Introduction

Coronary heart disease (CHD) is the leading cause of mortality,^1–4 and behavioral risk factors heighten the odds of developing CHD.^5–8 Cigarette smoking, poor-quality diet, physical inactivity, excessive alcohol consumption, and obesity are major preventable causes of CHD and premature mortality.^5–8

Clinical Perspective on p 17

Substantial epidemiological evidence associates the sustained presence of healthy lifestyle factors with reduced risk of myocardial infarction and CHD mortality.^9–12 Consequently, many advocate a public health policy of primordial prevention that preserves low risk from youth onward by preventing the development of risk factors.^13–15 However, the population prevalence of a behavioral low-risk profile is extremely low.^11,16 Behavioral Risk Factor Surveillance data show that only 5% of adults meet heart-healthy standards for all of 3 healthy behaviors: physical activity, fruit and vegetable consumption, and nonsmoking.¹⁶ The fact that most people reach young adulthood with at least 1 unhealthy behavior makes it essential to learn whether lifestyle changes in adulthood may still affect cardiovascular health.

We used data from the Coronary Artery Risk Development in Young Adults (CARDIA) study to determine whether health behavior changes made earlier in adulthood are associated with the burden and extent of subclinical atherosclerosis in middle age. We predicted that changes in healthy lifestyle behaviors would be related to the odds of coronary artery calcium (CAC) and carotid artery intima-media thickness (IMT), both markers of subclinical cardiovascular disease that predict future cardiovascular events.^17,18

Methods

Cohort Description and Selection

CARDIA, a longitudinal investigation of cardiovascular disease risk factors in a young adult population, has been described in detail elsewhere.¹⁹ The cohort of 5115 adults (51% of eligible persons contacted) was recruited from 4 metropolitan areas in the United States (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA). Participants, aged 18 to 30 years at year 0 (1985–1986), were reexamined at years 2 (1987–1988), 5 (1990–1991), 7 (1992–1993), 10 (1995–1996), 15 (2000–2001), and 20 (2005–2006), with follow-up rates of 91%, 86%, 81%, 79%, 74%, and 72%, respectively. Follow-up rates for vital status are >90% in each year. Institutional review boards at each site approved the protocol, and all participants provided informed consent.

Study Sample

Those who were not examined at year 20 were excluded. Of the 3549 participants examined at years 0 and 20, we excluded an individual who underwent sex reassignment (n=1) and women pregnant at year 0 (n=4) or year 20 (n=6), leaving 3538 participants for the primary analysis. The sample included 646 black men, 1000 black women, 889 white men, and 1003 white women.

Assessment of Demographic and Healthy Lifestyle Factors

Participants fasted for 12 hours and avoided smoking and heavy physical activity for 2 hours before each clinical examination. Three seated blood pressure measurements were obtained with a random-zero sphygmomanometer; the average of the second and third readings was used. Serum total cholesterol was determined by enzymatic procedures with the use of the ABA Biochromatic instrument.²⁰ Body weight was measured to the nearest 0.2 lb, with participants wearing light clothing and no shoes. Height was measured with participants without shoes to the nearest 0.5 cm. Body mass index (BMI) was computed as weight in kilograms divided by height in meters squared (kg/m²). Age, sex, race, education, and medical information (current treatment and history) were ascertained through questionnaires. At year 0, participants brought in a list of all medications used; at year 20, they physically brought in their medications. Moderate and vigorous physical activity over the previous 12 months²¹ and cigarette smoking were assessed by validated interviewer-administered questionnaires. Current smoking was defined as smoking at least 5 cigarettes per week almost every week. Weekly alcohol (beer, wine, and liquor) intake, assessed by questionnaire, was converted to milliliters of alcohol per day.²²

Diet history was obtained at years 0 and 20 with the use of the validated²³ CARDIA dietary history, an interviewer-administered quantitative food frequency questionnaire.^,24 Approximately 700 food items were used to develop a dietary assessment tool suitable for various populations and ethnic groups.^23,24 Participants were asked to recall their usual dietary intake for the previous 28 days and report the frequency, amount, and method of food preparation.

