| Title: | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| Journal: | Cardiovascular Diabetology |
| Published: | 13 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39806417/ |
| DOI: | https://doi.org/10.1186/s12933-025-02581-3 |
Publication 14521
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Abstract
BackgroundExisting cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients.MethodsData from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Sex-specific LASSO regression with bootstrapping identified significant metabolites. The enhanced model's predictive performance was evaluated using Harrell's C-index.ResultsSeven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly (P = 0.037) from 0.660 to 0.678 in the total sample. In external validation with ESTHER, the C-index increase was higher (+ 0.043) and remained statistically significant (P = 0.011).ConclusionsIncorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine.Graphical abstract</p>
20 Keywords
- Adult
- Aged
- Biomarkers
- Cardiovascular Diseases
- Decision Support Techniques
- Diabetes Mellitus, Type 2
- Female
- Germany
- Heart Disease Risk Factors
- Humans
- Magnetic Resonance Spectroscopy
- Male
- Metabolomics
- Middle Aged
- Predictive Value of Tests
- Prognosis
- Reproducibility of Results
- Risk Assessment
- Sex Factors
- Time Factors
7 Authors
- Ruijie Xie
- Teresa Seum
- Sha Sha
- Kira Trares
- Bernd Holleczek
- Hermann Brenner
- Ben Schöttker
1 Application
| Application ID | Title |
|---|---|
| 101633 | Use of omics data to understand better the etiology of age-related diseases and to improve their risk prediction |
Enabling scientific discoveries that improve human health