| Title: | A multi-omics atlas of multisystem complications in type 2 diabetes reveals molecular signatures and improves risk prediction |
| Journal: | Metabolism |
| Published: | 20 May 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42167398/ |
| DOI: | https://doi.org/10.1016/j.metabol.2026.156650 |
Publication 19982
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Abstract
BACKGROUND: Type 2 diabetes (T2D) causes multisystem complications, but an integrated multi-omics framework for cross-system, multi-outcome analysis is lacking. We aimed to comprehensively construct the proteomic and metabolomic atlas of major T2D outcomes and to identify predictive panels that balance performance and clinical feasibility.</p>
METHODS: Among UK Biobank participants with T2D, we established proteomic (n = 3104), metabolomic (n = 28,834), and multi-omics (n = 3059) subcohorts. Using cross-sectional and longitudinal analyses, we systematically evaluated the associations of plasma proteins and metabolites with 19 T2D-related outcomes. Predictive models were developed using machine learning-based molecular feature selection and were compared with the clinical risk model.</p>
RESULTS: The study identified molecular signals that consistently exhibited positive or negative associations across multiple T2D outcomes, revealing shared biological pathways. We also uncovered outcome-specific and heterogeneous molecular signatures. Furthermore, protein-based models substantially outperformed clinical models (median delta C-index = 0.108; range: 0.063-0.143), while combined models achieved the best performance (median delta C-index = 0.109; range: 0.080-0.150) with consistent improvements in reclassification metrics, whereas metabolites provided only modest incremental gains (median delta C-index = 0.027; range: 0.006-0.070). Evaluation across varying selection thresholds identified a simplified panel of 174 proteins that maintained robust predictive performance.</p>
CONCLUSION: This large-scale multi-omics study systematically constructs the molecular atlas of T2D complications, providing new insights into disease biology and potential therapeutic targets. It further defines the predictive value of proteomic and metabolomic profiles and proposes a clinically feasible and practical framework for risk prediction and precision intervention.</p>
12 Authors
- Hongqiang Zhang
- Tong Yue
- Suijian Wang
- Jie Shi
- Mengya Geng
- Haoqiang Zhang
- Lanxin Pan
- Zhaohe Gu
- Lijie Sun
- Minzhe Zhao
- Xueying Zheng
- Jianping Weng
Enabling scientific discoveries that improve human health