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Enregistrement W4412647992 · doi:10.1016/s2213-8587(25)00405-x

Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries

2025· preprint· en· W4412647992 sur OpenAlexaff
Alicia Huerta‐Chagoya, Joohyun Kim, Ravi Mandla, Yingchang Lu, Ken Suzuki, Lauren E. Petty, Hong Kiat Ng, Jaewon Choi, Simon Lee, Madhusmita Rout, Kuang Lin, Linda S. Adair, Adebowale Adeyemo, Habibul Ahsan, Masato Akiyama, Ping An, Sonia S. Anand, Diane M. Becker, Alain G. Bertoni, Zheng Bian, Lawrence F. Bielak, John Blangero, Michael Boehnke, Erwin P. Böttinger, Donald W. Bowden, Fiona Bragg, Jennifer A. Brody, Thomas A. Buchanan, Brian E. Cade, Jin Fang Chai, John C. Chambers, Giriraj R. Chandak, Li-Ching Chang, Kyong‐Mi Chang, Miao-Li Chee, Chien-Hsiun Chen, Yuan-Tsong Chen, Zhengming Chen, Yii‐Der Ida Chen, Jihua Chen, Guanjie Chen, Shyh‐Huei Chen, Wei‐Min Chen, Ching‐Yu Cheng, Yoon Shin Cho, Hyeok Sun Choi, Lee‐Ming Chuang, Miguel Cruz, Mary Cushman, Swapan K. Das, Ralph A. DeFronzo, H Janaka deSilva, Latchezar Dimitrov, Ayo P. Doumatey, Shufa Du, Qing Duan, Ravindranath Duggirala, Leslie S. Emery, James C. Engert, Daniel S. Evans, Michele K. Evans, Sarah Finer, José C. Florez, James S. Floyd, Myriam Fornage, Eitan Frankel, Barry I. Freedman, Lourdes García‐García, Pauline Genter, Hertzel C. Gerstein, Mark O. Goodarzi, Penny Gordon‐Larsen, Mariaelisa Graff, Myron Gross, Canqing Yu, Xiuqing Guo, Yang Hai, Craig L. Hanis, M. Geoffrey Hayes, Momoko Horikoshi, Annie-Green Howard, Sarah Hsu, Willa A. Hsueh, Wei Huang, Mengna Huang, Yi‐Jen Hung, Mi Yeong Hwang, Chii‐Min Hwu, Sahoko Ichihara, Michiya Igase, Eli Ipp, Mohammad Tariqul Islam, Masato Isono, Hye-Mi Jang, Farzana Jasmine, Jost B. Jonas, Yoonjung Yoonie Joo, Edmond K. Kabagambe, Takashi Kadowaki, Fouad Kandeel, Sharon L. R. Kardia, Elizabeth W. Karlson, Anuradhani Kasturiratne, Norihiro Kato, Tomohiro Katsuya, Varinderpal Kaur, Takahisa Kawaguchi, Jacob M. Keaton, Abel Kho, Chiea Chuen Khor, Muhammad G. Kibriya, Bong-Jo Kim, Woon-Puay Koh, Katsuhiko Kohara, Jaspal S. Kooner, Charles Kooperberg, Raymond J. Kreienkamp, Amel Lamri, Leslie A. Lange, Nanette R. Lee, Myung‐Shik Lee, Jung‐Jin Lee, Donna M. Lehman, Liming Li, Yun Li, Victor JY Lim, Jianjun Liu, Yongmei Liu, Simin Liu, Jirong Long, Tin Louie, Xi Luo, Jun Lv, Julie A. Lynch, Shiro Maeda, Anubha Mahajan, Nisa M. Maruthur, Fumihiko Matsuda, Mark I. McCarthy, Roberta McKean‐Cowdin, James B. Meigs, Iona Y. Millwood, Ayesha A. Motala, Girish N. Nadkarni, Jerry L. Nadler, Masahiro Nakatochi, Mike A. Nalls, Uma Nayak, Aude Nicolas, Kari E. North, Darryl Nousome, Yukinori Okada, Ian Pan, James S. Pankow, Guillaume Paré, Jae‐Hyun Park, Kyong Soo Park, Esteban J. Parra, Sanjay R. Patel, Mark A. Pereira, Patricia A. Peyser, Fraser Pirie, Michael Preuß, Michael A. Province, Bruce M. Psaty, Leslie J. Raffel, Laura M. Raffield, Laura J. Rasmussen‐Torvik, Susan Redline, Alexander P. Reiner, Stephen S. Rich, Rebecca Rohde, Kathryn Roll, Rashedeh Roshani, Charles N. Rotimi, Charumathi Sabanayagam, Danish Saleheen, Kevin Sandow, Claudia Schurmann, Hasan Shahriar, Douglas M. Shaw, Wayne Huey‐Herng Sheu, Jinxiu Shi, Xiao-Ou Shu, Megan M. Shuey, Moneeza K. Siddiqui, Jennifer A. Smith, Tamar Sofer, Cassandra N. Spracklen, Adrienne M. Stilp, Meng Sun, Yasuharu Tabara, E Shyong Tai, Salman M. Tajuddin, Atsushi Takahashi, Fumihiko Takeuchi, Jingyi Tan, Kent D. Taylor, Katherine Taylor, Farook Thameem, Lin Tong, Fuu‐Jen Tsai, Philip S. Tsao, Miriam S. Udler, Adán Valladares‐Salgado, David A. van Heel, Rob M. vanDam, Rohit Varma, Maheak Vora, Niels H. Wacher, Ya Xing Wang, Ellie Wheeler, Eric A. Whitsel, Ananda R. Wickremasinghe, Genevieve L. Wojcik, Tien Yin Wong, Jer‐Yuarn Wu, Yong-Bing Xiang, Anny H. Xiang, Chittaranjan S. Yajnik, Ken Yamamoto, Toshimasa Yamauchi, Lisa R. Yanek, Jie Yao, Mitsuhiro Yokota, Jian-Min Yuan, Salim Yusuf, Eleftheria Zeggini, Liang Zhang, Weihua Zhang, Wei Zheng, Alan B. Zonderman, Carlos A. Aguilar‐Salinas, Clicerio González‐Villalpando, Christopher A. Haiman, Young Jin Kim, Soo Heon Kwak, Aaron Leong, Ruth J. F. Loos, Andrés Moreno‐Estrada, Andrew P. Morris, Lorena Orozco, Jerome I. Rotter, Dharambir K. Sanghera, Teresa Tusié‐Luna, Benjamin F. Voight, Marijana Vujković, Robin Walters, Tian Ge, Marie Loh, Jennifer E. Below, Xueling Sim, Josep M. Mercader, Maggie C. Y. Ng

