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.

Beijing Hypertension League Institute Beijing China

Boden Institute University of Sydney Sydney NSW Australia

Center for Cardiovascular Prevention 1st Faculty of Medicine Charles University Prague and Thomayer Hospital Prague Czech Republic

Clinique Ambroise Paré Neuilly sur Seine and Centre de Recherches des Cordeliers Paris France

Department of Medicine Faculty of Medicine Université de Montréal Montréal Québec Canada

Department of Medicine University of Montréal CRCHUM Québec Canada

Department of Molecular Genetics and Dalla Lana School of Public Health University of Toronto Toronto ON Canada

Department of Physiology University of Melbourne Melbourne VIC Australia

Istituto Auxologico Italiano University of Milano Bicocca Italy

Julius Centre for Health Sciences and Primary Care University Medical Centre Utrecht Utrecht the Netherlands

Montreal Heart Institute Research Center Montréal Québec Canada

Ontario Institute for Cancer Research Toronto ON Canada

Oxford Centre for Diabetes Endocrinology and Metabolism University of Oxford Oxford UK

School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia

School of Public Health Faculty of Medicine Imperial College London London UK

The George Institute for Global Health School of Public Health Imperial College London London UK

The George Institute for Global Health University of New South Wales Sydney NSW Australia

University College London Institute of Cardiovascular Science London UK

Diabetologia. 2021 Oct;64(10):2355-2356. doi: 10.1007/s00125-021-05544-x. PubMed

See more in PubMed

The Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–2222. doi: 10.1016/S0140-6736(10)60484-9. PubMed DOI PMC

Jacobs E, Hoyer A, Brinks R, Kuss O, Rathmann W. Burden of mortality attributable to diagnosed diabetes: a nationwide analysis based on claims data from 65 million people in Germany. Diabetes Care. 2017;40(12):1703–1709. doi: 10.2337/dc17-0954. PubMed DOI

American Diabetes Association 9. Cardiovascular disease and risk management: standards of medical care in diabetes—2018. Diabetes Care. 2018;41(Supplement 1):S86–S104. doi: 10.2337/dc18-S009. PubMed DOI

Abraham G, Inouye M. Genomic risk prediction of complex human disease and its clinical application. Curr Opin Genet Dev. 2015;33:10–16. doi: 10.1016/j.gde.2015.06.005. PubMed DOI

Läll K, Mägi R, Morris A, Metspalu A, Fischer K. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med. 2017;19(3):322–329. doi: 10.1038/gim.2016.103. PubMed DOI PMC

Lei X, Huang S. Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population. PLoS One. 2017;12(11):e0187644. doi: 10.1371/journal.pone.0187644. PubMed DOI PMC

Ravizza S, Huschto T, Adamov A, et al. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat Med. 2019;25(1):57–59. doi: 10.1038/s41591-018-0239-8. PubMed DOI

Wuttke M, Li Y, Li M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019;51(6):957–972. doi: 10.1038/s41588-019-0407-x. PubMed DOI PMC

Anand SS, Meyre D, Pare G, et al. Genetic information and the prediction of incident type 2 diabetes in a high-risk multiethnic population. Diabetes Care. 2013;36(9):2836–2842. doi: 10.2337/dc12-2553. PubMed DOI PMC

Evangelou E, Warren HR, Mosen-Ansorena D, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50(10):1412–1425. doi: 10.1038/s41588-018-0205-x. PubMed DOI PMC

Mihaescu R, Moonesinghe R, Khoury MJ, Janssens A. Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability. Genome Med. 2011;3(7):51. doi: 10.1186/gm267. PubMed DOI PMC

Escott-Price V, Shoai M, Pither R, Williams J, Hardy J. Polygenic score prediction captures nearly all common genetic risk for Alzheimer’s disease. Neurobiol Aging. 2017;49:214.e7–214.e11. doi: 10.1016/j.neurobiolaging.2016.07.018. PubMed DOI

Rosenberg NA, Edge MD, Pritchard JK, Feldman MW. Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences. Evol Med Public Heal. 2019;2019(1):26–34. doi: 10.1093/emph/eoy036. PubMed DOI PMC

Gibson G. On the utilization of polygenic risk scores for therapeutic targeting. PLoS Genet. 2019;15(4):e1008060. doi: 10.1371/journal.pgen.1008060. PubMed DOI PMC

Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–1224. doi: 10.1038/s41588-018-0183-z. PubMed DOI PMC

Turley P, Walters RK, Maghzian O, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50(2):229–237. doi: 10.1038/s41588-017-0009-4. PubMed DOI PMC

Krapohl E, Patel H, Newhouse S, et al. Multi-polygenic score approach to trait prediction. Mol Psychiatry. 2018;23(5):1368–1374. doi: 10.1038/mp.2017.163. PubMed DOI PMC

Abraham G, Malik R, Yonova-Doing E, et al. Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke. Nat Commun. 2019;10(1):5819. doi: 10.1038/s41467-019-13848-1. PubMed DOI PMC

Morgan CL, Currie CJ, Stott NCH, Smithers M, Butler CC, Peters JR. The prevalence of multiple diabetes-related complications. Diabet Med. 2000;17(2):146–151. doi: 10.1046/j.1464-5491.2000.00222.x. PubMed DOI

