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@ARTICLE{Schmidt:143810,
      author       = {J. A. Schmidt and G. K. Fensom and S. Rinaldi and A.
                      Scalbert and P. N. Appleby and D. Achaintre and A. Gicquiau
                      and M. J. Gunter and P. Ferrari and R. Kaaks$^*$ and T.
                      Kühn$^*$ and H. Boeing$^*$ and A. Trichopoulou and A.
                      Karakatsani and E. Peppa and D. Palli and S. Sieri and R.
                      Tumino and B. Bueno-de-Mesquita and A. Agudo and M.-J.
                      Sánchez and M.-D. Chirlaque and E. Ardanaz and N.
                      Larrañaga and A. Perez-Cornago and N. Assi and E. Riboli
                      and K. K. Tsilidis and T. J. Key and R. C. Travis},
      title        = {{P}atterns in metabolite profile are associated with risk
                      of more aggressive prostate cancer: {A} prospective study of
                      3,057 matched case-control sets from {EPIC}.},
      journal      = {International journal of cancer},
      volume       = {146},
      number       = {3},
      issn         = {1097-0215},
      address      = {Bognor Regis},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2019-01372},
      pages        = {720-730},
      year         = {2020},
      note         = {Int J Cancer. 2020 Feb 1;146(3):720-730},
      abstract     = {Metabolomics may reveal novel insights into the etiology of
                      prostate cancer, for which few risk factors are established.
                      We investigated the association between patterns in baseline
                      plasma metabolite profile and subsequent prostate cancer
                      risk, using data from 3,057 matched case-control sets from
                      the European Prospective Investigation into Cancer and
                      Nutrition (EPIC). We measured 119 metabolite concentrations
                      in plasma samples, collected on average 9.4 years before
                      diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit,
                      Biocrates Life Sciences AG). Metabolite patterns were
                      identified using treelet transform, a statistical method for
                      identification of groups of correlated metabolites.
                      Associations of metabolite patterns with prostate cancer
                      risk (OR1SD ) were estimated by conditional logistic
                      regression. Supplementary analyses were conducted for
                      metabolite patterns derived using principal component
                      analysis and for individual metabolites. Men with metabolite
                      profiles characterized by higher concentrations of either
                      phosphatidylcholines or hydroxysphingomyelins (OR1SD
                       = 0.77, $95\%$ confidence interval 0.66-0.89),
                      acylcarnitines C18:1 and C18:2, glutamate, ornithine and
                      taurine (OR1SD  = 0.72, 0.57-0.90), or
                      lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had
                      lower risk of advanced stage prostate cancer at diagnosis,
                      with no evidence of heterogeneity by follow-up time. Similar
                      associations were observed for the two former patterns with
                      aggressive disease risk (the more aggressive subset of
                      advanced stage), while the latter pattern was inversely
                      related to risk of prostate cancer death (OR1SD  = 0.77,
                      0.61-0.96). No associations were observed for prostate
                      cancer overall or less aggressive tumor subtypes. In
                      conclusion, metabolite patterns may be related to lower risk
                      of more aggressive prostate tumors and prostate cancer
                      death, and might be relevant to etiology of advanced stage
                      prostate cancer.},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30951192},
      doi          = {10.1002/ijc.32314},
      url          = {https://inrepo02.dkfz.de/record/143810},
}