% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @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}, }