; 125:996–1004.LinkGoogle Scholar
    • 7 Howard G, Banach M, Cushman M, Goff DC, Howard VJ, Lackland DT, McVay J, Meschia JF, Muntner P, Oparil S, Rightmyer M, Taylor HA. Is blood pressure control for stroke prevention the correct goal? The lost opportunity of preventing hypertension. Stroke. 2015; 46:1595–1600.LinkGoogle Scholar
    • 8 Chiuve SE, Rexrode KM, Spiegelman D, Logroscino G, Manson JE, Rimm EB. Primary prevention of stroke by healthy lifestyle. Circulation. 2008; 118:947–954.LinkGoogle Scholar
    • 9 Chiuve SE, Fung TT, Rexrode KM, Spiegelman D, Manson JE, Stampfer MJ, Albert CM. Adherence to a low‐risk, healthy lifestyle and risk of sudden cardiac death among women. JAMA. 2011; 306:62–69.MedlineGoogle Scholar
    • 10 van Dam RM, Li T, Spiegelman D, Franco OH, Hu FB. Combined impact of lifestyle factors on mortality: prospective cohort study in US women. BMJ. 2008; 337:a1440.CrossrefMedlineGoogle Scholar
    • 11 Veronese N, Li Y, Manson JE, Willett WC, Fontana L, Hu FB. Combined associations of body weight and lifestyle factors with all cause and cause specific mortality in men and women: prospective cohort study. BMJ. 2016; 355:i5855.CrossrefMedlineGoogle Scholar
    • 12 Chiuve SE, Cook NR, Shay CM, Rexrode KM, Albert CM, Manson JE, Willett WC, Rimm EB. Lifestyle‐based prediction model for the prevention of CVD: the Healthy Heart Score. J Am Heart Assoc. 2014; 3:e000954. DOI: 10.1161/JAHA.114.000954.LinkGoogle Scholar
    • 13 Sotos‐Prieto M, Mattei J, Hu FB, Chomistek AK, Rimm EB, Willett WC, Eliassen AH, Chiuve SE. Association between a healthy heart score and the development of clinical cardiovascular risk factors among women: potential role for primordial prevention. Circ Cardiovasc Qual Outcomes. 2016; 9:S77–S85.LinkGoogle Scholar
    • 14 Gooding HC, Ning H, Gillman MW, Shay C, Allen N, Goff DC, Lloyd‐Jones D, Chiuve S. Application of a lifestyle‐based tool to estimate premature cardiovascular disease events in young adults: the Coronary Artery Risk Development in Young Adults (cardia) Study. JAMA Intern Med. 2017; 177:1354–1360.CrossrefMedlineGoogle Scholar
    • 15 Willett WC, Green A, Stampfer MJ, Speizer FE, Colditz GA, Rosner B, Monson R, Stason W, Hennekens CH. Relative and absolute excess risks of coronary heart disease among women who smoke cigarettes. N Engl J Med. 1987; 317:1303–1309.CrossrefMedlineGoogle Scholar
    • 16 Colditz GA, Rimm EB, Giovannucci E, Stampfer MJ, Rosner B, Willett WC. A prospective study of parental history of myocardial infarction and coronary artery disease in men. Am J Cardiol. 1991; 67:933–938.CrossrefMedlineGoogle Scholar
    • 17 Rimm EB, Stampfer MJ, Colditz GA, Chute CG, Litin LB, Willett WC. Validity of self‐reported waist and hip circumferences in men and women. Epidemiology. 1990; 1:466–473.CrossrefMedlineGoogle Scholar
    • 18 Wolf AM, Hunter DJ, Colditz GA, Manson JE, Stampfer MJ, Corsano KA, Rosner B, Kriska A, Willett WC. Reproducibility and validity of a self‐administered physical activity questionnaire. Int J Epidemiol. 1994; 23:991–999.CrossrefMedlineGoogle Scholar
    • 19 Ainsworth BE, Haskell WL, Leon AS, Jacobs DR, Montoye HJ, Sallis JF, Paffenbarger RS. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc. 1993; 25:71–80.CrossrefMedlineGoogle Scholar
    • 20 Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol. 1985; 122:51–65.CrossrefMedlineGoogle Scholar
