Population trends and variation in body mass index from 1971 to 2008 in the Framingham Heart Study Offspring Cohort

Abstract

Objective: We examined body mass index (BMI) across place and time to determine the pattern of BMI mean and standard deviation trajectories.

Methods: We included participants in the Framingham Heart Study (FHS) Offspring Cohort over eight waves of follow-up, from 1971 to 2008. After exclusions, the final sample size was 4569 subjects with 28,625 observations. We used multi-level models to examine population means and variation at the individual and neighborhood (census tracts) levels across time with measured BMI as the outcome, controlling for individual demographics and behaviors and neighborhood poverty. Because neighborhoods accounted for limited BMI variance, we removed this level as a source of variation in final models. We examined sex-stratified models with all subjects and models stratified by sex and baseline weight classification.

Results: Mean BMI increased from 24.0 kg/m(2) at Wave 1 to 27.7 at Wave 8 for women and from 26.6 kg/m(2) to 29.0 for men. In final models, BMI variation also increased from Waves 1 to 8, with the standard deviation increasing from 4.18 kg/m(2) to 6.15 for women and 3.31 kg/m(2) to 4.73 for men. BMI means increased in parallel across most baseline BMI weight classifications, except for more rapid increases through middle-age for obese women followed by declines in the last wave. BMI standard deviations also increased in parallel across baseline BMI classifications for women, with greater divergence of BMI variance for obese men compared to other weight classifications.

Conclusion: Over nearly 40 years, BMI mean and variation increased in parallel across most baseline weight classifications in our sample. Individual-level characteristics, especially baseline BMI, were the primary factors in rising BMI. These findings have important implications not only for understanding the sources of the obesity epidemic in the United States but also for the targeting of interventions to address the epidemic.

Figure 1. Flow Diagram for Framingham Heart Study Offspring Cohort Subjects and Observations Included in Analyses.

The final sample size for this study included 4569 subjects with 28,625 observations over a nearly 40 year period.

Figure 2. Adjusted Standard Deviation in Body Mass Index, Framingham Heart Study Offspring Cohort, 1971–2008.

In the fully adjusted models, the total unexplained variation in BMI attributed to individuals across time (individual-level standard deviation) steadily increased from 1971 to 2008 for both women and men. The error standard deviation represents the idiosyncratic pure error variance. We accounted for non-linear increases in between-individual BMI standard deviation by including a random intercept at the individual level and random slopes for time and the natural log of time.

Figure 3. Body Mass Index Trajectories by Baseline Weight Classification, Framingham Heart Study Offspring Cohort, 1979–2008.

Using results from the fully-adjusted models, we plotted the BMI trajectory for women (A) and men (B) based on their weight classification at baseline (during Wave 1, 1971–1975), controlling for covariates including baseline BMI. Weight classifications were underweight (BMI <18.5 kg/m²), normal weight (18.5 to 24.9), overweight (25 to 29.9), and obese (≥30). Lines represent trajectories for the typical male or female (mean age at each wave, married, employed,>high school education, non-smoker, consuming 1–2 alcoholic drinks daily, living in a census tract at mean poverty level, with mean baseline BMI for that weight classification).

Figure 4. Individual-Level Standard Deviation in Body Mass Index by Baseline Weight Classification, Framingham Heart Study Offspring Cohort, 1979–2008.

In the fully adjusted models, the individual-level standard deviation of BMI steadily increased from 1971 to 2008 for both women (A) and men (B) in all baseline weight classifications. Standard deviation increases were similar across most weight classifications with larger standard deviations for both obese women and men, and larger increases across time for obese men. We accounted for non-linear increases in between-individual BMI standard deviation by including a random intercept at the individual level and random slopes for time and the natural log of time.