Genetic Risk Scores in Diabetes: Potential for Disease Prediction, Classification, and Precision Medicine
DOI:
https://doi.org/10.69734/crvc7g84Keywords:
type 1 diabetes, type 2 diabetes, type 3c diabetes, insulin secretion, insulin resistance, single nucleotide polymorphism, genetic risk scores, polygenic risk scoreAbstract
Genome-wide association studies have discovered a large number of susceptibility variants for type 1 (T1DM) and type 2 diabetes mellitus (T2DM). This has facilitated numerous studies exploring the potential of genetic risk scores (GRS) to improve disease prediction and diabetes classification. Given the unique genetic architecture of T1DM, in which genetic variants explain ~90% of the heritability, GRS for T1DM are highly predictive for disease development, alone and in combination with clinical factors. T1DM GRS also effectively distinguish T1DM from other types of diabetes. Though composed of a greater number of variants, T2DM GRS have more modest ability to predict and classify diabetes. On the other hand, T2DM variants have been classified into subclusters that reflect diverse pathophysiologic processes underlying T2DM. GRS based on these clusters have been used to dissect the underpinnings not only of T2DM but also of related disorders such as polycystic ovary syndrome and pancreatogenic diabetes. They may also one day prove useful in precision medicine, allowing selection of drug therapy targeted to each patient’s underlying physiologic deficits. However, much work validating use of GRS in the clinic will need to be accomplished before the full potential of GRS can be realized.
References
Perry JR, Frayling TM. New gene variants alter type 2 diabetes risk predominantly through reduced beta-cell function. Curr Opin Clin Nutr Metab Care. 2008;11(4):371-7. doi: 10.1097/MCO.0b013e32830349a1.
Robertson CC, Inshaw JRJ, Onengut-Gumuscu S, et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes. Nat Genet. 2021;53(7):962-71. doi: 10.1038/s41588-021-00880-5.
Chiou J, Geusz RJ, Okino ML, et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature. 2021;594(7863):398-402. doi: 10.1038/s41586-021-03552-w.
Sharp SA, Weedon MN, Hagopian WA, Oram RA. Clinical and research uses of genetic risk scores in type 1 diabetes. Curr Opin Genet Dev. 2018;50:96-102. doi: 10.1016/j.gde.2018.03.009.
Winkler C, Krumsiek J, Buettner F, et al. Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes. Diabetologia. 2014;57(12):2521-9. doi: 10.1007/s00125-014-3362-1.
Lynam A, McDonald T, Hill A, et al. Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years. BMJ Open. 2019;9(9):e031586. doi: 10.1136/bmjopen-2019-031586.
Carr ALJ, Perry DJ, Lynam AL, et al. Histological validation of a type 1 diabetes clinical diagnostic model for classification of diabetes. Diabet Med. 2020;37(12):2160-8. doi: 10.1111/dme.14361.
Sharp SA, Rich SS, Wood AR, et al. Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis. Diabetes Care. 2019;42(2):200-7. doi: 10.2337/dc18-1785.
Oram RA, Sharp SA, Pihoker C, et al. Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth. Diabetes Care. 2022;45(5):1124-31. doi: 10.2337/dc20-2872.
Qu HQ, Qu J, Glessner J, et al. Improved genetic risk scoring algorithm for type 1 diabetes prediction. Pediatr Diabetes. 2022;23(3):320-3. doi: 10.1111/pedi.13310.
Ruiz-Esteves KN, Shank KR, Deutsch AJ, et al. Identification of Immune Checkpoint Inhibitor-Induced Diabetes. JAMA Oncol. 2024;10(10):1409-16. doi: 10.1001/jamaoncol.2024.3104.
Tosur M, Deen S, Huang X, et al. Random C-Peptide and Islet Antibodies at Onset Predict beta Cell Function Trajectory and Insulin Dependence in Pediatric Diabetes. Endocr Pract. 2024. doi: 10.1016/j.eprac.2024.09.116.
Triolo TM, Parikh HM, Tosur M, et al. Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives. J Clin Endocrinol Metab. 2024. doi: 10.1210/clinem/dgae349.
Fuhri Snethlage CM, Balvers M, Ferwerda B, et al. Associations between diabetes-related genetic risk scores and residual beta cell function in type 1 diabetes: the GUTDM1 study. Diabetologia. 2024;67(9):1865-76. doi: 10.1007/s00125-024-06204-6.
Jones AG, Shields BM, Oram RA, et al. Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study. Diabetes Care. 2024. doi: 10.2337/dc23-1815.
Steck AK, Parikh HM, Triolo TM, et al. Genetic Risk and Transition through Preclinical Stages of Type 1 Diabetes. J Clin Endocrinol Metab. 2025. doi: 10.1210/clinem/dgaf392.
Suzuki K, Hatzikotoulas K, Southam L, et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature. 2024;627(8003):347-57. doi: 10.1038/s41586-024-07019-6.
Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208-19. doi: 10.1056/NEJMoa0804742.
Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220-32. doi: 10.1056/NEJMoa0801869.
Wang J, Stancakova A, Kuusisto J, Laakso M. Identification of undiagnosed type 2 diabetic individuals by the finnish diabetes risk score and biochemical and genetic markers: a population-based study of 7232 Finnish men. J Clin Endocrinol Metab. 2010;95(8):3858-62. doi: 10.1210/jc.2010-0012.
