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Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control
AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382653/ https://www.ncbi.nlm.nih.gov/pubmed/34226943 http://dx.doi.org/10.1007/s00125-021-05491-7 |
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author | Tremblay, Johanne Haloui, Mounsif Attaoua, Redha Tahir, Ramzan Hishmih, Camil Harvey, François Marois-Blanchet, François-Christophe Long, Carole Simon, Paul Santucci, Lara Hizel, Candan Chalmers, John Marre, Michel Harrap, Stephen Cífková, Renata Krajčoviechová, Alena Matthews, David R. Williams, Bryan Poulter, Neil Zoungas, Sophia Colagiuri, Stephen Mancia, Giuseppe Grobbee, Diederick E. Rodgers, Anthony Liu, Liusheng Agbessi, Mawussé Bruat, Vanessa Favé, Marie-Julie Harwood, Michelle P. Awadalla, Philip Woodward, Mark Hussin, Julie G. Hamet, Pavel |
author_facet | Tremblay, Johanne Haloui, Mounsif Attaoua, Redha Tahir, Ramzan Hishmih, Camil Harvey, François Marois-Blanchet, François-Christophe Long, Carole Simon, Paul Santucci, Lara Hizel, Candan Chalmers, John Marre, Michel Harrap, Stephen Cífková, Renata Krajčoviechová, Alena Matthews, David R. Williams, Bryan Poulter, Neil Zoungas, Sophia Colagiuri, Stephen Mancia, Giuseppe Grobbee, Diederick E. Rodgers, Anthony Liu, Liusheng Agbessi, Mawussé Bruat, Vanessa Favé, Marie-Julie Harwood, Michelle P. Awadalla, Philip Woodward, Mark Hussin, Julie G. Hamet, Pavel |
author_sort | Tremblay, Johanne |
collection | PubMed |
description | AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS: The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10(−21) and p = 9.6 × 10(−31), respectively) and a 4.4-fold (p = 6.8 × 10(−33)) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION: This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05491-7. |
format | Online Article Text |
id | pubmed-8382653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83826532021-09-15 Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control Tremblay, Johanne Haloui, Mounsif Attaoua, Redha Tahir, Ramzan Hishmih, Camil Harvey, François Marois-Blanchet, François-Christophe Long, Carole Simon, Paul Santucci, Lara Hizel, Candan Chalmers, John Marre, Michel Harrap, Stephen Cífková, Renata Krajčoviechová, Alena Matthews, David R. Williams, Bryan Poulter, Neil Zoungas, Sophia Colagiuri, Stephen Mancia, Giuseppe Grobbee, Diederick E. Rodgers, Anthony Liu, Liusheng Agbessi, Mawussé Bruat, Vanessa Favé, Marie-Julie Harwood, Michelle P. Awadalla, Philip Woodward, Mark Hussin, Julie G. Hamet, Pavel Diabetologia Article AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS: The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10(−21) and p = 9.6 × 10(−31), respectively) and a 4.4-fold (p = 6.8 × 10(−33)) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION: This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-021-05491-7. Springer Berlin Heidelberg 2021-07-06 2021 /pmc/articles/PMC8382653/ /pubmed/34226943 http://dx.doi.org/10.1007/s00125-021-05491-7 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tremblay, Johanne Haloui, Mounsif Attaoua, Redha Tahir, Ramzan Hishmih, Camil Harvey, François Marois-Blanchet, François-Christophe Long, Carole Simon, Paul Santucci, Lara Hizel, Candan Chalmers, John Marre, Michel Harrap, Stephen Cífková, Renata Krajčoviechová, Alena Matthews, David R. Williams, Bryan Poulter, Neil Zoungas, Sophia Colagiuri, Stephen Mancia, Giuseppe Grobbee, Diederick E. Rodgers, Anthony Liu, Liusheng Agbessi, Mawussé Bruat, Vanessa Favé, Marie-Julie Harwood, Michelle P. Awadalla, Philip Woodward, Mark Hussin, Julie G. Hamet, Pavel Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title | Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title_full | Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title_fullStr | Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title_full_unstemmed | Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title_short | Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
title_sort | polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382653/ https://www.ncbi.nlm.nih.gov/pubmed/34226943 http://dx.doi.org/10.1007/s00125-021-05491-7 |
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