Changes in Body Mass Index and Obesity Risk in Married Couples Over 25 Years: The ARIC Cohort Study

Am J Epidemiol. 2016 Mar 1;183(5):435-43. doi: 10.1093/aje/kwv112. Epub 2015 Sep 23.

Abstract

Married couples might be an appropriate target for obesity prevention interventions. In the present study, we aimed to evaluate whether an individual's risk of obesity is associated with spousal risk of obesity and whether an individual's change in body mass index (BMI; weight in kilograms divided by height in meters squared) is associated with spousal BMI change. We analyzed data from 3,889 spouse pairs in the Atherosclerosis Risk in Communities Study cohort who were sampled at ages 45-65 years from 1986 to 1989 and followed for up to 25 years. We estimated hazard ratios for incident obesity by whether spouses remained nonobese, became obese, remained obese, or became nonobese. We estimated the association of participants' BMI changes with concurrent spousal BMI changes using linear mixed models. Analyses were stratified by sex. At baseline, 22.6% of men and 24.7% of women were obese. Nonobese participants whose spouses became obese were more likely to become obese themselves (for men, hazard ratio = 1.78, 95% confidence interval: 1.30, 2.43; for women, hazard ratio = 1.89, 95% confidence interval: 1.39, 2.57). With each 1-unit increase in spousal BMI change, women's BMI change increased by 0.15 (95% confidence interval: 0.13, 0.18) and men's BMI change increased by 0.10 (95% confidence interval: 0.09, 0.12). Having a spouse become obese nearly doubles one's risk of becoming obese. Future research should consider exploring the efficacy of obesity prevention interventions in couples.

Keywords: change; cohort study; obesity; spouses.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Body Mass Index*
  • Cohort Studies
  • Family Characteristics*
  • Female
  • Humans
  • Linear Models
  • Male
  • Marital Status
  • Maryland
  • Middle Aged
  • Minnesota
  • Mississippi
  • North Carolina
  • Obesity / etiology*
  • Proportional Hazards Models
  • Prospective Studies
  • Residence Characteristics
  • Risk Factors
  • Sex Factors
  • Spouses / statistics & numerical data*
  • Time Factors
  • Weight Gain