Hypothesis

Integrating single‑cell proteogenomic and metabolomic profiles with continuous wearable physiologic data will forecast the onset of a sepsis‑associated cytokine storm at least six hours before clinical deterioration, allowing pre‑emptive immunomodulatory intervention.

Mechanistic Rationale

Sepsis progression is driven by a feed‑forward loop where pathogen‑associated molecular patterns trigger trained immunity in monocytes, amplifying cytokine production via epigenetic reprogramming and metabolic rewiring【4】. Single‑cell proteogenomics can capture the emergent protein isoforms and post‑translational modifications that reflect this reprogrammed state, while metabolomics reveals the shift toward aerobic glycolysis and succinate accumulation that fuels inflammasome activation【6】. Wearable sensors provide real‑time surrogates of systemic stress—heart‑rate variability, skin temperature, and peripheral perfusion—that correlate with mitochondrial dysfunction and circulating catecholamine surges【5】. By aligning these layers in a dynamic graph neural network, the model learns signatures that precede the clinical cytokine surge, bridging the translational gap between static classification and prospective guidance【3】.

Testable Predictions

1. Patients who'll later develop cytokine storm will show a coordinated increase in monocyte‑specific proteoforms (e.g., phosphorylated STAT1, cleaved caspase‑1) and a rise in the lactate‑to‑succinate ratio at least six hours prior to fever spikes.
2. It's expected that the integrated risk score will outperform SOFA or qSOFA alone in predicting ICU transfer, with an expected increase in AUROC of ≥0.12.
3. It's likely that early administration of low‑dose corticosteroid guided by the score will reduce peak IL‑6 levels by 30% compared with standard care.

Experimental Design

Enroll 200 adult sepsis patients admitted to the emergency department. Collect peripheral blood at admission, then every hour for 12 hours for single‑cell proteogenomics (mass‑spec‑based protein sequencing) and targeted metabolomics (LC‑MS for lactate, succinate, itaconate). Simultaneously record wearable physiologic streams. Label outcome based on physician‑documented cytokine storm (IL‑6 >1000 pg/mL or vasopressor requirement). We'll train a variational autoencoder‑graph network to fuse the omics tensors with temporal sensor data, using sepsis‑free patients as controls. We'll validate on a held‑out 30% cohort and assess calibration with Brier score. Statistical analysis will use DeLong test for AUROC comparison and mixed‑effects modeling for cytokine trajectories.

Potential Impact

If validated, this framework could convert multi‑omics from a retrospective biomarker tool into a bedside early‑warning system, directly addressing the infrastructural and interpretability challenges noted in recent reviews【7】【8】.