Women and Men Often Respond Differently to Cannabinoids. Hormones May Be Part of the Story.

Narrative review of human and animal studies on sex differences in cannabinoid pharmacology, dependence, withdrawal, relapse, and task performance. The authors synthesize patterns across heterogeneous methods: epidemiologic observations, human laboratory tasks, and rodent experiments probing nociception, motor activity, reinforcement, and hormonal mechanisms. No systematic search strategy or pooled effect sizes were reported.

Sex can shape how drugs are experienced and how problems develop. By pulling together human data with mechanistic animal work, this review flags that women and men may not respond to cannabinoids the same way, and that hormonal context could matter. That has consequences for study design, interpretation of lab findings, and how dependence and withdrawal are evaluated across sexes.

Most cannabinoid research before and around 2013 rarely stratified outcomes by sex. This review pushed the field to consider that the same dose, timing, or exposure history may not translate across sexes, and that menstrual cycle phase or puberty timing could interact with cannabinoid responses. It also separates two evidence streams. Animal studies can map mechanisms and point to estradiol and sex-linked endocannabinoid signaling. Human findings suggest sex-linked differences in abuse liability, withdrawal, relapse, and task performance, but they are mixed, use different tasks, and often lack hormonal or cycle controls. The mechanistic story is stronger in rodents than in people.

This is a narrative review without a prespecified search strategy or quality ratings. Many human findings come from cross-sectional or laboratory task studies that do not establish causation and often do not control for menstrual phase, contraceptive use, or product potency. Mechanistic conclusions rest heavily on rodent data, which may not translate to humans. The paper predates today’s product diversity and higher-potency markets, and it does not quantify effect sizes.