The role of single-nucleotide polymorphisms of some candidate genes of carbohydrate and fat metabolism in predicting the risk of type 2 diabetes mellitus
- Authors: Valeeva F.V.1, Khasanova K.B.1, Valeeva E.V.1, Kiseleva T.A.1, Islamova D.R.1
-
Affiliations:
- Kazan State Medical University
- Issue: Vol 23, No 1 (2023)
- Pages: 47-56
- Section: ENDOCRINOLOGY
- URL: https://journal-vniispk.ru/2410-3764/article/view/126193
- DOI: https://doi.org/10.55531/2072-2354.2023.23.1.47-56
- ID: 126193
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Abstract
Over the past decade, some progress has been made in identifying and characterizing variants of DNA polymorphisms of genes associated with predisposition to type 2 diabetes mellitus (T2DM). The analysis of gene polymorphisms in combination with socio-demographic, clinical and metabolic parameters can be considered as a promising approach to identify high-risk groups for the development of T2DM. The review includes foreign and domestic studies of predictive models for the risk of developing T2DM comprising single-nucleotide polymorphisms, published in the period from 2006 to 2021. The search for the literature sources was carried out on the PubMed platform. The predictive accuracy of polygenic risk scores was assessed by comparing the area under the curve (AUC). The most commonly used clinical predictors of T2DM risk are sex, age, BMI, family history of diabetes, presence of arterial hypertension, waist circumference, waist-to-hip ratio. All genetic risk models for T2DM had lower AUC values than phenotypic (clinical) risk models. The addition of genetic factors has, in turn, improved AUC compared to purely clinical risk models in many studies, which may be a useful tool for primary prevention of T2DM. However, only those polymorphisms that strongly confirm their association with the risk of developing T2DM in different populations studies should be added to predictive risk scales.
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##article.viewOnOriginalSite##About the authors
Farida V. Valeeva
Kazan State Medical University
Email: val_farida@mail.ru
ORCID iD: 0000-0001-6000-8002
PhD, Professor, Head of the Endocrinology Department
Russian Federation, KazanKamilya B. Khasanova
Kazan State Medical University
Author for correspondence.
Email: kamilya_khasanova@mail.ru
ORCID iD: 0000-0003-1825-487X
Assistant of the Endocrinology Department
Russian Federation, KazanElena V. Valeeva
Kazan State Medical University
Email: vevaleeva@yanndex.ru
ORCID iD: 0000-0001-7080-3878
Junior Researcher of the Laboratory of Genetics of Aging and Longevity
Russian Federation, KazanTatyana A. Kiseleva
Kazan State Medical University
Email: tattiana@mail.ru
ORCID iD: 0000-0001-8959-093X
PhD, Associate Professor, Endocrinology Department
Russian Federation, KazanDiana R. Islamova
Kazan State Medical University
Email: radiana2007@yandex.ru
ORCID iD: 0000-0003-3639-6361
Clinical Resident of the Endocrinology Department
Russian Federation, KazanReferences
- International Diabetes Federation. IDF Diabetes Atlas, 9th dn. Brussels, Belgium; 2019.
