An Epidemiology-Constrained Framework for Mechanistic Hypothesis Restriction and Prioritization in Complex Disease.

rationaleMany chronic diseases remain mechanistically unresolved despite major advances in molecular biology, systems biology and high-throughput biomedical investigation. Disorders such as autoimmune disease, neurodegeneration and chronic inflammatory syndromes frequently exhibit prolonged latency, developmental susceptibility windows, heterogeneous phenotypes and multiscale biological interaction that limit direct experimental reconstruction of initiating causal events. Under such conditions, causal interpretation may proceed without sufficient inferential constraint, contributing to fragmentation between molecular association and coherent causal explanation. AIMS AND

objectivesTo propose an epidemiology-constrained framework for restricting and prioritising mechanistic hypotheses in complex chronic disease when direct experimental access to disease initiation is limited.

methodsThis conceptual framework was developed through a purposive conceptual synthesis of recurring epidemiologic structures and established inferential approaches relevant to complex chronic disease. The literature was organised across predefined conceptual domains, including temporality, sex distribution, geographic and migration patterns, multimorbidity, protective exposures, life-course epidemiology, causal inference, triangulation and systems epidemiology. Multiple sclerosis and Parkinson's disease were selected as illustrative examples because they exhibit several characteristics central to the framework, including prolonged latency, environmental modulation, heterogeneous phenotypes and limited experimental accessibility to initiating events.

resultsWithin the proposed model, structured epidemiologic observations, including sex distribution, geographic gradients, migration effects, age-of-onset patterns, multimorbidity networks and protective exposures, are first evaluated for reproducibility, bias, temporality and alternative explanations and are then treated as candidate constraints on competing mechanistic models. These constraints restrict and prioritise, rather than establish mechanistic hypotheses. Temporality and multimorbidity are incorporated as inferential filters that help distinguish antecedent causal processes from downstream consequences and treatment-related effects. Application to multiple sclerosis and Parkinson's disease illustrates how convergent population-level observations may narrow mechanistic hypothesis space prior to molecular validation.

conclusionsEpidemiologic structure may serve not merely as descriptive association but as inferential architecture for systematically restricting and prioritising mechanistic hypotheses under conditions of limited experimental accessibility. Epidemiology-constrained inference does not establish mechanisms or causality independently, but may provide a complementary framework for organising, comparing and prioritising competing mechanistic models.