Beyond recent BMI: BMI exposure metrics and their relationship to health. (August 2020)

• Record Type: Journal Article
• Authors: Ng, Carmen D.; Elliott, Michael R.; Riosmena, Fernando; Cunningham, Solveig A.
• Is Part Of: SSM - population health. Volume 11(2020)
• Publication Date: 2020-08
• Subjects: Body mass index -- Chronic health -- Longitudinal studies -- Obesity; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; 362.105
• DOI: 10.1016/j.ssmph.2020.100547
• Languages: English
• ISSNs: 2352-8273

Abstract

Abstract: Body mass index (BMI) is generally used to classify adiposity. Despite the fact that the consequences of adiposity for chronic health accumulate and manifest over time, most population health research exploring the implications of high BMI measures only its recent intensity. Some studies have used retrospective measures involving maximum weight, and even fewer have used BMI at multiple time points to estimate cumulative exposure to adiposity. The goal of this study was to compare BMI exposure metrics that captured different dimensions of body mass – intensity, history, and duration – in models of health indicators linked with adiposity. We used self-reported BMI of young adults (ages 18 – 33 years, n = 8, 608) across 11 waves of data from the National Longitudinal Survey of Youth 1997 to evaluate eight BMI exposure metrics: most recent, maximum, mean, and median BMI, proportion of time with overweight/obesity, and excess BMI-years with overweight/obesity. We used these metrics in models of self-reported general health, chronic condition, and diabetes, and ascertained how most recent BMI performed when compared with other metrics that better capture the dynamics of BMI. The Akaike information criteria and Vuong tests were used for model comparison, and the strengths of associations were also compared. Most recent BMI was the best metric for explaining general health. Median BMI was best for explaining diabetes, with most recent BMI under-estimating the association by 13% relative to median BMI. For chronic condition, there was no clear best metric. We concluded that most recent BMI is useful for explaining health outcomes, though other metrics should also be given consideration, particularly for conditions that develop over time. Metrics that accounted for both intensity and history performed quite well, but the duration measures might be less useful. Highlights: BMI is dynamic, so researchers need to consider beyond BMI at the time of survey. We calculated 8 BMI-based metrics accounting for intensity, history, and duration. Most recent BMI was the best metric, based on AIC, for explaining general health. Most recent BMI understated the association for diabetes, relative to median BMI.