In 2025, the convergence of genomic and multi-omic data integration is driving a paradigm shift toward "Preventive Medicine 2.0," moving healthcare from reactive symptom management to proactive disease interception. This approach leverages Artificial Intelligence (AI) to synthesize vast datasets—spanning genomics, proteomics, metabolomics, and spatial phenotyping—to detect disease at its molecular origins and tailor prevention strategies.

Genomic Risk Stratification and PRS Polygenic Risk Scores (PRS) have emerged as a critical tool for quantifying inherited risk by aggregating the effects of thousands of genetic variants. In cardiovascular medicine, integrating PRS with traditional clinical tools (like the PREVENT calculator) significantly improves risk prediction accuracy. This allows for the identification of "invisible" high-risk individuals who might otherwise be missed, enabling earlier interventions such as statin therapy or lifestyle modifications to prevent events like heart attacks and strokes.

Liquid Biopsies and Multi-Cancer Early Detection (MCED) Liquid biopsy technology has advanced beyond simple mutation detection to multi-modal frameworks that analyze cell-free DNA (cfDNA) methylation patterns, fragmentation profiles (fragmentomics), and end motifs. These features preserve tissue and chromatin context, enabling Multi-Cancer Early Detection (MCED) tests to identify multiple tumor types and infer their tissue-of-origin (TOO) with high specificity from a single blood draw. This "blood-first" approach supports diagnostic triage, potentially reducing unnecessary invasive procedures and shifting diagnoses to earlier, more treatable stages.

AI-Driven Multi-Omics Integration AI and deep learning models are essential for managing the high dimensionality of multi-omics data. Algorithms like COSMOS (COoperative integration of Spatially resolved Multi-OmicS) utilize graph neural networks to integrate spatially resolved transcriptomics, proteomics, and epigenomics. By extracting complementary features from different molecular layers, these models provide a holistic view of tissue architecture and disease mechanisms, outperforming single-omics analyses in tasks like domain segmentation and cell-type clustering. This integration facilitates the discovery of robust biomarkers that link genotype to phenotype.

Personalized Prevention and Precision Interventions The integration of these technologies enables highly personalized care pathways:

• Pharmacogenomics: Genetic profiling is increasingly used to optimize drug selection and dosing, minimizing adverse drug reactions and maximizing efficacy for conditions ranging from mental health to cardiovascular disease.

• Continuous Monitoring: Data from wearable biosensors is being integrated with molecular profiles to create dynamic, real-time health trajectories, allowing for immediate adjustments to treatment or lifestyle plans.

• Gene Editing: In select high-risk cases, CRISPR-based therapies (e.g., targeting PCSK9) offer the potential for "one-and-done" interventions to permanently lower risk factors like LDL cholesterol.

While promising, the widespread adoption of these strategies requires addressing challenges in cost-effectiveness, data interoperability, and health equity to ensure these innovations benefit diverse populations