Abstract Introduction Early diagnosis of cancer is essential for improving patient outcomes. Metabolomics analysis has shown promise in detecting cancer and distinguishing its metastatic burdens in previous studies. Objectives We hypothesized that metabolomics data can differentiate between people with and without cancer at a population level, uncovering new biomarkers and deepening our understanding of cancer metabolism. Methods A total of 1,386 metabolites were measured by two commonly used metabolomics platforms: Nightingale and Metabolon, in baseline plasma samples from participants in the population-based Rotterdam Study, with sample sizes of 2,538 and 5,057, respectively. Logistic regression and competing risk Cox proportional hazards models were employed to examine associations between these metabolites and both baseline prevalent and incident during follow-up of all and cause-specific cancers. Statistical significance was defined by a false discovery rate (FDR) < 0.05. Results There were 654 cancer cases at baseline, and 618 new cases also occurred during follow-up of nearly 10 years. In the cross-sectional study, 68, 7, and 10 metabolites were significantly associated with prevalent blood, colorectal, and all cancer, after multivariate adjustment. In the longitudinal study, 19, 11, 2, 3, and 1 metabolites were significantly associated with incident blood, colorectal, lung, prostate, and all cancer, respectively. Among these, 17 and 2 metabolites were associated with both prevalent and incident blood and colorectal cancer. Conclusions This study indicates several circulating metabolites that are associated with different cancers. These metabolites may contribute to better understanding of the metabolic pathways of cancer and serve as biomarkers for early cancer diagnosis.
Shuai et al. (2026) studied this question.