- Dedov II, Shestakova MV, Vikulova OK, et al. Diabetes mellitus in Russian Federation: prevalence, morbidity, mortality, parameters of glycaemic control and structure of hypoglycaemic therapy according to the Federal Diabetes Register, status 2017. Diabetes Mellitus. 2018;21(3):144-159. (In Russ.). [Дедов И.И., Шестакова М.В., Викулова О.К., и др. Сахарный диабет в Российской Федерации: распространенность, заболеваемость, смертность, параметры углеводного обмена и структура сахароснижающей терапии по данным Федерального регистра сахарного диабета, статус 2017 г. Сахарный диабет. 2018;21(3):144-159]. doi: 10.14341/DM9686
- Krentz NAJ, Gloyn AL. Insights into pancreatic islet cell dysfunction from type 2 diabetes mellitus genetics. Nat Rev Endocrinol. 2020;16(4):202-212. doi: 10.1038/s41574-020-0325-0
- Vujkovic M, Keaton JM, Lynch JA, et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet. 2020;52(7):680-691. doi: 10.1038/s41588-020-0637-y
- Ingelsson E, McCarthy MI. Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future. Circ Genom Precis Med. 2018;11(6):e002090. doi: 10.1161/CIRCGEN.118.002090
- Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33(1):46-62. doi: 10.1093/epirev/mxq019
- Wray NR, Yang J, Hayes BJ, et al. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet. 2013;14(7):507-515. doi: 10.1038/nrg3457
- Khera AV, Chaffin M, Wade KH, et al. Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood. Cell. 2019;177(3):587-596.e9. doi: 10.1016/j.cell.2019.03.028
- Liu W, Zhuang Z, Wang W, et al. An Improved Genome-Wide Polygenic Score Model for Predicting the Risk of Type 2 Diabetes. Front Genet. 2021;12:632385. doi: 10.3389/fgene.2021.632385
- Bramer WM, Rethlefsen ML, Kleijnen J, Franco OH. Optimal Database Combinations for Literature Searches in Systematic Reviews: A Prospective Exploratory Study. Syst Rev. 2017;6(1):245. doi: 10.1186/s13643-017-0644-y
- Mustafina SV, Simonova GI, Rymar OD. Comparative characteristics of diabetes risk scores. Diabetes mellitus. 2014;17(3):17-22. (In Russ.). [Мустафина С.В., Симонова Г.И., Рымар О.Д. Сравнительная характеристика шкал риска сахарного диабета 2 типа. Сахарный диабет. 2014;17(3):17-22]. doi: 10.14341/DM2014317-22
- Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163. doi: 10.1136/bmj.d7163
- Paulweber B, Valensi P, Lindström J, et al. A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res. 2010;42(1):S3-36. doi: 10.1055/s-0029-1240928
- Saaristo T, Peltonen M, Lindström J, et al. Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res. 2005;2:67-72. doi: 10.3132/dvdr.2005.011
- Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care. 2008;31(5):1040-5. doi: 10.2337/dc07-1150
- Meijnikman AS, De Block CE, Verrijken A, et al. Screening for type 2 diabetes mellitus in overweight and obese subjects made easy by the FINDRISC score. J Diabetes Complications. 2016;30(6):1043-9. doi: 10.1016/j.jdiacomp.2016.05.004
- Witte DR, Shipley MJ, Marmot MG, Brunner EJ. Performance of existing risk scores in screening for undiagnosed diabetes: an external validation study. Diabet Med. 2010;27(1):46-53. doi: 10.1111/j.1464-5491.2009.02891.x
- Zhang L, Zhang Z, Zhang Y, et al. Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010. PLoS One. 2014;9(5):e97865. doi: 10.1371/journal.pone.0097865
- Colagiuri S. Epidemiology of prediabetes. Med Clin North Am. 2011;95:299-307. doi: 10.1016/j.mcna.2010.11.003
- Wilson PWF, Meigs JB, Sullivan L, et al. Prediction of incident diabetes mellitus in middle-aged adults. Arch Intern Med. 2007;167:1068-74. doi: 10.1001/archinte.167.10.1068
- de Miguel-Yanes JM, Shrader P, Pencina MJ, et al. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care. 2011;34(1):121-125. doi: 10.2337/dc10-1265
- Holtzman NA, Marteau TM. Will genetics revolutionize medicine? N Engl J Med. 2000;13;343(2):141-4. doi: 10.1056/NEJM200007133430213
- Janssens AC, Moonesinghe R, Yang Q, et al. The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med. 2007;9(8):528-35. doi: 10.1097/gim.0b013e31812eece0
- Yang Q, Khoury MJ, Botto L, et al. Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes. Am J Hum Genet. 2003;72(3):636-649. doi: 10.1086/367923
- Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17(10):1520-1528. doi: 10.1101/gr.6665407
- Weedon MN, McCarthy MI, Hitman G, et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS Med. 2006;3(10):e374. doi: 10.1371/journal.pmed.0030374
