Study on the Anthropometric and Body Composition Indices for Prediction of Cold and Heat Patter

Abstract

Objectives: Many symptoms of cold and heat patterns are related to the thermoregulation of the body. Thus, we aimed to study the association of cold and heat patterns with anthropometry/body composition.Methods: The cold and heat patterns of 2000 individuals aged 30–55 years were evaluated using a self-administered questionnaire.Results: Among the anthropometric and body composition variables, body mass index (-0.37, 0.39) and fat mass index (-0.35, 0.38) had the highest correlation coefficients with the cold and heat pattern scores after adjustment for age and sex in the cold-heat group, while the correlation coefficients were relatively lower in the non-cold-heat group. In the cold-heat group, the most parsimonious model for the cold pattern with the variables selected by the best subset method and Lasso included sex, body mass index, waist-hip ratio, and extracellular water/total body water (adjusted R2 = 0.324), and the model for heat pattern additionally included age (adjusted R2 = 0.292).Conclusions: The variables related to obesity and water balance were the most useful for predicting cold and heat patterns. Further studies are required to improve the performance of prediction models.