Definition of Healthy Lifestyle Factor Scores

The 5 healthy lifestyle factors (HLFs) measured at years 0 and 20 were as follows: (1) not currently smoking cigarettes; (2) average physical activity ≥60th percentile at year 0 by race and sex)¹¹; (3) BMI <25.0 kg/m²; (4) alcohol intake ≤15 g/d (women) or ≤30 g/d (men); and (5) healthy diet reflecting low daily intake of saturated fatty acids and high intake of potassium, calcium, and fiber. For the dietary assessment, participants were assigned a score of 1 to 5 according to a race- and sex-specific quintile of each of the 4 nutrients, with 5 representing the more favorable quintile. The 4 scores were summed, and a score ≥60th percentile at year 0 by race and sex was considered a healthy diet. A HLF score (range, 0–5), summing the number of HLFs present, was computed for each participant at years 0 and 20, following the method used by Liu et al.²⁵ We also calculated a score representing change in HLF from year 0 to year 20 (HLF change; range, −5 to +5).

Measurement of CAC and Carotid IMT

CAC was measured at year 20 by computed tomography of the chest.^11,26 Electron beam computed tomography and multidetector computed tomography scanners were used to obtain 40 contiguous 2.5- to 3-mm-thick transverse images from the root of the aorta to the apex of the heart in 2 sequential scans. Participants were scanned over a hydroxylapatite phantom to allow monitoring of image brightness and noise and adjust for scanner differences. Data from both scans were transmitted electronically to the CARDIA Computed Tomography Reading Center. A calcium score in Agatston units was calculated for each calcified lesion, and the scores were summed across all lesions within a given artery and across all arteries (left anterior descending, left main, circumflex, and right coronary) to obtain the total calcium score for the participant. Because CAC is uncommon before midlife,^27,28 detectable CAC (>0) was used as an outcome.

High-resolution B-mode ultrasound was used to acquire a longitudinal image of the common carotid, 2 of the carotid artery bulb, and 2 of the internal carotid artery above the bulb on the right and left sides.²⁹ Measurements of the maximal carotid IMT were made at a central reading center by readers blinded to all clinical information. The maximum IMT of the common carotid was defined as the mean of the carotid IMT of the near and far walls on both the left and right sides, with 1 to 4 measurements available for the common carotid and 1 to 8 for the carotid artery bulb and internal carotid artery. Carotid IMT was analyzed as a continuous measure.

Statistical Analyses

Multiple Imputation of Missing Data

Most variables used in our analyses had <2% missingness, and the percent missing of our primary outcomes ranged from 8% to 14%. Even though the fraction of missing values in our data set was relatively small, a complete case analysis resulted in discarding 1270 observations. Compared with participants who had no missing values at year 20, those with missing values were somewhat younger and less educated (both by 10 months) and were more likely to be black, male, and smokers. To prevent reducing generalizability by discarding those with incomplete data, we imputed missing values in the primary cohort of 3538 using the sequential regression imputation approach³⁰ implemented in IVEware (software from the Survey Research Center, Institute for Social Research, University of Michigan). Five data sets were generated with the use of data from all 7 examinations, resulting in complete year 0 and year 20 data for the sample of n=3538. Each completed data set was analyzed separately, and results were combined with the use of Rubin’s rules.³¹

Sensitivity Analyses

A complete case analysis excluded participants with missing values on any variable in our model, resulting in an analysis of 2268 cases. Additional sensitivity analyses included baseline education, a potential confounder, in both the models that used imputed data and those based on the sample of 2268 with complete data.