Notice bibliographique

RevueThe Lancet Diabetes & Endocrinology · 2025
Typepreprint
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic Associations and Epidemiology
Établissements canadiensImpactMcGill UniversityHamilton Health SciencesUniversity of TorontoMcMaster UniversityPopulation Health Research Institute
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Human Genome Research InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute on AgingManchester Biomedical Research CentreAmerican Diabetes AssociationNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchNovo Nordisk UK Research FoundationNational Institutes of HealthU.S. Department of Health and Human ServicesFoundation for the National Institutes of Health
Mots-clésPolygenic risk scoreType 2 diabetesComputational biologyGeneticsDemographyDiabetes mellitusMedicineBiologyGerontologyEvolutionary biologyGeneEndocrinologySingle-nucleotide polymorphismGenotypeSociology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes. METHODS: We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities. FINDINGS: Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In individuals with type 2 diabetes, the association between multi-ancestry PRSs and risk of microvascular complications and comorbidity was studied in populations of African, Admixed American, and European ancestries and was significant in all three ancestry groups for diabetic retinopathy (ORs per SD 1·28-1·57), diabetic nephropathy (1·25-1·58), proliferative diabetic retinopathy (1·39-2·08), and end-stage diabetic nephropathy (1·44-1·87); PRS was associated with coronary artery disease in the Admixed American ancestry group only (1·16 [95% CI 1·08-1·25]). INTERPRETATION: These validated, publicly available PRSs can improve risk stratification for type 2 diabetes onset and complications across diverse ancestries, supporting their further evaluation in clinical settings. FUNDING: The National Human Genome Research Institute of the US National Institutes of Health.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,124

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0230,029
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,006
Bibliométrie0,0030,004
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,056
Tête enseignante GPT0,316
Écart entre enseignants0,260 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission1
Résumé présentoui

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