| Title: | Multi-omics signatures of chronic inflammation across immune-related disease states |
| Journal: | Frontiers in Immunology |
| Published: | 12 Feb 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41766899/ |
| DOI: | https://doi.org/10.3389/fimmu.2026.1753156 |
Publication 18296
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Abstract
Introduction: Chronic inflammation and immune cell communication underpin a wide range of chronic diseases, yet population-scale maps integrating systemic inflammatory, metabolic and proteomic signals across multiple disease states are scarce.</p>
Methods: Using UK Biobank, we classified participants into six baseline groups-healthy controls, cancer, autoimmune, infectious, metabolic diseases, and multiple comorbidities. We profiled clinical and hematological indices, NMR-based metabolites and Olink proteomics, and trained four multi-class deep learning models (clinical/inflammatory only; +NMR; +Olink; three-tower multi-omics) with 10-fold cross-validation. Out-of-fold predicted probabilities were combined in a stacking meta-model to derive machine-learning risk scores for "any chronic disease." Shapley value analyses were used to identify key features reflecting systemic immune and metabolic communication. Cause-specific cumulative incidence and Fine-Gray competing-risks models evaluated associations between these risk scores and cancer-related and non-cancer mortality, adjusting for conventional risk factors. To provide biological validation of model-prioritized immune mediators (BAFF [TNFSF13B], GDF15, IL-15 and CD276), we performed in vitro stimulation of healthy-donor PBMCs by ELISA, flow cytometry, and qPCR.</p>
Results: We observed pronounced and pathway-specific heterogeneity of inflammatory markers, lipid-related metabolites and immune-inflammatory proteins across disease groups. Omics-augmented deep learning models outperformed the clinical-only model, and the stacking ensemble achieved the best accuracy, macro-F1 and multi-class AUC. Machine-learning-derived risk scores showed monotonic gradients in cancer and other-cause death and remained independently associated with several cause-specific outcomes. In vitro validation supported myeloid inflammatory inducibility of model-highlighted mediators.</p>
Conclusions: By integrating multi-omics deep learning with competing-risks modelling, this study decodes population-level immune-metabolic communication patterns across chronic disease states, linking shared inflammatory and proteomic signatures to long-term mortality and providing a quantitative framework to support future, mechanism-focused and immunologically informed risk stratification.</p>
14 Keywords
- Adult
- Aged
- Biomarkers
- Chronic Disease
- Female
- Humans
- Immune System Diseases
- Inflammation
- Male
- Metabolomics
- Middle Aged
- Multiomics
- Neoplasms
- Proteomics
5 Authors
- Hui Li
- Xiaolin Xie
- Lirui Tang
- Chuanben Chen
- Jinluan Li
1 Application
| Application ID | Title |
|---|---|
| 194370 | Deep Machine Learning Algorithm for Cancer Risk Prediction Using Genes, Environment, and Clinical Data |
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