| Title: | Large-Scale Proteomics Uncovers Pre-Disease Inflammation-Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes |
| Journal: | Diabetes Obesity and Metabolism |
| Published: | 22 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42331617/ |
| DOI: | https://doi.org/10.1111/dom.71038 |
Publication 20822
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
AIMS: Type 2 diabetes exhibits substantial heterogeneity prior to disease onset, posing challenges for early prevention. This study aimed to identify pre-disease subgroups with distinct risk profiles and develop subgroup-specific prediction models to facilitate early detection and precision intervention.</p>
MATERIALS AND METHODS: Proteomic data from 41 030 participants without diabetes at baseline in the UK Biobank were analysed. Proteins associated with incident type 2 diabetes were identified using multivariable-adjusted Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finite Gaussian mixture model-based clustering was applied to define risk subgroups. Heterogeneity among subgroups was characterised according to proteomic patterns, clinical traits and disease risk. Subgroup-specific prediction models were subsequently developed using Cox regression.</p>
RESULTS: A total of 113 protein markers were identified, stratifying the population into three subgroups: metabolically healthy group (MHG), mild inflammation group (MIG) and Dyslipidemia with inflammation group (DLIG). Compared with MHG, DLIG showed the highest risk of type 2 diabetes (HR = 2.58, 95% CI: 2.34-2.84), followed by MIG (HR = 1.71, 95% CI: 1.49-1.98). Enrichment analysis indicated dysregulation of immune-inflammatory and lipid metabolism pathways in DLIG and MIG. Clinically, DLIG exhibited higher BMI, waist circumference, triglycerides, and C-reactive protein levels, whereas MIG showed moderately elevated C-reactive protein. Subgroup-specific proteomic models outperformed traditional clinical models in predicting diabetes risk (AUC 0.820-0.889 vs. 0.709-0.784; C-index 0.787-0.847 vs. 0.687-0.747).</p>
CONCLUSION: Proteomics-based clustering identified three pre-disease subtypes of type 2 diabetes characterized by inflammation-lipid profiles, improving risk prediction and supporting precision prevention.</p>
10 Authors
- Qiu Xiao
- Yi Zheng
- Hanhan Zhao
- Yucan Li
- Jianwei Wang
- Hao Wang
- Chen Suo
- Yanfeng Jiang
- Xingdong Chen
- Kelin Xu
1 Application
| Application ID | Title |
|---|---|
| 92718 | Deciphering chronic disease - association studies of genome, exposome and phenome |
Enabling scientific discoveries that improve human health