- Lango H; UK Type 2 Diabetes Genetics Consortium, Palmer CN, et al. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes. 2008;57(11):3129-3135. doi: 10.2337/db08-0504
- Willems SM, Mihaescu R, Sijbrands EJ, et al. A methodological perspective on genetic risk prediction studies in type 2 diabetes: recommendations for future research. Curr Diab Rep. 2011;11(6):511-518. doi: 10.1007/s11892-011-0235-6
- Schulze MB, Weikert C, Pischon T, et al. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care. 2009;32(11):2116-2119. doi: 10.2337/dc09-0197
- Talmud PJ, Hingorani AD, Cooper JA, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838. doi: 10.1136/bmj.b4838
- 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
- Vassy JL, Hivert MF, Porneala B, et al. Polygenic type 2 diabetes prediction at the limit of common variant detection. Diabetes. 2014;63(6):2172-2182. doi: 10.2337/db13-1663
- Läll K, Mägi R, Morris A, et al. 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
- 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
- van Hoek M, Dehghan A, Witteman JC, et al. Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Diabetes. 2008;57(11):3122-3128. doi: 10.2337/db08-0425
- Mühlenbruch K, Jeppesen C, Joost HG, et al. The value of genetic information for diabetes risk prediction – differences according to sex, age, family history and obesity. PLoS One. 2013;8(5):e64307. doi: 10.1371/journal.pone.0064307
- Goto A, Noda M, Goto M, et al. JPHC Study Group. Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study. Diabetic Med. 2018;35(5):602-11. doi: 10.1111/dme.13602
- Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score – the CoLaus Study. Diabetologia. 2009;52(4):600-8. doi: 10.1007/s00125-008-1254-y
- 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
- Mel'nikova ES, Rymar OD, Ivanova AA, et al. Association of polymorphisms of genes TCF7L2, FABP2, KCNQ1, ADIPOQ with the prognosis of the development of type 2 diabetes mellitus. Ter Arkh. 2020;92(10):40-47. (In Russ.). [Мельникова Е.С., Рымар О.Д., Иванова А.А., и др. Ассоциация полиморфизмов генов TCF7L2, FABP2, KCNQ1, ADIPOQ с прогнозом развития сахарного диабета 2-го типа. Терапевтический архив. 2020;92(10):40-47]. doi: 10.26442/00403660.2020.10.000393
- 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
- Cauchi S, Meyre D, Durand E, et al. Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. PLoS One. 2008;3(5):e2031. doi: 10.1371/journal.pone.0002031
- Cornelis MC, Qi L, Zhang C, et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann Intern Med. 2009;150(8):541-550. doi: 10.7326/0003-4819-150-8-200904210-00008
- Sparsø T, Grarup N, Andreasen C, et al. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene-gene interaction. Diabetologia. 2009;52(7):1308-14. doi: 10.1007/s00125-009-1362-3
- Miyake K, Yang W, Hara K, et al. Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association. J Hum Genet. 2009;54(4):236-41. doi: 10.1038/jhg.2009.17
- Hu C, Zhang R, Wang C, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PLoS One. 2009;4(10):e7643. doi: 10.1371/journal.pone.0007643
- Fontaine-Bisson B, Renström F, Rolandsson O, et al. Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia. 2010;53(10):2155-2162. doi: 10.1007/s00125-010-1792-y
- Wang J, Stancáková 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
- Xu M, Bi Y, Xu Y, et al. Combined effects of 19 common variations on type 2 diabetes in Chinese: results from two community-based studies. PLoS One. 2010;5(11):e14022. doi: 10.1371/journal.pone.0014022
- Qi Q, Li H, Wu Y, et al. Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population. Diabetologia. 2010;53(10):2163-6. doi: 10.1007/s00125-010-1826-5
- Ruchat SM, Vohl MC, Weisnagel SJ, et al. Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism. Ann Med. 2010;42(3):196-206. doi: 10.3109/07853890903559716
- Rees SD, Hydrie MZ, Shera AS, et al. Replication of 13 genome-wide association (GWA)-validated risk variants for type 2 diabetes in Pakistani populations. Diabetologia. 2011;54(6):1368-74. doi: 10.1007/s00125-011-2063-2
- Janipalli CS, Kumar MV, Vinay DG, et al. Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of Type 2 diabetes and related traits in Indians. Diabet Med. 2012;29(1):121-7. doi: 10.1111/j.1464-5491.2011.03438.x
- Tam CH, Ho JS, Wang Y, et al. Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes. PLoS One. 2013;8(12):e83093. doi: 10.1371/journal.pone.0083093
