Systolic Blood Pressure, Socioeconomic Status, and Biobehavioral Risk Factors in a Nationally Representative US Young Adult Sample
In the National Longitudinal Study of Adolescent Health, a US longitudinal study of >15 000 young adults, we examined the extent to which socioeconomic status is linked to systolic blood pressure (SBP) and whether biobehavioral risk factors mediate the association. More than 62% of the participants had SBP >120 mm Hg and 12% had SBP >140 mm Hg. More than 66% were classified as at least overweight (body mass index >25 kg/m2), with >36% meeting criteria for at least class I obesity (body mass index >30 kg/m2). Multivariate models showed that higher household income and being married were independently associated with lower SBP. Higher body mass index, greater waist circumference, smoking, and higher alcohol intake were each independently associated with higher SBP. Meditational analyses suggested that higher education level was associated with lower SBP by way of lower body mass, smaller waist circumference, and lower resting heart rate. When these indirect effects were accounted for, education was not significantly associated with SBP. In contrast, household income remained associated with SBP even with control for all of the covariates. Results reinforce current public health concerns about rates of obesity and high blood pressure among young adults and suggest that disparities in education level and household income may play an important role in the observed decrements in health. Identifying modifiable mechanisms that link socioeconomic status to SBP using data from a large representative sample may improve risk stratification and guide the development of effective interventions.
See Editorial Commentary, pp 140–141
High blood pressure continues to be prevalent in the United States, conferring increased morbidity and mortality, for example,1,2 and remains a significant economic burden on the healthcare system.3 Recently much attention has been paid in the scientific literature4 and popular press5 to substantial increases in the prevalence of biobehavioral risk factors for high blood pressure among young adults. Although advances in treatment of high blood pressure have apparently stabilized the rates of high blood pressure for the present,6 further elucidating how modifiable biobehavioral risk factors are related to high blood pressure may provide additional opportunities for maintaining or improving on these advances in this population.
Modifiable biobehavioral risk factors for high blood pressure include body mass index (BMI), waist circumference, heart rate (HR), alcohol consumption, exercise, and smoking.7 Lower socioeconomic status (SES) also has been associated with a poorer biobehavioral risk profile and, in turn, with higher systolic blood pressure (SBP).8–10 Recent evidence from a French sample aged 30 to 79 years10 has shown that body shape, HR, and health behaviors may account for a sizable amount of the association between SES and SBP. In the present study, we adopted the theoretical framework used in the French study10 to further examine the association between SES and SBP using data from a nationally representative sample of ≈15 000 young adults in the United States. Our aims were to assess the independent predictive association among SES indices, biobehavioral factors, and SBP and to examine these biobehavioral risk factors as possible mediators of the association between SBP and SES. We focused on SBP in the present study, because SBP has been shown to be more important than diastolic blood pressure with respect to health risk11–13 and also possibly to be more responsive than diastolic blood pressure to changes in modifiable risk factors.14 Results from the present study could improve risk stratification in clinical settings and inform interventions aimed at reductions in social disparities in health and also further inform the generalizability of the association between SES and SBP across cultures and age cohorts.
The current study uses data from the National Longitudinal Study of Adolescent Health, a nationally representative sample of ≈15 000 young adults that was designed to assess the effects of health-related behaviors during adolescence and into young adulthood. The study was reviewed and approved by the institutional review board at the University of North Carolina-Chapel Hill. Written consent was obtained for all of the data collection. The participants were followed from grades 7 through 12 in 1995 through early adulthood in 2008 in 4 waves of data collection.15 Participants without SBP measures or survey sample weights were excluded, leaving a final sample of 14 299.
Age and marital status (yes/no) were recorded assessed at Wave IV. Race was constructed from a series of queries at Wave I.
SBP, Resting HR, and Cardiac Medication Use
Certified field interviewers measured respondents' resting, seated systolic, and diastolic blood pressures (in millimeters of mercury) and pulse rate (in beats per minute).16 After a 5-minute seated rest, 3 serial measurements were performed at 30-second intervals using a factory calibrated, MicroLife BP3MC1-PC-IB oscillometric blood pressure monitor (MicroLife USA, Inc, Dunedin, FL), and SBP was constructed as the average of measures 2 and 3 and are highly reliable.17 Cardiac medication status was assessed at the Wave IV in-home interview.
