Disease Onset Models
1 Triggering Events
ME/CFS onset typically follows an identifiable trigger, most commonly acute infection, though physical trauma, surgery, and severe psychological stress are also reported (Chu et al. 2019). The model represents triggering events as large perturbations to the integrated system. For infection-triggered onset, the perturbation is a sustained increase in viral load \(V(t)\) (Equation viral dynamics) over a period of days to weeks. The immune response generates cytokine elevations, which via the neuroimmune pathway (Section Neuroimmune Interactions) affect CNS function, and via the energy–immune coupling (Section Energy–Immune Coupling) deplete energy reserves.
The critical question is: why do some individuals recover fully while others develop ME/CFS? The model framework provides two distinct answers.
2 Tipping Point Dynamics
If the energy–immune system is bistable (as hypothesized in Chapter Integrated Multi-System Models), disease onset corresponds to crossing a separatrix in state space. The pre-infection state determines proximity to this separatrix, and the infection-induced perturbation determines whether it is crossed. The model predicts that individuals with pre-existing subclinical vulnerabilities—slightly reduced mitochondrial function, elevated baseline inflammation, lower cortisol reserve—reside closer to the separatrix and therefore require a smaller perturbation to trigger the transition. This is consistent with epidemiological observations that ME/CFS risk factors include prior immune challenges, genetic predisposition (DecodeME Consortium, Ponting, et al. 2025), and female sex (Lim et al. 2020).
Formally, the separatrix can be characterized by computing the boundary of the basin of attraction for the healthy steady state. Points inside this basin return to health after perturbation; points outside converge to the disease attractor. The basin boundary depends on all system parameters, making it patient-specific. The tipping point model predicts that:
- The same infection can cause ME/CFS in one individual and full recovery in another, depending on pre-existing parameter values
- More severe infections (larger perturbations) are more likely to cross the separatrix, consistent with epidemiological evidence suggesting that acute infection severity correlates with risk of post-infectious sequelae
- Multiple sub-threshold perturbations can progressively erode the basin of attraction (through cumulative parameter damage), eventually causing a transition after a seemingly mild trigger
3 Failure of Recovery Mechanisms
An alternative model, not requiring bistability, posits that ME/CFS onset reflects irreversible damage accumulated during the acute infection. ROS-mediated mitochondrial DNA damage, epigenetic modifications to immune cell precursors Vega, Vernon, and McGowan (2021), or permanent alterations in microglial priming could shift system parameters to values that no longer support the healthy steady state. In this model, there is only one attractor (the disease state) for the post-infection parameter values, and recovery requires parameter restoration (mitochondrial biogenesis, epigenetic reprogramming) rather than state-space perturbation. The two models make distinct predictions about spontaneous recovery: the bistability model permits recovery through large perturbations, while the damage model requires parameter repair.
4 Consolidated Infection-to-Onset Cascade
The preceding subsections describe the tipping point and recovery failure models separately. This subsection traces a single infection event through all interacting subsystems to show how the cascade produces ME/CFS onset. The walkthrough uses the same patient profile as the tipping point worked example (Section Disease Onset Models): a 35-year-old female with baseline \(\alpha_\text{CI} = 0.75\), hs-CRP \(= 1.8\) mg/L, and morning cortisol \(= 8\) \(\mu\)g/dL (separatrix distance \(d_\text{sep} = 0.12\)).
Day 0–3: Acute infection and immune activation. Viral infection triggers innate immune activation: NK cells and macrophages engage the pathogen. Activated immune cells consume substantial ATP through the immune energy demand term \(J_\text{immune}\) (Equation energy immune coupling). Pro-inflammatory cytokines (TNF-\(\alpha\), IL-6, IL-1\(\beta\)) rise, peaking at 24–72 hours. Cortisol increases in response but, starting from a low-normal baseline of $ 8$ \(\mu\)g/dL, the HPA axis reaches its ceiling quickly, limiting the anti-inflammatory brake (Equation cortisol immune). For this patient, the immune energy demand increases \(J_\text{demand}\) by approximately 30% above baseline during peak infection.