Wu Y, Jing R, Dong Y, et al. Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk prediction. Sci Rep. 2017;7:43709. doi: 10.1038/srep43709.
Talmud PJ, Cooper JA, Morris RW, et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes. 2015;64(5):1830-40. doi: 10.2337/db14-1504.
Inaishi J, Hirakawa Y, Horikoshi M, et al. Association between genetic risk and development of type 2 diabetes in a general Japanese population: The Hisayama Study. J Clin Endocrinol Metab. 2019;104(8):3213-22. doi: 10.1210/jc.2018-01782.
Pitkanen N, Juonala M, Ronnemaa T, et al. Role of conventional childhood risk factors versus genetic risk in the development of type 2 diabetes and impaired fasting glucose in adulthood: The Cardiovascular Risk in Young Finns Study. Diabetes Care. 2016;39(8):1393-9. doi: 10.2337/dc16-0167.
Abdullah N, Abdul Murad NA, Mohd Haniff EA, et al. Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort. Public Health. 2017;149:31-8. doi: 10.1016/j.puhe.2017.04.003.
Kwak SH, Choi SH, Kim K, et al. Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score. Diabetologia. 2013;56(12):2556-63. doi: 10.1007/s00125-013-3059-x.
Lambert SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet. 2019;28(R2):R133-R42. doi: 10.1093/hmg/ddz187.
Lall K, Magi 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):322-9. doi: 10.1038/gim.2016.103.
Choi SW, O'Reilly PF. PRSice-2: Polygenic Risk Score software for biobank-scale data. Gigascience. 2019;8(7). doi: 10.1093/gigascience/giz082.
Mandla R, Schroeder P, Porneala B, et al. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study. Genome Med. 2024;16(1):63. doi: 10.1186/s13073-024-01337-0.
Kim NY, Lee H, Kim S, et al. The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population. Sci Rep. 2024;14(1):5749. doi: 10.1038/s41598-024-55313-0.
Rout M, Wander GS, Ralhan S, et al. Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations. Ther Adv Endocrinol Metab. 2023;14:20420188231220120. doi: 10.1177/20420188231220120.
Liu X, Littlejohns TJ, Besevic J, et al. Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes. Diabetes Metab Syndr. 2024;18(4):102996. doi: 10.1016/j.dsx.2024.102996.
Oram RA, Patel K, Hill A, et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care. 2016;39(3):337-44. doi: 10.2337/dc15-1111.
Thomas NJ, Jones SE, Weedon MN, et al. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 2018;6(2):122-9. doi: 10.1016/S2213-8587(17)30362-5.
Thomas NJ, Lynam AL, Hill AV, et al. Type 1 diabetes defined by severe insulin deficiency occurs after 30 years of age and is commonly treated as type 2 diabetes. Diabetologia. 2019;62(7):1167-72. doi: 10.1007/s00125-019-4863-8.
Patel KA, Oram RA, Flanagan SE, et al. Type 1 diabetes genetic risk score: a novel tool to discriminate monogenic and type 1 diabetes. Diabetes. 2016;65(7):2094-9. doi: 10.2337/db15-1690.
Patel KA, Weedon MN, Shields BM, et al. Zinc transporter 8 autoantibodies (ZnT8A) and a type 1 diabetes genetic risk score can exclude individuals with type 1 diabetes from inappropriate genetic testing for monogenic diabetes. Diabetes Care. 2019;42(2):e16-e7. doi: 10.2337/dc18-0373.
Kavvoura FK, Moutsianas L, Bennett AJ, et al. Can genomic information assist in establishing aetiology of young adult onset diabetes? Diabetes. 2015;64(Suppl 1):A452 (abstract).
Mishra R, Akerlund M, Cousminer DL, et al. Genetic discrimination between LADA and childhood-onset type 1 diabetes within the MHC. Diabetes Care. 2020;43(2):418-25. doi: 10.2337/dc19-0986.
Cousminer DL, Ahlqvist E, Mishra R, et al. First genome-wide association study of latent autoimmune diabetes in adults reveals novel insights linking immune and metabolic diabetes. Diabetes Care. 2018;41(11):2396-403. doi: 10.2337/dc18-1032.
Goodarzi MO, Nagpal T, Greer P, et al. Genetic risk score in diabetes associated with chronic pancreatitis versus type 2 diabetes mellitus. Clin Transl Gastroenterol. 2019;10(7):e00057. doi: 10.14309/ctg.0000000000000057.
Jeon C, Hart PA, Li L, et al. Development of a clinical prediction model for diabetes in chronic pancreatitis: The PREDICT3c Study. Diabetes Care. 2023;46(1):46-55. doi: 10.2337/dc22-1414.
Brower MA, Hai Y, Jones MR, et al. Bidirectional Mendelian randomization to explore the causal relationships between body mass index and polycystic ovary syndrome. Hum Reprod. 2019;34(1):127-36. doi: 10.1093/humrep/dey343.
Yang Y, Li L, Su X, et al., editors. Genetic risk scores improve the prediction of chronic pancreatitis-associated diabetes and provide insights into its pathophysiology. Digestive Disease Week; 2025 May 3-6; San Diego, CA.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 SMART-MD Journal of Precision Medicine
This work is licensed under a Creative Commons Attribution 4.0 International License.
Journal information