BMI and Waist Circumference
Height, weight, and waist circumference were assessed at Wave IV. BMI was calculated as BMI=weight (in kilograms)/height (in meters squared). BMI was modeled in its continuous form in our primary analyses, but for descriptive purposes also was reported in the following categories: (1) BMI <25.0=normal weight; (2) 25.0 to 29.9=overweight; (3) 30.0 to 34.5=obese class I; (4) 35.0 to 39.5=obese class II; and (5) ≥40.0=morbidly obese.18 Waist circumference was measured to the nearest 0.5 cm at the superior border of the iliac crest.16
Physical Exercise, Alcohol Consumption, and Smoking Behavior
Exercise was represented by a yes/no variable that assessed regular (on a weekly basis) participation in any bouts of physical activity, such as walking or strenuous sports.19 Alcohol consumption was defined as follows: 0=nondrinker; 1=occasional drinker, drink ≤2 days of the week; 2=light, drink 5 to 7 days per week and ≤2 drinks (≤1 if female); 3=moderate, drink 5 to 7 days per week, 3 drinks for males and 2 drinks for females; and 4=heavy, drink 5 to 7 days per week, >3 drinks for males and >2 for females. Smoking was coded yes/no, indicating current daily smoking. All of the above variables were assessed at Wave IV, concurrent with the SBP measurement.
Individual and Parental Education
Education was coded as the highest level reported (at Wave IV for respondent and Wave I for parent), as follows: 1=some high school or less; 2=graduated high school; 3=some college or vocational tech; 4=bachelor's degree; and 5=some graduate school or more. Participants completing General Educational Development tests were grouped in the lower level of education class.20
Financial Strain, Home Ownership, Built Environment, and Household Income
Income, financial strain, and home ownership were assessed at Wave IV. Financial strain was derived from 6 questions that assessed whether individuals reported the inability to pay bills, buy food, and so forth. A “built environment” measure (rated by the field interviewer) was the sum of 2 Likert-type items assessed at Wave I regarding how well the building in which the respondent lived was maintained and the surrounding buildings were maintained. The summed score had a possible range of 2 to 8, with higher scores reflecting poorer maintenance. Annual household income was assessed using ordered categories. To create an ordinal income measure, we assigned the midpoint value to each category, resulting in the 13 values ranging from $2500 to more than $150 000.
Sample characteristics were described as median (interquartile range) for continuous variables and frequency (percentage) for categorical measures. Analyses were carried out using SAS version 9.2 (SAS Institute Inc, Cary, NC), R software (http://cran.r-project.org), and Mplus.21 Statistical models were weighted using grand sample weights and adjusted for individual school membership. We first conducted a series of linear regression models estimating the independent associations between the predictor variables and SBP, first adjusted for only age, sex, and cardiac medication, and then adjusted for all of the predictors under study, a “full” model. To capture nonlinearity for several variables we used a piecewise regression or “hockey stick” approach.
We then estimated a path model in which the associations between respondent education and household income on SBP were mediated by the biobehavioral variables. Significance tests were 2 sided, and a value of P<0.05 was considered “significant.”
Further details on the above assessments and statistical analyses can be found in the online Data Supplement at http://hyper.ahajournals.org.
Table 1 shows the unweighted sample characteristics. The majority of participants were white women, with mean age of 29 years. Median SBP was 123.5 mm Hg, and median BMI was 27.6. Approximately 3.7% of the sample was taking some form of cardiac medication. More than 62% of the participants had SBP >120 mm Hg and 12% had SBP >140 mm Hg. More than 66% were classified as at least overweight (BMI: 25.0 to 29.9).
Predictors of SBP
Among the background variables, black race/ethnicity, male sex, age, and taking cardiac medication were positively associated with higher SBP (see Table 2). Adjusting only for age, sex, and cardiac medication, financial strain, built environment, alcohol intake, tobacco smoking, BMI, resting HR, and waist circumference were associated with SBP, whereas higher respondent and parental education, owning a home, being married, regular exercise, and annual household income were inversely associated with SBP.
