Potentially modifiable predictors in models for undiagnosed and incident type 2 diabetes: A systematic review

Table 1 Summary of study design, modeling techniques, and validation strategies in type 2 diabetes mellitus models

Characteristic	Category		n	Percentage (%)
Overall	Studies included		82
Country/region	China		30	36.6
	Japan		12	14.6
	South Korea		4	4.9
	Iran		3	3.7
	Mexico		3	3.7
	United States		3	3.7
	Germany		2	2.4
	Qatar		2	2.4
	Singapore		2	2.4
	United Kingdom		2	2.4
	United Arab Emirates		2	2.4
	Other1		17	20.7
Study design	Prospective cohort		39	47.6
	Cross-sectional		20	24.4
	Retrospective cohort		14	17.1
	Mathematical modeling		2	2.4
	Case-control		2	2.4
	Population-based cohort		2	2.4
	Nested case-control		1	1.2
	Multi-cohort prospective		1	1.2
	Meta-analysis + prospective cohort validation		1	1.2
Modeling method	Traditional regression	Logistic regression	42	-2
		Cox proportional hazards model	32	-
	Regularized regression	LASSO/elastic net	17	-
	Machine learning	Support vector machine	5	-
		Decision tree	4	-
		Neural network	2	-
		Random forest	1	-
	Competing risk model	Fine–Gray model	2	-
	Other methods	Meta-analysis–based model	1	-
Model validation	Internal validation		67	-
		Bootstrap	21	-
		Cross-validation	13	-
		Split-sample	32	-
	External validation		28	-
	Both internal and external validation		15	-
	No validation		0	-

¹Other includes Denmark, Finland, Indonesia, Jordan, Malaysia, the Netherlands, Nigeria, Oman, Peru, Portugal, Russia, Saudi Arabia, Spain, Switzerland, Taiwan (China), 12 European countries, and the United Kingdom/Germany.

²Modeling methods and validation categories were not mutually exclusive because some studies applied more than one modeling or validation approach.

LASSO: Least absolute shrinkage and selection operator; SVM: Support vector machine.

Table 2 Methodological characteristics and predictive performance of the included type 2 diabetes mellitus prediction models

Ref.	Country/region	Study design	Sample size	Modeling method	Key predictors	AUC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	Validation	Model format	Target population
Bernabe-Ortiz et al[17], 2016	Peru	Derivation: Cross-sectional national survey (ENINBSC); validation: Prospective cohort (CRONICAS)	Derivation: 2472 (48 events); validation: 2945 (85 events at baseline; 121 incident cases)	Logistic regression with stepwise backward elimination	Age, family history of diabetes, WC	Derivation: 0.73 (0.65–0.78)	Derivation: 69.6%	Derivation: 65.8%	Internal validation: Bootstrap; external validation: CRONICAS cohort	Points-based scoring system	Peruvian adults aged ≥ 35 years
						Validation (cross-sectional): 0.68 (0.62–0.73)	Validation: 70.2%	Validation: 58.9%
						Validation (incident): 0.66 (0.61–0.71)
Liu et al[14], 2016	China	Prospective cohort (Beijing Longitudinal Study of Ageing)	1857 (144 events over 20 years)	Subdistribution hazards model (Fine–Gray) accounting for competing risk of non-diabetes death	Age, BMI, FPG, self-rated health, physical activity	0.76 (0.72–0.80)	0.74	0.65	Internal validation by bootstrapping	Points-based scoring system	Community-dwelling Chinese adults aged ≥ 55 years
Zhang et al[60], 2016	China	Prospective cohort (rural Henan province)	Derivation: 11564 (659 events); validation: 1285 (70 events)	Cox proportional hazards with forward selection	Age, BMI, TG, FPG	Derivation: 0.768 (0.760–0.776)	Derivation: 66.7%	Derivation: 74.0%	Internal validation: Random split (90/10); external validation: Compared with 5 existing models in validation set	Risk score formula	Rural Chinese adults aged ≥ 18 years
						Validation: 0.766 (0.742–0.789)	Validation: 70.0%	Validation: 72.5%
Han et al[49], 2016	China	Derivation: Cross-sectional (HDNNCDS); validation: Prospective cohort (HPHS)	Derivation: 8764 (1096 prevalent cases); validation: 3430 (394 incident cases over 4.2 years)	Multivariable logistic regression with backward selection	Classic model: Age, sex, hypertension, family history of diabetes, alcohol consumption, regular exercise, BMI, WC	Derivation (classic): 0.78 (0.77–0.80)			External validation in independent cohort (HPHS)	Points-based scoring system	Northern Chinese adults aged 20–74 years
						Validation (classic): 0.76 (0.73–0.78)
					Diet model: Above + staple foods, livestock, eggs, potato, fresh fruit, fresh vegetables, dairy products	Derivation (diet): 0.81 (0.79–0.82)	Derivation (diet): 57.6%	Derivation (diet): 84.5%
						Validation (diet): 0.79 (0.77–0.81)	Validation (diet): 49.0%	Validation (diet): 85.8%
Miyakoshi et al[20], 2016	Japan	Retrospective cohort (health examinees)	Total: 4159 (279 events); derivation: 2080 (138 events); validation: 2079 (141 events)	Multivariate Cox proportional hazards with backward stepwise selection	RSFull: Age, family history of diabetes, SBP, sex, smoking, FPG, 2hPG, HbA1c	RSFull: 0.80 (0.76–0.84)	NR	NR	Internal validation (random split 50/50)	Points-based scoring system	Japanese adults undergoing health checkups (baseline non-diabetic)
					RS-2hPG: Age, family history of diabetes, SBP, sex, FPG, HbA1c, TG	RS-2hPG: 0.75 (0.70–0.78)
					RSNI: Age, family history of diabetes, SBP, sex, BMI	RSNI: 0.68 (0.63–0.72)
Zhang et al[55], 2017	China	Prospective cohort (rural Henan, 6-year follow-up)	Total: 15768 (702 events); derivation: 12654; validation: 3114	Cox proportional hazards with forward selection; scores = β × 10 (rounded down)	Drinking tea, BMI, WHtR, TG, FPG	Derivation: 0.791 (0.783–0.799)	69.6% (derivation)	75.6% (derivation)	Internal validation: Random split (80/20)	Points-based scoring system	Rural Chinese adults aged ≥ 18 years
						Validation: 0.731 (0.713–0.748)
Asadollahi et al[18], 2017	Iran	Cross-sectional (populationbased survey)	Derivation: 2158 (254 diabetes, 168 IFG); validation: 404	Multivariate logistic regression with univariate prescreening	Family history of diabetes, place of life, age, hypertension, daily exercise, marital status, sex, WC, smoking, BMI	Derivation: 0.71	Range: 12.9%–30.7% (for diabetes at different score cut-offs)	Range: 72.8%–88% (for diabetes at different score cut-offs)	External validation in independent clinical sample (n = 404)	Points-based scoring system	Iranian adults aged ≥ 25 years
						Validation: 0.872
Hippisley-Cox and Coupland[32], 2017	United Kingdom (England)	Prospective open cohort (QResearch database)	Derivation: 8186705 (178314 events); validation: 2629940 (62326 events)	Cox proportional hazards with fractional polynomials; multiple imputation for missing data	Model A: Age, ethnicity, deprivation, BMI, smoking, family history of diabetes, CVD, treated hypertension, corticosteroids, atypical antipsychotics, statins, schizophrenia/bipolar disorder, learning disability, gestational diabetes, PCOS	Model A: Women: Harrell’s C 0.834 (0.832–0.837); men: 0.814 (0.812–0.816)	Model A (threshold 5.6%): 68.1% (top 20%)	Model A: 81.1% (top 20%)	External validation in separate set of 363 practices (not used in derivation)	Cox regression equations; web calculator (qdiabetes.org)	United Kingdom adults aged 25–84 years without diabetes
					Model B: Model A + FPG	Model B: Women: 0.889 (0.887–0.891); men: 0.866 (0.863–0.868)
					Model C: Model A + HbA1c	Model C: Women: 0.878 (0.875–0.881); men: 0.855 (0.849–0.861)
