Machine learning and deep learning in predicting the risk of diabetic kidney disease: A systematic review and meta-analysis

Table 1 Characteristics of included studies

Ref.	Country	Data source	Recruitment period	Single-center/multi-center	Sample size	Inclusion criteria	Definition of positive outcome
Cho et al[22], 2008	South Korea	EHRs	1996-2005	Single-center	292	Confirmed T2DM; age ≥ 18 years; patients underwent up to 20 clinical tests	20 μg/minute to 200 μg/minute in urinary albumin; no microalbumin or renal failure at diabetes diagnosis; prior evidence of diabetic retinopathy
Dagliati et al[23], 2018	Italy	EHRs	2012-2016	Multi-center	943	Confirmed T2DM; no pre-existing complications; sufficient follow-up records	eGFR < 60 mL/minute/1.73 m2; UACR=30 mg/gram to 299 mg/gram (≥ 2 morning samples)
Rodriguez-Romero et al[24], 2019	United States	ACCORD dataset	2001.01-2001.06; 2003.02-2005.10	Multi-center	10251	Confirmed T2DM by ADA criteria; age ≥ 40 with CVD history; age ≥ 55 with high CVD risk; HbA1c 7.5%-9% (multiple drugs); 7.5%-11% (fewer drugs)	Baseline SCr doubling; eGFR < 60 mL/minute/1.73 m2; UACR ≥ 30 mg/gram or 3.4 mg/mmol; UACR ≥ 300 mg/gram or 33.9 mg/mmol; renal failure
Allen et al[25], 2022	United States	EHRs	2007-2020	Multi-center	111046	Confirmed T2DM by ICD-9/10 codes; age ≥ 18 years; ≥5 years follow-up records; required baseline tests (1-year pre-T2DM); albuminuria/reduced eGFR allowed at baseline; no pre-existing CKD or renal transplant	DKD confirmed by ICD-9/10 codes
Dong et al[26], 2022	China	EMRs	2008.10-2019.12	Single-center	2809	Confirmed T2DM by ADA criteria; age ≥ 18 years; 3 years follow-up records	UACR > 30 mg/gram; protein excretion rate > 150 mg/24 hours; urine dipstick test ≥ 1+; eGFR < 60 mL/minute/1.73 m2
Nicolucci et al[27], 2022	Italy	EMRs	NA	Multi-center	147664	Confirmed T2DM by ICD-9 CM codes	Confirmed DKD by ICD-9 CM codes; no pre-existing complication at baseline
Sabanayagam et al[28], 2023	Singapore	SEED study	2004-2011	Multi-center	1365	Random glucose ≥ 11.1 mmol/L; HbA1c ≥ 6.5% (48 mmol/mol); self-reported antidiabetic medication use; diagnosed with diabetes by a physician	eGFR < 60 mL/minute/1.73 m2; ≥ 25% eGFR decline during follow-up
Hosseini Sarkhosh et al[29], 2023	Iran	EHRs	2012-2021	Single-center	1907	Confirmed T2DM by ADA criteria; 5 years follow-up records; no pre-existing DKD at baseline	UACR ≥ 30 mg/gram; eGFR ≤ 60 mL/minute/1.73 m2
Yun et al[30], 2024	China	EHRs	NA	Multi-center	6040	Confirmed T2DM; age ≥ 18 years; ≥ 7 years follow-up records; ≥ 2 hospital visits per year; no pre-existing DKD at baseline or within 2 years	UACR ≥ 30 mg/gram (≥ 3 months); eGFR < 60 mL/minute/1.73 m2; exclude other kidney diseases
Lin et al[31], 2025	China	EHRs	2011-2023	Single-center	3291	Confirmed T2DM by ICD-9/10 codes; age ≥ 18 years; at least one hospital visit following the end of the baseline time window	eGFR < 60 mL/minute/1.73 m2; UACR ≥ 30 mg/gram over 3 months; DKD confirmed by ICD-9/10 codes; protein in urine dipstick test ≥ 1+
Zou et al[32], 2025	China	EHRs	2014.01-2022.12	Single-center	9572	Confirmed T2DM; no pre-existing DKD; ≥ 3 years follow-up records; exclude glomerular/systemic diseases	UACR ≥ 30 mg/gram in 2/3 tests within 3 months to 6 months; eGFR < 60 mL/minute/1.73 m2 for more than 3 months; renal biopsy was consistent with DKD pathological changes
Dei Cas et al[33], 2025	Italy	DARWIN-Renal	2015.01-2021.09	Multi-center	22379	Confirmed T2DM; age ≥ 18 years; ≥ 1 year follow-up records	5 renal disease severity thresholds established by the KDOQI scale based on the eGFR

EHRs: Electronic health records; T2DM: Type 2 diabetes mellitus; eGFR: Estimated glomerular filtration rate; UACR: Urine albumin-creatinine ratio; ACCORD: Action to Control Cardiovascular Risk in Diabetes; ADA: American Diabetes Association; CVD: Cardiovascular disease; SCr: Serum creatinine; ICD-9/10 codes: International Classification of Diseases, 9th/10th Revision; CKD: Chronic kidney disease; DKD: Diabetic kidney disease; EMRs: Electronic medical records; NA: Not available; SEED: Singapore Epidemiology of Eye Diseases; HbA1c: Glycated hemoglobin; DARWIN-Renal: DApagliflozin Real-World evIdeNce-Renal; KDOQI: Kidney Disease Outcomes Quality Initiative.

