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@ARTICLE{AlmanzaAguilera:295915,
      author       = {E. Almanza-Aguilera and M. Martínez-Huélamo and Y.
                      López-Hernández and D. Guiñón-Fort and A. Guadall and M.
                      Cruz and A. Perez-Cornago and A. L. Rostgaard-Hansen and A.
                      Tjønneland and C. C. Dahm and V. Katzke$^*$ and M. B.
                      Schulze and G. Masala and C. Agnoli and R. Tumino and F.
                      Ricceri and C. Lasheras and M. Crous-Bou and M.-J. Sánchez
                      and A. Aizpurua-Atxega and M. Guevara and K. K. Tsilidis and
                      A. C. Chatziioannou and E. Weiderpass and R. C. Travis and
                      D. S. Wishart and C. Andrés-Lacueva and R. Zamora-Ros},
      title        = {{P}rediagnostic {P}lasma {N}utrimetabolomics and {P}rostate
                      {C}ancer {R}isk: {A} {N}ested {C}ase-{C}ontrol {A}nalysis
                      {W}ithin the {EPIC} {S}tudy.},
      journal      = {Cancers},
      volume       = {16},
      number       = {23},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2024-02728},
      pages        = {4116},
      year         = {2024},
      abstract     = {Background and Objective: Nutrimetabolomics may reveal
                      novel insights into early metabolic alterations and the role
                      of dietary exposures on prostate cancer (PCa) risk. We aimed
                      to prospectively investigate the associations between plasma
                      metabolite concentrations and PCa risk, including clinically
                      relevant tumor subtypes. Methods: We used a targeted and
                      large-scale metabolomics approach to analyze plasma samples
                      of 851 matched PCa case-control pairs from the European
                      Prospective Investigation into Cancer and Nutrition (EPIC)
                      cohort. Associations between metabolite concentrations and
                      PCa risk were estimated by multivariate conditional logistic
                      regression analysis. False discovery rate (FDR) was used to
                      control for multiple testing correction. Results: Thirty-one
                      metabolites (predominately derivatives of food intake and
                      microbial metabolism) were associated with overall PCa risk
                      and its clinical subtypes (p < 0.05), but none of the
                      associations exceeded the FDR threshold. The strongest
                      positive and negative associations were for dimethylglycine
                      (OR = 2.13; $95\%$ CI 1.16-3.91) with advanced PCa risk (n =
                      157) and indole-3-lactic acid (OR = 0.28; $95\%$ CI
                      0.09-0.87) with fatal PCa risk (n = 57), respectively;
                      however, these associations did not survive correction for
                      multiple testing. Conclusions: The results from the current
                      nutrimetabolomics study suggest that apart from early
                      metabolic deregulations, some biomarkers of food intake
                      might be related to PCa risk, especially advanced and fatal
                      PCa. Further independent and larger studies are needed to
                      validate our results.},
      keywords     = {EPIC (Other) / nested case–control (Other) /
                      nutrimetabolomics (Other) / prostate cancer (Other)},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39682302},
      doi          = {10.3390/cancers16234116},
      url          = {https://inrepo02.dkfz.de/record/295915},
}