This project investigates how gut microbiota and their metabolites contribute to the onset and progression of cardiometabolic diseases (CMD). By integrating large-scale metagenomic, metabolomic, and clinical datasets (e.g., SUPERB, SCAPIS, IGT), we aim to identify robust microbiome- and metabolite-based predictive markers and explore preventive strategies by prioritizing actionable microbial pathways. The work involves cohort-scale processing of thousands of shotgun metagenomes, including computationally intensive metagenomic assembly and genome/gene-catalog construction, high-throughput functional annotation and pathway reconstruction, and the generation of high-dimensional feature matrices for downstream analyses. We further perform large-scale multi-omics integration and repeated cross-validated machine learning across multiple CMD endpoints with extensive sensitivity analyses to ensure robustness and interpretability. Collectively, these steps impose heavy demands on both CPU time and memory, require efficient parallel execution, and necessitate substantial temporary and intermediate storage throughout the analysis pipeline.