Abstract
Body weight, height, and other simple, noninvasive anthropometric measures are the cornerstones of epidemiological research. Body composition determinants such as fat and lean tissue masses and their distributions are better associated with metabolic conditions, such as diabetes, than anthropometrics alone. However, body composition is generally more challenging to measure. This analysis article comments on the manuscript by Cichosz et al that appeared in this issue of the Journal of Diabetes Science and Technology, where a machine-learning approach was developed to predict body composition using measured anthropometric parameters for potentially easier estimations of risk factors of metabolic diseases in the future.