    • 21 Hu FB, Stampfer MJ, Rimm E, Ascherio A, Rosner BA, Spiegelman D, Willett WC. Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am J Epidemiol. 1999; 149:531–540.CrossrefMedlineGoogle Scholar
    • 22 Rich‐Edwards JW, Corsano KA, Stampfer MJ. Test of the national death index and Equifax nationwide death search. Am J Epidemiol. 1994; 140:1016–1019.CrossrefMedlineGoogle Scholar
    • 23 Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med. 1989; 8:551–561.CrossrefMedlineGoogle Scholar
    • 24 Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW. Body‐mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med. 1999; 341:1097–1105.CrossrefMedlineGoogle Scholar
    • 25 Borgundvaag E, Janssen I. Objectively measured physical activity and mortality risk among American adults. Am J Prev Med. 2017; 52:e25–e31.CrossrefMedlineGoogle Scholar
    • 26 Carter BD, Abnet CC, Feskanich D, Freedman ND, Hartge P, Lewis CE, Ockene JK, Prentice RL, Speizer FE, Thun MJ, Jacobs EJ. Smoking and mortality—beyond established causes. N Engl J Med. 2015; 372:631–640.CrossrefMedlineGoogle Scholar
    • 27 Sotos‐Prieto M, Bhupathiraju SN, Mattei J, Fung TT, Li Y, Pan A, Willett WC, Rimm EB, Hu FB. Association of changes in diet quality with total and cause‐specific mortality. N Engl J Med. 2017; 377:143–153.CrossrefMedlineGoogle Scholar
    • 28 Knoops KT, de Groot LC, Kromhout D, Perrin AE, Moreiras‐Varela O, Menotti A, van Staveren WA. Mediterranean diet, lifestyle factors, and 10‐year mortality in elderly European men and women: the HALE project. JAMA. 2004; 292:1433–1439.CrossrefMedlineGoogle Scholar
    • 29 Behrens G, Fischer B, Kohler S, Park Y, Hollenbeck AR, Leitzmann MF. Healthy lifestyle behaviors and decreased risk of mortality in a large prospective study of U.S. women and men. Eur J Epidemiol. 2013; 28:361–372.CrossrefMedlineGoogle Scholar
    • 30 van den Brandt PA. The impact of a Mediterranean diet and healthy lifestyle on premature mortality in men and women. Am J Clin Nutr. 2011; 94:913–920.CrossrefMedlineGoogle Scholar
    • 31 Bress AP, Colantonio LD, Booth JN, Spruill TM, Ravenell J, Butler M, Shallcross AJ, Seals SR, Reynolds K, Ogedegbe G, Shimbo D, Muntner P. Modifiable risk factors versus age on developing high predicted cardiovascular disease risk in blacks. J Am Heart Assoc. 2017; 6:e005054. DOI: 10.1161/JAHA.116.005054.LinkGoogle Scholar
    • 32 Stampfer MJ, Hu FB, Manson JE, Rimm EB, Willett WC. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med. 2000; 343:16–22.CrossrefMedlineGoogle Scholar
    • 33 Chiuve SE, McCullough ML, Sacks FM, Rimm EB. Healthy lifestyle factors in the primary prevention of coronary heart disease among men: benefits among users and nonusers of lipid‐lowering and antihypertensive medications. Circulation. 2006; 114:160–167.LinkGoogle Scholar
    • 34 Grosso G, Bella F, Godos J, Sciacca S, Del Rio D, Ray S, Galvano F, Giovannucci EL. Possible role of diet in cancer: systematic review and multiple meta‐analyses of dietary patterns, lifestyle factors, and cancer risk. Nutr Rev. 2017; 75:405–419.CrossrefMedlineGoogle Scholar
    • 35 Mosca L, Linfante AH, Benjamin EJ, Berra K, Hayes SN, Walsh BW, Fabunmi RP, Kwan J, Mills T, Simpson SL. National study of physician awareness and adherence to cardiovascular disease prevention guidelines. Circulation. 2005; 111:499–510.LinkGoogle Scholar

    eLetters(0)

    eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

    Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.