Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics
Background: Existing 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 (MA...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 January 2025
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| In: |
Cardiovascular diabetology
Year: 2025, Jahrgang: 24, Pages: 1-12 |
| ISSN: | 1475-2840 |
| DOI: | 10.1186/s12933-025-02581-3 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12933-025-02581-3 Verlag, kostenfrei, Volltext: https://cardiab.biomedcentral.com/articles/10.1186/s12933-025-02581-3 
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| Verfasserangaben: | Ruijie Xie, Teresa Seum, Sha Sha, Kira Trares, Bernd Holleczek, Hermann Brenner and Ben Schöttker |
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| 245 | 1 | 0 | |a Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |c Ruijie Xie, Teresa Seum, Sha Sha, Kira Trares, Bernd Holleczek, Hermann Brenner and Ben Schöttker |
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| 520 | |a Background: Existing 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. Methods: Data 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. Results: Seven 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). Conclusions: Incorporating 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. | ||
| 650 | 4 | |a Cardiovascular risk | |
| 650 | 4 | |a Metabolomics | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a Type 2 diabetes | |
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