Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes

doi:10.4239/wjd.122555

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World Journal of Diabetes

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1948-9358

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Cohort Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Diabetes. Sep 15, 2026; 17(9): 122555
Published online Sep 15, 2026. doi: 10.4239/wjd.122555

Table 1 Baseline characteristics of study population, mean ± SD/n (%)/median (interquartile range)

Variable	Training cohort (n = 15228)	Testing cohort (n = 9700)	Validation cohort (n = 2681)
Age (years)	48.75 ± 13.81	46.44 ± 12.43	52.53 ± 12.63
Sex
Male	8935 (58.67)	6142 (63.32)	1945 (72.55)
Female	6293 (41.33)	3558 (36.68)	736 (27.45)
BMI (kg/m²)	25.02 ± 3.49	25.20 ± 3.57	26.52 ± 3.33
SBP (mmHg)	127.80 ± 17.69	127.32 ± 16.74	142.26 ± 19.46
DBP (mmHg)	77.36 ± 11.61	76.55 ± 11.53	86.27 ± 11.91
FBG (mmol/L)	5.85 (5.68, 6.11)	5.66 (5.30, 5.87)	5.88 (5.71, 6.17)
WBC (10⁹/L)	5.71 (4.60, 6.88)	6.06 (5.18, 7.07)	6.12 (5.28, 7.11)
Neutrophil count (10⁹/L)	3.48 (2.83, 4.23)	3.40 (2.76, 4.12)	3.50 (2.89, 4.30)
Lymphocyte count (10⁹/L)	1.99 (1.65, 2.39)	2.00 (1.68, 2.40)	1.90 (1.60, 2.30)
MONO (10⁹/L)	0.34 (0.28, 0.43)	0.40 (0.33, 0.50)	0.40 (0.40, 0.50)
EOS (10⁹/L)	0.14 ± 0.12	0.15 ± 0.13	0.14 ± 0.14
BASO (10⁹/L)	0.03 ± 0.02	0.03 ± 0.03	0.01 ± 0.03
PLT (10⁹/L)	238.68 ± 56.19	243.77 ± 55.88	228.17 ± 57.74
MPV (fL)	8.60 (8.00, 9.50)	9.70 (8.80, 10.50)	8.40 (7.76, 9.15)
PDW (fL)	15.31 ± 1.87	13.48 ± 2.72	16.86 ± 1.12
HGB (g/L)	146.96 ± 14.82	145.28 ± 15.16	145.29 ± 14.44
TC (mmol/L)	5.20 ± 0.93	5.61 ± 0.47	4.89 ± 0.94
TG (mmol/L)	1.34 (0.93, 1.95)	1.34 (0.93, 1.95)	1.60 (1.16, 2.24)
HDL-C (mmol/L)	1.28 (1.09, 1.53)	1.18 (1.01, 1.41)	1.20 (1.01, 1.43)
LDL-C (mmol/L)	2.96 ± 0.77	3.09 ± 0.80	2.63 ± 0.67
ALT (U/L)	21.00 (15.00, 30.00)	23.00 (16.00, 34.00)	20.00 (15.00, 29.00)
AST (U/L)	20.00 (17.00, 24.00)	21.00 (18.00, 25.00)	20.00 (17.00, 24.00)
TBil (μmol/L)	13.25 (10.28, 17.03)	11.73 (9.19, 15.14)	15.20 (12.00, 19.20)
FH of T2DM
Yes	1579 (10.37)	1136 (11.71)	90 (3.36)
No	11891 (78.09)	6535 (67.37)	2213 (82.54)
Missing	1758 (11.54)	2029 (20.92)	378 (14.10)
Progression to T2DM
Yes	2413 (15.85)	914 (9.42)	454 (16.93)
No	12815 (84.15)	8786 (90.58)	2227 (83.07)

BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FBG: Fasting blood glucose; WBC: White blood cell count; MONO: Monocyte count; EOS: Eosinophil count; BASO: Basophil count; PLT: Platelets; MPV: Mean platelet volume; PDW: Platelet distribution width; HGB: Hemoglobin; TC: Total cholesterol; TG: Triglycerides; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TBil: Total bilirubin; FH: Family history; T2DM: Type 2 diabetes mellitus.

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Table 2 Predictive performance of four developed models

C-index (95%CI)	CoxPH	XGB	RSF	GBSA
Training cohort	0.789 (0.778-0.801)	0.803 (0.787-0.819)	0.880 (0.868-0.891)	0.807 (0.795-0.819)
Testing cohort	0.802 (0.783-0.821)	0.806 (0.786-0.826)	0.717 (0.697-0.736)	0.813 (0.793-0.832)
Validation cohort	0.758 (0.730-0.786)	0.756 (0.727-0.785)	0.762 (0.734-0.790)	0.759 (0.731-0.787)

C-index: Concordance index; CoxPH: Cox proportional hazards; XGB: Extreme gradient boosting survival model; RSF: Random survival forest; GBSA: Gradient boosting survival analysis.

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Citation: Fan ZY, Ran XH, Wang N, Zhao TY, Li H, Liu X, Wu J, Yang Z, Chen G, Yang L, Ma X. Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes. World J Diabetes 2026; 17(9): 122555
URL: https://www.wjgnet.com/1948-9358/full/v17/i9/122555.htm
DOI: https://dx.doi.org/10.4239/wjd.122555

Fan ZY, Ran XH, Wang N, Zhao TY, Li H, Liu X, Wu J, Yang Z, Chen G, Yang L, Ma X. Development and validation of an interpretable machine learning model for predicting progression from prediabetes to type 2 diabetes. World J Diabetes 2026; 17(9): 122555 [DOI: 10.4239/wjd.122555]

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