Primary Analyses

Baseline (year 0) characteristics were calculated for the 6 HLF groups (HLF 0–5), and trends across the groups were tested by linear or logistic regression. We used logistic regression to estimate the odds of CAC (>0) and linear regression to estimate carotid IMT expressed as a continuous variable at year 20. All models included age, sex, and race, year 0 HLF score, and HLF change score (year 20 HLFs minus year 0 HLFs). All analyses were conducted with SAS statistical software version 9.2.

Results

Sample Demographics

Table 1 shows baseline (year 0) characteristics as a function of number of HLFs. Table I in the online-only Data Supplement compares the baseline characteristics of those included in the analysis with those who were not included. HLFs were inversely related to total cholesterol, blood pressure, BMI, triglycerides, lipids, and fasting glucose. The sample (n=3538) included approximately equal proportions of men and women and black and white participants. Mean age at year 0 was 25.1 years (SD=3.6), and mean education was 14.0 years (SD=2.2). The modal number of HLFs was 3 (36% of the sample); only 8% had all 5 HLFs at year 0.

HLF Change and CAC

At year 20, 19.2% of the sample had CAC >0 (13.3% with CAC 1–100; 3.4% with CAC 101–400; 2.5% with CAC >400). Table 2 reports results of the logistic regression model estimating the odds of having CAC at year 20. In addition to year 0 HLF score, the following variables were included as covariates: age, sex, race, education, and use of hypertension, cholesterol-lowering, or diabetes mellitus medication. Results indicate that being older, being male, being white, being less educated, using hypertension or cholesterol-lowering medication, or having fewer HLFs at baseline were all associated with greater odds of having CAC at year 20. Importantly, after adjustment for these factors, a change in HLF was associated with significantly altered odds of having CAC at year 20 (odds ratio [OR]=0.85; 95% confidence interval, 0.77–0.94). A 1-HLF increase was associated with a 15% reduction in odds of detectable CAC. The relation between HLF change and odds of detectable CAC was graded: Those with a greater increase in HLFs had a proportionally lower prevalence of CAC; those with a greater decrease in HLFs had proportionally higher prevalence of CAC and greater odds of CAC (Figure 1). The pattern of results was consistent when models were run with nonimputed data (Table II in the online-only Data Supplement).

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Assessment of HLF change in these models assumes a uniform linear effect for both healthy (positive) and unhealthy (negative) changes in lifestyle. To test this assumption, we also ran the aforementioned model with number of healthy and unhealthy HLF changes entered as independent variables. The results confirmed a uniform linear effect: Each additional healthy HLF change reduced the odds of detectable CAC (OR=0.85; 95% confidence interval, 0.74–0.98), and each additional unhealthy HLF change increased the odds of detectable CAC (OR=1.17; 95% confidence interval, 1.02–1.33). Note that the effect associated with healthy change was nearly identical in magnitude (OR=1/0.85=1.18) to that associated with unhealthy change (OR=1.17).

HLF Change and Carotid IMT

At year 20, 6.8% of the sample had common carotid IMT >1; 45.5% had carotid bulb IMT >1; and 12.3% had internal carotid IMT >1%. Linear regression adjusted for age; sex; race; education; use of hypertension, cholesterol-lowering, or diabetes mellitus medication; and year 0 HLF score showed that an increase in HLFs was associated with lower carotid IMT at year 20, as follows: common carotid IMT, β=−0.008, P=0.001; carotid bulb IMT, β=−0.022, P<0.0001; internal carotid IMT, β=−0.014, P=0.002 (Table 3). The pattern of results was consistent when models were run with nonimputed data (Table III in the online-only Data Supplement).

