Multi-scale phenomenological framework applying TSTU formalism (scalar field, effective mass, criticality) to pathological transitions in biology. Prediction S1 empirically corroborated on NHANES (n=3,138) and calibrated via gold-standard CGM simulation.
Elevated glucose variability (CV ≥ 30%) is associated with increased prediabetes risk across 3 independent datasets. Prediction S1 — derived from TSTU bifurcation theory — survives progressively stricter measurement constraints: RR=1.50 (synthetic) → RR=2.36 (NHANES real-world) → RR=1.68 (CGM gold-standard). The observed gradient is consistent with the longitudinal literature (HR ~1.5–2.0).
Paper V extends the TSTU framework to biology via a formal analogy: a biological system approaching a pathological transition behaves like a scalar field approaching a critical point. Near the threshold, the effective mass m²eff tends to zero, producing two observable signatures: critical slowing down (slow recovery from perturbation) and variance amplification (fluctuations grow). The S1 glucose-variability prediction follows directly from this second mechanism.
Addendum A (Dec. 2025) clarifies a critical point: Paper V uses three distinct mathematical frameworks across biological scales. They are not interchangeable and each has its own characteristic timescale.
| Scale | Model | Variables | Timescale |
|---|---|---|---|
| Tissue (§3) | Klein-Gordon PDE∂²Φ/∂t² + Γ∂Φ/∂t − c²∇²Φ + V'(Φ) = J |
Scalar field Φ (proliferation) | Hours–days (fast) |
| Neural (§4) | ODEs E-I-S-NdE/dt, dI/dt, dS/dt, dN/dt |
Excitation, inhibition, sleep, neuro-inflammation | 10–30 days (slow) |
| Systemic (§5) | Kuramoto multi-organesdθᵢ/dt = ωᵢ + ΣKᵢⱼ sin(θⱼ−θᵢ) |
Organ phases (liver, heart, ...) | Circadian period (24 h) |
S1 statement: a glucose coefficient of variation CV ≥ 30% is associated with increased risk of progression to diabetes. Three complementary datasets test this prediction.
The observed gradient (synthetic 1.50 → NHANES 2.36 → CGM 1.68) is consistent with two expected mechanisms: (1) cross-sectional confounding inflates associations in NHANES, (2) gold-standard CGM measurement provides stricter calibration. All three results point in the same direction and survive label permutation (null AUC ≈ 0.5).
Under gold-standard CGM measurement, logistic regression assigns variability (CV) a standardized coefficient of +1.07, versus +0.41 for mean glucose and −0.09 for time-above-range (TAR). Variability is therefore the dominant predictor, surpassing mean glucose. This is consistent with the theoretical framework: dynamic destabilization precedes mean shift — exactly as predicted by variance amplification near a bifurcation.
The items below are potential research directions that become real only after strict longitudinal validation. No product, no patent, no promise — only possible trajectories.
CV ≥ 30% scoring can be integrated as an early-warning flag in existing continuous glucose monitors. Light computation, on-device executable, complementary to current metrics (TIR, GMI).
UK Biobank has ~2,500 participants with 14-day CGM monitoring. Direct test: baseline CV → incident diabetes at 5–10 years, Cox proportional hazards.
Addendum A narrows the detection window to 6–24 hours (vs 2–5 days). If verified, daily imaging could detect tissue bifurcations (oncology, hepatic fibrosis) much earlier than current protocols.
Beyond CV, indicators like lag-1 autocorrelation, variance and skewness can capture critical slowing down. A composite score may improve the current AUC (0.74).
Working on diabetes, CGM, biological systems dynamics, or early detection? Predictions are ready for independent testing, code is public under MIT license.