Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.

IF 15.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Nature Communications Pub Date : 2025-10-01 DOI:10.1038/s41467-025-63546-4

Boya Guo, Yanwei Cai, Daeeun Kim, Roelof A J Smit, Zhe Wang, Kruthika R Iyer, Austin T Hilliard, Jeffrey Haessler, Ran Tao, K Alaine Broadaway, Yujie Wang, Nikita Pozdeyev, Frederik F Stæger, Chaojie Yang, Brett Vanderwerff, Amit D Patki, Lauren Stalbow, Meng Lin, Nicholas Rafaels, Jonathan Shortt, Laura Wiley, Maggie Stanislawski, Jack Pattee, Lea Davis, Peter S Straub, Megan M Shuey, Nancy J Cox, Nanette R Lee, Marit E Jørgensen, Peter Bjerregaard, Christina Larsen, Torben Hansen, Ida Moltke, James B Meigs, Daniel O Stram, Xianyong Yin, Xiang Zhou, Kyong-Mi Chang, Shoa L Clarke, Rodrigo Guarischi-Sousa, Joanna Lankester, Philip S Tsao, Steven Buyske, Mariaelisa Graff, Laura M Raffield, Quan Sun, Lynne R Wilkens, Christopher S Carlson, Charles B Easton, Simin Liu, JoAnn E Manson, Loïc L Marchand, Christopher A Haiman, Karen L Mohlke, Penny Gordon-Larsen, Anders Albrechtsen, Michael Boehnke, Stephen S Rich, Ani Manichaikul, Jerome I Rotter, Noha A Yousri, Ryan M Irvin, Chris Gignoux, Kari E North, Ruth J F Loos, Themistocles L Assimes, Ulrike Peters, Charles Kooperberg, Sridharan Raghavan, Heather M Highland, Burcu F Darst

{"title":"Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.","authors":"Boya Guo, Yanwei Cai, Daeeun Kim, Roelof A J Smit, Zhe Wang, Kruthika R Iyer, Austin T Hilliard, Jeffrey Haessler, Ran Tao, K Alaine Broadaway, Yujie Wang, Nikita Pozdeyev, Frederik F Stæger, Chaojie Yang, Brett Vanderwerff, Amit D Patki, Lauren Stalbow, Meng Lin, Nicholas Rafaels, Jonathan Shortt, Laura Wiley, Maggie Stanislawski, Jack Pattee, Lea Davis, Peter S Straub, Megan M Shuey, Nancy J Cox, Nanette R Lee, Marit E Jørgensen, Peter Bjerregaard, Christina Larsen, Torben Hansen, Ida Moltke, James B Meigs, Daniel O Stram, Xianyong Yin, Xiang Zhou, Kyong-Mi Chang, Shoa L Clarke, Rodrigo Guarischi-Sousa, Joanna Lankester, Philip S Tsao, Steven Buyske, Mariaelisa Graff, Laura M Raffield, Quan Sun, Lynne R Wilkens, Christopher S Carlson, Charles B Easton, Simin Liu, JoAnn E Manson, Loïc L Marchand, Christopher A Haiman, Karen L Mohlke, Penny Gordon-Larsen, Anders Albrechtsen, Michael Boehnke, Stephen S Rich, Ani Manichaikul, Jerome I Rotter, Noha A Yousri, Ryan M Irvin, Chris Gignoux, Kari E North, Ruth J F Loos, Themistocles L Assimes, Ulrike Peters, Charles Kooperberg, Sridharan Raghavan, Heather M Highland, Burcu F Darst","doi":"10.1038/s41467-025-63546-4","DOIUrl":null,"url":null,"abstract":"<p><p>Polygenic risk scores hold prognostic value for identifying individuals at higher risk of type 2 diabetes. However, further characterization is needed to understand the generalizability of type 2 diabetes polygenic risk scores in diverse populations across various contexts. We systematically characterize a multi-ancestry type 2 diabetes polygenic risk score among 244,637 cases and 637,891 controls across diverse populations from the Population Architecture Genomics and Epidemiology Study and 13 additional biobanks and cohorts. Polygenic risk score performance is context dependent, with better performance in those who are younger, male, without hypertension, and not obese or overweight. Additionally, the polygenic risk score is associated with various diabetes-related cardiometabolic traits and type 2 diabetes complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between type 2 diabetes and other diseases. These findings highlight the need to account for context when evaluating polygenic risk score as a tool for type 2 diabetes risk prognostication and the potentially generalizable associations of type 2 diabetes polygenic risk score with diabetes-related traits, despite differential performance in type 2 diabetes prediction across diverse populations. Our study provides a comprehensive resource to characterize a type 2 diabetes polygenic risk score.</p>","PeriodicalId":19066,"journal":{"name":"Nature Communications","volume":"16 1","pages":"8632"},"PeriodicalIF":15.7000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Communications","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41467-025-63546-4","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Polygenic risk scores hold prognostic value for identifying individuals at higher risk of type 2 diabetes. However, further characterization is needed to understand the generalizability of type 2 diabetes polygenic risk scores in diverse populations across various contexts. We systematically characterize a multi-ancestry type 2 diabetes polygenic risk score among 244,637 cases and 637,891 controls across diverse populations from the Population Architecture Genomics and Epidemiology Study and 13 additional biobanks and cohorts. Polygenic risk score performance is context dependent, with better performance in those who are younger, male, without hypertension, and not obese or overweight. Additionally, the polygenic risk score is associated with various diabetes-related cardiometabolic traits and type 2 diabetes complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between type 2 diabetes and other diseases. These findings highlight the need to account for context when evaluating polygenic risk score as a tool for type 2 diabetes risk prognostication and the potentially generalizable associations of type 2 diabetes polygenic risk score with diabetes-related traits, despite differential performance in type 2 diabetes prediction across diverse populations. Our study provides a comprehensive resource to characterize a type 2 diabetes polygenic risk score.

查看原文本刊更多论文

2型糖尿病的多基因风险评分在人群中显示出环境依赖效应。

多基因风险评分对于识别2型糖尿病高危人群具有预后价值。然而，需要进一步的表征来了解2型糖尿病多基因风险评分在不同背景下不同人群中的普遍性。我们系统地描述了来自人群结构基因组学和流行病学研究以及13个其他生物库和队列的不同人群的244,637例和637,891例对照的多祖先2型糖尿病多基因风险评分。多基因风险评分的表现与环境有关，年轻、男性、无高血压、不肥胖或超重的人表现更好。此外，多基因风险评分与各种糖尿病相关的心脏代谢特征和2型糖尿病并发症相关，表明其在并发症风险分层和识别2型糖尿病和其他疾病之间共享的遗传结构方面的效用。这些发现强调了在评估多基因风险评分作为2型糖尿病风险预测工具时需要考虑背景，以及2型糖尿病多基因风险评分与糖尿病相关特征的潜在可推广关联，尽管不同人群在2型糖尿病预测中的表现存在差异。我们的研究提供了一个全面的资源来表征2型糖尿病多基因风险评分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Nature Communications Biological Science Disciplines-

CiteScore

24.90

自引率

2.40%

发文量

6928

审稿时长

3.7 months

期刊介绍： Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.