We next ran the aforementioned model with number of healthy and unhealthy HLF changes entered as independent covariates. Results confirmed a uniform linear effect such that healthy HLF change predicted significantly lower carotid IMT at year 20 on 2 of the 3 indicators, as follows: common carotid IMT, β=−0.005, P=0.12; carotid bulb IMT, β=−0.024, P=0.001; internal carotid IMT, β=−0.015, P<0.01. Conversely, negative HLF change predicted significantly higher carotid IMT at year 20 on all 3 indicators: common carotid IMT, β=+0.009, P=0.001; carotid bulb IMT, β=+0.020, P<0.01; internal carotid IMT, β=+0.012, P<0.05. Note that effects associated with healthy lifestyle change were nearly identical in magnitude to those associated with unhealthy change. Figure 2 shows the graded relationship between HLF change from year 0 to year 20 and incidence of bulb IMT above the 80th percentile at year 20.

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Individual HLFs

We also ran separate models predicting year 20 CAC >0 and IMT in the top 80% for each individual HLF in which the HLF was treated either as a continuous variable (Table 4) or as a categorical variable (Tables IV through VII in the online-only Data Supplement). Heavier smoking at year 0 predicted increased odds of having CAC or IMT at year 20, and achieving or maintaining nonsmoking was associated with reduced odds. Having a higher BMI at year 0 predicted increased odds of having CAC and IMT at year 20. Maintaining a nonoverweight body weight was associated with reduced odds of CAC and IMT, and gaining more weight was associated with increased odds of having common and internal IMT in the top 80%. Neither baseline nor change in physical activity and alcohol intake was significantly associated with CAC or IMT, and healthy change in diet was only associated with reduced odds of having year 20 bulb IMT in the top 80%.

To learn whether any single HLF accounted for the associations between the combined HLF change score and year 20 CAC and IMT, we removed each HLF 1 at a time and reexamined these associations (Table 5). The residual composite HLF change score remained significantly associated with year 20 CAC and IMT.

Discussion

Change in HLFs from young adulthood to middle age is significantly associated with the presence and extent of subclinical atherosclerosis measured after 20 years of follow-up. The association held after adjustment for demographics, medications, and the baseline number of HLFs in young adulthood. The effect also was evident for 2 indicators of subclinical disease, CAC and carotid IMT, both of which are predictive of future coronary events.^17,18 Furthermore, the relation between HLF change and subclinical atherosclerosis was graded, such that adding HLFs was associated with improved CAC and IMT outcomes, whereas reducing HLFs was associated with poorer outcomes.

Associations between health behaviors and subclinical atherosclerosis may shed light on pathways toward the development of CHD, coronary events, and mortality. HLFs, assessed at a single time point, have been predictive of cardiovascular and all-cause mortality in several studies.^10,32–34 Individual HLFs (BMI, smoking, alcohol consumption, poor diet, and physical inactivity) are also associated with CAC or IMT.^35–38 Using the same combined HLF score as this study, Liu and colleagues²⁵ found that having more HLFs in young adulthood was associated with having a low cardiovascular disease risk profile in middle age (ie, untreated cholesterol <200 mg/dL, untreated blood pressure <120/<80 mm Hg, no history of diabetes mellitus or myocardial infarction).

The stark reality, however, is that few adults have multiple HLFs. Indeed, our observation that only 10% of the young adults in our sample had all 5 HLFs is consistent with earlier estimates.³⁹ In terms of individual health behaviors, <25% of US adults meet dietary guidelines,⁹ and 25% report no leisure time physical activity.¹⁰ Presently, 21% of adults in the United States⁴⁰ report cigarette smoking.⁴¹ Suboptimal diet and a sedentary behavior pattern are pervasive, and risk behaviors tend to cluster,^11–13,16 heightening disease risk.¹⁷ The fact that most people reach young adulthood already having acquired at least 1 unhealthy behavior raises a pressing question for clinical practice policy: Does healthy lifestyle change in adulthood actually reduce the risk of cardiovascular disease? If poor health habits acquired by early adulthood have already done their damage, subsequent intervention to promote healthy lifestyle change might be futile.

