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@ARTICLE{Xie:300191,
      author       = {R. Xie$^*$ and T. Vlaski$^*$ and S. Sha$^*$ and H.
                      Brenner$^*$ and B. Schöttker$^*$},
      title        = {{S}ex-specific proteomic signatures improve cardiovascular
                      risk prediction for the general population without
                      cardiovascular disease or diabetes.},
      journal      = {Journal of advanced research},
      volume       = {nn},
      issn         = {2090-1232},
      address      = {Amsterdam ˜[u.a.]œ},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2025-00668},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:C070#LA:C070# / epub},
      abstract     = {Accurate prediction of 10-year major adverse cardiovascular
                      events (MACE) is critical for effective disease prevention
                      and management. Although the SCORE2 model introduced
                      sex-specific algorithms, opportunities remain to further
                      refine prediction.To evaluate whether adding sex-specific
                      proteomic profiles to the SCORE2 model enhances 10-year MACE
                      risk prediction in the large UK Biobank (UKB) cohort.Data
                      from 47,382 UKB participants, aged 40 to 69 years without
                      prior cardiovascular disease or diabetes, were utilized.
                      Proteomic profiling of plasma samples was conducted using
                      the Olink Explore 3072 platform, measuring 2,923 unique
                      proteins, of which 2,085 could be used. Sex-specific Least
                      Absolute Shrinkage and Selection Operator (LASSO) regression
                      was used for biomarker selection. Model performance was
                      assessed by changes in Harrell's C-index (a measure of
                      discrimination), net reclassification index (NRI), and
                      integrated discrimination index (IDI).During 10-year
                      follow-up, 2,163 participants experienced MACE. Overall, 18
                      proteins were selected by LASSO regression, with 5 of them
                      identified in both sexes, 7 only in males, and 6 only in
                      females. Incorporating these proteins significantly improved
                      the C-index of the SCORE2 model from 0.713 to 0.778 (P <
                      0.001) in the total population. The improvement was greater
                      in males (C-index increase from 0.684 to 0.771; Δ = +0.087)
                      than in females (from 0.720 to 0.769; Δ = +0.049). The WAP
                      four-disulfide core domain protein (WFDC2) and the
                      growth/differentiation factor 15 (GDF15) were the proteins
                      contributing the strongest C-index increase in both sexes,
                      even more than the N-terminal prohormone of brain
                      natriuretic peptide (NTproBNP).The derived sex-specific
                      10-year MACE risk prediction models, combining 12 protein
                      concentrations among men and 11 protein concentrations among
                      women with the SCORE2 model, significantly improved the
                      discriminative abilities of the SCORE2 model. This study
                      shows the potential of sex-specific proteomic profiles for
                      enhanced cardiovascular risk stratification and personalized
                      prevention strategies.},
      keywords     = {Cardiovascular risk (Other) / Prediction model (Other) /
                      Proteomics (Other) / SCORE2 (Other) / Sex-specific (Other)},
      cin          = {C070},
      ddc          = {500},
      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:40154735},
      doi          = {10.1016/j.jare.2025.03.034},
      url          = {https://inrepo02.dkfz.de/record/300191},
}