Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics
Abstract 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 ev...
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| Format: | Article |
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BMC
2025-01-01
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| Series: | Cardiovascular Diabetology |
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| Online Access: | https://doi.org/10.1186/s12933-025-02581-3 |
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| author | Ruijie Xie Teresa Seum Sha Sha Kira Trares Bernd Holleczek Hermann Brenner Ben Schöttker |
| author_facet | Ruijie Xie Teresa Seum Sha Sha Kira Trares Bernd Holleczek Hermann Brenner Ben Schöttker |
| author_sort | Ruijie Xie |
| collection | DOAJ |
| description | Abstract 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. Graphical abstract |
| format | Article |
| id | doaj-art-0833be8d5f5f4eb1a2be21888a255d03 |
| institution | Kabale University |
| issn | 1475-2840 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj-art-0833be8d5f5f4eb1a2be21888a255d032025-01-19T12:09:14ZengBMCCardiovascular Diabetology1475-28402025-01-0124111210.1186/s12933-025-02581-3Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomicsRuijie Xie0Teresa Seum1Sha Sha2Kira Trares3Bernd Holleczek4Hermann Brenner5Ben Schöttker6Division of Clinical Epidemiology and Aging Research, German Cancer Research CenterDivision of Clinical Epidemiology and Aging Research, German Cancer Research CenterDivision of Clinical Epidemiology and Aging Research, German Cancer Research CenterDivision of Clinical Epidemiology and Aging Research, German Cancer Research CenterSaarland Cancer RegistryDivision of Clinical Epidemiology and Aging Research, German Cancer Research CenterDivision of Clinical Epidemiology and Aging Research, German Cancer Research CenterAbstract 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. Graphical abstracthttps://doi.org/10.1186/s12933-025-02581-3Type 2 diabetesMetabolomicsCardiovascular riskPrediction model |
| spellingShingle | Ruijie Xie Teresa Seum Sha Sha Kira Trares Bernd Holleczek Hermann Brenner Ben Schöttker Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics Cardiovascular Diabetology Type 2 diabetes Metabolomics Cardiovascular risk Prediction model |
| title | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| title_full | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| title_fullStr | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| title_full_unstemmed | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| title_short | Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| title_sort | improving 10 year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics |
| topic | Type 2 diabetes Metabolomics Cardiovascular risk Prediction model |
| url | https://doi.org/10.1186/s12933-025-02581-3 |
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