Modeling Oxidative Stress–Linked Telogen Effluvium: A Monte Carlo Simulation Using Published Trichoscopy Norms and Cannabis Exposure Distributions

Abstract Cannabis exposure has increased with greater legalization, yet little is known about cannabis and its possibly relevant impacts on hair biology. The literature suggests that inhalative byproducts and endocannabinoid signaling can affect processes that may affect anagen stability, and, as hair follicles are metabolically active organs susceptible to oxidative and inflammatory damage, they could be affected as well. Therefore, this study intends to understand whether population-based cannabis exposure behaviors statistically correlate with symptoms characteristic of diffuse shedding when applied to a virtual population. A synthetic dataset of 140 individuals was generated using a Monte Carlo framework parameterized by published trichoscopy-based follicular density values, national cannabis use statistics, and psychometric properties reported for hair loss and dermatologic quality of life measures. Cannabis exposure, density, and SAHL scores were sampled from probability distributions specified by published means, standard deviations, and plausible covariance structures rather than from individual-level patient data. The simulated dataset was then subjected to Pearson correlations, linear regression, and 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, and female-assigned profiles showed larger effect magnitudes. These associations reflect patterns in the modeled population structure rather than clinical observations, but they parallel mechanistic pathways described in oxidative stress and hair cycle research. As a simulation study, these results cannot establish causality and are limited by underlying assumptions in the source distributions. However, they demonstrate that publicly available data and computational modeling can be combined to generate preliminary hypotheses about how cannabis exposure patterns may align with characteristics observed in telogen effluvium. Future work should integrate empirical biomarkers, longitudinal imaging, and clinical datasets to determine whether these modeled patterns correspond to measurable biological effects.