Evidence map›Paper›PMID 42061389›Full record

SynthesisThe lancet. Diabetes & endocrinology2026

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

Alicia Huerta-Chagoya, Joohyun Kim, Ravi Mandla, Yingchang Lu, Ken Suzuki, Lauren E Petty, Hong Kiat Ng, Jaewon Choi, Simon Lee, Madhusmita Rout and 30 more

Abstract readMeta-Analysis
In one paragraph

Synthesis in The lancet. Diabetes & endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

40 authors.

Alicia Huerta-ChagoyaPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.
Joohyun KimVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Ravi MandlaPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Yingchang LuVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Ken SuzukiDepartment of Statistical Genetics, Osaka University, Osaka, Japan; Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
Lauren E PettyVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Hong Kiat NgLee Kong Chian School of Medicine, Clinical Sciences Building, Nanyang Technological University, Singapore.
Jaewon ChoiDepartment of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
Simon LeeThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Madhusmita RoutDepartment of Pediatrics, Section of Genetics, University of Oklahoma, Oklahoma City, OK, USA.
Kuang LinNuffield Department of Population Health, University of Oxford, Oxford, UK.
Katherine TaylorPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
ENSA Genomics Consortium
Genes & Health Research Team
VA Million Veteran Program
Carlos A Aguilar-SalinasUnidad de Investigación de Enfermedades Metabólicas, Research Direction of the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Tecnológico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Mexico City, Mexico.
Lourdes García-GarcíaCenter for Infectious Disease Research, National Institute of Public Health, Cuernavaca, Mexico.
Clicerio González-VillalpandoCentro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico.
Christopher A HaimanDepartment of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA.
Young Jin KimDepartment of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea.
Soo Heon KwakDepartment of Internal Medicine, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, South Korea.
Aaron LeongPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA; Endocrine Division, Massachusetts General Hospital, Boston, MA USA.
Ruth J F LoosThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Andres Moreno-EstradaAging Research Center, Cinvestav Sede Sur, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City, Mexico.
Andrew P MorrisCentre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
Lorena OrozcoInstituto Nacional de Medicina Genómica, Mexico City, Mexico.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Dharambir SangheraThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Teresa Tusie-LunaUnidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
Benjamin F VoightDepartment of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Corporal Michael J Crescenz Philadelphia VA Medical Center, Philadelphia, PA, USA; Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Marijana VujkovicCorporal Michael J Crescenz Philadelphia VA Medical Center, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Robin G WaltersNuffield Department of Population Health, University of Oxford, Oxford, UK.
Tian GePsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Alisa K ManningPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Marie LohLee Kong Chian School of Medicine, Clinical Sciences Building, Nanyang Technological University, Singapore; Department of Epidemiology and Biostatistics, Imperial College London, London, UK; Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore.
Jennifer E BelowVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Xueling SimSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
Josep M MercaderPrograms in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Electronic address: jmercader@mgb.org.
Maggie C Y NgVanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: maggie.ng@vumc.org.
D-PRISM Consortium

Funding

Development of Polygenic Risk Scores for Diabetes and Complications across the Life-Span in Populations of Multiple AncestriesU01HG011723 · NHGRI · BROAD INSTITUTE, INC. · PI Alisa Knodle Manning, Josep Maria Mercader · 2021 to 2026
$5.7M
Multi-omics for obesity-associated liver disease in a high-risk population cohortU01CA288325 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Jennifer Below, JOSEPH MCCORMICK · 2023 to 2026
$3.3M
GEneration and assessment of Multi-omic informed Subtypes of Type 2 Diabetes in Diverse Populations (GEMS-T2D)U01DK140757 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Josep Maria Mercader, KRISTINA Marie UTZSCHNEIDER · 2024 to 2026
$2.3M
Cataloging multi-ancestry 'omic readouts of the environmental and genetic determinants of type 2 diabetesR01DK137993 · NIDDK · HARVARD MEDICAL SCHOOL · PI ARJUN KUMAR MANRAI, Josep Maria Mercader · 2024 to 2026
$2.0M
Massive-scale genomic risk assessment to inform precision medicine across the spectrum of monogenic and common forms of diabetesR01DK140545 · NIDDK · BROAD INSTITUTE, INC. · PI Josep Maria Mercader, Miriam Sargon Udler · 2025 to 2026
$1.6M
BLRD VA I01 BX003362NCI NIH HHS U01 CA288325NHGRI NIH HHS U01 HG011723NIDDK NIH HHS L30 DK106874NIDDK NIH HHS R01 DK137993NIDDK NIH HHS R01 DK140545NIDDK NIH HHS U01 DK140757
6 · The paper itself

Abstract

backgroundPolygenic 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.

methodsWe 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.

findingsOur 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]).

interpretationThese 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.

fundingThe National Human Genome Research Institute of the US National Institutes of Health.

Indexed as

Diabetes ComplicationsDiabetes Mellitus, Type 2Genetic Predisposition to DiseaseMultifactorial InheritanceBlack or African AmericanEast Asian PeopleEuropean PeopleGenetic Risk ScoreGenome-Wide Association StudyHumansPolymorphism, Single NucleotideSouth Asian PeopleWhite People

Identifiers

PMID42061389
PMCPMC13290298

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.