Key result
Deep learning multi-omics strongly outperforms single-omics models in identifying clinically distinct T2D subtypes.
Why the study?
Type 2 diabetes is highly heterogeneous, and accurate stratification is essential for understanding its pathophysiology and advancing precision medicine.
Does a deep learning-based multi-omics integration framework improve the identification of Type 2 diabetes subtypes compared to single-omics models?
Observational (n=670)
Yes
Does a deep learning-based multi-omics integration framework improve the identification of Type 2 diabetes subtypes compared to single-omics models?
Absolute Event Rate: 0.67% vs 0.46%
A deep learning-based multi-omics integration framework effectively identifies clinically distinct Type 2 diabetes subtypes, outperforming single-omics models and supporting personalized management.
May refine T2D subtyping beyond single-omics; extends observational classifications but remains hypothesis-generating and should not yet change practice.
Introduction and Objective: Type 2 diabetes (T2D) is highly heterogeneous; accurate stratification is essential for understanding its pathophysiology and advancing precision medicine. We developed a deep learning-based framework to integrate multi-omics data and identify subtypes reflecting molecular and lifestyle factors. Methods: In this observational study of 670 T2D patients from the Seoul National University Hospital cohort, we integrated genetic, proteomic, metabolomic, and longitudinal clinical data with a deep learning model. Subtypes were identified via K-means clustering in a clinically informed latent space. Feature importance was assessed using Integrated Gradients, with validation in the UK Biobank cohort. Results: Among 670 patients (median age 65 years; mean duration 11.9 years), four distinct subtypes were identified. The subtypes showed high concordance with Ahlqvist et al. subtypes (Adjusted Rand Index [ARI] 0.78), with the multi-omics model outperforming the best single-omics (proteomics) model (mean ARI 0.67 vs. 0.46). Proteomic and metabolomic patterns were major discriminators, while genetic variation provided weak background risk. Omics attribution varied: proteomics was dominant in Mild Age-Related Diabetes (MARD; 82%) but less prominent in Severe Insulin-Resistant Diabetes (SIRD; 28%). Key markers included decreased APOM in SIRD, increased LEP in Mild Obesity-Related Diabetes (MOD), and decreased FABP4 in MARD. Complication prevalence differed significantly (p < 0.05); SIRD was most associated with CKD, while Severe Insulin-Deficient Diabetes (SIDD) showed the highest diabetic retinopathy prevalence. Findings were replicated in the UK Biobank. Conclusion: Deep learning-based multi-omics integration effectively identifies clinically distinct T2D subtypes, outperforming single-omics models. This approach provides a comprehensive insight into T2D heterogeneity, supporting personalized management. Disclosure H. Jin: None. J. Lim: None. S. Kwak: None. B. Han: None. M. Kho: None. S. Lee: None. H. Lee: None. M. Moon: None. S. Choi: None. J. Choi: None. Funding The Ministry of Food and Drug Safety, Republic of Korea (23212MFDS202)
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JIN et al. (2026) conducted an observational in Type 2 diabetes (n=670). Deep learning-based multi-omics integration vs. Single-omics (proteomics) model was evaluated on Subtype identification performance (mean Adjusted Rand Index). Deep learning-based multi-omics integration effectively identified clinically distinct Type 2 diabetes subtypes, outperforming the best single-omics model (mean Adjusted Rand Index 0.67 vs. 0.46).
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