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Mutagenesis. 2015 Nov;30(6):743-53. doi: 10.1093/mutage/gev045. Epub 2015 Jun 30.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Author information

1
International Agency for Research in Cancer (IARC-WHO), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France.
2
Centre de RMN à Très Hauts Champs, Institut des Sciences Analytiques (CNRS/ENS Lyon/UCB Lyon 1), Université de Lyon, 69100 Villeurbanne, France, Present address: Chemical Physics Department, Weizmann Institute of Science, Rehovot, Israel.
3
Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK.
4
Centre de RMN à Très Hauts Champs, Institut des Sciences Analytiques (CNRS/ENS Lyon/UCB Lyon 1), Université de Lyon, 69100 Villeurbanne, France.
5
Department of Epidemiology, German Institute of Human Nutrition, Potsdam-Rehbruecke, 14558 Nuthetal, Germany.
6
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
7
Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute - ISPO, Florence, Italy.
8
Department of Hygiene, Epidemiology and Medical Statistics, WHO Collaborating Center for Food and Nutrition Policies, University of Athens Medical School, Athens, Greece.
9
National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
10
Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Institut Català d'Oncologia, L'Hospitalet de Llobregat, Spain.
11
The Department of Epidemiology, School of Public Health, Aarhus University, Aarhus, Denmark.
12
International Agency for Research in Cancer (IARC-WHO), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France, The Department for Biobank Research, Umeå University, Umeå, Sweden.
13
Cancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford, UK.
14
The Institute of Community Medicine, University of Tromsø, Tromsø, Norway.
15
Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Lifestyle, Genes and Health: Integrative Trans-generational Epidemiology Team, Villejuif, France, Université Paris Sud, Villejuif, France.
16
International Agency for Research in Cancer (IARC-WHO), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France, ferrarip@iarc.fr.

Abstract

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 (1)H 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 'meeting-in-the-middle' approach finds natural applications in settings characterised by high-dimensional data, increasingly frequent in the omics generation.

PMID:
26130468
PMCID:
PMC5909887
DOI:
10.1093/mutage/gev045
[Indexed for MEDLINE]
Free PMC Article

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