Chen et al[50], 2017	China	Prospective cohort (Rural Deqing Cohort Study)	Whole cohort: 28251 (387 events over mean 4.2 years); sub-cohort: 3043 (191 events over mean 8.7 years)	Cox proportional hazards; scores based on β-coefficients	Non-invasive model: Age, BMI, family history of diabetes, meat diet, hypertension	Non-invasive: Whole cohort: 0.705; subcohort: 0.638	Noninvasive: 70.4%	Noninvasive: 60.4%	Internal validation: Bootstrap; external validation: Subcohort validation	Points-based scoring system	Rural Chinese adults aged ≥ 18 years
					Plus-FPG model: Age, BMI, family history of diabetes, meat diet, hypertension, IFG	Plus-FPG: Whole cohort: 0.754; sub-cohort: 0.667	Plus-FPG: 63.0%	Plus-FPG: 73.1%
Tian et al[72], 2017	China	Cross-sectional (oil field workers)	1995 (776 dysglycemia)	Multivariable logistic regression	Sex, age, BMI, HHBG, smoking, TG, FPG	0.791 (0.773–0.809)	0.736	0.735	Internal validation; compared with m-FINDRISC)	Pointsbased scoring system	Chinese oil field workers aged 20–64 years
Yatsuya et al[15], 2018	Japan	Prospective cohort (Aichi Workers’ Cohort Study)	3540 (342 events over median 12.2 years)	Cox proportional hazards with backward selection; Framingham point system method	Age, BMI, smoking status, family history of diabetes, TG, FPG	C-index: 0.77	0.66	0.76	Internal validation; cross-validated with Ibaraki model	Points-based scoring system	Japanese male workers aged 35–64 years
Abdullah et al[26], 2017	Malaysia	Casecontrol (nested within Malaysian Cohort Project)	Total: 4077 (cases: 1962; controls: 2115); Malay: 1323, Chinese: 1344, Indian: 1410	Multivariable logistic regression; GRS based on 62 T2DM-associated SNPs	Environmental model: Sex, age, WHR, physical inactivity	Environmental only: Malay: 0.75 (0.72–0.78); Chinese: 0.83 (0.80–0.86); Indian: 0.78 (0.75–0.81); Combined: 0.79 (0.77–0.81)	NR	NR	Internal validation; compared model fit with/without GRS)	GRS combined with environmental risk factors in logistic regression model	Multiethnic Malaysian adults (Malay, Chinese, Indian) aged 35–70 years
					Full model: Environmental + GRS	Environmental + GRS: Malay: 0.77 (0.73–0.80); Chinese: 0.84 (0.82–0.87); Indian: 0.79 (0.76–0.82); Combined: 0.80 (0.78–0.82)
Wang et al[65], 2018	China	Prospective cohort (health checkup attendees)	9735 (1099 events over 3 years)	Cox regression (HR); logistic regression for prediction models (full, stepwise, MFP)	Male: Age, hypertension, 2hPG, FPG, TG	Male (stepwise model): 0.779 (0.759–0.799)	Male: 74%	Male: 67%	Internal validation using 500 bootstrap resamples	Nomogram; points-based scoring system (separate for male and female)	Chinese adults (aged 17–96 years) attending annual health check-ups in Wuhan, China
					Female: Age, FPG, 2hPG, TG	Female (stepwise model): 0.860 (0.836–0.883)	Female: 88%	Female: 69%
Han et al[37], 2018	China	Prospective cohort (Dongfeng-Tongji cohort)	17690 (1390 events over 5 years)	Cox proportional hazards	BMI, FPG, hypertension, hyperlipidemia, current smoking, family history of diabetes	0.751 (0.737–0.764)	0.656	0.729	Internal validation using 90/10 random split (repeated 10 times)	Points-based scoring system	Middle-aged and older Chinese adults (mean age 63.3 years, retired workers)
Sulaiman et al[19], 2018	United Arab Emirates	Cross-sectional (United Arab Emirates National Diabetes and Lifestyle Study)	872 (219 undiagnosed diabetes)	Stepwise forward logistic regression	Age, family history of diabetes, hypertension, BMI, WHR	0.82 (0.78–0.86)	0.754	0.7	External validation in a separate sample of Arab non-UAE citizens residing in the UAE (n = 631)	Pointsbased scoring system	United Arab Emirates citizens aged ≥ 18 years (without known diabetes)
Hu et al[83], 2018	Japan	Prospective cohort (J-ECOH Study)	Total: 46198 (3385 events over 7 years); derivation: 30500 (2216 events); validation: 15698 (1169 events)	Cox proportional hazards with backward selection	Noninvasive: Sex, age, BMI, abdominal obesity, smoking, hypertension	Noninvasive: 0.73 (0.72–0.74)	NR	NR	Internal validation (random split 2/3-1/3)	Cox regression equations; risk calculators and charts	Japanese employees aged 30–59 years
					Invasive: Age, BMI, smoking, hypertension, dyslipidemia, FPG, HbA1c	Invasive: 0.89 (0.89–0.90)
Félix-Martínez and Godínez-Fernández[84], 2018	Mexico	Cross-sectional (National Health and Nutrition Surveys)	Model 1 (2006): 8743 (258 UDM); model 2 (2012): 5103 (63 UDM)	Multivariable logistic regression with backward stepwise selection (AIC-based, 10-fold CV)	Model 1: Age, WC, SBP	Model 1: 0.73 (validation)	Model 1: 74%	Model 1: 62%	external validation in NHS 2000 (n = 28913)	Logistic regression equation; interactive web applications	Mexican adults aged ≥ 20 years
					Model 2: Age, WC, height, family history of diabetes	Model 2: 0.71 (validation)	Model 2: 76%	Model 2: 55%
Kim et al[77], 2019	Korea	Prospective cohort (KoGES)	Clinical model: 7912 (1345 events); integrated model: 6818 (1162 events)	Cox proportional hazards with backward elimination (clinical) + forward selection (SNPs)	Clinical model: Age, sex, FBS, ALT, γ-GTP, HDL, TG, SBP, BMI, parental history of DM	Clinical model (validated C-index): 5-year 0.744, 7-year 0.732, 10-year 0.732	NR	NR	Internal validation (bootstrap 200 iterations)	Cox regression equations; points-based scoring system	Korean adults aged 40–69 years (urban/rural)
					Integrated model: Above + 5-SNP point score	Integrated model (validated C-index): 5-year 0.747, 7-year 0.736, 10-year 0.738
Zhang et al[51], 2019	China	Crosssectional (community-based)	1432 (142 newly diagnosed diabetes)	Multivariable logistic regression	Age, family history of diabetes, hypertension, WC, BMI, smoking, vegetable intake, fruit intake	0.758 (0.714–0.803)	0.831	0.6	Internal validation	Pointsbased scoring system	Chinese communitydwelling adults aged 18–60 years without known diabetes
Wang et al[38], 2019	China	Prospective cohort (3-year followup)	Training: 5557 (595 events); validation: 1870 (206 events)	Multivariable logistic regression with stepwise selection	Female: Age, BMI, FPG, TG	Training: Female 0.863 (0.837–0.888); male 0.751 (0.729–0.774)	Training: Female 82.6%; male 73.8%	Training: Female 79.1%; male 65.7%	Internal validation (500 bootstrap); temporal validation using later cohort	Nomogram	Central Chinese adults attending annual health check-ups
					Male: Age, BMI, FPG, HDL-C, TG	Validation: Female 0.847 (0.801–0.892); male 0.755 (0.717–0.794)	Validation: Female 90.2%; male 63.3%	Validation: Female 66.7%; male 76.0%
Kraege et al[41], 2019	Switzerland	Prospective cohort (CoLaus/PsyCoLaus)	5277 (405 events over mean 10.9 years)	Logistic regression with bivariate prescreening	Age, sex, WC, family history of diabetes, hypertension, physical inactivity	0.772 (0.752–0.792)	0.605	0.771	External validation in 3 cohorts: EPIC-Europe (n = 26270), Tlalpan 2020 (Mexico, n = 1076), Shahedieh (Iran, n = 904)	Points-based scoring system	European adults aged 35–75 years, generalizable to populations with different diabetes prevalence
