Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data.

Objective: This study aimed to illustrate the use and value of measurement error models for reducing bias when evaluating associations between body fat and having type 2 diabetes (T2D) or being physically active.

Methods: Logistic regression models were used to evaluate T2D and physical activity among adults aged 19 to 80 years from the Photobody Study (n = 558). Self-reported T2D and physical activity were categorized as "yes" or "no." Body fat measured by two-dimensional photographs was adjusted for bias using dual-energy x-ray absorptiometry scans as a reference. Three approaches were applied: regression calibration (RC), simulation extrapolation (SIMEX), and multiple imputation (MI).

Results: Unadjusted two-dimensional measures of body fat had upward biases of 30% and 233% for physical activity and T2D, respectively. For the physical activity model, RC-adjusted values had a 13% upward bias, whereas MI and SIMEX decreased the bias to 9% and 91%, respectively. For the T2D model, MI reduced the bias to 0%, whereas RC and SIMEX increased the upward bias to > 300%.

Conclusions: Of three statistical approaches to reducing bias due to measurement errors, MI performed best in comparison to RC and SIMEX. Measurement error methods can improve the reliability of analyses that look for relations between body fat measures and health outcomes.