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.
Highlights
Metabolomic profiles from blood and other biological samples collected from large-scale epidemiologic studies are increasingly being investigated [1], following recent developments in nuclear magnetic resonance (NMR) and mass spectrometry (MS) enabling the assessment of metabolic profiles for large numbers of individuals
These approaches explore a variety of aetiological hypotheses; they usually focus on one aspect at a time, combining metabolomics with either epidemiologic/phenotypic data on lifestyle exposures [3] or with disease outcomes [4,5]
The MITM was previously implemented as a proof of concept in a case–control study nested within a cohort of healthy individuals [7], where a list of putative intermediate 1H NMR biomarkers linking exposure to dietary compounds, mainly micro- and macronutrients, and disease outcomes were investigated
Summary
Metabolomic profiles from blood and other biological samples collected from large-scale epidemiologic studies are increasingly being investigated [1], following recent developments in nuclear magnetic resonance (NMR) and mass spectrometry (MS) enabling the assessment of metabolic profiles for large numbers of individuals. Metabolomic data is gradually playing a key part in clinical and observational studies; and new statistical methodologies [2] are increasingly being sought to explore insights into pathological processes that metabolomics may provide in order to better understand determinants of disease development These approaches explore a variety of aetiological hypotheses; they usually focus on one aspect at a time, combining metabolomics with either epidemiologic/phenotypic data on lifestyle exposures [3] or with disease outcomes [4,5]. Prospective studies are conceptually suitable for this purpose, since they rely on biological samples collected before disease onset, and are marginally influenced by metabolic changes due to processes of disease development In this scenario, the ‘meeting-in-the-middle’ (MITM) approach [6] has been conceived as a research strategy to identify biomarkers that are related to specific exposures and that are, at the same time, predictive of disease outcome. The MITM was previously implemented as a proof of concept in a case–control study nested within a cohort of healthy individuals [7], where a list of putative intermediate 1H NMR biomarkers linking exposure to dietary compounds, mainly micro- and macronutrients, and disease outcomes (colon and breast cancer) were investigated
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