Xie et al[80], 2019	United States	Cross-sectional (BRFSS 2014)	138146 (20467 T2DM)	Multiple machine-learning methods: SVM, decision tree, logistic regression, random forest, neural network, Naïve Bayes	Age, BMI, income, marital status, race/ethnicity, sleep duration, check-up frequency, physical activity, smoking, etc. (27 variables)	Neural network: 0.795	Decision tree: 51.6% (highest)	Neural network: 90.2% (highest)	Internal validation (random split 2/3 training, 1/3 test)	Machine learning models	United States adults aged ≥ 30 years (Behavioral Risk Factor Surveillance System)
						Logistic regression: 0.793
						Linear SVM: 0.781
						Rbf SVM: 0.779
						Random forest: 0.761
						Naïve Bayes: 0.760
						Polynomial SVM: 0.759
						Decision tree: 0.718
Pei et al[82], 2019	China	Cross-sectional (retrospective, health checkup data)	10436 (1570 with diabetes)	Decision tree (J48 algorithm, C4.5)	Age, BMI, family history of diabetes, work-related stress, physical activity, salty food preference, hypertension, sex, history of CVD/stroke	0.948	0.942	94.2% (accuracy)	Internal validation (random split 70/30)	Decision tree with 19 IF-THEN rules	Chinese adults undergoing annual health check-ups in Shenyang, China
Wang et al[29], 2020	Singapore	Nested case-control within prospective cohort (Singapore Chinese Health Study)	485 cases, 485 controls (matched on age, sex, dialect, date of blood collection)	Weighted biomarker score based on logistic regression β coefficients; 10-fold cross-validation	TG/HDL ratio, ALT, ferritin, adiponectin (all in quartiles)	Base model + biomarker score: 0.83 (0.81–0.86); increment from base model (AUC 0.81) significant (P = 0.002)	NR	NR	Internal validation (10-fold cross-validation)	Weighted biomarker score	Singapore Chinese adults aged 45–74 years
Wu et al[76], 2019	China	Crosssectional (Shanghai Nicheng Cohort)	15309 (2344 prevalent undiagnosed diabetes)	Multivariable logistic regression with backward stepwise selection	Age group, family history of diabetes, hypertension, overweight/obesity (BMI ≥ 25 kg/m2), central obesity (WC ≥ 90/85 cm)	Exploratory: 0.654 (0.637–0.670)	0.655	0.587	Internal validation (random split 50/50)	Pointsbased scoring system	Middleaged and elderly Chinese (45–70 years) in suburban Shanghai
						Validation: 0.669 (0.653–0.686)
Arellano-Campos et al[67], 2019	Mexico	Prospective cohort (metabolic syndrome cohort)	6144 (331 events over median 2.4 years)	Cox proportional hazards; points based on β-coefficient categories	Office-based model: Age, family history of diabetes, WHtR, hypertension, BMI	Office-based model: C-statistic 0.656 (after cross-validation)	NR	NR	Internal validation (k-fold cross-validation, bootstrap)	Points-based scoring system	Middle-aged Mexican adults (≥ 20 years, urban settings)
					Biochemical model: Age, TG, FPG, hypertension, abdominal obesity	Biochemical model: C-statistic 0.741 (after cross-validation)
Cai et al[23], 2020	China	Retrospective cohort (Rich Healthcare database)	Total: 1688 (103 events); derivation: 1273 (81 events); validation: 415 (22 events)	LASSO regression for feature selection; multivariate Cox regression	Age, BMI, FPG, TC	Derivation: AUC 0.878 (0.861–0.895), C-index 0.862 (0.830–0.894)	NR	NR	Internal validation (random split 75/25); 1000 bootstrap resampling	Nomogram (continuous variables; no simplified pointsbased score)	Chinese adults with hypertension (aged ≥ 20 years) without baseline diabetes
						Validation: AUC 0.855 (0.836–0.874), C-index 0.841 (0.817–0.865)
Bahijri et al[85], 2020	Saudi Arabia	Cross-sectional (communitybased, stratified 2-stage cluster sampling)	1403 (328 with dysglycemia: 69 diabetes, 259 prediabetes)	Multivariable logistic regression; points assigned based on β-coefficient categories	Sex, age, WC, history of hyperglycemia, family history of diabetes	0.76 (0.73–0.79)	Approximately 70% (at cutoff 5 or 6)	Approximately 70% (at cutoff 5 or 6)	Internal validation; model performance assessed on development sample	Pointsbased scoring system (range 0–15)	Saudi adults aged ≥ 20 years without known diabetes
Hu et al[86], 2020	China	Prospective cohort (Dongfeng-Tongji cohort)	4833 (171 events over 4.6 years)	Cox proportional hazards; restricted cubic splines; Youden index for optimal cut-offs	Age, sex, smoking, physical activity, BMI, TG, FPG	0.75 (0.70–0.81)	44.9% (BMI ≥ 24 kg/m2)	75.4% (BMI ≥ 24 kg/m2)	Internal validation	Cox regression model (no simplified pointsbased score)	Chinese adults aged ≥ 50 years without major underlying diseases (relatively low-risk population)
							46.3% (TG ≥ 1.1 mmol/L)	67.6% (TG ≥ 1.1 mmol/L)
							52.9% (FPG ≥ 5.89 mmol/L)	81.7% (FPG ≥ 5.89 mmol/L)
Liu et al[58], 2020	China	Prospective cohort (Beijing Health Management Cohort)	43404 (2623 events over median 6.8 years)	Logistic regression with forward selection	Age, education, parental history of diabetes, WHR, BMI, sleep duration, FPG	Comprehensive nomogram: C-index 0.74 (0.71–0.76) in test set	NR	NR	Internal validation (bootstrap); random split (80/20)	Nomogram (points-based scoring system; risk groups defined)	Chinese adults aged 18–80 years from urban Beijing
						Concise nomogram: C-index 0.73 (0.71–0.75)
Wilkinson et al[61], 2020	United States	Prospective cohort (REGARDS, ARIC)	Development: 12043 (1602 events) over approximately 10 years; validation: 9710 (927 events)	Bayesian logistic regression with weakly informative Cauchy priors	Age, sex, race, BMI, TG, HDL, SBP, DBP, blood glucose	Development (REGARDS): 0.79 (0.78–0.80)	NR	NR	External validation in ARIC (independent cohort)	Bayesian logistic regression equation; probability calculation formula provided	United States Black and White adults aged ≥ 45 years (REGARDS) and 45–64 years (ARIC)
						External validation (ARIC): 0.85 (0.83–0.86)
Zhang et al[78], 2020	China	Cross-sectional (Henan Rural Cohort)	36652 (3356 T2DM)	Multiple machine-learning methods: LR, CART, ANN, SVM, RF, GBM; SMOTE for class imbalance	60 variables including sociodemographic, lifestyle, anthropometric, laboratory, and medical history variables. Top 10: Sweet flavor, urine glucose, age, heart rate, creatinine, WC, uric acid, pulse pressure, insulin, hypertension	GBM (with laboratory data): 0.872	GBM: NR	GBM: 81.7%	Internal validation (10-fold cross-validation, repeated 100 times)	Machine learning models	Rural Chinese adults aged 18–79 years
						RF: 0.868	RF: NR
						ANN: 0.858	ANN: Highest sensitivity among all models
						LR: 0.841
						SVM: 0.835
						CART: 0.811
						Non-laboratory models ranged from 0.767 to 0.817
Shao et al[36], 2020	China	Prospective cohort (China Health and Nutrition Survey, 2004-2015)	Total: 6023 (349 events over 10 years); derivation: 4498 (257 events); validation: 1525 (92 events)	Multivariable logistic regression; LASSO for feature selection; nomogram	Model A: Age, sex, ethnicity, hypertension, smoking, alcohol, WC, BMI	Model A: 0.788 (0.761–0.816) derivation; 0.818 (0.775–0.861) validation	Model A: 74.7%	Model A: 71.2%	Internal validation: Bootstrap; external validation: Validation in independent provinces	Nomogram (4 separate models with increasing complexity)	Chinese adults aged 20–80 years
					Model B: Model A + education, soft drinks, tea, physical activity, calories, carbohydrates, fat, protein, triceps skinfold, sleep hours	Model B: 0.807 (0.780–0.834) derivation; 0.823 (0.780–0.865) validation	Model B: 77.5%	Model B: 70.8%
