Women face hidden psychosis risk from cannabis despite lower use rates

This scoping review of 30 studies published between 2000-2024 reveals a striking paradox in cannabis-induced psychosis: while men use cannabis at higher rates, women may face greater vulnerability to developing psychotic outcomes despite consuming less. The research challenges conventional understanding by showing that females lose their typical biological advantage in age of psychosis onset when exposed to cannabis, suggesting that cannabis exposure fundamentally alters disease patterns differently in men and women. Key mechanisms underlying these differences include BDNF gene polymorphisms, hormonal factors, and developmental timing, which interact in sex-specific ways that current prevention and treatment models often overlook.

The review identifies several critical sex-based differences in how cannabis affects the brain and behavior. Differential neurobiological responses to cannabis exposure between sexes suggest that women's brains may process cannabis compounds like THC differently, potentially making them more susceptible to psychotic episodes. Additionally, age-dependent vulnerability patterns vary significantly by gender, meaning that adolescent girls and young women may face different critical windows of risk compared to males during the same developmental periods. These findings highlight the importance of recognizing that cannabis risk isn't uniform across the population.

The implications are substantial for public health and clinical practice. Sex-specific approaches are now essential for prevention strategies, clinical assessment, and treatment planning to account for these biological and developmental differences. For women considering cannabis use, particularly during adolescence and young adulthood, this research suggests heightened caution regarding psychotic risk. Healthcare providers need to evaluate male and female cannabis users differently, considering hormonal status, genetic predisposition, and developmental stage rather than applying one-size-fits-all risk models.