| Title: | Use of omics data to understand better the etiology of age-related diseases and to improve their risk prediction |
| Lead Institution: | German Cancer Research Center (DKFZ) |
| Principal investigator: |
Application 101633
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About
A biomarker is a measurable indicator of some biological state or condition. Biomarkers are often measured and evaluated using blood, urine, or soft tissues to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The Nightingale Metabolomics technology measured 250 biomarkers at once in the blood samples of the study participants of the UK Biobank. Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. The cell metabolism changes in mid-life and late-life and some age-related diseases are characterized by high concentrations of specific metabolites in the blood. The best example is diabetes mellitus, which manifests in high concentrations of glucose in blood. For cardiovascular diseases, mainly lipids are important but also inflammatory biomarkers play a role. Inflammation has also been shown to be involved in the development of cancer, dementia, and frailty. Thus, by studying Nightingale Metabolomics, we hope to understand better the physiological mechanisms underlying the aforementioned age-related diseases. We aim to identify metabolites that are strongly associated with specific age-related diseases. These metabolites could be novel targets that are amenable to interventions. Furthermore, these biomarkers could be helpful for the early identification of individuals at high risk of disease development to assign them to prevention programs. The research project is initially planned for 3 years but might be prolonged if promising first results are obtained.3 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14521 | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics | Ruijie Xie (+6) | 2025 | Cardiovascular Diabetology |
| 14778 | Large-Scale Proteomics Improve Risk Prediction for Type 2 Diabetes. | Ruijie Xie (+6) | 2025 | Diabetes Care |
| 14038 | Novel type 2 diabetes prediction score based on traditional risk factors and circulating metabolites: model derivation and validation in two large cohort studies | Ruijie Xie (+5) | 2024 | EClinicalMedicine |
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