Life-course trajectories of body mass index from adolescence to old age: Racial and educational disparities

Abstract

No research exists on how body mass index (BMI) changes with age over the full life span and social disparities therein. This study aims to fill the gap using an innovative life-course research design and analytic methods to model BMI trajectories from early adolescence to old age across 20th-century birth cohorts and test sociodemographic variation in such trajectories. We conducted the pooled integrative data analysis (IDA) to combine data from four national population-based NIH longitudinal cohort studies that collectively cover multiple stages of the life course (Add Health, MIDUS, ACL, and HRS) and estimate mixed-effects models of age trajectories of BMI for men and women. We examined associations of BMI trajectories with birth cohort, race/ethnicity, parental education, and adult educational attainment. We found higher mean levels of and larger increases in BMI with age across more recent birth cohorts as compared with earlier-born cohorts. Black and Hispanic excesses in BMI compared with Whites were present early in life and persisted at all ages, and, in the case of Black–White disparities, were of larger magnitude for more recent cohorts. Higher parental and adulthood educational attainment were associated with lower levels of BMI at all ages. Women with college-educated parents also experienced less cohort increase in mean BMI. Both race and education disparities in BMI trajectories were larger for women compared with men.

Relationship between paternal factors and embryonic aneuploidy of paternal origin

“…This is evident in our study as the paternal BMI was missing in 13.6% male patients and the BMI data were collected an average of 385 days from the date of the index IVF cycle. This timeline appears extensive; however, the amount of weight fluctuations in a standard man in their 30s generally is only 1 pound per year (34,35). Given this information, it is safe to assume that paternal BMI likely did not vary a significant amount over the 385 days.…”

Prevalence and correlates of weight gain attempts across five countries

“…Specifically, future research should investigate the utility of such efforts that focus on weight-and appearance-change behaviors, particularly weight gain attempts, as well as considering populations for whom these efforts may have particular applicability (i.e., adolescent boys and young adult men). However, despite an overall dearth of research explicitly exploring weight gain attempts among men in middle-and older-adulthood (Brown & Lavender, 2021), it is notable that men in these older age groups do experience body dissatisfaction, including concerns related to perceived reductions in athleticism and muscularity (Lodge & Umberson, 2013), which may be driven by natural weight gain as one ages (Chu et al, 2021;Viner, Costa, & Johnson, 2019;Yang et al, 2021). As such, this warrants additional research to inform a Race/ethnicity categories in each country as per census questions asked in each country: Australia: majority = only speaks English at home, minority = speaks a language besides English at home; Canada: majority = "White (European descent)," minority = any other race/ ethnicity; Mexico: majority = nonindigenous, minority = indigenous; United Kingdom: majority = "White," minority = any other race/ethnicity; United States: majority = "White," minority = any other race/ethnicity.…”

Life-Course Trajectories of Risk-Taking Propensity: A Coordinated Analysis of Longitudinal Studies

“…Thirdly and finally, our analyses suggest some role for period and cohort effects but our approach was relatively simple and distinguished only periods as the starting year of data collection and compared only two cohorts. Future work might consider more sophisticated methods to provide a more continuous assessment of age, period, and cohort effects on risk taking across studies and domains (Yang et al, 2021). We hope such studies considering individual-specific differences and time-varying covariates will profit from our panel selection and publicly available code.…”