A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population

References

• Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98. doi:10.1038/nrendo.2017.151
   PubMed Web of Science ®Google Scholar
• Cai XT, Ji LW, Liu SS, et al. Derivation and validation of a prediction model for predicting the 5-year incidence of type 2 diabetes in non-obese adults: a population-based cohort study. Diabetes Metab Syndr Obes. 2021;14:2087–2101. doi:10.2147/DMSO.S304994
   PubMed Web of Science ®Google Scholar
• Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocr Rev. 2016;37(3):278–316. doi:10.1210/er.2015-1137
   PubMed Web of Science ®Google Scholar
• American Diabetes A. Economic costs of diabetes in the U.S. in 2017. Diabetes Care. 2018;41(5):917–928. doi:10.2337/dci18-0007
   Web of Science ®Google Scholar
• Nair GG, Tzanakakis ES, Hebrok M. Emerging routes to the generation of functional beta-cells for diabetes mellitus cell therapy. Nat Rev Endocrinol. 2020;16(9):506–518. doi:10.1038/s41574-020-0375-3
   Web of Science ®Google Scholar
• Malone JI, Hansen BC. Does obesity cause type 2 diabetes mellitus (T2DM)? Or is it the opposite? Pediatr Diabetes. 2019;20(1):5–9. doi:10.1111/pedi.12787
   PubMed Web of Science ®Google Scholar
• Okamura T, Hashimoto Y, Hamaguchi M, et al. Ectopic fat obesity presents the greatest risk for incident type 2 diabetes: a population-based longitudinal study. Int J Obes. 2019;43(1):139–148. doi:10.1038/s41366-018-0076-3
   Web of Science ®Google Scholar
• Zhao W, Tong JJ, Cao YT, et al. A linear relationship between a body shape index and risk of incident type 2 diabetes: a secondary analysis based on a retrospective cohort study in Japan. Diabetes Metab Syndr Obes. 2020;13:2139–2146. doi:10.2147/DMSO.S256031
   PubMed Web of Science ®Google Scholar
• Tanamas SK, Reddy SP, Chambers MA, et al. Effect of severe obesity in childhood and adolescence on risk of type 2 diabetes in youth and early adulthood in an American Indian population. Pediatr Diabetes. 2018;19(4):622–629. doi:10.1111/pedi.12627
   Web of Science ®Google Scholar
• Hales CM, Carroll MD, Fryar CD, et al. Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS Data Brief. 2017;2017(288):1–8.
   Google Scholar
• Shulman GI. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med. 2014;371(23):2237–2238. doi:10.1056/NEJMc1412427
   PubMed Web of Science ®Google Scholar
• Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000;21(6):697–738. doi:10.1210/edrv.21.6.0415
   PubMed Web of Science ®Google Scholar
• Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173–80. doi:10.1016/S1470-2045(14)71116-7
   PubMed Web of Science ®Google Scholar
• Misra A, Vikram NK, Gupta R, et al. Waist circumference cutoff points and action levels for Asian Indians for identification of abdominal obesity. Int J Obes. 2006;30(1):106–111. doi:10.1038/sj.ijo.0803111
   Web of Science ®Google Scholar
• Hashimoto Y, Hamaguchi M, Kojima T, et al. Modest alcohol consumption reduces the incidence of fatty liver in men: a population-based large-scale cohort study. J Gastroenterol Hepatol. 2015;30(3):546–552. doi:10.1111/jgh.12786
   PubMed Web of Science ®Google Scholar
• American Diabetes A. Standards of medical care in diabetes--2011. Diabetes Care. 2011;34(Suppl 1):S11–61. doi:10.2337/dc11-S011
   Google Scholar
• Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/bmj.g7594
   PubMed Web of Science ®Google Scholar
• Bhupathiraju SN, Hu FB. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res. 2016;118(11):1723–1735. doi:10.1161/CIRCRESAHA.115.306825
   PubMed Web of Science ®Google Scholar
• Cai X, Zhu Q, Cao Y, et al. A prediction model based on noninvasive indicators to predict the 8-year incidence of type 2 diabetes in patients with nonalcoholic fatty liver disease: a population-based retrospective cohort study. Biomed Res Int. 2021;2021:5527460. doi:10.1155/2021/5527460
   PubMed Web of Science ®Google Scholar
• Wei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of visceral adiposity index, body shape index, body mass index and waist circumference and their associations with diabetes mellitus in adults. Nutrients. 2019;11(7):1580. doi:10.3390/nu11071580
   PubMed Web of Science ®Google Scholar
• Venkatrao M, Nagarathna R, Patil SS, et al. A composite of BMI and waist circumference may be a better obesity metric in Indians with high risk for type 2 diabetes: an analysis of NMB-2017, a nationwide cross-sectional study. Diabetes Res Clin Pract. 2020;161:108037. doi:10.1016/j.diabres.2020.108037
   PubMed Web of Science ®Google Scholar
• Wilkinson L, Yi N, Mehta T, et al. Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study. PLoS Med. 2020;17(8):e1003232. doi:10.1371/journal.pmed.1003232
   Web of Science ®Google Scholar
• Cai X, Zhu Q, Wu T, et al. Development and validation of a novel model for predicting the 5-year risk of type 2 diabetes in patients with hypertension: a retrospective cohort study. Biomed Res Int. 2020;2020:9108216. doi:10.1155/2020/9108216
   Web of Science ®Google Scholar
• Lin Z, Guo D, Chen J, et al. A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population. Endocrine. 2020;67(3):561–568. doi:10.1007/s12020-019-02154-x
   Web of Science ®Google Scholar
• Wang K, Gong M, Xie S, et al. Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents. EPMA J. 2019;10(3):227–237. doi:10.1007/s13167-019-00181-2
   PubMed Web of Science ®Google Scholar
• Hirani V. Generalised and abdominal adiposity are important risk factors for chronic disease in older people: results from a nationally representative survey. J Nutr Health Aging. 2011;15(6):469–478. doi:10.1007/s12603-011-0051-3
   Web of Science ®Google Scholar
• Krishnan S, Rosenberg L, Djousse L, et al. Overall and central obesity and risk of type 2 diabetes in U.S. black women. Obesity. 2007;15(7):1860–1866. doi:10.1038/oby.2007.220
   PubMed Web of Science ®Google Scholar
• Zhou Y, Yang G, Peng W, et al. Relationship between depression symptoms and different types of measures of obesity (BMI, SAD) in US women. Behav Neurol. 2020;2020:9624106. doi:10.1155/2020/9624106
   Web of Science ®Google Scholar
• Yang T, Zhao B, Pei D. Evaluation of the association between obesity markers and type 2 diabetes: a cohort study based on a physical examination population. J Diabetes Res. 2021;2021:6503339. doi:10.1155/2021/6503339
   Web of Science ®Google Scholar
• Klisic A, Kavaric N, Vujcic S, et al. Factorial analysis of the cardiometabolic risk influence on redox status components in adult population. Oxid Med Cell Longev. 2021;2021:6661940. doi:10.1155/2021/6661940
   Web of Science ®Google Scholar
• Ates E, Set T, Karahan SC, et al. Thiol/Disulphide homeostasis, ischemia modified albumin, and ferroxidase as oxidative stress markers in women with obesity with insulin resistance. J Med Biochem. 2019;38(4):445–451. doi:10.2478/jomb-2019-0015
   PubMed Web of Science ®Google Scholar
• Bragg F, Tang K, Guo Y, et al. Associations of general and central adiposity with incident diabetes in chinese men and women. Diabetes Care. 2018;41(3):494–502. doi:10.2337/dc17-1852
   Web of Science ®Google Scholar
• Athyros VG, Doumas M, Imprialos KP, et al. Diabetes and lipid metabolism. Hormones. 2018;17(1):61–67. doi:10.1007/s42000-018-0014-8
   PubMedGoogle Scholar
• Chapman MJ. HDL functionality in type 1 and type 2 diabetes: new insights. Curr Opin Endocrinol Diabetes Obes. 2022;29(2):112–123. doi:10.1097/MED.0000000000000705
   Google Scholar
• Akter S, Goto A, Mizoue T. Smoking and the risk of type 2 diabetes in Japan: a systematic review and meta-analysis. J Epidemiol. 2017;27(12):553–561. doi:10.1016/j.je.2016.12.017
   PubMed Web of Science ®Google Scholar
• Pan A, Wang Y, Talaei M, et al. Relation of active, passive, and quitting smoking with incident type 2 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2015;3(12):958–967. doi:10.1016/S2213-8587(15)00316-2
   PubMed Web of Science ®Google Scholar
• Anker MS, Kalantar-Zadeh K, Doehner W. Smoking and other risk factors in type 2 diabetes. N Engl J Med. 2018;379(26):2572–2573. doi:10.1056/NEJMc1812842
   Web of Science ®Google Scholar
• Sliwinska-Mosson M, Milnerowicz H. The impact of smoking on the development of diabetes and its complications. Diab Vasc Dis Res. 2017;14(4):265–276. doi:10.1177/1479164117701876
   PubMed Web of Science ®Google Scholar
• American Diabetes A. 6. Glycemic targets: standards of medical care in diabetes-2021. Diabetes Care. 2021;44(Suppl1):S73–S84. doi:10.2337/dc21-S006
   Web of Science ®Google Scholar
• Thamakaison S, Anothaisintawee T, Sukhato K, et al. Hemoglobin A1c in combination with fasting plasma glucose trumps fasting plasma glucose alone as predictive indicators for diabetes mellitus: an ambidirectional cohort study of Thai people with impaired fasting glucose. BMJ Open Diabetes Res Care. 2021;9(2):e002427. doi:10.1136/bmjdrc-2021-002427
   Web of Science ®Google Scholar
• Ogata E, Asahi K, Yamaguchi S, et al. Low fasting plasma glucose level as a predictor of new-onset diabetes mellitus on a large cohort from a Japanese general population. Sci Rep. 2018;8(1):13927. doi:10.1038/s41598-018-31744-4
   Web of Science ®Google Scholar