Modeling Oxidative Stress-Linked Telogen Effluvium Using Monte Carlo Simulation of Published Trichoscopy Norms and...

Cannabis exposure has increased alongside broader legalization, yet its potential relationship to hair follicle biology and diffuse shedding patterns remains incompletely characterized. This study aimed to examine whether simulated cannabis exposure levels are associated with markers of diffuse hair shedding using a Monte Carlo-generated cohort. A synthetic dataset of 140 subjects was generated using a Monte Carlo framework parameterized by published trichoscopic follicular density values, nationally reported cannabis exposure distributions, and psychometric properties of validated hair loss and dermatology-related quality-of-life instruments. Cannabis exposure, follicular density, and Self-Assessment of Hair Loss (SAHL) scores were sampled from probability distributions informed by published means, standard deviations, and hypothetical covariance structures rather than from individual-level patient data. The simulated dataset was analyzed using Pearson correlations, linear regression, and analysis of covariance (ANCOVA) with demographic covariates. Higher cannabis exposure was associated with increased SAHL severity (r = 0.31, p < 0.01) and reduced follicular density (r = -0.38, p < 0.05). Both associations remained statistically significant after covariate adjustment, with larger effect magnitudes observed in female-assigned profiles. As these modeled relationships are non-causal and arise from simulated rather than clinical data, they serve only as hypothesis-generating signals. However, this exposure-level simulation demonstrates that publicly available epidemiologic and trichoscopic data can be integrated through computational modeling to generate falsifiable hypotheses regarding cannabis exposure and diffuse shedding phenotypes. Future studies should incorporate objective biological mediators, longitudinal imaging, and clinical datasets to determine whether these modeled associations correspond to measurable biological effects.