Large‐Scale Proteomics Uncovers Pre‐Disease Inflammation–Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes
Authors' 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.
Study context
- Population
- Participants without diabetes at baseline in the UK Biobank with proteomic data
- Participants
- 41,030 participants
- Exposure
- Proteomic risk subgroups: Dyslipidemia with inflammation group (DLIG) and mild inflammation group (MIG) vs metabolically healthy group (MHG)
- Outcome
- Incident T2D
Methodological detailsproteomics-based clustering, finite Gaussian mixture model, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable-adjusted Cox regression
- Proteomics-based clustering
- Finite Gaussian mixture model
- LASSO and multivariable-adjusted Cox regression
Main finding
As reported in the abstract
Risk of incident T2D
Each group vs MHGHazard ratio (95% CI)
DLIG
2.58(2.34–2.84)
MIG
1.71(1.49–1.98)
Observational data: associations, not effects.
Further findings
| Outcome | Subgroup-specific proteomic models | Traditional clinical models |
|---|---|---|
| Prediction of diabetes risk: subgroup-specific proteomic models vs traditional clinical models | ||
| AUC | 0.820-0.889 | 0.709-0.784 |
| C-index | 0.787-0.847 | 0.687-0.747 |
Prediction of diabetes risk: subgroup-specific proteomic models vs traditional clinical models
- Subgroup-specific proteomic models outperformed traditional clinical models
Pathways and clinical traits
- Dysregulation of immune-inflammatory and lipid metabolism pathways in DLIG and MIG
- DLIG had higher BMI, waist circumference, TG and C-reactive protein; MIG moderately elevated C-reactive protein
Original publication
Large‐Scale Proteomics Uncovers Pre‐Disease Inflammation–Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes. Diabetes Obes Metab. Published 22 June 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →