B. Zhou, Y. Lu, K. Hajifathalian, J. Bentham, M. Di Cesare, G. Danaei, H. Bixby, M.J. Cowan, M.K. Ali, C. Taddei, Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4· 4 million participants. Lancet 387(10027), 1513–1530 (2016)
Y. Zheng, S.H. Ley, F.B. Hu, Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14(2), 88–98 (2018)
J.M. Forbes, M.E. Cooper, Mechanisms of diabetic complications. Physiol. Rev. 93(1), 137–188 (2013)
Article PubMed CAS Google Scholar
S.Y. Tan, J.L.M. Wong, Y.J. Sim, S.S. Wong, S.A.M. Elhassan, S.H. Tan, G.P.L. Lim, N.W.R. Tay, N.C. Annan, S.K. Bhattamisra, Type 1 and 2 diabetes mellitus: a review on current treatment approach and gene therapy as potential intervention. Diab. Metab. Syndr. Clin. Res. Rev. 13(1), 364–372 (2019)
M.A. Reddy, E. Zhang, R. Natarajan, Epigenetic mechanisms in diabetic complications and metabolic memory. Diabetologia 58, 443–455 (2015)
Article PubMed CAS Google Scholar
M. Brownlee, The pathobiology of diabetic complications: a unifying mechanism. Diabetes 54(6), 1615–1625 (2005)
Article PubMed CAS Google Scholar
M. Roden, G.I. Shulman, The integrative biology of type 2 diabetes. Nature 576(7785), 51–60 (2019)
Article PubMed CAS Google Scholar
U. Galicia-Garcia, A. Benito-Vicente, S. Jebari, A. Larrea-Sebal, H. Siddiqi, K.B. Uribe, H. Ostolaza, C. Martín, Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci. 21(17), 6275 (2020)
Article PubMed PubMed Central CAS Google Scholar
S. Ley, S. Harris, P. Connelly, M. Mamakeesick, J. Gittelsohn, T. Wolever, R. Hegele, B. Zinman, A. Hanley, Utility of non‐high‐density lipoprotein cholesterol in assessing incident type 2 diabetes risk. Diab. Obes. Metab. 14(9), 821–825 (2012)
A. von Eckardstein, R.A. Sibler, Possible contributions of lipoproteins and cholesterol to the pathogenesis of diabetes mellitus type 2. Curr. Opin. Lipidol. 22(1), 26–32 (2011)
A. Tirosh, I. Shai, R. Bitzur, I. Kochba, D. Tekes-Manova, E. Israeli, T. Shochat, A. Rudich, Changes in triglyceride levels over time and risk of type 2 diabetes in young men. Diab. Care 31(10), 2032–2037 (2008)
P.W. Wilson, J.B. Meigs, L. Sullivan, C.S. Fox, D.M. Nathan, R.B. D’Agostino,, Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch. Intern. Med. 167(10), 1068–1074 (2007).
L. Li, P. Li, J. Yang, X. Huang, H. Bao, C. Zhang, Y. Song, M. Zhao, M. Ji, Y. Wang, Lipid levels and new-onset diabetes in a hypertensive population: the China Stroke Primary Prevention Trial. Sci. Rep. 7(1), 7014 (2017)
Article PubMed PubMed Central Google Scholar
X. Bao, Y. Borné, L. Johnson, I.F. Muhammad, M. Persson, K. Niu, G. Engström, Comparing the inflammatory profiles for incidence of diabetes mellitus and cardiovascular diseases: a prospective study exploring the ‘common soil’hypothesis. Cardiovasc. Diabetol. 17(1), 1–11 (2018)
K.M. Peper, B. Guo, D. Leann Long, G. Howard, A.P. Carson, V.J. Howard, S.E. Judd, N.A. Zakai, A. Cherrington, M. Cushman, C-reactive protein and racial differences in type 2 diabetes incidence: the REGARDS study. J. Clin. Endocrinol. Metab. 107(6), e2523–e2531 (2022)
Article PubMed PubMed Central Google Scholar
B.S. Nayak, A. Sobrian, K. Latiff, D. Pope, A. Rampersad, K. Lourenço, N. Samuel, The association of age, gender, ethnicity, family history, obesity, and hypertension with type 2 diabetes mellitus in Trinidad. Diab. Metab. Syndrome: Clin. Res. Rev. 8(2), 91–95 (2014)
M. Halim, A. Halim, The effects of inflammation, aging, and oxidative stress on the pathogenesis of diabetes mellitus (type 2 diabetes). Diabetes & metabolic syndrome. Clin. Res. Rev. 13(2), 1165–1172 (2019)
Y. Kim, B.G. Han, Cohort profile: the Korean Genome and Epidemiology Study (KoGES) Consortium. Int J. Epidemiol. 46(2), e20 (2017). https://doi.org/10.1093/ije/dyv316
