Associations between genetic loci related to lean mass and body composition in type 2 diabetes patients

Aim Several genetic loci related to lean mass have been identified in healthy individuals by genome-wide association studies; however, the contribution of these loci to body composition in type 2 diabetes remains to be investigated. Here, we aimed to clarify the genetic determinants of body composition in individuals with type 2 diabetes. Methods A total of 176 Japanese outpatients (70 women and 106 men) with type 2 diabetes were studied using a cross-sectional design. Body composition was measured using bioimpedance analysis with a commercially available device (InBody770). Single-nucleotide polymorphisms in IRS1 (rs2943656), HSD17B11 (rs9991501), VCAN (rs2287926), ADAMTSL3 (rs4842924) and FTO (rs9936385) were evaluated by genotyping. The contributions of single-nucleotide polymorphisms to body composition were examined, considering known clinical determinants. Results Sex, body composition and age were identified as clinical predictors. IRS1 rs2934656 was identified as an independent predictor of skeletal muscle mass (beta = 0.11, P = 0.026), and ADAMTSL3 rs4842924 was an independent predictor of body fat mass (beta = 0.15, P = 0.0095) and appendicular lean mass (beta = -0.13, P = 0.017). Conclusions The findings clarified the contribution of genetic factors - IRS1 and ADAMTSL3 - to interindividual variation in body composition, independent of clinical factors, in type 2 diabetes patients. These data will contribute to the establishment of effective methods for the prediction, prevention, and intervention of sarcopenia and frailty in diabetes patients. Geriatr Gerontol Int 2021; center dot center dot: center dot center dot-center dot center dot.

Automated summary

This study aimed to investigate the genetic determinants of body composition in individuals with type 2 diabetes. The findings identified IRS1 and ADAMTSL3 as important genetic factors contributing to variation in body composition in type 2 diabetes patients, independent of clinical factors. These data will help in the development of effective methods for predicting, preventing, and intervening in sarcopenia and frailty in diabetes patients.