Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes.

Autor: Timasheva Y; Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia.; Department of Medical Genetics and Fundamental Medicine, Bashkir State Medical University, 450008 Ufa, Russia., Balkhiyarova Z; Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK.; Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia., Avzaletdinova D; Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia., Rassoleeva I; Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia., Morugova TV; Department of Endocrinology, Bashkir State Medical University, 450008 Ufa, Russia., Korytina G; Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia., Prokopenko I; Section of Statistical Multi-Omics, Department of Clinical & Experimental Medicine, School of Biosciences & Medicine, University of Surrey, Guildford GU2 7XH, UK., Kochetova O; Institute of Biochemistry and Genetics, Ufa Federal Research Centre of Russian Academy of Sciences, 450054 Ufa, Russia.
Jazyk: angličtina
Zdroj: International journal of molecular sciences [Int J Mol Sci] 2023 Jan 04; Vol. 24 (2). Date of Electronic Publication: 2023 Jan 04.
DOI: 10.3390/ijms24020984
Abstrakt: We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive 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. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P FDR = 3.40 × 10 -5 ), CCR5 rs333 (OR = 1.99, P FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P FDR = 2.64 × 10 -4 ) , TCF7L2 rs114758349 (OR = 1.77, P FDR = 9.37 × 10 -5 ), and CCL2 rs1024611 (OR = 1.38, P FDR = 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%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10 -6 ). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). 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 elucidate the molecular mechanisms of the disease development.
Databáze: MEDLINE
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