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@ARTICLE{Xie:300675,
      author       = {R. Xie$^*$ and S. Sha$^*$ and L. Peng$^*$ and B. Holleczek
                      and H. Brenner$^*$ and B. Schöttker$^*$},
      title        = {{M}etabolomics data improve 10-year cardiovascular risk
                      prediction with the {SCORE}2 algorithm for the general
                      population without cardiovascular disease or diabetes.},
      journal      = {European journal of preventive cardiology},
      volume       = {nn},
      issn         = {2047-4873},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-00869},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:C070#LA:C070# / epub},
      abstract     = {The value of metabolomic biomarkers for cardiovascular risk
                      prediction is unclear. This study aimed to evaluate the
                      potential of improved prediction of the 10-year risk of
                      major adverse cardiovascular events (MACE) in large
                      population-based cohorts by adding metabolomic biomarkers to
                      the novel SCORE2 model, which was introduced in 2021 for the
                      European population without previous cardiovascular disease
                      or diabetes.Data from 187,039 and 5,578 participants 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.
                      LASSO regression with bootstrapping was used to identify
                      metabolites in sex-specific analyses and the predictive
                      performance of metabolites added to the SCORE2 model was
                      primarily evaluated with Harrell's C-index.Thirteen
                      metabolomic biomarkers were selected by LASSO regression for
                      enhanced MACE risk prediction (three for both sexes, six
                      male- and four female-specific metabolites) in the UKB
                      derivation set. In internal validation with the UKB, adding
                      the selected metabolites to the SCORE2 model increased the
                      C-index statistically significantly (P<0.001) from 0.691 to
                      0.710. In external validation with ESTHER, the C-index
                      increase was similar (from 0.673 to 0.688, P=0.042). The
                      inflammation biomarker, glycoprotein acetyls, contributed
                      the most to the increased C-index in both men and women.The
                      integration of metabolomic biomarkers into the SCORE2 model
                      markedly improves the prediction of 10-year cardiovascular
                      risk. With recent advancements in reducing costs and
                      standardizing processes, NMR metabolomics holds considerable
                      promise for implementation in clinical practice.},
      keywords     = {SCORE2 (Other) / cardiovascular risk (Other) / metabolomics
                      (Other) / prediction model (Other)},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40269530},
      doi          = {10.1093/eurjpc/zwaf254},
      url          = {https://inrepo02.dkfz.de/record/300675},
}