How to measure obesity in public health research? Problems with using BMI for population inference

BackgroundThough viewed as problematic for measuring individual-level adiposity, Body Mass Index (BMI) is often considered "good enough" for population inference and epidemiological research. However, we demonstrate that BMI produces statistically invalid population-level estimates of associations between key demographic risk factors (e.g., self-reported sex, race, age) and obesity when compared to more direct adiposity measurements. Further, we demonstrate how novel statistical calibration techniques can enable more valid population inference using widely available BMI data alongside a limited subset of "gold standard" measurements. MethodsUsing National Health and Nutrition Examination Survey data (2011-2023), we compare associations, broken down by demographic groups, across three different purported adiposity measures: BMI, Waist Circumference (WC), and whole-body total fat percentage from Dual-energy X-ray absorptiometry (DXA) scans. We then apply a statistical procedure for conducting inference on predicted data to calibrate BMI-based prevalence estimates toward the "gold standard" DXA-based measurements, allowing for valid population inference even for time periods where only BMI data are available. FindingsBMI-measured adiposity yields substantially different - and often contradictory - conclusions about the association between obesity status and lifestyle factors compared to the more direct, DXA-based measurements. Most concerning, the directions and magnitudes of the associations between racial groups may differ depending on whether BMI or DXA-based measurements are used. Similarly, self-reported sex-based differences in obesity prevalence show opposite patterns across measurement types. Our validation results confirm that our calibration method overcomes this challenge and successfully approximates DXA-based associations using primarily BMI-based measurements. InterpretationOur study provides empirical evidence that uncorrected BMI-based inference leads to invalid population-level estimates about the associations between obesity status and key predictors. The statistical calibration approach we present offers a practical solution for obesity researchers who must rely on BMI or similar anthropometric measures due to cost or data availability constraints, enabling more valid population inference without requiring comprehensive "gold standard" adiposity measurements.