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@ARTICLE{Assi:136013,
      author       = {N. Assi and D. C. Thomas and M. Leitzmann and M. Stepien
                      and V. Chajès and T. Philip and P. Vineis and C. Bamia and
                      M.-C. Boutron-Ruault and T. M. Sandanger and A. Molinuevo
                      and H. C. Boshuizen and A. Sundkvist and T. Kühn$^*$ and R.
                      C. Travis and K. Overvad and E. Riboli and M. J. Gunter and
                      A. Scalbert and M. Jenab and P. Ferrari and V. Viallon},
      title        = {{A}re {M}etabolic {S}ignatures {M}ediating the
                      {R}elationship between {L}ifestyle {F}actors and
                      {H}epatocellular {C}arcinoma {R}isk? {R}esults from a
                      {N}ested {C}ase-{C}ontrol {S}tudy in {EPIC}.},
      journal      = {Cancer epidemiology, biomarkers $\&$ prevention},
      volume       = {27},
      number       = {5},
      issn         = {1538-7755},
      address      = {Philadelphia, Pa.},
      publisher    = {AACR},
      reportid     = {DKFZ-2018-00713},
      pages        = {531 - 540},
      year         = {2018},
      abstract     = {Background: The 'meeting-in-the-middle' (MITM) is a
                      principle to identify exposure biomarkers that are also
                      predictors of disease. The MITM statistical framework was
                      applied in a nested case-control study of hepatocellular
                      carcinoma (HCC) within European Prospective Investigation
                      into Cancer and Nutrition (EPIC), where healthy lifestyle
                      index (HLI) variables were related to targeted serum
                      metabolites.Methods: Lifestyle and targeted metabolomic data
                      were available from 147 incident HCC cases and 147 matched
                      controls. Partial least squares analysis related 7 lifestyle
                      variables from a modified HLI to a set of 132 serum-measured
                      metabolites and a liver function score. Mediation analysis
                      evaluated whether metabolic profiles mediated the
                      relationship between each lifestyle exposure and HCC
                      risk.Results: Exposure-related metabolic signatures were
                      identified. Particularly, the body mass index
                      (BMI)-associated metabolic component was positively related
                      to glutamic acid, tyrosine, PC aaC38:3, and liver function
                      score and negatively to lysoPC aC17:0 and aC18:2. The
                      lifetime alcohol-specific signature had negative loadings on
                      sphingomyelins (SM C16:1, C18:1, SM(OH) C14:1, C16:1 and
                      C22:2). Both exposures were associated with increased HCC
                      with total effects (TE) = 1.23 $(95\%$ confidence interval =
                      0.93-1.62) and 1.40 (1.14-1.72), respectively, for BMI and
                      alcohol consumption. Both metabolic signatures mediated the
                      association between BMI and lifetime alcohol consumption and
                      HCC with natural indirect effects, respectively, equal to
                      1.56 (1.24-1.96) and 1.09 (1.03-1.15), accounting for a
                      proportion mediated of $100\%$ and $24\%.Conclusions:$ In a
                      refined MITM framework, relevant metabolic signatures were
                      identified as mediators in the relationship between
                      lifestyle exposures and HCC risk.Impact: The understanding
                      of the biological basis for the relationship between
                      modifiable exposures and cancer would pave avenues for
                      clinical and public health interventions on metabolic
                      mediators. Cancer Epidemiol Biomarkers Prev; 27(5); 531-40.
                      ©2018 AACR.},
      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:29563134},
      doi          = {10.1158/1055-9965.EPI-17-0649},
      url          = {https://inrepo02.dkfz.de/record/136013},
}