Obsessive-Compulsive Disorder (OCD) in Elderly Populations: A Precision Medicine Framework

obsessive-compulsive disorder (OCD) in elderly populations represents a critical yet understudied frontier in mental health, characterized by diagnostic ambiguities, treatment resistance, and dynamic symptom evolution. This review synthesizes current evidence on the epidemiology, neurobiology, and clinical management of late-life OCD, while proposing a novel polymorphic systems framework to reconceptualize its evolving nature. Drawing parallels to adaptive computational algorithms, we posit that OCD symptoms "mutate" contextually (e.g., contamination fears → symmetry obsessions) while retaining core anxiety-neutralizing functions, necessitating precision medicine approaches that map real-time symptom trajectories. Key findings include: (1) Genetic (SLC1A1 polymorphisms) and epigenetic (NR3C1 hypermethylation) factors driving late-onset OCD; (2) Neuroimaging biomarkers (orbitofrontal cortex hyperactivity, white matter hyperintensities) predictive of SSRI resistance; (3) Elevated pro-inflammatory cytokines (IL-6, TNF-α) correlating with symptom severity; and (4) Pandemic-induced exacerbations (68% increased compulsive handwashing). We critique traditional diagnostic frameworks and advance Bayesian hierarchical models to address cohort heterogeneity, integrating socio-cultural, genetic, and neuroimaging data for personalized interventions. Emerging tools such as virtual reality exposure therapy, AI-driven compulsion tracking, and caregiver-mediated CBT demonstrate promises but require age-specific adaptations. The review underscores the urgency of transcending static diagnostic paradigms in favor of dynamic, computationally inspired models that mirror OCD’s fluid architecture. By aligning geriatric psychiatry with precision medicine innovations, this work charts a roadmap for scalable, cost-effective interventions tailored to the unique needs of aging populations.