Drivers: A Biologically Contextualized, Cross-Inferential View of the Epidemiology of Neurodegenerative Disorders

Background: Sutherland et al. (2011) suggested that, instead of risk factors for single neurodegenerative disorders (NDDs), there was a need to identify specific drivers, i.e., risk factors with impact on specific deposits, such as amyloid-beta, tau, or alpha-synuclein, acting across entities. Objectives and Methods: Redefining drivers as neither protein/gene-nor entity-specific features identifiable in the clinical and general epidemiology of conformational NDDs (CNDDs) as potential footprints of templating/spread/transfer mechanisms, we conducted an analysis of the epidemiology of ten CNDDs, searching for patterns. Results: We identified seven potential drivers, each of which was shared by at least two CNDDs: 1) an age-at-exposure-related susceptibility to Creutzfeldt-Jakob disease (CJD) and several late-life CNDDs; 2) a relationship between age at onset, survival, and incidence; 3) shared genetic risk factors for CJD and late-life CNNDs; 4) partly shared personal (diagnostic, educational, behavioral, and social risk factors) predating clinical onset of late-life CNDDs; 5) two environmental risk factors, namely, surgery for sporadic CJD and amyotrophic lateral sclerosis, and Bordetella pertussis infection for Parkinson's disease; 6) reticulo-endothelial system stressors or general drivers (andropause or premenopausal estrogen deficiency, APOE epsilon 4, and vascular risk factors) for late-life CNDDs such as dementia/Alzheimer's disease, type-2 diabetes mellitus, and some sporadic cardiac and vascular degenerative diseases; and 7) a high, invariant incidence ratio of sporadic to genetic forms of mid- and late-life CNDDs, and type-2 diabetes mellitus. clusion: There might be a systematic epidemiologic pattern induced by specific proteins (PrP, TDP-43, SOD1, alpha-synuclein, amyloid-beta, tau, Langerhans islet peptide, and transthyretin) or established combinations of these.