					Model C: Model B + LDL, HDL, TC, TG, insulin, FPG, HbA1c	Model C: 0.905 (0.879–0.932) derivation; 0.915 (0.877–0.953) validation	Model C: 84.1%	Model C: 83.0%
					Model D: LDL, HDL, TC, TG, insulin, FPG, HbA1c	Model D: 0.882 (0.853–0.912) derivation; 0.862 (0.813–0.912) validation	Model D: 73.1%	Model D: 92.3%
Wang et al[87], 2020	Japan	Retrospective cohort (DRYAD database, Murakami Memorial Hospital, 2004–2015)	Total: 11113 (373 events); training: 8296 (275 events); validation: 2817 (98 events)	Multivariable Cox regression; nomogram	Age, WC, smoking, fatty liver, FPG, HbA1c	Training: 0.87 (0.85–0.89)	Training: 82%	Training: 79%	Internal validation (random split 75/25)	Nomogram (points-based scoring system)	Japanese adults attending annual health checkups (healthy at baseline)
						Validation: 0.87 (0.86–0.90)	Validation: 87%	Validation: 73%
Yang et al[88], 2020	United States	Crosssectional (NHANES 2011–2016)	Total: 8057 (1336 T2DM); training: 4246 (664 T2DM); test: 1060 (165 T2DM); validation: 2751 (507 T2DM)	Multiple machine-learning methods: LDA, RF, SVM; easy ensemble methods with 100 base models	Age, sex, WC, sagittal abdominal diameter, relative leg length, 60-second pulse, smoking, alcohol, hypertension, family history, general health condition, weight control or loss	LDA ensemble: 0.849 (validation)	LDA ensemble: 81.9%	LDA ensemble: 70.9%	Internal validation (random split 80/20); external validation using NHANES 2015–2016	Machine-learning models	United States adults aged 18–80 years
						RF ensemble: 0.836 (validation)	RF ensemble: 81.3%	RF ensemble: 71.3%
						SVM ensemble: 0.848 (validation)	SVM ensemble: 82.4%	SVM ensemble: 71.4%
Del Razo-Olvera et al[33], 2020	Mexico	Prospective cohort (derivation: Metabolic syndrome cohort; validation: INPer-IMSS cohort)	Derivation: 3161 (147 events over 2.4 years); Validation: 925 (97 events over 2.8 years)	Cox proportional hazards; stepwise selection; points based on β-coefficients (Framingham method)	History of gestational diabetes, BMI, hypertriglyceridemia, FPG	Derivation: 0.75 (0.73–0.80)	73% (at cut-off ≥ 15)	67% (at cut-off ≥ 15)	External validation in independent cohort (INPer-IMSS)	Points-based scoring system	Mexican women of reproductive age (20–49 years)
						Validation: 0.91 (0.87–0.95)
Katsimpris et al[52], 2021	Germany	Crosssectional (KORA FF4 study)	1591 (139 T2DM)	Elastic net-penalized regression with bootstrap (5000 samples); logistic regression	Potatoes, mushrooms, onions and garlic, whipping cream, cookies, soft drinks, coffee, tomato sauce, mayonnaise, xylitol, galactose	0.79 (0.76–0.83)	10%–90% (range across thresholds)	53%–98% (range across thresholds)	Internal validation (5-fold crossvalidation; bootstrap)	Logistic regression equation (coefficients provided)	German adults aged 39–88 years
Rhee et al[34], 2021	Korea	Prospective cohort (NHIS-HEALS)	Total: 335302 (23420 events over 10.4 years); training: 268241; validation: 67061	Deep learning (RNNLSTM); Cox longitudinal summary model for comparison	Age, sex, BMI, SBP, DBP, FPG, TC, hemoglobin, AST, ALT, GGT, proteinuria, smoking, alcohol, exercise, personal history (hypertension, heart disease, stroke, others), family history (hypertension, heart disease, stroke, DM, others)	DL model 5-year: 0.877 (0.869–0.885)	DL 5-year: 81.6%	DL 5-year: 76.5%	Internal validation (random split 80/20)	Deep learning (RNN-LSTM) algorithm	Korean adults aged ≥ 40 years
						DL model 10-year: 0.827 (0.821–0.833)	DL 10-year: 75.1%	DL 10-year: 74.0%
						Cox model 5-year: 0.842 (0.832–0.852)
						Cox model 10-year: 0.807 (0.801–0.813)
Awad et al[24], 2021	Qatar	Mathematical modeling (simulated population based on epidemiological model)	5000 simulated individuals per time point (2020, 2030, 2050)	Logistic regression on simulated samples	Age, sex, obesity, smoking, physical inactivity	2020: 0.79 (0.77–0.80)	2020: 79.0%	2020: 66.8%	External validation using 2012 Qatar STEPwise survey data	Pointsbased scoring system	Qatari adults aged 15–79 years
						2030: 0.78 (0.76–0.79)	2030: 77.5%	2030: 65.8%
						2050: 0.76 (0.75–0.78)	2050: 74.4%	2050: 64.5%
Cai et al[63], 2021	Japan	Retrospective cohort (NAGALA database)	Total: 12940 (201 events); derivation: 9651 (154 events); validation: 3289 (47 events)	LASSO regression for feature selection; multivariate Cox regression	Age, fatty liver, GGT, TG, HbA1c, FPG	Derivation: C-index 0.906 (0.878–0.934), AUC 0.916 (0.889–0.943)	NR	NR	Internal validation (random split 75/25); 1000 bootstrap resampling	Nomogram (continuous variables)	Japanese nonobese adults (BMI < 25 kg/m2) attending health checkups
						Validation: C-index 0.837 (0.760–0.914), AUC 0.829 (0.753–0.905)
Wang et al[89], 2021	China	Crosssectional (national health examination)	Total: 458153 (38335 T2DM); development: 366523 (30758 T2DM); validation: 91630 (7577 T2DM)	LASSO regression for feature selection; multivariable logistic regression	Age, family history of T2D, WC, TC, TG, BMI, HDL-C, hypertension	Male: Dev 0.864 (0.861–0.866), Val 0.865 (0.859–0.871)	Male: 80.1%	Male: 77.7%	Internal validation (random split 80/20)	Nomogram (sex-specific)	Adults in Western Xinjiang, China (aged ≥ 19 years)
						Female: Dev 0.816 (0.813–0.819), Val 0.815 (0.808–0.821)	Female: 77.9%	Female: 70.7%
Cai et al[21], 2021	Japan	Retrospective cohort (NAGALA database)	Total: 2741 (223 events over 8 years); training: 2058 (157 events); validation: 683 (66 events)	LASSO regression for feature selection; multivariate Cox regression	Smoking status, WC, FPG, HbA1c	Training: C-index 0.839 (0.804–0.874), AUC 0.811	Training: 77.5%	Training: 72.4%	Internal validation (random split 75/25); 1000 bootstrap resampling	Nomogram (continuous variables)	Japanese adults with NAFLD attending health check-ups
						Validation: C-index 0.822 (0.777–0.868), AUC 0.805	Validation: 69.9%	Validation: 75.6%
de Sousa Tavares and Jorge[90], 2021	Portugal (Azores)	Cross-sectional (retrospective, primary care data)	6834 (4661 with T2DM)	Binary logistic regression with interaction terms; backward selection	CVD history, hypertension history, sex, age, BMI, TG, glucose	0.863 (0.854–0.872)	NR	NR	Internal validation (bootstrapping)	Logistic regression equation with interaction terms	Azorean adults aged ≥ 18 years
Mustafina et al[39], 2021	Russia	Prospective cohort (HAPIEE)	7739 (915 events over 14 years)	Cox proportional hazards	Full model: Age, BMI, WC, FPG, TG, HDL-C, BP, family history of T2DM	Full model: NR	Full model: Male 76.0%, female 71.7%	Full model: Male 71.5%, female 69.2%	Internal validation; performance assessed on development sample	Points-based scoring system	Russian adults aged 45–69 years
					Simplified model: WC, hypertension, family history of T2DM	Simplified model: 0.71	Simplified model: 74.7%	Simplified model: 60.0%
Deberneh and Kim[91], 2021	Korea	Retrospective cohort (EHRs)	253395 subjects; after preprocessing: 17131 per class for training, 200 per class for testing	Machine learning (LR, RF, SVM, XGBoost, CIM, stacking, soft voting) with ANOVA, χ2, RFE feature selection	FPG, HbA1c, TG, BMI, γ-GGT, age, uric acid, sex, smoking, drinking, physical activity, family history	Test: 0.71–0.73 (accuracy-based, no AUC reported)	Test: 71%–74% (accuracy)	Test: 71%–74% (accuracy)	Internal validation: 10-fold cross-validation	Machine-learning models	Korean adults attending health check-ups