- Chatterjee N, Wheeler B, Sampson J, et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet. 2013;45(4):400-5, 405e1-3. doi: 10.1038/ng.2579
- Imamura M, Shigemizu D, Tsunoda T, et al. Assessing the clinical utility of a genetic risk score constructed using 49 susceptibility alleles for type 2 diabetes in a Japanese population. J Clin Endocrinol Metab. 2013; 98:e1667-73. doi: 10.1210/jc.2013-1642
- Shigemizu D, Abe T, Morizono T, et al. The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort. PLoS One. 2014;9(3):e92549. doi: 10.1371/journal.pone.0092549
- Qian Y, Lu F, Dong M, et al. Cumulative effect and predictive value of genetic variants associated with type 2 diabetes in Han Chinese: a case-control study. PLoS One. 2015;10:e0116537. doi: 10.1371/journal.pone.0116537
- Chikowore T, van Zyl T, Feskens EJ, Conradie KR. Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population. Diabetes Res Clin Pract. 2016;122:1-8. doi: 10.1016/j.diabres.2016.09.019
- Janssens AC, Gwinn M, Khoury MJ, Subramonia-Iyer S. Does genetic testing really improve the prediction of future type 2 diabetes? PLoS Med. 2006;3(2):e114-e127. doi: 10.1371/journal.pmed.0030114
- Vaxillaire M, Veslot J, Dina C, et al. DESIR Study Group. Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study. Diabetes. 2008;57(1):244-54. doi: 10.2337/db07-0615
- Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care. 2008;31(10):2056-2061. doi: 10.2337/dc08-0368
- Rotger M, Gsponer T, Martinez R, et al. Swiss HIV Cohort Study. Impact of single nucleotide polymorphisms and of clinical risk factors on new-onset diabetes mellitus in HIV-infected individuals. Clin Infect Dis. 2010;51(9):1090-8. doi: 10.1086/656630
- Hivert MF, Jablonski KA, Perreault L, et al. Updated genetic score based on 34 confirmed type 2 diabetes loci is associated with diabetes incidence and regression to normoglycemia in the diabetes prevention program. Diabetes. 2011;60(4):1340-1348. doi: 10.2337/db10-1119
- Schmid R, Vollenweider P, Bastardot F, et al. Current genetic data do not improve the prediction of type 2 diabetes mellitus: The CoLaus study. J Clin Endocrinol Metab. 2012;97(7):E1338-41. doi: 10.1210/jc.2011-3412
- Raynor LA, Pankow JS, Duncan BB, et al. Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2013;36(1):70-76. doi: 10.2337/dc12-0609
- Anand SS, Meyre D, Pare G, et al. Genetic information and the prediction of incident type 2 diabetes in a high-risk multiethnic population: the EpiDREAM genetic study. Diabetes Care. 2013;36(9):2836-2842. doi: 10.2337/dc12-2553
- Walford GA, Porneala BC, Dauriz M, et al. Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care. 2014;37(9):2508-2514. doi: 10.2337/dc14-0560
- Vaxillaire M, Yengo L, Lobbens S, et al. Type 2 diabetes-related genetic risk scores associated with variations in fasting plasma glucose and development of impaired glucose homeostasis in the prospective DESIR study. Diabetologia. 2014;57:1601-10. doi: 10.1007/s00125-014-3277-x
- Talmud PJ, Cooper JA, Morris RW, et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes. 2015;64:1830-1840. doi: 10.2337/db14-1504
- Park HY, Choi HJ, Hong YC. Utilizing Genetic Predisposition Score in Predicting Risk of Type 2 Diabetes Mellitus Incidence: A Community-based Cohort Study on Middle-aged Koreans. J Korean Med Sci. 2015;30(8):1101-1109. doi: 10.3346/jkms.2015.30.8.1101
- Go MJ, Lee Y, Park S, et al. Genetic-risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10 year follow-up of a population-based cohort study. J Hum Genet. 2016;61(12):1009-1012. doi: 10.1038/jhg.2016.93
- Stančáková A, Kuulasmaa T, Kuusisto J, et al. Genetic risk scores in the prediction of plasma glucose, impaired insulin secretion, insulin resistance and incident type 2 diabetes in the METSIM study. Diabetologia. 2017;60(9):1722-1730. doi: 10.1007/s00125-017-4313-4
- Kim J, Kim J, Kwak MJ, Bajaj M. Genetic prediction of type 2 diabetes using deep neural network. Clin Genet. 2018;93(4):822-829. doi: 10.1111/cge.13175
- Wang Y, Zhang L, Niu M, et al. Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study. Front Public Health. 2021;9:606711. doi: 10.3389/fpubh.2021.606711
- Chen X, Liu C, Si S, et al. Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank. Acta Diabetol. 2021;58(4):467-474. doi: 10.1007/s00592-020-01650-1
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