Day 3–7: Energy depletion and metabolic crisis. The 30% increase in total energy demand pushes the system beyond its already-limited production capacity (\(\alpha_\text{CI} = 0.75\), already mildly impaired). ATP levels fall as demand exceeds production (Equation atp balance). Falling ATP triggers the cascade described in the energy metabolism model (Chapter Energy Metabolism Models): + AMPK activation signals energy deficit, upregulating glycolysis + Increased glycolytic flux produces lactate at lower-than-normal workloads + The ETC, pushed harder to compensate, generates elevated ROS (Equation ros production): at \(\alpha_\text{CI} = 0.75\), the ROS factor is \((1 - 0.75) = 0.25\) versus 0.05 in health—a 5-fold increase, which worsens further as infection-mediated oxidative bursts from immune cells add to the ROS load + Elevated ROS damages ETC complexes, reducing \(\alpha_\text{CI}\) from 0.75 toward 0.65 over days
Day 7–14: The tipping point. As \(\alpha_\text{CI}\) drops from 0.75 to \(~\) 0.65, two critical thresholds approach. First, the ATP synthase cliff effect (Section Consolidated Cascade Example): the membrane potential \(\Delta \Psi\) falls from \(~\) 145 mV toward 135 mV, producing disproportionate ATP loss. Second, the mitophagy threshold: at \([\text{ATP}] < [\text{ATP}]_{\text{crit,autophagy}}\), the cell can no longer remove damaged mitochondria (Equation mitophagy), and damaged organelles accumulate.
Simultaneously, the energy–immune vicious cycle engages (Equation immune energy feedback): ATP depletion impairs immune cell function (\(k_\text{act}^\text{eff}\) decreases), slowing viral clearance, prolonging immune activation, and sustaining the energy drain. The state-space trajectory crosses the separatrix (\(d_\text{sep} = 0.12\), perturbation magnitude \(\approx 0.18\)). The system enters the basin of attraction of the disease state.
Week 2–4: Infection resolves, disease persists. The adaptive immune response eventually clears the virus. Cytokines decline. But the system does not return to its pre-infection state because:
- \(\alpha_\text{CI}\) has been reduced by ROS-mediated damage (now \(~\) 0.65 instead of 0.75)
- NAD⁺ pool is depleted (\(\gamma\) reduced from \(~\) 0.85 to \(~\) 0.70) by the sustained metabolic stress
- Mitochondrial quality control is impaired: biogenesis cannot keep pace with accumulated damage at the new, lower ATP level (Equation biogenesis)
- The energy–immune coupling maintains low-grade immune activation even without the original pathogen, because the depleted energy state impairs immune regulation
The patient experiences persistent fatigue, cognitive dysfunction, and exercise intolerance. The disease attractor is now the stable equilibrium: without intervention, the system will remain there indefinitely.
What determined the outcome. A different patient with \(\alpha_\text{CI} = 0.90\) and morning cortisol \(= 14\) \(\mu\)g/dL (separatrix distance \(d_\text{sep} = 0.35\)) experiencing the same infection would generate a perturbation of similar magnitude (\(~\) 0.18) but would not cross the separatrix ($ 0.18 < 0.35$). That patient recovers fully. The difference is not the infection—it is the pre-existing proximity to the tipping point.
This walkthrough is a qualitative trace through the coupled model, not a numerical simulation. The specific timescales (days 0–3, 3–7, etc.) and parameter trajectories (\(\alpha_\text{CI}\) dropping from 0.75 to 0.65) are illustrative estimates consistent with the model structure and clinical observations, not computed outputs. Quantitative validation requires numerical integration of the full coupled ODE system with infection-specific initial conditions, which has not been performed.