Oh et al[40], 2021	Korea	Prospective cohort (KoGES)	Derivation: 3973 (NR); validation: 1700 (NR)	Multivariable logistic regression; points based on β coefficients	KDR: Age, living location, WC, hypertension, family history of T2D, smoking	KDR: 0.657 (0.626–0.715)	NR	NR	Internal validation (random split 70/30)	Points-based scoring system (sex-specific risk scorecard with 10-year risk table)	Korean adults aged 40–69 years without diabetes
					KDR + FPG: KDR + FPG	KDR + FPG: 0.690 (0.660–0.720)
					KDR + HbA1c: KDR + HbA1c	KDR + HbA1c: 0.746 (0.717–0.775)
Stiglic et al[12], 2021	12 European countries (Austria, Belgium, Denmark, France, Germany, Greece, The Netherlands, Israel, Italy, Spain, Sweden, Switzerland)	Prospective cohort (SHARE survey)	16363 (1360 events over 10 years)	LASSO logistic regression with inverse probability weighting	Global model: BMI, alcohol consumption, physical activity, CASP score, orientation in time, chronic health problems, hypertension, high cholesterol, self-rated health, country indicators (Spain, Israel, Italy)	0.702 (0.698–0.706)	0.642 (0.633–0.650)	0.648 (0.643–0.652)	Internal validation (repeated 10-fold cross-validation with 10 runs)	LASSO logistic regression equation	European adults aged ≥ 50 years without diabetes at baseline
Al-Lawati et al[92], 2022	Oman	Cross-sectional (WHS 2008 for development; Sur Survey 2006 for validation)	Development: 2720 (315 T2DM); validation: 1355 (189 T2DM)	Multivariable logistic regression with backward stepwise elimination	Age, male sex, no education, marital status, urban residence, hypertension, BMI, current tobacco use	Development: 0.80 (0.78–0.82); Validation: 0.74 (0.70–0.78)	Development: 71.1%; Validation: 54.0%	Development: 74.4%; Validation: 79.0%	External validation in independent cohort (Sur Survey 2006)	Points-based scoring system; risk assessment table provided	Omani adults aged ≥ 20 years
Sulistiowati and Pradono[93], 2022	Indonesia	Prospective cohort (Bogor Cohort Study)	4418 (568 events over 6 years)	Multivariable logistic regression	Age, obesity, central obesity, hypertension, physical activity	0.70 (0.675–0.721)	0.766	0.503	Internal validation (split-sample not specified)	Bogor Diabetes Risk Prediction Chart	Indonesian adults aged 25–60 years without diabetes at baseline
Tan et al[48], 2022	Japan	Prospective cohort (NAGALA)	2012 (incident T2DM 6.81%)	LASSO regression; Cox proportional hazards	WC, HbA1c, FPG, HDL, TG, smoking	Training: 0.819 (0.776–0.858); validation: 0.853 (0.820–0.886)	Training: 74.7%; validation: 77.6%	Training: 77.0%; validation: 88.1%	Internal validation (random split 70/30 with 1000 bootstrap resampling)	Nomogram; online calculator	Japanese adults with abdominal obesity
Wang et al[35], 2022	China	Prospective cohort (Guangzhou Biobank Cohort Study)	15934 (1302 events over mean 4.1 years)	Bayesian network with structural expectation maximization algorithm	Hypertension, impaired fasting glucose, WC, age, sex, education, occupation, family income, smoking, drinking, physical activity, daytime napping, snoring, self-rated health, heart rate, lipid-lowering drugs, coronary heart disease history, family history of diabetes, BMI, WHR, TG, HDL-C, LDL-C	0.746 (0.732–0.760)	0.727 (0.703–0.752)	0.660 (0.652–0.667)	Internal validation (5-fold cross-validation)	Bayesian network with conditional probability table; post-test probability table for risk combinations	Chinese adults aged ≥ 50 years without diabetes at baseline
Nicolaisen et al[28], 2022	Denmark	Population-based cohort (Danish National Health Survey linked to healthcare registries)	26007 with incident HbA1c-defined pre-diabetes (2449 progressed to T2D within 5 years)	Fine–Gray competing risk survival model with LASSO variable selection	Baseline HbA1c, age, sex, BMI, antihypertensive drug use, pancreatic disease, cancer, self-reported unhealthy diet, doctor’s advice to lose weight/change diet, lack of social support, good self-rated health	72.7 (71.2–74.3)	68.3 (63.9–72.7)	66.3 (65.4–67.1)	Internal validation (random split 80/20)	Fine–Gray competing risk regression model	Danish adults aged ≥ 30 years with incident HbA1c-defined pre-diabetes
Zhao et al[44], 2022	China	Prospective cohort (Rural Chinese Cohort Study)	9860 (653 events over median 6.01 years)	Cox proportional hazards	Age, sex, BMI, WC, SBP, family history of diabetes	0.675 (0.666–0.684)	72.6% (at cut-off 24)	53.6% (at cut-off 24)	External validation of existing New Chinese Diabetes Risk Score in independent cohort	New Chinese Diabetes Risk Score: Points-based scoring system	Chinese rural adults aged 20–74 years without diabetes at baseline
Sadek et al[94], 2022	Qatar	Cross-sectional (Qatar Biobank)	Development: 1660 (169 T2DM); Validation: 930 (61 T2DM)	LASSO logistic regression	Age, sex, WHR, history of hypertension, history of hyperlipidemia, education level	Development: 0.870 (0.843–0.896); validation: 0.815 (0.765–0.864)	Development (cut-off 26): 84.6%; Validation: NR	Development: 76.2%; validation: NR	External validation in independent QBB cohort (n = 930)	Qatari Diabetes Mellitus Risk Score: Points-based scoring system	Qatari adults aged 18–89 years
Edlitz and Segal[16], 2022	United Kingdom	Prospective cohort (United Kingdom Biobank) with external validation in Israeli Clalit cohort	Development: 44709 (1.79% incident T2D over mean 7.3 years); validation: 17132 (4.1% T2D)	Logistic regression; Cox regression; scorecard based on β coefficients	Anthropometric model: Age, sex, weight, height, WC, hip circumference, BMI, WHR	Anthropometric: 0.81 (0.77–0.84)	NR	NR	Internal validation (train 56%/val 24%/test 20%); external validation of 4-blood-test model only	Scorecard (points-based); logistic regression; Cox regression	United Kingdom adults aged 40–69 years without diabetes at baseline
					Four-blood-test model: Age, sex, HbA1c, GGT, HDL, TG	Four blood test: 0.87 (0.85–0.90); external validation: 0.75 (0.74–0.75)
Liu et al[22], 2022	China	Retrospective cohort (health screening data, Wuhan)	127031 (8298 incident T2D over 2 years)	LASSO feature selection; machine-learning algorithms: LR, DT, RF, XGBoost	Age, sex, education, marital status, hypertension, exercise, current smoking, current drinking, WC, SBP, FPG, TC, TG, HDL-C, LDL-C, ALT, AST, TBIL, Scr, BUN, uric acid	XGBoost: 0.7805; RF: 0.7772; LR: 0.7601; DT: 0.7280	XGBoost: 0.6452; RF: 0.6428; LR: 0.6320; DT: 0.5821	XGBoost: 0.7577; RF: 0.7524; LR: 0.7636; DT: 0.7633	Internal validation (random split 80/20)	Machine learning models (no simplified scoring system); SHAP for feature importance	Chinese adults aged ≥ 65 years without diabetes at baseline
Martínez-Hervás et al[95], 2022	Spain	Prospective cohort (Di@bet.es Study)	2408 (156 incident T2D over 7.5 years)	CHAID decision tree algorithm	FPG, fasting TG, age, sex, BMI	0.764 (0.722–0.807) for FPG alone; model correctly classified 93.5%	NR	NR	Internal validation (cross-validation)	CHAID decision tree diagram with risk stratification nodes	Spanish adults aged ≥ 18 years without diabetes at baseline
