Normative reference data for body composition in healthy indigenous populations on the Qinghai-Tibet Plateau.

Body composition represents an important aspect of physical fitness and has received increasing attention in recent years. The present study was designed and conducted to provide reference values for age-, sex-, and altitude-specific body composition for healthy indigenous Tibetan adults living on the Qinghai-Tibet Plateau, which has not yet been studied. A cross-sectional survey was conducted among 2,356 healthy Tibetan adults (1,433 females and 923 males) living on the Qinghai-Tibet Plateau for generations. The body composition of the participants was measured by bioelectrical impedance analysis (BIA). The measurements included weight, basal metabolic rate (BMR), fat-free mass (FFM), skeletal muscle mass (SMM), protein mass (PM), bone mass (BM), fat mass (FM), percentage of body fat (PBF), subcutaneous fat mass (SFM), visceral fat mass (VFM), total body water (TBW), intracellular water (ICW), and extracellular water (ECW). Overall, males had greater FFM, SMM, PM, BM, and water mass, whereas females were high in fat mass. Participants from plains generally had higher body composition values, and PM, BM, FM and body water values decreased with increasing altitude, with the lowest values found in >4,000 m Shigatse. FFM, SMM, PM, and BM tended to decrease with increasing age, whereas fat mass and ECW tended to increase with increasing age. This study provides the first normative reference data of body composition for healthy indigenous individuals on the Qinghai-Tibet Plateau. These datasets are important for future research related to body composition. A considerable decrease in body composition values in > 4,000 m-altitude areas suggested that body composition cutoff values should be established by altitude. Age-, sex-, and altitude-specific alterations in body composition values also inform the prevention and amendment of abnormal body composition changes and resultant health complications.

Copyright © 2022 Li, Li, Huang, Chen, Zhang, Dai and Wen.