D.R. Matthews, J.P. Hosker, A.S. Rudenski, B.A. Naylor, D.F. Treacher, R.C. Turner, Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7), 412–419 (1985). https://doi.org/10.1007/bf00280883
Article PubMed CAS Google Scholar
A.D, Association, Diagnosis, and classification of diabetes mellitus. Diab. Care 37(Supplement_1), S81–S90 (2014)
B. Chen, P.C. Tai, R. Harrison, Y. Pan, Novel hybrid hierarchical-K-means clustering method (HK-means) for microarray analysis 2005 IEEE computational systems bioinformatics conference-workshops (CSBW'05), pp. 105–108. IEEE (2005)
T.-S. Xu, H.-D. Chiang, G.-Y. Liu, C.-W. Tan, Hierarchical K-means method for clustering large-scale advanced metering infrastructure data. IEEE Trans. Power Deliv. 32(2), 609–616 (2015)
E. Ahlqvist, P. Storm, A. Käräjämäki, M. Martinell, M. Dorkhan, A. Carlsson, P. Vikman, R.B. Prasad, D.M. Aly, P. Almgren, Y. Wessman, N. Shaat, P. Spégel, H. Mulder, E. Lindholm, O. Melander, O. Hansson, U. Malmqvist, Å. Lernmark, K. Lahti, T. Forsén, T. Tuomi, A.H. Rosengren, L. Groop, Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diab. Endocrinol. 6(5), 361–369 (2018). https://doi.org/10.1016/s2213-8587(18)30051-2
P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
S. Chatterjee, K. Khunti, M.J. Davies, Type 2 diabetes. Lancet 389(10085), 2239–2251 (2017)
Article PubMed CAS Google Scholar
R.A. DeFronzo, D. Tripathy, Skeletal muscle insulin resistance is the primary defect in type 2 diabetes. Diab. Care 32(Suppl 2), S157 (2009)
S. Lillioja, D.M. Mott, B.V. Howard, P.H. Bennett, H. Yki-Järvinen, D. Freymond, B.L. Nyomba, F. Zurlo, B. Swinburn, C. Bogardus, Impaired glucose tolerance as a disorder of insulin action. Longitud. Cross-Sect. Stud. Pima Indians N. Engl. J. Med. 318(19), 1217–1225 (1988). https://doi.org/10.1056/nejm198805123181901
Y. Kim, A.L. Han, The correlation between triglyceride to HDL cholesterol ratio and metabolic syndrome, nutrition intake in Korean adults: Korean National Health and Nutrition Examination Survey 2016. J. Nutr. Health 52(3), 268–276 (2019)
G.F. Lewis, K.D. Uffelman, L.W. Szeto, G. Steiner, Effects of acute hyperinsulinemia on VLDL triglyceride and VLDL apoB production in normal weight and obese individuals. Diabetes 42(6), 833–842 (1993). https://doi.org/10.2337/diab.42.6.833
Article PubMed CAS Google Scholar
R.D. Siegel, A. Cupples, E.J. Schaefer, P.W. Wilson, Lipoproteins, apolipoproteins, and low-density lipoprotein size among diabetics in the Framingham offspring study. Metabolism 45(10), 1267–1272 (1996)
Article PubMed CAS Google Scholar
H.N. Ginsberg, L.-S. Huang, The insulin resistance syndrome: impact on lipoprotein metabolism and atherothrombosis. J. Cardiovasc. Risk 7(5), 325–331 (2000)
Article PubMed CAS Google Scholar
G. Kolovou, K. Anagnostopoulou, D. Cokkinos, Pathophysiology of dyslipidaemia in the metabolic syndrome. Postgrad. Med. J. 81(956), 358–366 (2005)
Article PubMed PubMed Central CAS Google Scholar
R. Gong, G. Luo, M. Wang, L. Ma, S. Sun, X. Wei, Associations between TG/HDL ratio and insulin resistance in the US population: a cross-sectional study. Endocr. Connect. 10(11), 1502–1512 (2021)
Article PubMed PubMed Central Google Scholar
H. Liu, J. Liu, J. Liu, S. Xin, Z. Lyu, X. Fu, Triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, a simple but effective indicator in predicting type 2 diabetes mellitus in older adults. Front. Endocrinol. 13, 828581 (2022). https://doi.org/10.3389/fendo.2022.828581
B. Che, C. Zhong, R. Zhang, L. Pu, T. Zhao, Y. Zhang, L. Han, Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc. Diabetol. 22(1), 34 (2023). https://doi.org/10.1186/s12933-023-01762-2
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