Dong et al[57], 2022	China (Hong Kong)	Cross-sectional (Hong Kong Population Health Survey 2014/15)	1857 (280 newly detected DM/pre-DM)	Logistic regression (AIC stepwise)	Age, BMI, WHR, smoking, sleep duration, vigorous recreational activity time, fruit consumption	LR: 0.812 (0.769–0.853)	LR at 15% threshold: 77.6%	LR: 68.1%	Internal validation (random split 2/3 development, 1/3 validation)	Logistic regression equation; online risk calculator	Chinese adults aged 18–84 years without self-reported DM/pre-DM or major chronic diseases
				XGBoost with isotonic calibration	Age, BMI, WHR, SBP, WC, sleep duration, smoking, vigorous recreational activity time	XGBoost: 0.822 (0.779–0.863)	XGBoost at 15% threshold: 72.4%	XGBoost: 77.9%		XGBoost model with SHAP interpretation; online risk calculator
Tong et al[75], 2022	China	Retrospective cohort (Beijing Health Management Cohort)	44852 (2912 incident T2D over median 3.58 years)	Multivariable Cox regression	Sex, age, BMI, FPG, hypertension, dyslipidemia, sCr	Dynamic nomogram: 0.909 (0.897–0.920) (10-fold CV)	NR	NR	Internal validation (random split 70/30); 10-fold cross-validation	Dynamic nomogram	Urban Chinese current or retired employees aged 16–68 years without diabetes at baseline
Boersma et al[59], 2022	Netherlands	Prospective cohort (Lifelines)	Derivation: 58377 (2146 new diabetes)	Multivariable logistic regression	Model 1: Age group, BMI group, SAF group	Model 1: 0.783 (0.774–0.793)	Model 1: 78	Model 1: 66	Internal validation (bootstrapping); external validation comparison to concise FINDRISC model	Points-based scoring system	Dutch general population adults (aged 25–50 years and their family members)
					Model 2: Age group, BMI group, SAF group, parental history of diabetes	Model 2: 0.792 (0.783–0.802)	Model 2: 74	Model 2: 70
Wang et al[54], 2023	China	Cross-sectional	Pre-SMOTE: 4106 (149 T2DM); Post-SMOTE: 8013 (4023 T2DM)	BP neural network, logistic regression, SVM, CART, C4.5, DNN	Age, DBP, FPG, TC, TG, HDL-C, LDL-C, drinking, consumption frequency of cereals, potatoes, beans, fruits, eggs, milk, poultry, fish	BPNN: 0.977 LR: 0.962 SVM: 0.911 CART: 0.906 C4.5: 0.888 DNN: 0.845	BPNN: 92.8 LR: 86.0 SVM: 89.0 CART: 84.8 C4.5: 84.9 DNN: 82.9	BPNN: 94.6 LR: 93.0 SVM: 93.3 CART: 93.3 C4.5: 92.7 DNN: 86.1	Internal validation (10-fold cross-validation, random split 70/30)	Machine learning models; logistic regression equation provided	Chinese adults aged ≥ 18 years in Dongguan, China
Zheng et al[46], 2023	China	Prospective cohort (Kailuan Study)	Total: 18384 (5697 events); training: 12870; internal validation: 5514; external validation: 10620 + 8513	Cox regression with 2-step selection (univariate + LASSO-Cox)	Age, BMI, WC, DBP, TG, FPG, fatty liver	Men: 0.741 (0.723–0.760)	NR	NR	Internal validation (random split 70/30) + external validation (2 independent cohorts)	Points-based scoring system; Web calculator	Chinese adults with IFG
						Women: 0.762 (0.720–0.802)
Yonel et al[74], 2023	Germany	Population-based cohort (SHIP-TREND-0 for development; SHIP-START-0 for external validation)	Development: 3339 (329 events); external validation: 2381 (403 events)	Logistic regression with backward selection; Heuristic shrinkage	Age, sex, BMI, smoking status, first-degree relative with diabetes, presence of dental prosthesis, presence of mobile teeth, history of periodontal treatment, probing pocket depths ≥ 5 mm	Development: 0.72 (0.69–0.75)	Point score (development): 77.0 (76.8–77.2)	Point score (development): 51.5 (51.4–51.7)	Internal validation (backward selection with multiple imputation, heuristic shrinkage); external validation in independent cohort	Full model (coefficients); Simplified point-based scoring system	German adults aged ≥ 40 years without known diabetes, attending dental settings
						External validation (full model): 0.69 (0.67–0.72)	Point score (external validation): 79.2 (79.0–79.4)	Point score (external validation): 49.9 (49.8–50.0)
						External validation (point score): 0.69 (0.66–0.71)
Hu et al[62], 2023	China	Retrospective cohort	Total: 184188 (19699 events); training: 92177; validation: 92011	LASSO regression (compared with full Cox, stepwise Cox, MFP, XGBoost)	Age, BMI, FPG, TG, SBP, Scr	Training: 0.7341 (0.7290–0.7392)	Training: 68.00	Training: 67.13	Internal validation (random split 50/50)	Nomogram; Risk prediction formula	Chinese adults aged ≥ 20 years without prediabetes at baseline
						Validation: 0.7336 (0.7285–0.7387)	Validation: 65.35	Validation: 69.91
Yang et al[96], 2024	China	Prospective cohort (physical examination center)	Total: 4602 (760 events); training: 3221; test: 1381	Logistic regression with stepwise selection	Age, sex, BMI, FPG, HDL-C, LDL-C, fatty liver, liver dysfunction (ALT > 50 U/L and AST > 40 U/L)	Training: 0.787 (0.765–0.808)	NR	NR	Internal validation (random split 70/30; 1000 bootstrap resampling)	Nomogram; logistic regression equation	Chinese adults aged ≥ 20 years with newly diagnosed prediabetes
						Test: 0.800 (0.770–0.829)
Ojurongbe et al[27], 2024	Nigeria	Case-control (hospital-based)	Total: 444 (172 cases, 272 controls); training: 312; test: 132	Penalized logistic regression with nested cross-validation and sequential backward feature selection	WHR, TG, catalase, atherogenic indices of plasma	Training: 0.99 (0.97–1.00)	Training: 96	Training: 93	Internal validation (10-fold nested cross-validation with 20 repetitions)	Web-based application; logistic regression equation	Nigerian adults aged > 18 years attending a diabetes clinic
						Test: 0.94 (0.89–0.99)	Test: 94	Test: 88
Hafezi et al[68], 2024	Iran	Prospective cohort (MASHAD study)	Total: 6710 (1256 events); training: 5032; test: 1678	Logistic regression; decision tree (CHAID)	Model A: BMI, VAI, LAP, abdominal volume index	Model A (LR): Approximately 0.67	NR	Model A (train): 99%; model A (test): 100%	Internal validation (random split 75/25)	Logistic regression equations; Decision tree with 14 IF-THEN rules	Iranian adults aged 35–65 years without diabetes at baseline
					Model B: Model A + age, sex, physical activity level, anxiety, depression, smoking, dietary composition, energy intake	Model B (LR): Approximately 0.70		Model B (train): 99%; model B (test): 100%
Xu et al[97], 2024	Japan	Prospective cohort (JPHC Study)	Development: 10986 (707 events); external validation: 11345 (673 events)	Logistic regression with backward selection	Model 1 (non-invasive): Sex, BMI, family history of DM, DBP	Model 1: 0.643	NR	NR	Internal validation (bootstrap, 1000 iterations); internal –external cross-validation (leave-one-area-out); external validation (J-ECOH Study)	Logistic regression equations; Excel spreadsheet for risk calculation	Japanese adults aged 46–75 years without diabetes at baseline
					Model 2 (invasive): Family history of DM, HbA1c	Model 2: 0.786
					Model 3 (invasive): Family history of DM, HbA1c, FPG	Model 3: 0.845
Lin et al[70], 2024	China	Retrospective cohort	Total: 2235 (99 events); training: 1564; validation: 671	LASSO logistic regression	Age, sex, BMI, FPG, TG, ALT, ALT/AST, BUN, Hb	Training: 0.824 (0.765–0.883)	NR	NR	Internal validation (random split 70/30; 1000 bootstrap resampling)	Dynamic online nomogram; logistic regression equation	Chinese older adults (≥ 60 years) with normal FBG
						Validation: 0.809 (0.732–0.886)