Nickerson HD, Dutta S. Diabetic complications: current challenges and opportunities. J Cardiovasc Transl Res. 2012;5(4):375–379. doi: 10.1007/s12265-012-9388-1. PubMed DOI PMC

Avogaro A, Fadini GP. Microvascular complications in diabetes: a growing concern for cardiologists. Int J Cardiol. 2019;291:29–35. doi: 10.1016/j.ijcard.2019.02.030. PubMed DOI

Ninomiya T, Perkovic V, de Galan BE, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009;20(8):1813–1821. doi: 10.1681/ASN.2008121270. PubMed DOI PMC

Ohkuma T, Jun M, Chalmers J, et al. Combination of changes in estimated GFR and albuminuria and the risk of major clinical outcomes. Clin J Am Soc Nephrol. 2019;14(6):862–872. doi: 10.2215/CJN.13391118. PubMed DOI PMC

Patel A, ADVANCE Collaborative Group. MacMahon, et al. Effects of a fixed combination of perindopril and indapamide on macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE trial): a randomised controlled trial. Lancet. 2007;370(9590):829–840. doi: 10.1016/S0140-6736(07)61303-8. PubMed DOI

Hamet P, Haloui M, Harvey F, et al. PROX1 gene CC genotype as a major determinant of early onset of type 2 diabetes in slavic study participants from action in diabetes and vascular disease. J Hypertens. 2017;35:S24–S32. doi: 10.1097/HJH.0000000000001241. PubMed DOI PMC

Zoungas S, Chalmers J, Neal B, et al. Follow-up of blood-pressure lowering and glucose control in type 2 diabetes. N Engl J Med. 2014;371(15):1392–1406. doi: 10.1056/NEJMoa1407963. PubMed DOI

Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779. PubMed DOI PMC

Patel A, MacMahon S, Chalmers J, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008;358(24):2560–2572. doi: 10.1056/NEJMoa0802987. PubMed DOI

Zoungas S, de Galan BE, Ninomiya T, et al. Combined effects of routine blood pressure lowering and intensive glucose control on macrovascular and microvascular outcomes in patients with type 2 diabetes: new results from the ADVANCE trial. Diabetes Care. 2009;32(11):2068–2074. doi: 10.2337/dc09-0959. PubMed DOI PMC

Krajcoviechova A, Tremblay J, Wohlfahrt P, et al. The impact of blood pressure and visceral adiposity on the association of serum uric acid with albuminuria in adults without full metabolic syndrome. Am J Hypertens. 2016;29(12):1335–1342. doi: 10.1093/ajh/hpw098. PubMed DOI

R Core team . R: a language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing; 2018.

Warnes GR, Bolker B, Bonebakker L et al (2016) gplots: various R programming tools for plotting data, R package version 3.0.1. R foundation for statistical computing, Vienna, Austria

Kengne AP, Patel A, Marre M, et al. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prev Rehabil. 2011;18(3):393–398. doi: 10.1177/1741826710394270. PubMed DOI

D’Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care. Circulation. 2008;117(6):743–753. doi: 10.1161/CIRCULATIONAHA.107.699579. PubMed DOI

Visscher PM, Wray NR, Zhang Q, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005. PubMed DOI PMC

Young BA, Lin E, Von Korff M, et al. Diabetes complications severity index and risk of mortality, hospitalization, and healthcare utilization. Am J Manag Care. 2008;14(1):15–23. PubMed PMC

Holliday EG, Traylor M, Malik R, et al. Polygenic overlap between kidney function and large artery atherosclerotic stroke. Stroke. 2014;45(12):3508–3513. doi: 10.1161/STROKEAHA.114.006609. PubMed DOI PMC

Morieri ML, Gao H, Pigeyre M, et al. Genetic tools for coronary risk assessment in type 2 diabetes: a cohort study from the ACCORD clinical trial. Diabetes Care. 2018;41(11):2404–2413. doi: 10.2337/dc18-0709. PubMed DOI PMC

Zoungas S, Woodward M, Li Q, et al. Impact of age, age at diagnosis and duration of diabetes on the risk of macrovascular and microvascular complications and death in type 2 diabetes. Diabetologia. 2014;57(12):2465–2474. doi: 10.1007/s00125-014-3369-7. PubMed DOI

Mega JL, Stitziel NO, Smith JG, et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015;385(9984):2264–2271. doi: 10.1016/S0140-6736(14)61730-X. PubMed DOI PMC

Guinan K, Beauchemin C, Tremblay J, et al. Economic evaluation of a new polygenic risk score to predict nephropathy in adult patients with type 2 diabetes. Can J Diabetes. 2021;45(2):129–136. doi: 10.1016/j.jcjd.2020.06.010. PubMed DOI

Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):70. doi: 10.1186/s13073-019-0689-8. PubMed DOI PMC

Zannad F, Ferreira JP, Pocock SJ, et al. SGLT2 inhibitors in patients with heart failure with reduced ejection fraction: a meta-analysis of the EMPEROR-reduced and DAPA-HF trials. Lancet. 2020;396(10254):819–829. doi: 10.1016/S0140-6736(20)31824-9. PubMed DOI