Hypothesis
Serial multi-omic trajectories (proteomics, metabolomics, and flow cytometry immunophenotyping) sampled every 2–4 weeks in ANCA-associated vasculitis (AAV) patients in remission encode topological features — persistent homology barcodes and Betti numbers — that identify hidden dynamical attractors. Patients whose trajectories converge toward a specific attractor basin will relapse within 8–16 weeks, detectable 6–10 weeks before clinical manifestation (BVAS ≥ 1).
Background and Rationale
AAV relapses remain unpredictable. ANCA titers alone have poor positive predictive value (~30–50%), and no single biomarker reliably forecasts flare timing. The immune system in AAV behaves as a high-dimensional nonlinear dynamical system: cytokine networks, neutrophil activation cascades, and B-cell reconstitution interact through feedback loops that resist linear modeling.
Topological Data Analysis (TDA) — specifically persistent homology — captures the shape of data manifolds without requiring linearity or distributional assumptions. In dynamical systems theory, attractors represent stable states toward which trajectories converge. We hypothesize that the pre-relapse immune state constitutes a distinct attractor basin distinguishable from stable remission via TDA applied to time-delay embeddings of multi-omic panels.
Proposed Methodology
- Cohort: n ≥ 80 AAV patients (GPA + MPA) in clinical remission (BVAS = 0, prednisone ≤ 5 mg/day), followed prospectively for 12 months with sampling every 2 weeks.
- Multi-omic panel: 92-plex Olink proteomics (inflam





























