Cluster-Based Diagnostic for Diabetes, Insights from Europe and Asia

Abstract

Type 2 diabetes mellitus (T2DM) has emerged as a major global health challenge, with its prevalence steadily rising in recent decades. The International Diabetes Federation (IDF) Diabetes Atlas projecting that the number of individuals living with diabetes will reach 783 million by 2045. The Asian region is particularly affected, with over 157 million diagnosed cases in 2021, representing about 11% of the region’s total adult population. This review aims to shed light on the heterogeneity within T2DM, emphasizing the importance on the diversity of disease and exhibits the different phenotypic characteristics. The focus of the review is to highlight the significance of understanding these variations in glycemic levels, insulin resistance, complications, heredity, lifestyle, and patient preferences to tailor effective prevention and management strategies. A comprehensive review of recent studies is presented, revealing the importance of personalized approaches in combating the multifaceted challenges related to T2DM. The key findings underscore the critical need for adapting treatment strategies to individual patient profiles, thus mitigating the burden of T2DM and its health consequences. This manuscript underscores the pressing need for additional research endeavors and the formulation of customized interventions to tackle the escalating prevalence of T2DM, both on a global scale and within the Asian region.Cluster-Based Diagnostic for Diabetes, Insights from Europe and Asia

Keywords:

diabetes mellitus, noninsulin-dependent, patient centered care, phenotypic variabilities, personalized medicine

References
[1] IDF_Atlas_10th_Edition_2021.

[2] Radtke MA, Midthjell K, Nilsen TIL, Grill V. Heterogeneity of patients with latent autoimmune diabetesin adults: Linkage to autoimmunity is apparent only in those with perceived need for insulin treatment results from the nord-trondelag health (HUNT) study. Diabetes Care. 2009;32(2). doi:10.2337/dc08-1468

[3] Redondo MJ, Hagopian WA, Oram R, et al. The clinical consequences of heterogeneity within and between different diabetes types. Diabetologia. 2020;63(10). doi:10.1007/s00125-020-05211-7

[4] Gore MO, McGuire DK, Lingvay I, Rosenstock J. Predicting cardiovascular risk in type 2 diabetes: The heterogeneity challenges. Curr Cardiol Rep. 2015;17(7). doi:10.1007/s11886-015-0607-7

[5] Nair ATN, Wesolowska-Andersen A, Brorsson C, et al. Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nat Med. 2022;28(5). doi:10.1038/s41591-022-01790-7

[6] Silvia P, Simona Z, Ernesto M, Raffaella B. “H” for heterogeneity in the algorithm for type 2 diabetes management. Curr Diab Rep. 2020;20(5). doi:10.1007/s11892-020-01297-w

[7] Venkatasubramaniam A, Mateen BA, Shields BM, et al. Comparison of causal forest and regressionbased approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine. BMC Med Inform Decis Mak. 2023;23(1). doi:10.1186/s12911-023-02207-2

[8] Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5). doi:10.1016/S2213-8587(18)30051-2

[9] Zaharia OP, Strassburger K, Strom A, et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 2019;7(9):684-694. doi:10.1016/S2213- 8587(19)30187-1

[10] Safai N, Ali A, Rossing P, Ridderstråle M. Stratification of type 2 diabetes based on routine clinical markers. Diabetes Res Clin Pract. 2018;141. doi:10.1016/j.diabres.2018.05.014

[11] Rončáková M, Davani A, Mikušová V, Ságová I, Novodvorský P, Martinka E. Prevalence of positivity for diabetes-associated autoantibodies in individuals with type 2 diabetes and their further characterisation: Cross-sectional study from Slovakia. Diabetes Ther. 2023;14(9):1537-1548. doi:10.1007/s13300-023-01440-2

[12] Qiu J, Xiao Z, Zhang Z, Luo S, Zhou Z. Latent autoimmune diabetes in adults in China. Front Immunol. 2022;13(August):1-14. doi:10.3389/fimmu.2022.977413

[13] Anjana RM, Baskar V, Nair ATN, et al. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: A data-driven cluster analysis: The INSPIRED study. BMJ Open Diabetes Res Care. 2020;8(1). doi:10.1136/bmjdrc-2020-001506

[14] Preechasuk L, Khaedon N, Lapinee V, et al. Cluster analysis of Thai patients with newly diagnosed type 2 diabetes mellitus to predict disease progression and treatment outcomes: A prospective cohort study. BMJ Open Diabetes Res Care. 2022;10(6). doi:10.1136/bmjdrc-2022-003145

[15] Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442-451. doi:10.1016/S2213-8587(19)30087-7

[16] Zheng R, Xu Y, Li M, et al. Data-driven subgroups of prediabetes and the associations with outcomes in Chinese adults. Cell Reports Med. 2023;4(3). doi:10.1016/j.xcrm.2023.100958

[17] Lu J, He J, Li M, et al. Predictive value of fasting glucose, postload glucose, and hemoglobin A1c on risk of diabetes and complications in Chinese adults. Diabetes Care. 2019;42(8). doi:10.2337/dc18-1390

[18] Zheng R, Li M, Xu M, et al. Chinese adults are more susceptible to effects of overall obesity and fat distribution on cardiometabolic riskfactors. J Clin Endocrinol Metab. 2021;106(7). doi:10.1210/clinem/dgab049

[19] Li M, Xu Y, Wan Q, et al. Individual and combined associations of modifiable lifestyle and metabolic health status with new-onset diabetes and major cardiovascular events: The China cardiometabolic disease and cancer cohort (4C) study. Diabetes Care. 2020;43(8). doi:10.2337/dc20-0256

[20] Unnikrishnan R, Anjana RM, Mohan V. Diabetes in South Asians: Is the phenotype different? Diabetes. 2014;63(1). doi:10.2337/db13-1592

[21] Anjana RM, Pradeepa R, Unnikrishnan R, Tiwaskar M, Aravind SR, Saboo B, Joshi SR, Mohan V. New and unique clusters of type 2 diabetes identified in indians. J Assoc Physicians India. 2021 Feb;69(2):58- 61. PMID: 33527813.

[22] Roden M, Shulman GI. The integrative biology of type 2 diabetes. Nature. 2019;576(7785). doi:10.1038/s41586-019-1797-8

[23] Ahlqvist E, Storm P, Karajamaki A, et al. Clustering of adult-onset diabetes into novel subgroups guides therapy and improves prediction of outcome. bioRxiv. Published online 2017.

[24] Kumar A, De Leiva A. Latent autoimmune diabetes in adults in north Indian region: Assessment of β-cell function, metabolic and immunological features. In: Metabolic Syndrome and Related Disorders. Vol 15. ; 2017. doi:10.1089/met.2017.0103

[25] Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycaemia in type 2 diabetes: A patient-centered approach. Position statement of the american diabetes association (ADA) and the european association for the study of diabetes (EASD). Diabetologia. 2012;55(6). doi:10.1007/s00125-012- 2534-0

[26] Cordiner RLM, Mari A, Tura A, Pearson ER. The impact of low-dose gliclazide on the incretin effect and indices of beta-cell function. J Clin Endocrinol Metab. 2021;106(7). doi:10.1210/clinem/dgab151