In the fully adjusted model, the background variables age, male sex, cardiac medication use, and black race remained significantly, positively associated with SBP. Annual income and being married maintained their significant negative association with SBP, whereas moderate and heavy alcohol intake remained strongly associated with higher SBP. Cigarette smoking also was associated with higher SBP. Strong, independent associations were observed for BMI and waist circumference (see Figure 1), with higher values corresponding with higher SBP. Higher resting HR was also associated with higher SBP, although the P value was 0.06.
Mediation path model results are displayed in Figure 2. Estimates for respondent education (top panel) and household income (bottom panel) were produced from a single model that included all of the variables simultaneously but are separated in Figure 2 for presentational clarity. Higher levels of respondent education were associated with lower BMI, lower resting HR, less smoking, and more frequent exercise. Higher education level was associated with greater alcohol intake. The indirect effects of respondent education through BMI and through resting HR were statistically significant (see Table S2, available in the online Data Supplement at http://hyper.ahajournals.org). For every one category increase in education, there was roughly a 0.50-mm Hg decrease in SBP by way of BMI and a 0.20-mm Hg decrease by way of resting HR. In contrast, for every one level increase in education, there also was a 0.13-mm Hg increase in SBP by way of alcohol intake. The indirect effects of education through exercise and smoking were not statistically significant. Waist circumference and BMI were not simultaneously modeled because of their strong correlation. We, therefore, re-estimated the primary path model replacing BMI with the waist measure. In this model, education was inversely associated with waist circumference (unstandardized path coefficient=−2.71; 95% CI=−3.20 to −2.26). The unstandardized indirect effect for education via waist was −0.64 (95% CI=−0.76 to −0.53). (The primary path model also specified indirect effects of education on SBP by way of household income and biobehavioral variables; these associations were all trivial in magnitude and not statistically significant.) The overall indirect effect of education across the entire set of biobehavioral variables was −0.91 (95% CI=−1.19 to −0.63). Finally, after accounting for all of the indirect effects via biobehavioral mediators, the direct effect of respondent education on SBP was no longer statistically significant. Combining the direct effect with all of the indirect effects yielded a total effect for education on SBP of −0.59 (95% CI=−0.91 to −0.26).
Higher household income was associated with lower resting HR and higher alcohol intake (see Figure 2). In contrast to respondent education, although the specific indirect effects of income by way of alcohol and resting HR were statistically significant (see bottom section of Table S2), the total indirect effect was not. Moreover, the direct effect was statistically significant, with each $50 000 increase associated with a decrease in SBP of ≈0.61 mm Hg. The total effect of household income on SBP was −0.74 (95% CI=−1.19 to −0.29) and statistically significant. As with BMI, income was inversely associated with waist circumference (unstandardized path coefficient=−1.71; 95% CI=−2.43 to −1.03). The unstandardized indirect effect of household income on SBP via waist circumference was also significant (−0.41; 95% CI=−0.54 to −0.24).
A striking number of these young adults displayed clinically relevant elevations in both SBP and BMI. Among the most noteworthy findings from the conventional regression model was that, among the SES indices, only income and marital status remained significantly related to SBP after adjustment for biobehavioral risk factors and other SES indicators. Our findings regarding the mediating path from education level to SBP by way of BMI, resting HR, and alcohol consumption are consistent with those of Chaix et al.10 However, our findings diverge from the French study in that we did not find that the total effect of household income on SBP was accounted for by these indirect biobehavioral mediators. This discrepancy may be the result of cultural differences between French and American cultures, the younger age of our sample, and/or our larger sample size. In addition, the availability of government-sponsored healthcare in France, for example, could have buffered the SBP-raising effects of lower household income there. Another intriguing possibility is that the recent economic recession resulted in loss of jobs and diminished household income among National Longitudinal Study of Adolescent Health participants at just about the time that the Wave IV data collection was under way, thereby potentiating the impact of such a recent reduction in household income on SBP. Supporting this possibility is the recent report22 that there was an increase in acute myocardial infarction rates during the stock market decline of October 2008 to April 2009.
