Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men.

ABSTRACT

BACKGROUND: Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer. METHODS: We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk. FINDINGS: After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI 0.81-0.87) to 0.89 (95% CI 0.86-0.91). INTERPRETATION: This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer. FUNDING: National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].