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@ARTICLE{Xie:296215,
      author       = {R. Xie$^*$ and T. Seum$^*$ and S. Sha$^*$ and K. Trares$^*$
                      and B. Holleczek and H. Brenner$^*$ and B. Schöttker$^*$},
      title        = {{I}mproving 10-year cardiovascular risk prediction in
                      patients with type 2 diabetes with metabolomics.},
      journal      = {Cardiovascular diabetology},
      volume       = {24},
      number       = {1},
      issn         = {1475-2840},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2025-00128},
      pages        = {18},
      year         = {2025},
      note         = {#EA:C070#LA:C070#},
      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.},
      keywords     = {Humans / Diabetes Mellitus, Type 2: diagnosis / Diabetes
                      Mellitus, Type 2: epidemiology / Diabetes Mellitus, Type 2:
                      blood / Male / Female / Metabolomics / Middle Aged /
                      Cardiovascular Diseases: epidemiology / Cardiovascular
                      Diseases: diagnosis / Risk Assessment / Aged / Predictive
                      Value of Tests / Biomarkers: blood / Time Factors / Heart
                      Disease Risk Factors / Magnetic Resonance Spectroscopy /
                      Prognosis / Reproducibility of Results / Germany:
                      epidemiology / Decision Support Techniques / Adult / Sex
                      Factors / Cardiovascular risk (Other) / Metabolomics (Other)
                      / Prediction model (Other) / Type 2 diabetes (Other) /
                      Biomarkers (NLM Chemicals)},
      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:39806417},
      doi          = {10.1186/s12933-025-02581-3},
      url          = {https://inrepo02.dkfz.de/record/296215},
}