Ultra-Processed Foods and the Cardiovascular-Kidney-Metabolic Continuum: Integrating Epidemiological, Multi-Omics, and Translational Evidence.

Cardiovascular-kidney-metabolic (CKM) syndrome integrates excess adiposity, metabolic dysfunction, kidney impairment, subclinical cardiovascular diseases, and clinical events along a staged continuum that invites unified prevention and treatment. Ultra-processed foods (UPFs) are a complex, high-prevalence exposure that may influence risk across CKM stages through nutrient profiles, additives, processing-induced compounds, and packaging-related contaminants. This review synthesizes epidemiologic, mechanistic, and translational evidence with attention to exposure definition and analytic rigor. We summarize NOVA-based UPF operationalization across dietary assessment tools, highlighting misclassification of mixed dishes, brand heterogeneity, and energy under-reporting, and we propose further examination of energy-adjusted models, calibration, and harmonized metrics. Observational studies consistently associate higher UPF intake with adiposity, diabetes, chronic kidney disease, cardiovascular events, and mortality, with modest to moderate effect sizes that are heterogeneous across populations. Mechanistic data from metabolomics, lipidomics, proteomics, and the gut microbiome converge on pathways of inflammation, lipid metabolism, oxidative and metabolic stress, and intestinal barrier dysfunction; in selected cohorts, multi-omics modules account for a substantial minority of UPF-outcome associations. We outline quality-control pipelines, batch-effect prevention/correction, and multiple-testing control necessary for reproducible diet-omics. Translationally, targeted lipidomic and proteomic panels show promise for CKM risk stratification and monitoring but require validation, clinical thresholds, and guideline endorsement. Equity and global context, including differences in product mix, food systems, and care capacity, modify population impact. We conclude with a research agenda prioritizing harmonized exposure metrics, error-aware modeling, standardized multi-omics workflows, and adequately powered, stage-specific interventions capable of testing mediation and prognostic utility.