Our findings may have important implications for approaches to prevention of high blood pressure. Mediation of the low education association with SBP via increased BMI and waist circumference is consistent with the long-recognized importance of steps to decrease BMI and central obesity in any cardiopreventive program. The independent effect of lower HR to mediate the effects of both low education and household income on SBP points to the importance of also identifying interventions that can reduce resting HR. One such intervention is aerobic exercise, which is also long recognized as an important cardiopreventive measure.
Finally, the emergence of smoking, being married, and increased alcohol consumption as independent correlates of SBP points to the importance of smoking cessation and limiting alcohol consumption as potentially important preventive approaches. The salutatory effects of marriage and social support in general have been widely studied, for example,23 with the majority of findings being consistent with ours.
The cross-sectional nature of this study precludes conclusions regarding causality, and the present findings may generalize only to individuals 24 to 32 years of age. In addition, as with any observational study, unmeasured factors may have significantly contributed to the present associations. For example, genetic profiles could be associated with SES, as well as SBP.
We have shown that indices of lower SES are associated with increased SBP and that increased BMI and waist circumference and higher resting HR are significant mediators of these associations. These findings strengthen the case that lower SES is a risk factor for cardiovascular disease and that increased BMI and central obesity are important mediators of this effect. Interventions that promote weight loss and reduce resting HR have the potential to reduce the impact of low SES on SBP, especially among young adults, which will further reduce the cardiovascular health burden of the US population as they age into middle and older adulthood.
Sources of Funding
This research uses data from the National Longitudinal Study of Adolescent Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and K.M.H. at the University of North Carolina at Chapel Hill, and funded by grant P01 HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the National Longitudinal Study of Adolescent Health data files is available on the National Longitudinal Study of Adolescent Health Web site (http://www.cpc.unc.edu/addhealth). This research was also supported by grant P01 HL36587 from the National Heart, Lung and Blood Institute.
Dr Jon Hussey provided helpful commentary on the initial drafts of this article.
- Received February 8, 2011.
- Revision received March 14, 2011.
- Accepted June 9, 2011.
- © 2011 American Heart Association, Inc.
- Crews DC,
- Plantinga LC,
- Miller ER,
- Hedgeman SR,
- Saydah SH,
- Williams DE,
- Powe NR,
- for Centers for Disease Control and Prevention Cronic Kidney Disease Surveillance Team
Centers for Disease Control and Prevention. US Obesity Trends 2009. Atanta, GA: Centers for Disease Control and Prevention; 2010.
Reuters. Obesity rates will reach 42 percent: study. http://www.reuters.com/article/idUSTRE6A35SO20101104. Accessed June 21, 2011.
- Niskanen L,
- Laaksonen DE,
- Nyyssonen K,
- Punnonen K,
- Valkonen VP,
- Fuentes R,
- Tuomainen TP,
- Salonen R,
- Salonen JT
- Manuck SB,
- Phillips JE,
- Gianaros PJ,
- Flory JD,
- Muldoon MF
- Chaix B,
- Bean K,
- Leal C,
- Thomas F,
- Harvard S,
- Evans D,
- Jego B,
- Pannier B
- Izzo JL Jr..,
- Levy D,
- Black HR
- Blumenthal JA,
- Babyak MA,
- Hinderliter A,
- Watkins LL,
- Craighead L,
- Lin PH,
- Caccia C,
- Johnson J,
- Waugh R,
- Sherwood A
- Harris CM,
- Halpern CT,
- Whitsel AE,
- Hussey DJ,
- Tabor JW,
- Entzel PP,
- Udry JR
- Entzel PP,
- Whitsel AE,
- Richardson A,
- Tabor JW,
- Hallquist S,
- Hussey DJ,
- Halpern CT,
- Harris KM
World Health Organization. Technical Report Series 894. Obesity: Preventing and Managing the Global Epidemic. Geneva, Switzerland: World Health Organization; 2000.
- Muthen LK,
- Muthen BO
- Brummett BH,
- Barefoot JC,
- Siegler IC,
- Clapp-Channing NE,
- Lytle BL,
- Bosworth HB,
- Williams RB,
- Mark DB