POLYGENIC SCORES TOGETHER WITH COMMON RISK FACTORS ARE INFORMATIVE PREDICTORS OF METABOLIC HEALTH

Abstract

Objective: Type 2 diabetes (T2D) is a complex metabolic disorder influenced by various genetic, behavioural, and environmental factors, with ageing and obesity most prominently contributing to its development. Our aim was to examine an association between common variants in genes implicated in the development of T2D, and evaluate the predictive value of polygenic scores and other risk factors. Design and method: We tested associations between 13 established genetic variants and T2D in 1371 study participants from the Volga-Ural region of Eurasian continent, and evaluated the prognostic ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors, such as age and sex. Results: Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, PFDR = 3.40 × 10-5), CCR5 rs333 (OR = 1.99, PFDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, PFDR = 2.64 × 10-4), TCF7L2 rs114758349 (OR = 1.77, PFDR = 9.37 × 10-5), and CCL2 rs1024611 (OR = 1.38, PFDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7%-87.8%). Conclusions: The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and further elucidate the molecular mechanisms of the disease development. Figure 1. Receiver operator characteristic (ROC) curves visualising the predictive prognostic ability abilities of the models to predict type 2 diabetes: A. model constructed using the unweighted polygenic score calculated for the five genetic variants associated with type 2 diabetes in our study; B. model constructed using the weighted polygenic score; C. model constructed using the unweighted polygenic score in combination with age and sex; D. model constructed using the weighted polygenic score in combination with age and sex polygenic scores for type 2 diabetes. This research was funded by THE MINISTRY OF SCIENCE AND HIGHER EDUCATION OF RUSSIAN FEDERATION, grant number 075-15-2021-595; RUSSIAN SCIENCE FOUNDATION, grant number 22-25-00010.