Schmidt MI, Bracco PA, Yudkin JS, Bensenor IM, Griep RH, Barreto SM, et al. Intermediate hyperglycaemia to predict progression to type 2 diabetes (ELSA-Brasil): an occupational cohort study in Brazil. Lancet Diabetes Endocrinol. 2019;7:267–77.
Richter B, Hemmingsen B, Metzendorf M-I, Takwoingi Y. Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia. In: Cochrane Metabolic and Endocrine Disorders Group, editor. Cochrane Database Syst Rev. 2018.
Wagner R, Heni M, Tabák AG, Machann J, Schick F, Randrianarisoa E, et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med. 2021;27:49–57.
International Diabetes Federation. IDF diabetes atlas. 10th ed. Brussels: International Diabetes Federation; 2021.
Endocrinology CS of, Society CD, Association CE, Association E and MDB of CRH, Association DB of CRH. Intervention for adults with pre-diabetes: a Chinese expert consensus. Chin J Endocrinol Metab. 2020;36:371–80.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Moons KGM, de Groot JAH, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS Checklist. PLoS Med. 2014;11:e1001744.
Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med. 2002;136:575–81.
Lindstrom J, Tuomilehto J. The Diabetes Risk Score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26:725–31.
Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, et al. Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care. 2005;28:2013–8.
Wilson PWF. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007;167:1068.
Tuomilehto J, Lindström J, Hellmich M, Lehmacher W, Westermeier T, Evers T, et al. Development and validation of a risk-score model for subjects with impaired glucose tolerance for the assessment of the risk of type 2 diabetes mellitus—The STOP-NIDDM risk-score. Diabetes Res Clin Pract. 2010;87:267–74.
Venema E, Wessler BS, Paulus JK, Salah R, Raman G, Leung LY, et al. Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination. J Clin Epidemiol. 2021;138:32–9.
Holman RR, Coleman RL, Chan JCN, Chiasson J-L, Feng H, Ge J, et al. Effects of acarbose on cardiovascular and diabetes outcomes in patients with coronary heart disease and impaired glucose tolerance (ACE): a randomised, double-blind, placebo-controlled trial. Lancet Diabetes Endocrinol. 2017;5:877–86.
Bi Y, Lu J, Wang W, Mu Y, Zhao J, Liu C, et al. Cohort profile: risk evaluation of cancers in Chinese diabetic individuals: a longitudinal (REACTION) study. J Diabetes. 2014;6:147–57.
Song K, Du H, Zhang Q, Wang C, Guo Y, Wu H, et al. Serum immunoglobulin M concentration is positively related to metabolic syndrome in an adult population: Tianjin Chronic Low-Grade Systemic Inflammation and Health (TCLSIH) Cohort Study. PLoS ONE. 2014;9:e88701.
World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: report of a WHO consultation. Part 1, Diagnosis and classification of diabetes mellitus. Geneva: World Health Organization; 1999.
Tuomilehto J, Peltonen M, Eriksson JG, Ilanne-Parikka P. Improved lifestyle and decreased diabetes risk over 13 years: long-term follow-up of the randomised Finnish Diabetes Prevention Study (DPS). Diabetologia. 2013;56:284–93.
Hosmer D, Lemeshow S. Applied logistic regression. Chapter 5. 3rd ed. New York: Wiley; 2013.
Bethel MA, Chacra AR, Deedwania P, Fulcher GR, Holman RR, Jenssen T, et al. A novel risk classification paradigm for patients with impaired glucose tolerance and high cardiovascular risk. Am J Cardiol. 2013;112:231–7.
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594.
Aekplakorn W, Bunnag P, Woodward M, Sritara P, Cheepudomwit S, Yamwong S, et al. A risk score for predicting incident diabetes in the Thai population. Diabetes Care. 2006;29:1872–7.
Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, et al. A prediction model for type 2 diabetes risk among Chinese people. Diabetologia. 2009;52:443–50.
Gao WG, Qiao Q, Pitkäniemi J, Wild S, Magliano D, Shaw J, et al. Risk prediction models for the development of diabetes in Mauritian Indians. Diabet Med. 2009;26:996–1002.
Sun F, Tao Q, Zhan S. An accurate risk score for estimation 5-year risk of type 2 diabetes based on a health screening population in Taiwan. Diabetes Res Clin Pract. 2009;85:228–34.
