Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population

doi:10.4239/wjd.119604

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World Journal of Diabetes

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1948-9358

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World J Diabetes. Jul 15, 2026; 17(7): 119604
Published online Jul 15, 2026. doi: 10.4239/wjd.119604

Table 2 Cluster-specific dysglycemia transitions and intervention effects: Adjusted hazard ratios and cumulative incidence

Variable	n	Events	Unadjusted 2-year CIF (%)	Adjusted 2-year CIF (%)¹	95%CI (adjusted CIF)	aHR²	95%CI³ (aHR)	P value
Progression to diabetes
MARD	104	35	5.8	7.28	4.27-11.10	Reference
MOD	99	17	5.1	3.38	1.70-5.95	0.52	0.29-0.93	0.028^a
SID	115	28	7.0	5.20	2.66-8.49	0.84	0.51-1.39	0.496
SIR	49	19	4.1	8.32	4.69-12.84	0.88	0.50-1.57	0.673
Intervention (yes vs no)						0.52	0.30-0.89	0.017^a
Sex (male vs female)						1.24	0.82-1.85	0.305
Regression to normoglycemia
MARD	104	11	1.9	3.03	1.41-5.17	Reference
MOD	99	21	7.1	6.49	3.58-10.48	2.08	1.00-4.33	0.049^a
SID	115	19	5.2	4.97	2.38-8.28	1.82	0.87-3.84	0.113
SIR	49	9	6.1	5.41	2.09-10.34	1.51	0.63-3.66	0.358
Intervention (yes vs no)						0.63	0.33-1.20	0.159
Sex (male vs female)						0.94	0.56-1.59	0.829
Persistence of prediabetes
MARD	104	58	14.4	15.49	10.99-20.46	Reference
MOD	99	61	14.1	17.50	12.53-22.74	1.18	0.82-1.69	0.380
SID	115	68	22.6	17.21	12.40-22.55	1.25	0.88-1.77	0.222
SIR	49	21	8.2	11.02	6.48-16.28	0.67	0.41-1.11	0.120
Intervention (yes vs no)						0.57	0.40-0.81	0.002^a
Sex (male vs female)						0.90	0.68-1.19	0.459

¹Adjusted 2-year cumulative incidence function from Fine-Gray competing risk model with marginal standardization.

²Adjusted hazard ratio from cause-specific Cox models (reference: Mild age-related dysmetabolism, adjusted for intervention and sex).

³95% confidence interval from 500 bootstrap resamples.

^aP < 0.05.

aHR: Adjusted hazard ratio; CIF: Cumulative incidence function; MARD: Mild age-related dysmetabolism; MOD: Mild obesity-related dysmetabolism; SID: Severe insulin deficiency; SIR: Severe insulin resistance; CI: Confidence interval.

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Citation: Zhang SH, Zhang JP, Song LL, Wu LL, Li ZQ, He YF, Deng RF, Ma WL, Zhang C, Zhang B, Yu LP. Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population. World J Diabetes 2026; 17(7): 119604
URL: https://www.wjgnet.com/1948-9358/full/v17/i7/119604.htm
DOI: https://dx.doi.org/10.4239/wjd.119604

Zhang SH, Zhang JP, Song LL, Wu LL, Li ZQ, He YF, Deng RF, Ma WL, Zhang C, Zhang B, Yu LP. Metabolic feature-based clustering for subtype identification and longitudinal outcome predictions in the Chinese prediabetic population. World J Diabetes 2026; 17(7): 119604 [DOI: 10.4239/wjd.119604]

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