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

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,...

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Autores principales: Timasheva, Yanina, Balkhiyarova, Zhanna, Avzaletdinova, Diana, Rassoleeva, Irina, Morugova, Tatiana V., Korytina, Gulnaz, Prokopenko, Inga, Kochetova, Olga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866792/
https://www.ncbi.nlm.nih.gov/pubmed/36674502
http://dx.doi.org/10.3390/ijms24020984
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author Timasheva, Yanina
Balkhiyarova, Zhanna
Avzaletdinova, Diana
Rassoleeva, Irina
Morugova, Tatiana V.
Korytina, Gulnaz
Prokopenko, Inga
Kochetova, Olga
author_facet Timasheva, Yanina
Balkhiyarova, Zhanna
Avzaletdinova, Diana
Rassoleeva, Irina
Morugova, Tatiana V.
Korytina, Gulnaz
Prokopenko, Inga
Kochetova, Olga
author_sort Timasheva, Yanina
collection PubMed
description 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.
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spelling pubmed-98667922023-01-22 Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes Timasheva, Yanina Balkhiyarova, Zhanna Avzaletdinova, Diana Rassoleeva, Irina Morugova, Tatiana V. Korytina, Gulnaz Prokopenko, Inga Kochetova, Olga Int J Mol Sci Article 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. MDPI 2023-01-04 /pmc/articles/PMC9866792/ /pubmed/36674502 http://dx.doi.org/10.3390/ijms24020984 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Timasheva, Yanina
Balkhiyarova, Zhanna
Avzaletdinova, Diana
Rassoleeva, Irina
Morugova, Tatiana V.
Korytina, Gulnaz
Prokopenko, Inga
Kochetova, Olga
Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title_full Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title_fullStr Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title_full_unstemmed Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title_short Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes
title_sort integrating common risk factors with polygenic scores improves the prediction of type 2 diabetes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866792/
https://www.ncbi.nlm.nih.gov/pubmed/36674502
http://dx.doi.org/10.3390/ijms24020984
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