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Hepatocellular carcinoma (HCC) is the most common form of liver cancer and its incidence is increasing including in regions where hepatitis infection rates are low. This trend may be the result of increases in 'unhealthy lifestyle' factors. The main aim of this study is to identify metabolic signatures associated with healthy lifestyle behaviours and to relate these signatures to risk of developing HCC to investigate whether the metabolites were of predictive utility for HCC beyond data procured from questionnaires. To address this question, we exploited data from a large European cohort (EPIC) which includes detailed questionnaire-based data as well as metabolomic data.
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Studies using metabolomic data have identified metabolites from several compound classes that are associated with disease-related lifestyle factors. This study identified metabolic signatures reflecting lifestyle patterns and related them to hepatocellular carcinoma (HCC) risk in the EPIC cohort. Partial Least Squares (PLS) analysis related seven modified Healthy Lifestyle Index variables (diet, BMI, physical activity, lifetime alcohol, smoking, diabetes, hepatitis) to 132 targeted serum-measured metabolites, and a liver function score in a nested study of HCC with 147 case-control pairs. The association between the resulting PLS scores and HCC risk was examined in multivariable conditional logistic regression models where odds ratios (OR) and their 95% confidence intervals (95%CI) were computed. The PLS-derived lifestyle component reflected a high propensity towards healthy behaviours. Its metabolic counterpart was positively related to the following metabolites: SM(OH) C14:1, C16:1 and C22:2, and negatively to glutamate, hexoses, and PC aaC32:1. The lifestyle and metabolomics components were inversely associated with HCC risk with OR for a 1-SD increase in scores equal to 0.49(95%CI=0.35 to 0.68) and 0.28(0.18 to 0.43).
Measuring a specific metabolites panel may identify strata of the population at higher risk for HCC and can add substantial discrimination compared to questionnaire data
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