Only a few studies have prospectively examined whether change in HLFs during adulthood is related to cardiovascular morbidity and mortality or to early markers of coronary disease. The Atherosclerosis Risk in Communities (ARIC) study found fewer cardiovascular events and lower all-cause mortality among those who adopted a healthy lifestyle in middle age.⁴² Hu et al⁴³ found that reduced smoking and improved diet explained a significant portion of the decline in coronary disease incidence in the Nurses’ Health Study cohort, whereas increased obesity apparently slowed the decline. Gregg et al⁴⁴ found decreased all-cause and cardiovascular mortality among elderly women who increased physical activity. Quitting smoking during young and middle adulthood has been associated with diminished incidence of CAC,⁴⁵ and increased obesity has been linked to increased incidence of CAC⁴⁶ and IMT.⁴⁷ In the only prior study to relate naturalistic, bidirectional change in a healthy lifestyle factor to measures of atherosclerosis, Buscemi et al⁴⁶ observed that carotid IMT decreased among those who lost weight and increased for those who gained weight over a 10-year period. In a clinical trial, Ornish and colleagues^48–50 randomized adults with bioverified coronary artery disease to either intensive lifestyle intervention (low-fat vegetarian diet, cessation of smoking, stress management training, and moderate exercise) compared with usual care control. The investigators observed decreased stenosis and fewer cardiac events over a 5-year follow-up period among treated participants versus increased stenosis and events for controls.

The present results are consistent with prior findings and suggest that, even after a person reaches young adulthood, making changes in lifestyle behaviors can still affect the odds of coronary atherosclerosis. The graded, linear association we observed between changes in HLFs and presence and extent of subclinical disease suggests that making healthy lifestyle changes during early life or midlife might be able to reverse or change the natural progression of coronary artery disease. Making changes in smoking and body weight appears to have greatest impact, and changing other HLFs, including physical activity, diet, and alcohol intake, combines in a graded manner to affect the odds of subclinical atherosclerosis in midlife. It is encouraging to note that the healthy behavior changes that participants made naturally and that were associated with reduced odds of CAC and IMT were much less extreme and demanding than those required by the Ornish program.

Our findings provide grounds for optimism to the vast majority of adults who have already acquired behavioral risk factors for chronic disease by young adulthood. More than 25% of our sample spontaneously made healthy lifestyle changes that were associated with lowered odds of subclinical atherosclerosis. The proportion of adults who make healthy lifestyle changes can very likely be increased by implementing healthcare reimbursement for behavior change interventions or other health promotion policies. At the same time, our findings also convey a cautionary note to those who reach young adulthood free of lifestyle risk factors. Those individuals were not necessarily free of cardiovascular risk 20 years later, indicating that primordial prevention is not a panacea. Rather, the tendency to adopt unhealthy lifestyles after young adulthood was substantial, greater than the tendency to acquire healthier habits, and was associated to the same degree with altered odds of developing CAC and IMT. These observations suggest that health behavior changes in adulthood, whether positive or negative, continue to have important consequences for later health outcomes.

A strength of our study is the relatively young age of our large biracial cohort (n=3538; mean age, 25.1 years at year 0) and the excellent retention through 20-year follow-up. The finding of a robust, bidirectional, graded effect of healthy lifestyle change across both markers of subclinical disease (CAC and IMT) also is a major strength. The results lend weight to the recommendation that counseling for healthy lifestyle change is clinically indicated both to promote change from unhealthy to healthy lifestyles and to prevent loss of existing healthy habits.⁴⁷

Conclusion

Independent of the healthy lifestyle profile present in young adulthood, making subsequent changes in health behaviors is linked to alterations in the burden of subclinical atherosclerosis in middle age. Adopting a healthier lifestyle during early life or midlife may reduce the risk of developing coronary artery disease; conversely, discontinuing healthy behaviors may increase risk. Promotion of healthy lifestyles is essential not only primordially at early stages of life but also among adults to control and potentially even reverse the natural progression of coronary artery disease.

Footnotes