guest ::
| Home > Publications database > Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics. |
| Home > Publications database > Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics. |
| Journal Article | DKFZ-2025-00128 |
; ; ; ; ; ;
2025
BioMed Central
London
This record in other databases: 
Please use a persistent id in citations: doi:10.1186/s12933-025-02581-3
Abstract: 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.
Keyword(s): Humans (MeSH) ; Diabetes Mellitus, Type 2: diagnosis (MeSH) ; Diabetes Mellitus, Type 2: epidemiology (MeSH) ; Diabetes Mellitus, Type 2: blood (MeSH) ; Male (MeSH) ; Female (MeSH) ; Metabolomics (MeSH) ; Middle Aged (MeSH) ; Cardiovascular Diseases: epidemiology (MeSH) ; Cardiovascular Diseases: diagnosis (MeSH) ; Risk Assessment (MeSH) ; Aged (MeSH) ; Predictive Value of Tests (MeSH) ; Biomarkers: blood (MeSH) ; Time Factors (MeSH) ; Heart Disease Risk Factors (MeSH) ; Magnetic Resonance Spectroscopy (MeSH) ; Prognosis (MeSH) ; Reproducibility of Results (MeSH) ; Germany: epidemiology (MeSH) ; Decision Support Techniques (MeSH) ; Adult (MeSH) ; Sex Factors (MeSH) ; Cardiovascular risk ; Metabolomics ; Prediction model ; Type 2 diabetes ; Biomarkers
; 
; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |
