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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,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9866792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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