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@article{Assi:2015:mutage/gev045,
author = {Assi, N and Fages, A and Vineis, P and Chadeau-Hyam, M and Stepien, M and Duarte-Salles, T and Byrnes, G and Boumaza, H and Knueppel, S and Kuehn, T and Palli, D and Bamia, C and Boshuizen, H and Bonet, C and Overvad, K and Johansson, M and Travis, R and Gunter, MJ and Lund, E and Dossus, L and Elena-Herrmann, B and Riboli, E and Jenab, M and Viallon, V and Ferrari, P},
doi = {mutage/gev045},
journal = {Mutagenesis},
pages = {743--753},
title = {A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study},
url = {http://dx.doi.org/10.1093/mutage/gev045},
volume = {30},
year = {2015}
}

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TY  - JOUR
AB  - Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the ‘meeting-in-the-middle’ concept, for which an analytical framework was developed in this study. In a nested case–control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum 1H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls. Through partial least square (PLS) analysis, 21 lifestyle variables (the ‘predictors’, including information on diet, anthropometry and clinical characteristics) were linked to a set of 285 metabolic variables (the ‘responses’). The three resulting scores were related to HCC risk by means of conditional logistic regressions. The first PLS factor was not associated with HCC risk. The second PLS metabolomic factor was positively associated with tyrosine and glucose, and was related to a significantly increased HCC risk with OR = 1.11 (95% CI: 1.02, 1.22, P = 0.02) for a 1SD change in the responses score, and a similar association was found for the corresponding lifestyle component of the factor. The third PLS lifestyle factor was associated with lifetime alcohol consumption, hepatitis and smoking, and had negative loadings on vegetables intake. Its metabolomic counterpart displayed positive loadings on ethanol, glutamate and phenylalanine. These factors were positively and statistically significantly associated with HCC risk, with 1.37 (1.05, 1.79, P = 0.02) and 1.22 (1.04, 1.44, P = 0.01), respectively. Evidence of mediation was found in both the second and third PLS factors, where the metabolomic signals mediated the relation between the lifestyle component and HCC outcome. This study devised a way to bridge lifestyle variables to HCC risk through NMR metabolomics data. This implementation of the ‘meet
AU  - Assi,N
AU  - Fages,A
AU  - Vineis,P
AU  - Chadeau-Hyam,M
AU  - Stepien,M
AU  - Duarte-Salles,T
AU  - Byrnes,G
AU  - Boumaza,H
AU  - Knueppel,S
AU  - Kuehn,T
AU  - Palli,D
AU  - Bamia,C
AU  - Boshuizen,H
AU  - Bonet,C
AU  - Overvad,K
AU  - Johansson,M
AU  - Travis,R
AU  - Gunter,MJ
AU  - Lund,E
AU  - Dossus,L
AU  - Elena-Herrmann,B
AU  - Riboli,E
AU  - Jenab,M
AU  - Viallon,V
AU  - Ferrari,P
DO  - mutage/gev045
EP  - 753
PY  - 2015///
SN  - 1464-3804
SP  - 743
TI  - A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study
T2  - Mutagenesis
UR  - http://dx.doi.org/10.1093/mutage/gev045
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000368265500004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/42777
VL  - 30
ER  -

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