The Perfect Storm Framework: A Testable Multi-Hit Model Linking Connective Tissue Variance, Neurodivergence, and Gene–Environment Interaction in Complex Chronic Illness

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Eccles, J. A., Cole, N., Critchley, H. D., Davies, K. A., Tewfik, I., Quadt, L., Witte, K., Gajwani, R., Minnis, H., & Colasanti, A. (2026). The Perfect Storm Framework: A Testable Multi-Hit Model Linking Connective Tissue Variance, Neurodivergence, and Gene–Environment Interaction in Complex Chronic Illness [Preprint]. Center for Open Science. https://doi.org/10.31234/osf.io/upvk3_v1

Publication date: 4 Aug 2026 Added to AutiHub: 25 Sep 2026 Type: Preprint Article language: English

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Abstract

Individuals with joint hypermobility and neurodivergent traits (autism, ADHD) show markedly elevated rates of psychiatric, autonomic and multi-system complaints, yet no single causal pathway has been established. We propose the Perfect Storm Framework: a multi-hit, threshold-based model in which constitutional connective-tissue and neurodevelopmental variance interacts multiplicatively, not additively, with discrete environmental perturbations (infection/inflammation, chronic stress, nutritional deficiency) to precipitate multi-system breakdown once cumulative allostatic load exceeds an individual's buffering capacity. We formalise this using four organising metaphors and derive falsifiable, quantitative predictions from existing pilot data, including a typhoid-vaccine inflammatory-challenge paradigm (ISRCTN78820481) showing that baseline neural network modularity and hypermobility status jointly determine the magnitude of inflammation-induced pain, fatigue and transcriptomic disease-ontology shift. We further address the causal-direction problem ("chicken or egg") using gene–environment correlation theory, proposing that hypermobility and neurodivergence arise from partially shared developmental genetics while also causally potentiating one another via proprioceptive, interoceptive and inflammatory pathways influenced dynamically by genes and environment. The framework yields testable, falsifiable predictions amenable to prospective cohort, challenge, intervention and computational-modelling designs.

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