The association between Major Depressive Disorder and Cannabis Use Disorder: a meta-analysis and meta-regression analysis

Introduction Major Depressive Disorder (MDD) is one of the most common psychiatric conditions, while cannabis is the most widely used illicit drug globally. Both MDD and Cannabis Use Disorder (CUD) hold significant epidemiological and health implications. Emerging evidence suggests a co-occurrence between cannabis abuse, dependence and depression, though studies remain limited. Objectives To estimate the percentage of individuals with CUD who have comorbid MDD and those with MDD who have comorbid CUD. Methods PubMed, SciELO, and Google Scholar were searched using keywords: ((abuse, cannabis[MeSH Terms]) OR (cannabis dependence[MeSH Terms])) AND ((depressive disorder, major[MeSH Terms]) OR (depressive disorder[MeSH Terms])). Original articles in English or Portuguese were included. Data collection followed PRISMA, MOOSE guidelines, and JBI critical appraisal. The final sample included 53 articles: 36 for the first meta-analysis and 17 for the second. A heterogeneity test (Q test) and "leave-one-out meta-analysis" were used. Prevalence rates were aggregated using random-effects models. Meta-regression and sensitivity analyses were conducted. Results MDD showed a high prevalence among individuals with CUD, at 31.12% (95% CI: 25.71% to 36.80%). Prevalence was not significantly influenced by year, age, gender, population type, assessment period, region, or diagnostic criteria. CUD prevalence among those with MDD was 10.95% (95% CI: 7.08% to 15.53%), with higher rates in men and younger individuals. CUD prevalence appears to be increasing over time, though population type and assessment period did not significantly affect overall prevalence. Conclusions This meta-analysis reveals a high prevalence of MDD among individuals with CUD and a significant prevalence of CUD among those with MDD, confirming a strong comorbidity. Cannabis use may exacerbate depressive symptoms, while those with MDD are at higher risk of developing CUD. Age, gender, and geographical factors influence this relationship. With increasing cannabis use, particularly among younger populations, the CUD-MDD comorbidity presents a growing public health issue. Further research is needed to explore the longitudinal link between these disorders. Disclosure of Interest None Declared

Authors

Institutions

Publication Details

Journal
European Psychiatry
Published
2025-04-01
DOI
https://doi.org/10.1192/j.eurpsy.2025.923
Primary Topic
Psychosocial Factors Impacting Youth
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The association between Major Depressive Disorder and Cannabis Use Disorder: a meta-analysis and meta-regression analysis

Maria Olívia Pozzolo Pedro, João Maurício Castaldelli-Maia, João Pini Alemar
European Psychiatry
Psychosocial Factors Impacting Youth
article

The association between Major Depressive Disorder and Cannabis Use Disorder: a meta-analysis and meta-regression analysis

Maria Olívia Pozzolo Pedro, João Maurício Castaldelli-Maia, João Pini Alemar
article en

Abstract

Introduction Major Depressive Disorder (MDD) is one of the most common psychiatric conditions, while cannabis is the most widely used illicit drug globally. Both MDD and Cannabis Use Disorder (CUD) hold significant epidemiological and health implications. Emerging evidence suggests a co-occurrence between cannabis abuse, dependence and depression, though studies remain limited. Objectives To estimate the percentage of individuals with CUD who have comorbid MDD and those with MDD who have comorbid CUD. Methods PubMed, SciELO, and Google Scholar were searched using keywords: ((abuse, cannabis[MeSH Terms]) OR (cannabis dependence[MeSH Terms])) AND ((depressive disorder, major[MeSH Terms]) OR (depressive disorder[MeSH Terms])). Original articles in English or Portuguese were included. Data collection followed PRISMA, MOOSE guidelines, and JBI critical appraisal. The final sample included 53 articles: 36 for the first meta-analysis and 17 for the second. A heterogeneity test (Q test) and "leave-one-out meta-analysis" were used. Prevalence rates were aggregated using random-effects models. Meta-regression and sensitivity analyses were conducted. Results MDD showed a high prevalence among individuals with CUD, at 31.12% (95% CI: 25.71% to 36.80%). Prevalence was not significantly influenced by year, age, gender, population type, assessment period, region, or diagnostic criteria. CUD prevalence among those with MDD was 10.95% (95% CI: 7.08% to 15.53%), with higher rates in men and younger individuals. CUD prevalence appears to be increasing over time, though population type and assessment period did not significantly affect overall prevalence. Conclusions This meta-analysis reveals a high prevalence of MDD among individuals with CUD and a significant prevalence of CUD among those with MDD, confirming a strong comorbidity. Cannabis use may exacerbate depressive symptoms, while those with MDD are at higher risk of developing CUD. Age, gender, and geographical factors influence this relationship. With increasing cannabis use, particularly among younger populations, the CUD-MDD comorbidity presents a growing public health issue. Further research is needed to explore the longitudinal link between these disorders. Disclosure of Interest None Declared

European PsychiatryVol. 68(S1)
Faculdade de Medicina do ABC (BR), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR)
Openalex Percentile: Top 67%
Psychosocial Factors Impacting Youth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.