Abstract
Liquid-chromatography-mass-spectrometry-based metabolomics is widely used in prospective case-control studies for disease prediction. Given the large amount of clinical and metabolomics data involved, data integration and analyses are crucial to provide an accurate understanding of the disease. We provide a comprehensive analysis approach to explore associations among clinical risk factors, metabolites, and disease. We describe steps for performing Spearman correlation, conditional logistic regression, casual mediation, and variance partitioning to investigate the potential effects of metabolites on disease. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).(1).