Table 2 Characteristics of the best-performing model from each included study

Ref.	Number	Algorithm type	Missing data imputation	Oversample	Train-test split	Validation type	Internal validation method	Best model	Prediction horizon, year	AUC (95%CI)
Cho et al[22], 2008	292	ML	NA	NA	NA	Internal	LOOCV	SVM	1	0.969 (0.941-0.997)
Dagliati et al[23], 2018	943	ML	missForest	Oversampling	NA	Internal	LOOCV	LR	3	0.808 (0.772-0.845)
Rodriguez-Romero et al[24], 2019	10251	ML	NA	SMOTE	66:34	Internal	CV-10	RF	1-1.9	0.730 (0.715-0.745)
Allen et al[25], 2022	111046	ML	missForest	NA	7:1	External	Holdout	XGBoost	5	0.750 (0.734-0.766)
Dong et al[26], 2022	861	ML	missForest	NA	8:2	Internal	CV-5	LightGBM	3	0.815 (0.747-0.882)
Nicolucci et al[27], 2022	147664	ML	Extra-values imputation	SMOTE	Yes	External	CV-10	XGBoost	2	0.970 (0.968-0.972)
Sabanayagam et al[28], 2023	1365	ML	Mean/mode	NA	8:2	Internal	CV-5	Elastic Net	6	0.851 (0.847-0.856)
Hosseini Sarkhosh et al[29], 2023	3444	ML	Mean/mode	NA	8:2	External	RFECV	RF	5	0.790 (0.770-0.820)
Yun et al[30], 2024	6040	DL	NA	NA	7:3	Internal	Holdout	LSTM	5	0.830 (0.807-0.853)
Lin et al[31], 2025	3291	ML	MICE	NA	7:3	Internal	Holdout	SuperLearner	2.53	0.714 (0.673-0.755)
Zou et al[32], 2025	12190	ML	MICE	SMOTE	8:2	Internal	CV-5	LightGBM	3	0.918 (0.906-0.930)
Dei Cas et al[33], 2025	32379	DL	MICE	NA	8:2	External	CV-5	RNN	2	0.887 (0.869-0.904)

AUC: Area under the receiver operating characteristic curve; ML: Machine learning; NA: Not available; LOOCV: Leave-one-out cross-validation; SVM: Support vector machine; LR: Logistic regression; SMOTE: Synthetic minority oversampling technique; CV-10: 10-fold cross-validation; RF: Random forest; CV-5; 5-fold cross-validation; RFECV: Recursive feature elimination with cross-validation; DL: Deep learning; LSTM: Long short-term memory; RNN: Recurrent neural network.

Table 3 Subgroup analysis of pooled area under the receiver operating characteristic curve values in internal validation

Subgroup variable	Level	Studies	AUC (95%CI)	I2	t11	P value	χ2	P value
Region	Overall				7.34	< 0.0001	0.01	0.9422
	Asian	7	0.86 (0.74-0.92)	96.7%
	Western	5	0.86 (0.78-0.91)	99.7%
Study type	Overall	12	0.86 (0.78-0.91)	85.8%	7.34	< 0.0001	0.47	0.4951
	Prospective study	3	0.90 (0.40-0.99)	99.7%
	Retrospective study	9	0.85 (0.74-0.91)	99.7%
Algorithm type	Overall				7.34	< 0.0001	0.00	0.9563
	ML	10	0.86 (0.76-0.92)	99.7%
	DL	2	0.86 (0.23-0.99)	99.7%
Center	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.02	0.8770
	Single-center	5	0.86 (0.66-0.96)	97.7%
	Multicenter	7	0.85 (0.78-0.91)	99.8%
Validation	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.26	0.6107
	Internal validation	8	0.84 (0.74-0.91)	98.5%
	External validation	4	0.86 (0.78-0.91)	99.9%
Prediction horizon	Overall	12	0.86 (0.78-0.91)	99.7%	7.34	< 0.0001	0.92	0.3362
	< 3 years	5	0.90 (0.64-0.98)	99.8%
	≥ 3 years	7	0.83 (0.77-0.88)	98.0%

AUC: Area under the receiver operating characteristic curve; CI: Confidence interval.

