Schoeler Tabea, Baldwin Jessie R, Martin Ellen, Barkhuizen Wikus, Pingault Jean-Baptiste
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK.
Nat Ment Health. 2024;2(7):865-876. doi: 10.1038/s44220-024-00261-x. Epub 2024 Jun 3.
Cannabis, one of the most widely used psychoactive substances worldwide, can give rise to acute cannabis-associated psychotic symptoms (CAPS). While distinct study designs have been used to examine CAPS, an overarching synthesis of the existing findings has not yet been carried forward. To that end, we quantitatively pooled the evidence on rates and predictors of CAPS ( = 162 studies, = 210,283 cannabis-exposed individuals) as studied in (1) observational research, (2) experimental tetrahydrocannabinol (THC) studies, and (3) medicinal cannabis research. We found that rates of CAPS varied substantially across the study designs, given the high rates reported by observational and experimental research (19% and 21%, respectively) but not medicinal cannabis studies (2%). CAPS was predicted by THC administration (for example, single dose, Cohen's = 0.7), mental health liabilities (for example, bipolar disorder, = 0.8), dopamine activity ( = 0.4), younger age ( = -0.2), and female gender ( = -0.09). Neither candidate genes (for example, , ) nor other demographic variables (for example, education) predicted CAPS in meta-analytical models. The results reinforce the need to more closely monitor adverse cannabis-related outcomes in vulnerable individuals as these individuals may benefit most from harm-reduction efforts.
大麻是全球使用最广泛的精神活性物质之一，可引发急性大麻相关精神病性症状（CAPS）。虽然已采用不同的研究设计来研究CAPS，但尚未对现有研究结果进行全面综合分析。为此，我们对观察性研究、实验性四氢大麻酚（THC）研究以及药用大麻研究中关于CAPS发生率及预测因素的证据进行了定量汇总（n = 162项研究，n = 210,283名接触大麻的个体）。我们发现，不同研究设计中CAPS的发生率差异很大，观察性研究和实验性研究报告的发生率较高（分别为19%和21%），而药用大麻研究的发生率较低（2%）。THC给药（例如单剂量，Cohen's d = 0.7）、心理健康问题（例如双相情感障碍，d = 0.8）、多巴胺活性（d = 0.4）、较年轻年龄（d = -0.2）以及女性（d = -0.09）可预测CAPS。在荟萃分析模型中，候选基因（例如， ， ）和其他人口统计学变量（例如教育程度）均无法预测CAPS。这些结果强化了对易受伤害个体中与大麻相关的不良后果进行更密切监测的必要性，因为这些个体可能从减少伤害的措施中获益最多。
