%0 Journal Article
%A Xie, Ruijie
%A Seum, Teresa
%A Sha, Sha
%A Trares, Kira
%A Holleczek, Bernd
%A Brenner, Hermann
%A Schöttker, Ben
%T Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics.
%J Cardiovascular diabetology
%V 24
%N 1
%@ 1475-2840
%C London
%I BioMed Central
%M DKFZ-2025-00128
%P 18
%D 2025
%Z #EA:C070#LA:C070#
%X 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
%K Humans
%K Diabetes Mellitus, Type 2: diagnosis
%K Diabetes Mellitus, Type 2: epidemiology
%K Diabetes Mellitus, Type 2: blood
%K Male
%K Female
%K Metabolomics
%K Middle Aged
%K Cardiovascular Diseases: epidemiology
%K Cardiovascular Diseases: diagnosis
%K Risk Assessment
%K Aged
%K Predictive Value of Tests
%K Biomarkers: blood
%K Time Factors
%K Heart Disease Risk Factors
%K Magnetic Resonance Spectroscopy
%K Prognosis
%K Reproducibility of Results
%K Germany: epidemiology
%K Decision Support Techniques
%K Adult
%K Sex Factors
%K Cardiovascular risk (Other)
%K Metabolomics (Other)
%K Prediction model (Other)
%K Type 2 diabetes (Other)
%K Biomarkers (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39806417
%R 10.1186/s12933-025-02581-3
%U https://inrepo02.dkfz.de/record/296215