Table 4 Predictors in diabetic kidney disease prediction models

Ref.	Number	Demographic	Past history	Laboratory
Cho et al[22], 2008	39	BMI (mean, minimum)	NA	SBP (mean, EST, and initial), DBP (slope and maximum), WBC (initial), WBC (latest), hemoglobin, platelet (slope), platelet (var), platelet (K), platelet (EST), cholesterol (K and EST), AST (K and initial), ALT (initial, minimum, and K), ALP (minimum and latest), creatinine (mean and var), uric acid (var), Na (EST), K (slope and K), triglyceride (EST), HDL-C (var and initial), LDL-C (EST and initial), microalbumin (mean, var, maximum, and minimum)
Dagliati et al[23], 2018	4	BMI	Smoking, hypertension	HbA1c
Rodriguez-Romero et al[24], 2019	18	Age	NA	UAlb at baseline, eGFR at baseline, UCr at baseline, eGFR from baseline to year 1, trig at baseline, FPG at baseline, CPK at baseline, age, eGFR at month 4, K at baseline, LDL at baseline, Chol at baseline, eGFR at month 12, FPG at month 4, FPG from baseline to year 1, trig from baseline to year 1, UACR at baseline, FPG at month 8
Allen et al[25], 2022	15	Age, gender, BMI	Acute kidney injury, chronic heart failure, smoking, drinking	SBP, DBP, blood urea nitrogen, creatinine, eGFR, cholesterol (HDL and LDL), white cell count
Dong et al[26], 2022	8	Age, BMI	NA	Hcy, HbA1c, ALB, eGFR, bicarbonate, LDL
Nicolucci et al[27], 2022	46	Gender, age, height, weight, BMI, waist circumference, diabetes duration	NA	SBP, DBP, ankle/brachial index DX, ankle/brachial index SX, fasting blood glucose, blood glucose after breakfast, blood glucose before dinner, blood glucose before lunch, blood glucose at 11:00 pm, blood glucose after lunch, blood glucose after dinner, pre-prandial blood glucose, post-prandial blood glucose, HbA1c, albuminuria, serum creatinine, creatinine clearance, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, fibrinogen, GGT, ALT, AST, alkaline phosphatase, amylase, CPK, hemoglobin, platelets, BUN, uric acid, glycosuria, urinary amylase, urinary ketones, urinary potassium, urinary sodium, urine creatinine, urine culture
Sabanayagam et al[28], 2023	15	Ethnicity (Malay/Chinese)	Diabetic retinopathy, hypertension	Acetate, diabetic retinopathy, SBP, DHA, GFR-EPI, HbA1c, IDL-CE%, M-HDL-PL%, M-VLDL-PL%, S-HDL-FC%, XL-HDL-CE%, anti-DM meds
Hosseini Sarkhosh et al[29], 2023	6	Diabetes duration	Hypertension, CVD	HbA1c, eGFR, ACR
Yun et al[30], 2024	10	Age	NA	PP, SBP, variabilities of PP and SBP, Scr, HDL-C, HbA1c, TG, variabilities of HbA1c
Lin et al[31], 2025	46	Gender, age	Smoking, drinking	WBC count, neutrophil count, lymph count, mono count, eosinophil count, basophil count, red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, red cell distribution width-coefficient of variation, platelet, mean platelet volume, platelet distribution width, lymph, mono, eosinophil, basophil, ALT, AST, AST/ALT (AST ALT), total bilirubin, creatinine, Ca, total cholesterol, triglyceride, HDL, LDL, prealbumin, HbA1c, D-Dimer, pH, glucose, billing, ketone, obstetric, nitrogen, urology
Zou et al[32], 2025	5	Age	NA	UACR, Cystatin C, eGFR, Neutrophil
Dei Cas et al[33], 2025	34	Gender, age, weight, weight-past, BMI, diabetes duration	CKD	SBP, DBP, SBP in the past, DBP in the past, HbA1c, HbA1c in the past, eGFR, eGFR in the past, AER, ACR > 30, AER in the past, and anti-DM Meds

BMI: Body mass index; NA: Not available; SBP: Systolic blood pressure; EST: Estimated value on the date of prediction; DBP: Diastolic blood pressure; WBC: White blood cell; AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; ALP: Alkaline phosphatase; var: Variance; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; HbA1C: Glycosylated hemoglobin; eGFR, Estimated glomerular filtration rate; UAlb: Uninary albumin; UCr: Urinary creatinine; Trig: Triglycerides; FPG: Fasting plasma glucose; CPK: Creatinine phosphokinase; UACR: Urine albumin-creatinine ratio; Chol: Cholesterol; Hcy: Homocysteine; ALB: Albumin; GGT: Gamma-glutamyl transferase; CVD: Cardiovascular disease; BUN: Blood urea nitrogen; DHA: Docosahexaenoic acid; GFR-EPI: Glomerular filtration rate estimated using; IDL-CE%: Cholesterol esters to total lipids ratio in IDL; M-HDL-PL%: Phospholipids to total lipids ratio in medium HDL; M-VLDL-PL%: Phospholipids to total lipids ratio in medium VLDL; S-HDL-FC%: Free cholesterol to total lipids ratio in small HDL; XL-HDL-CE%: Cholesterol esters to total lipids ratio in very large HDL; AER: Albumin excretion rate; ACR: Albumin-creatinine ratio.