Chuang S-Y, Yeh W-T, Wu Y-L, Chang H-Y, Pan W-H, Tsao C-K. Prediction equations and point system derived from large-scale health check-up data for estimating diabetic risk in the Chinese population of Taiwan. Diabetes Res Clin Pract. 2011;92:128–36.
Liu M, Pan C, Jin M. A Chinese diabetes risk score for screening of undiagnosed diabetes and abnormal glucose tolerance. Diabetes Technol Ther. 2011;13:501–7.
Doi Y, Ninomiya T, Hata J, Hirakawa Y, Mukai N, Iwase M, et al. Two risk score models for predicting incident Type 2 diabetes in Japan: two diabetes risk score models in Japan. Diabet Med. 2012;29:107–14.
Heianza Y, Arase Y, Hsieh SD, Saito K, Tsuji H, Kodama S, et al. Development of a new scoring system for predicting the 5 year incidence of type 2 diabetes in Japan: the Toranomon Hospital Health Management Center Study 6 (TOPICS 6). Diabetologia. 2012;55:3213–23.
Lim N-K, Park S-H, Choi S-J, Lee K-S, Park H-Y. A risk score for predicting the incidence of type 2 diabetes in a middle-aged Korean cohort. Circ J. 2012;76:1904–10.
Xu L, Jiang CQ, Schooling CM, Zhang WS, Cheng KK, Lam TH. Prediction of 4-year incident diabetes in older Chinese: recalibration of the Framingham diabetes score on Guangzhou Biobank Cohort Study. Prev Med. 2014;69:63–8.
Ye X, Zong G, Liu X, Liu G, Gan W, Zhu J, et al. Development of a new risk score for incident type 2 diabetes using updated diagnostic criteria in middle-aged and older Chinese. PLoS ONE. 2014;9:e97042.
Nanri A, Nakagawa T, Kuwahara K, Yamamoto S, Honda T, Okazaki H, et al. Development of risk score for predicting 3-year incidence of type 2 diabetes: Japan Epidemiology Collaboration on Occupational Health Study. PLoS ONE. 2015;10:e0142779.
Liu X, Chen Z, Fine JP, Liu L, Wang A, Guo J, et al. A competing-risk-based score for predicting twenty-year risk of incident diabetes: the Beijing Longitudinal Study of Ageing study. Sci Rep. 2016;6:37248.
Wang A, Chen G, Su Z, Liu X, Liu X, Li H, et al. Risk scores for predicting incidence of type 2 diabetes in the Chinese population: the Kailuan prospective study. Sci Rep. 2016;6:26548.
Zhang M, Zhang H, Wang C, Ren Y, Wang B, Zhang L, et al. Development and validation of a risk-score model for type 2 diabetes: a cohort study of a rural adult Chinese population. PLoS ONE. 2016;11:e0152054.
Miyakoshi T, Oka R, Nakasone Y, Sato Y, Yamauchi K, Hashikura R, et al. Development of new diabetes risk scores on the basis of the current definition of diabetes in Japanese subjects [Rapid Communication]. Endocr J. 2016;63:857–65.
Chen X, Wu Z, Chen Y, Wang X, Zhu J, Wang N, et al. Risk score model of type 2 diabetes prediction for rural Chinese adults: the Rural Deqing Cohort Study. J Endocrinol Invest. 2017;40:1115–23.
Wen J, Hao J, Liang Y, Li S, Cao K, Lu X, et al. A non-invasive risk score for predicting incident diabetes among rural Chinese people: a village-based cohort study. PLoS ONE. 2017;12:e0186172.
Yokota N, Miyakoshi T, Sato Y, Nakasone Y, Yamashita K, Imai T, et al. Predictive models for conversion of prediabetes to diabetes. J Diabetes Complicat. 2017;31:1266–71.
Zhang H, Wang C, Ren Y, Wang B, Yang X, Zhao Y, et al. A risk-score model for predicting risk of type 2 diabetes mellitus in a rural Chinese adult population: a cohort study with a 6-year follow-up. Diabetes Metab Res Rev. 2017;33:e2911.
Ha KH, Lee Y, Song SO, Lee J, Kim DW, Cho K, et al. Development and validation of the Korean Diabetes Risk Score: a 10-year national cohort study. Diabetes Metab J. 2018;42:402.