Kawasoe et al[64], 2025	Japan	Retrospective cohort	Total: 31084 (1548 events); derivation: 15542; validation: 15542	Logistic regression	Model 1 (invasive): Age, sex, BMI, FPG, HbA1c	Model 1: 0.89	Model 1: 81	Model 1: 81	Internal validation (random split 50/50)	Logistic regression equations	Japanese adults aged 30–69 years without diabetes at baseline
					Model 2 (non-invasive): Age, sex, BMI, hypertension	Model 2: 0.71	Model 2: 75	Model 2: 54
Xie et al[13], 2025	United Kingdom/Germany	Prospective cohort (United Kingdom Biobank; ESTHER)	Development: 60362 (2477 events); internal validation: 25870 (1059 events); external validation: 4383 (495 events)	LASSO-Cox regression with bootstrap (1000 resamples)	Clinical CDRS: Age, sex, BMI, smoking, family history of diabetes, antihypertensive medication, steroid medication, HbA1c; plus 11 metabolites (glucose, citrate, lactate, pyruvate, 3-hydroxybutyrate, acetate, glutamine, tyrosine, IDL-CE-pct, M-LDL-TG-pct, LA-pct)	Clinical CDRS: 0.815 (0.800–0.829)	NR	NR	Internal validation (random split 70/30); external validation (ESTHER cohort)	Cox regression equations	European adults (United Kingdom and Germany) aged 50–69 years without diabetes at baseline
						United Kingdom Biobank-DRS (full): 0.834 (0.820–0.847)
						United Kingdom Biobank-DRS (concise): 0.830 (0.815–0.844)
Wang et al[98], 2025	Japan	Prospective cohort (NAGALA)	Total: 15464 (262 events)	Cox proportional hazards regression	Age, WC, ALT, HbA1c, FPG, fatty liver, current smoking, heavy alcohol consumption, HDL-C	Combined model: 0.90 (0.88–0.92)	86.3	82.8	Internal validation (not specified)	Risk score combining 9 factors	Japanese adults without diabetes at baseline (mean age 43.7 years)
Shahraki et al[66], 2025	Iran	Prospective cohort (Isfahan Diabetes Prevention Study)	Total: 1765 (diabetes: 7.4%; prediabetes: 32.6%)	Cox proportional hazards regression with optimal cutoff categorization	Diabetes Model 1: WHR, HbA1c, OGTT-AUC, lipid index	Diabetes model 1: 0.71 (0.66–0.77)	Diabetes model 1: 78	Diabetes model 1: 52	Internal validation (ROC analysis; compared with FINDRISC)	Points-based scoring system	First-degree relatives of Iranian patients with type 2 diabetes, with normal glucose tolerance at baseline
					Diabetes model 2: WHR, HbA1c, FPG, lipid index	Diabetes model 2: 0.69 (0.63–0.74)	Diabetes model 2: 77	Diabetes model 2: 52
Chee et al[73], 2025	Singapore	Cross-sectional	1074 (65 confirmed diabetes, 83 confirmed prediabetes)	Logistic regression with stepwise variable selection	Model IV: BMI ≥ 23 kg/m2, family history of DM, smoking, stage III/IV periodontitis	Model IV: 0.717 (0.689–0.744)	Model IV: 71	Model IV: 62	Internal validation	Clinical risk model based on ORs; risk stratification using 4 simple factors	Asian dental patients (Singapore) aged ≥ 21 years without prior diabetes diagnosis
					Model V: BMI ≥ 23 kg/m2, family history of DM, smoking, severe periodontitis	Model V: 0.721 (0.693–0.748)	Model V: 71	Model V: 60
Munekawa et al[47], 2025	Japan	Retrospective cohort (Panasonic Cohort)	Development: 72124 (5133 events); external validation: 12885 (712 events)	Cox proportional hazards model with interval censoring; natural cubic splines; bootstrap variable selection	Age, sex, BMI, SBP, log-TG, HDL, log-ALT, FPG, weight gain, smoking status	Internal validation (optimism-corrected C-index): 0.877 (0.852–0.882)	NR	NR	Internal validation (2000 bootstrap resamples); external validation (NAGALA cohort)	Cox regression equation	Japanese adults aged ≥ 40 years without diabetes at baseline
						External validation C-index: 0.882
Kiviniemi et al[30], 2025	Finland	Multi-cohort prospective (FINRISK 1997-2012, NFBC1966)	Development: 4926 (96 events); validation: 17385 (397 events)	Logistic regression with penalization (ridge, elastic net, LASSO) and stepwise selection	Baseline model: Age, sex, BMI, WC, systolic/diastolic BP, HDL-C, TC, TG, glucose, current smoking, BP medication, lipid medication, diabetes family history	Baseline model: 0.832–0.913 (validation cohorts)	NR	NR	External validation (4 independent cohorts)	Logistic regression equations	Finnish adults aged 25–74 years without diabetes at baseline
					Extended models: Baseline + 154 NMR metabolites	Best extended model: 0.840–0.915 (validation cohorts)
Alareeki et al[25], 2025	Jordan	Mathematical modeling (simulated population based on epidemiological model)	Derivation: 5000 simulated individuals per time point (2020, 2030, 2050)	Multivariable logistic regression	Age group, sex, obesity, smoking, physical inactivity	2020: 0.79 (0.77–0.80)	2020: 78.7 (77.5–79.8)	2020: 64.2 (62.9–65.6)	Internal validation (compared with survey-based risk score in 2017 data); external validation comparison with regional and international risk scores	Points-based scoring system	Jordanian adults aged 20–79 years without known diabetes
						2030: 0.78 (0.77–0.80)	2030: 76.1 (74.9–77.3)	2030: 68.4 (67.1–69.6)
						2050: 0.77 (0.76–0.79)	2050: 74.2 (73.0–75.4)	2050: 65.6 (64.2–66.9)
Chen et al[99], 2025	China (Taiwan)	Prospective cohort (Taiwan Biobank)	Development: 65112 (2485 events); external validation (United Kingdom Biobank): 86484 (1187 events); external validation (APHS): > 4300000	LASSO logistic regression	HbA1c, blood glucose, PEF, PRS, age, WHR, family history of diabetes, BMI, TG, SGPT, prediabetes, self-reported hyperlipidemia, mixed hyperlipidemia (ICD)	Taiwan Biobank testing: 0.9058 (0.8915–0.9202)	Taiwan Biobank testing: 78.34 (71.65–86.61)	Taiwan Biobank testing: 85.88 (77.08–91.20)	External validation (United Kingdom Biobank, APHS)	Logistic regression coefficients; risk prediction equation	Han Chinese adults (Taiwan) aged ≥ 20 years without diabetes at baseline
						United Kingdom Biobank external validation: 0.8869 (0.8543–0.9112)	United Kingdom Biobank external validation: NR	United Kingdom Biobank external validation: NR
Han et al[31], 2025	China	Meta-analysis + prospective cohort validation	Development: Meta-analysis of 70 studies; validation: 3 cohorts (Hongguang, Bao’an, Kailuan)	Pooled effect estimates from meta-analysis; Framingham method for risk scoring	Sex, age, smoking, central obesity, exercise, BMI, WHtR, hypertension, fatty liver, family history of diabetes, FPG, TG, TyG, HDL-C, uric acid	Hongguang: 0.783	Hongguang: NR	Hongguang: NR	External validation (3 independent Chinese cohorts)	Points-based scoring system (iCARE-DM)	Chinese elderly adults (≥ 50 years) without diabetes at baseline
						Bao’an: 0.766	Bao’an: NR	Bao’an: NR
						Kailuan: 0.741	Kailuan: NR	Kailuan: NR
Zhang et al[53], 2025	China	Prospective cohort (HAELS)	Total: 1127 (107 events); training/validation: Bootstrap resampling	Cox proportional hazards regression with stepwise selection (SLE/SLS = 0.1)	Age, exercise, WC, FPG, TG, SBP, dessert intake	Training: 0.752	NR	NR	Internal validation (bootstrap resampling, 2 iterations)	Nomogram	Chinese older adults (≥ 65 years) with prediabetes (FPG 5.6–7.0 mmol/L)
						Validation: 0.793
Baynouna AlKetbi et al[71], 2025	United Arab Emirates	Retrospective cohort (Weqaya screening program)	Total: 8699; analysis: 6772 (643 events)	Cox proportional hazards regression with stepwise variable selection	HbA1c, random blood glucose, BMI, current smoking, sex, age, mean BP, eGFR percentile, HDL, hypertension at baseline	Full model: 0.837 (0.818–0.856)	75.1	78.1	Internal validation (time-dependent ROC)	Cox regression HRs; risk score based on coefficients	United Arab Emirates adults (Emirati nationals) aged ≥ 18 years without diabetes at baseline
