Proteomic health archetypes identified in disease-free adults enable risk assessment for diverse chronic diseases

The prevention of common chronic diseases is hampered by the lack of tools to identify at-risk individuals. The plasma proteome could enable a systems-level assessment of health, but its clinical translation is limited by platform-specific biases and a focus on single diseases.

We aimed to develop and validate a clinically deployable proteomic classifier for multi-system risk prediction. Using data from 11,900 disease-free adults, we defined five ProteoHealth Archetypes (PHAs) via unsupervised learning. Critically, we trained a classifier using paired protein ratios to ensure robustness across measurement platforms.

The ratio-based classifier successfully transferred PHA signatures in an internal validation cohort (n = 3,570) and, importantly, in three external cohorts profiled by different technologies (SomaScan and mass spectrometry). Each PHA exhibited distinct, highly reproducible risks for developing cardiometabolic, inflammatory/immune, neurovascular, and psychiatric diseases. Individuals in high-risk archetypes experienced significantly steeper declines in survival. Genome-wide association analyses identified loci associated with PHA liability, and two-sample Mendelian randomisation produced effect directions consistent with the observational disease association. The differential proteomes and enriched pathways aligned with the specific disease profiles of each archetype.

PHAs provide an externally transferable, mechanistically interpretable map of baseline proteomic health that forecasts multisystem disease and survival, offering a scalable substrate for prevention, risk communication, and biomarker-guided stratification.