Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes. (13th August 2021)

• Record Type: Journal Article
• Authors: Shuey, Megan M.; Huang, Shi; Levinson, Rebecca T.; Farber‐Eger, Eric; Cahill, Katherine N.; Beckman, Joshua A.; Koethe, John R.; Silver, Heidi J.; Niswender, Kevin D.; Cox, Nancy J.; Harrell, Frank E.; Wells, Quinn S.
• Is Part Of: Obesity science and practice. Volume 8:Number 1(2022)
• Page Start: 124
• Page End: 130
• Publication Date: 2021-08-13
• Subjects: body mass index -- cardiovascular disease -- metabolic syndrome -- prediction modeling; Obesity -- Periodicals; 616.398005
• DOI: 10.1002/osp4.543
• Languages: English
• ISSNs: 2055-2238

Abstract

Abstract: Objective: Body mass index (BMI) is the most commonly used predictor of weight‐related comorbidities and outcomes. However, the presumed relationship between height and weight intrinsic to BMI may introduce bias with respect to prediction of clinical outcomes. A series of analyses comparing the performance of models representing weight and height as separate interacting variables to models using BMI were performed using Vanderbilt University Medical Center's deidentified electronic health records and landmark methodology. Methods: Use of BMI or height‐weight interaction in prediction models for established weight‐related cardiometabolic traits and metabolic syndrome was evaluated. Specifically, prediction models for hypertension, diabetes mellitus, low high‐density lipoprotein, and elevated triglycerides, atrial fibrillation, coronary artery disease, heart failure, and peripheral artery disease were developed. Model performance was evaluated using likelihood ratio, R 2, and Somers' Dxy rank correlation. Differences in model predictions were visualized using heat maps. Results: Compared to BMI, the maximally flexible height‐weight interaction model demonstrated improved prediction, higher likelihood ratio, R 2, and Somers' Dxy rank correlation, for event‐free probability for all outcomes. The degree of improvement to the prediction model differed based on the outcome and across the height and weight range. Conclusions: Because alternative measures of body composition such as waist‐to‐hip ratio are not routinely collected in the clinic clinical risk models quantifying risk based on height and weight measurements alone are essential to improve practice. Compared to BMI, modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits. Considering an individual's height and weight opposed to BMI is a better method for quantifying individual disease risk.