Han X, Wang J, Li Y, Hu H, Li X, Yuan J, et al. Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese. Acta Diabetol. 2018;55:13–9.
Hu H, Nakagawa T, Yamamoto S, Honda T, Okazaki H, Uehara A, et al. Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study. J Diabetes Investig. 2018;9:1052–9.
Ustulin M, Rhee SY, Chon S, Ahn KK, Lim JE, Oh B, et al. Importance of family history of diabetes in computing a diabetes risk score in Korean prediabetic population. Sci Rep. 2018;8:15958.
Yatsuya H, Li Y, Hirakawa Y, Ota A, Matsunaga M, Haregot HE, et al. A point system for predicting 10-year risk of developing type 2 diabetes mellitus in Japanese men: Aichi workers’ cohort study. J Epidemiol. 2018;28:347–52.
Wang K, Gong M, Xie S, Zhang M, Zheng H, Zhao X, et al. Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents. EPMA J. 2019;10:227–37.
Cai X, Zhu Q, Wu T, Zhu B, Aierken X, Ahmat A, 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.
Hu H, Wang J, Han X, Li Y, Miao X, Yuan J, et al. Prediction of 5-year risk of diabetes mellitus in relatively low risk middle-aged and elderly adults. Acta Diabetol. 2020;57:63–70.
Lin Z, Guo D, Chen J, Zheng B. A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population. Endocrine. 2020;67:561–8.
Liu Q, Yuan J, Bakeyi M, Li J, Zhang Z, Yang X, et al. Development and validation of a nomogram to predict type 2 diabetes mellitus in overweight and obese adults: a prospective cohort study from 82938 adults in China. Int J Endocrinol. 2020;2020:8899556.
Liu X, Li Z, Zhang J, Chen S, Tao L, Luo Y, et al. A novel risk score for type 2 diabetes containing sleep duration: a 7-year prospective cohort study among Chinese participants. J Diabetes Res. 2020;2020:2969105.
Ma C-M, Yin F-Z. Glycosylated hemoglobin A1c improves the performance of the nomogram for predicting the 5-year incidence of type 2 diabetes. Diabetes Metab Syndr Obes. 2020;13:1753–62.
Shao X, Wang Y, Huang S, Liu H, Zhou S, Zhang R, et al. Development and validation of a prediction model estimating the 10-year risk for type 2 diabetes in China. PLoS ONE. 2020;15:e0237936.
Wang H, Zheng X, Bai Z-H, Lv J-H, Sun J-L, Shi Y, et al. A retrospective population study to develop a predictive model of prediabetes and incident type 2 diabetes mellitus from a hospital database in Japan between 2004 and 2015. Med Sci Monit. 2020;26:e920880.
Wu Y, Hu H, Cai J, Chen R, Zuo X, Cheng H, et al. A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults. Sci Rep. 2020;10:21716.
Cai X-T, Ji L-W, Liu S-S, Wang M-R, Heizhati M, Li N-F. 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;14:2087–101.
Cai X, Zhu Q, Cao Y, Liu S, Wang M, Wu T, 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.
Li L, Wang Z, Zhang M, Ruan H, Zhou L, Wei X, et al. New risk score model for identifying individuals at risk for diabetes in southwest China. Prev Med Rep. 2021;24:101618.
Liang K, Guo X, Wang C, Yan F, Wang L, Liu J, et al. Nomogram predicting the risk of progression from prediabetes to diabetes after a 3-year follow-up in Chinese adults. Diabetes Metab Syndr Obes. 2021;14:2641–9.
Wu Y, Hu H, Cai J, Chen R, Zuo X, Cheng H, et al. Machine learning for predicting the 3-year risk of incident diabetes in Chinese adults. Front Public Health. 2021;9:626331.
Xu S, Scott CAB, Coleman RL, Tuomilehto J, Holman RR. Predicting the risk of developing type 2 diabetes in Chinese people who have coronary heart disease and impaired glucose tolerance. J Diabetes. 2021;13:817–26.
Chen L, Magliano DJ, Balkau B, Colagiuri S, Zimmet PZ, Tonkin AM, et al. AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192:6.
Hippisley-Cox J, Coupland C. Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study. BMJ. 2017;359:j5019.
Kengne AP, Beulens JW, Peelen LM, Moons KG, van der Schouw YT, Schulze MB, et al. Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. Lancet Diabetes Endocrinol. 2014;2:19–29.