A review of cardiovascular-kidney-metabolic (CKM) syndrome argues that AI-enabled decision support could help clinicians turn multidimensional biomarker data into individualized prevention and treatment strategies across cardiovascular disease, chronic kidney disease, and metabolic disorders. The paper outlines how multi-omics, accessible sampling such as dried blood spots, and advanced models including multi-organ-on-a-chip may improve early detection, risk stratification, and dynamic risk prediction beyond traditional biomarkers.

Why It Matters To Your Practice

• CKM reframes heart, kidney, and metabolic disease as an interconnected continuum rather than separate problems.

• That matters clinically because traditional biomarkers can miss early disease, multisystem risk, and heterogeneity in progression.

• AI may help synthesize high-dimensional biomarker inputs into more actionable bedside risk estimates.

Clinical Implications

• Expect growing interest in integrated panels spanning metabolism, immuno-inflammation, oxidative stress, and biologic aging.

• Decision-support tools could eventually identify which patients need earlier prevention, tighter follow-up, or more personalized therapy.

• For clinicians, the near-term shift is conceptual: moving from siloed disease management toward coordinated CKM risk assessment.

Insights

• The review emphasizes enabling platforms, not just biomarkers: dried blood spots, multi-omics, and organ-on-a-chip systems.

• These tools may clarify disease mechanisms and organ crosstalk while generating richer datasets for AI models.

• AI's proposed role is translation—converting complex molecular profiles into dynamic risk prediction and clinical decision support.

The Bottom Line

• CKM care is moving toward integrated, data-rich risk assessment.

• AI will likely be most useful if it helps clinicians personalize prevention and management earlier, before overt multisystem disease is established.

• This is a review, so the promise is directional rather than practice-changing today.