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

Nada Assi, Anne Fages, Paolo Vineis, Marc Chadeau-Hyam, Magdalena Stepien, Talita Duarte-Salles, Graham Byrnes, Houda Boumaza, Sven Knüppe, Tilman Kühn, Domenico Palli, Christina Bamia, Hendriek Boshuizen, Catalina Bonet, Kim Overvad, Mattias Johansson, Ruth Travis, Marc J. Gunter, Eiliv Lund, Laure DossusBénédicte Elena-Herrmann, Elio Riboli, Mazda Jenab, Vivian Viallon, Pietro Ferrari*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

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

Original languageEnglish
Pages (from-to)743-753
JournalMutagenesis
Volume30
Issue number6
DOIs
Publication statusPublished - 30 Jun 2015
Externally publishedYes

ASJC Scopus subject areas

  • Genetics
  • Toxicology
  • Genetics(clinical)
  • Health, Toxicology and Mutagenesis

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