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024 7 _ |a 10.1186/s12933-025-02581-3
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100 1 _ |a Xie, Ruijie
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245 _ _ |a Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics.
260 _ _ |a London
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|b BioMed Central
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520 _ _ |a 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.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.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).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.
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650 _ 7 |a Cardiovascular risk
|2 Other
650 _ 7 |a Metabolomics
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650 _ 7 |a Prediction model
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650 _ 7 |a Type 2 diabetes
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650 _ 7 |a Biomarkers
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650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Diabetes Mellitus, Type 2: diagnosis
|2 MeSH
650 _ 2 |a Diabetes Mellitus, Type 2: epidemiology
|2 MeSH
650 _ 2 |a Diabetes Mellitus, Type 2: blood
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Metabolomics
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Cardiovascular Diseases: epidemiology
|2 MeSH
650 _ 2 |a Cardiovascular Diseases: diagnosis
|2 MeSH
650 _ 2 |a Risk Assessment
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Predictive Value of Tests
|2 MeSH
650 _ 2 |a Biomarkers: blood
|2 MeSH
650 _ 2 |a Time Factors
|2 MeSH
650 _ 2 |a Heart Disease Risk Factors
|2 MeSH
650 _ 2 |a Magnetic Resonance Spectroscopy
|2 MeSH
650 _ 2 |a Prognosis
|2 MeSH
650 _ 2 |a Reproducibility of Results
|2 MeSH
650 _ 2 |a Germany: epidemiology
|2 MeSH
650 _ 2 |a Decision Support Techniques
|2 MeSH
650 _ 2 |a Adult
|2 MeSH
650 _ 2 |a Sex Factors
|2 MeSH
700 1 _ |a Seum, Teresa
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700 1 _ |a Sha, Sha
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700 1 _ |a Trares, Kira
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700 1 _ |a Holleczek, Bernd
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700 1 _ |a Brenner, Hermann
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700 1 _ |a Schöttker, Ben
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773 _ _ |a 10.1186/s12933-025-02581-3
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|t Cardiovascular diabetology
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