						HBA1C alone: 0.784 (0.761–0.807)
						RBS alone: 0.698 (0.672–0.723)
Satoh et al[45], 2025	Japan	Retrospective cohort (SKDB)	Total: 463248 (78431 events); derivation: 308832 (52152 events); validation: 154416 (26279 events)	Cox proportional hazards regression with stepwise selection	Age, sex, BMI, SBP, TG, HDL-C, LDL-C, AST, γ-GTP, HbA1c, eGFR, urine protein, antihypertensive medication, dyslipidemia medication, heavy drinking, exercise habits	C-index (validation): 0.656 (0.652–0.659)	NR	NR	Internal validation (random split 2:1)	Points-based scoring system	Japanese adults aged ≥ 40 years without diabetes at baseline

2hPG: 2-hour post-load plasma glucose; 95%CI: 95% confidence interval; AIC: Akaike information criterion; ALT: Alanine aminotransferase; ANN: Artificial neural network; ANOVA: Analysis of variance; APHS: Adult Preventive Healthcare Services; ARIC: Atherosclerosis Risk in Communities; AST: Aspartate aminotransferase; AUC: Area under the receiver operating characteristic curve; BMI: Body mass index; BP: Blood pressure; BPNN: Backpropagation neural network; BRFSS: Behavioral Risk Factor Surveillance System; BUN: Blood urea nitrogen; CART: Classification and regression tree; CASP: Control, Autonomy, Self-Realization, and Pleasure; CDRS: Cambridge Diabetes Risk Score; CHAID: χ²d Automatic Interaction Detector; CIM: Confusion matrix-based classifier integration approach; CoLaus: Cohorte Lausannoise; CRONICAS: CRONICAS Cohort Study; CV: Cross-validation; CVD: Cardiovascular disease; DBP: Diastolic blood pressure; DL: Deep learning; DM: Diabetes mellitus; DNN: Deep neural network; DRS: Diabetes risk score; DT: Decision tree; eGFR: Estimated glomerular filtration rate; EHR: Electronic health record; ENINBSC: National Survey of Nutritional and Biochemical Indicators for Noncommunicable Diseases; EPIC-Europe: European Prospective Investigation into Cancer and Nutrition; ESTHER: Epidemiological Study on Potentials for Prevention, Early Detection and Optimized Treatment of Chronic Diseases in the Elderly Population; FBG: Fasting blood glucose; FBS: Fasting blood sugar; FF4: Second follow-up examination of the KORA S4 cohort; FINDRISC: Finnish Diabetes Risk Score; FINRISK: National FINRISK Study; FPG: Fasting plasma glucose; GBM: Gradient boosting machine; GGT: Gamma-glutamyl transferase; GRS: Genetic risk score; HAELS: Health Aging Evaluation Longitudinal Survey; HAPIEE: Health, Alcohol and Psychosocial Factors in Eastern Europe; Hb: Hemoglobin; HbA1c: Glycated hemoglobin; HDL: High-density lipoprotein; HDL-C: High-density lipoprotein cholesterol; HDNNCDS: Harbin Cohort Study on Diet, Nutrition and Chronic Noncommunicable Disease; HHBG: History of high blood glucose; HPHS: Harbin People Health Study; HR: Hazard ratio; iCARE-DM: Integrated Chinese Adapted Risk Evaluation for Diabetes Mellitus; ICD: International Classification of Diseases; IDL-CE-pct: Cholesteryl esters to total lipids in intermediate-density lipoprotein percentage; IFG: Impaired fasting glucose; INPer-IMSS: Instituto Nacional de Perinatología–Instituto Mexicano del Seguro Social; J-ECOH: Japan Epidemiology Collaboration on Occupational Health Study; JPHC: Japan Public Health Center-based Prospective Study; KDR: Korean Diabetes Risk; KoGES: Korean Genome and Epidemiology Study; KORA: Cooperative Health Research in the Region of Augsburg; LA-pct: Linoleic acid to total fatty acids percentage; LAP: Lipid accumulation product; LASSO: Least absolute shrinkage and selection operator; LDA: Linear discriminant analysis; LDL: Low-density lipoprotein; LDL-C: Low-density lipoprotein cholesterol; LR: Logistic regression; m-FINDRISC: Modified Finnish Diabetes Risk Score; M-LDL-TG-pct: Triglycerides to total lipids in medium low-density lipoprotein percentage; MASHAD: Mashhad Stroke and Heart Atherosclerotic Disorder Study; MFP: Multivariable fractional polynomial; NAFLD: Non-alcoholic fatty liver disease; NAGALA: NAfld in Gifu Area, Longitudinal Analysis; NFBC1966: Northern Finland Birth Cohort 1966; NHANES: National Health and Nutrition Examination Survey; NHIS-HEALS: National Health Insurance Service–Health Screening Cohort; NHS: National Health Survey; NMR: Nuclear magnetic resonance; NR: Not reported; OGTT: Oral glucose tolerance test; OGTT-AUC: Oral glucose tolerance test area under the curve; OR: Odds ratio; PCOS: Polycystic ovary syndrome; PEF: Peak expiratory flow; PRS: Polygenic risk score; PsyCoLaus: Psychiatric arm of the CoLaus cohort; QBB: Qatar Biobank; RBS: Random blood sugar; REGARDS: REasons for Geographic And Racial Differences in Stroke; RF: Random forest; RFE: Recursive feature elimination; RNN-LSTM: Recurrent neural network long short-term memory; ROC: Receiver operating characteristic; RS-2hPG: Risk score without 2-hour post-load plasma glucose; RSFull: Full risk score; RSNI: Non-invasive risk score; SAF: Skin autofluorescence; SBP: Systolic blood pressure; Scr: Serum creatinine; SGPT: Serum glutamate pyruvate transaminase; SHAP: SHapley Additive exPlanations; SHARE: Survey of Health, Ageing and Retirement in Europe; SHIP-START-0: Baseline examination of the first Study of Health in Pomerania cohort; SHIP-TREND-0: Baseline examination of the second Study of Health in Pomerania cohort; SKDB: Shizuoka Kokuho Database; SLE: Significance level for entry; SLS: Significance level for stay; SMOTE: Synthetic minority over-sampling technique; SNP: Single nucleotide polymorphism; SVM: Support vector machine; T2D: Type 2 diabetes; T2DM: Type 2 diabetes mellitus; TBIL: Total bilirubin; TC: Total cholesterol; TG: Triglycerides; TyG: Triglyceride–glucose index; UDM: Undiagnosed diabetes mellitus; UKB-DRS: UK Biobank Diabetes Risk Score; VAI: Visceral adiposity index; WC: Waist circumference; WHR: Waist-to-hip ratio; WHS: World Health Survey; WHtR: Waist-to-height ratio; XGBoost: Extreme Gradient Boosting; γ-GGT: Gamma-glutamyl transferase; γ-GTP: Gamma-glutamyl transpeptidase.

Table 3 Frequency of predictor variables across the included models

Category	Predictor	Frequency
Non-modifiable	Age	83
	Sex	48
	Family history of diabetes	41
	Education	7
	Race/ethnicity	5
Obesity	Body mass index	68
	Waist circumference	28
	Waisttohip ratio	12
	Obesity/central obesity	5
	Waist-to-height ratio	3
Lifestyle	Smoking	36
	Physical activity	23
	Dietary factors (e.g., fruit, vegetable, meat, dessert intake, tea)	14
	Alcohol	11
	Sleep duration	5
Glycemic	Glucose (FPG or random glucose)	51
	HbA1c	24
	Post-load glucose measures (2hPG or OGTT-AUC)	4
	Insulin	3
Lipid	Triglycerides	39
	HDL	20
	Total cholesterol	9
	LDL	8
	Hyperlipidemia/dyslipidemia medication use	8
	Lipid accumulation product/lipid indices	3
Blood pressure	Hypertension	34
	SBP	15
	DBP	6
Liver	Liver dysfunction/ALT/AST	8
	Fatty liver	6
Metabolic	Uric acid	4
	Creatinine/eGFR/renal function	3

2hPG: 2-hour post-load glucose; OGTT-AUC: Oral glucose tolerance test area under the curve; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; DBP: Diastolic blood pressure; eGFR: Estimated glomerular filtration rate; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; SBP: Systolic blood pressure.