Abstract
Background
There is growing evidence that smoking increases the risk of developing psychiatric disorders, but the underlying mechanisms are largely unknown. We examine brain structure as a potential pathway between smoking and psychiatric disease liability.
Methods
We test associations between smoking (initiation, cigarettes per day, cessation, lifetime use) and depression, bipolar disorder, and schizophrenia, with and without correcting for volume of the amygdala, hippocampus, lateral and medial orbitofrontal cortex, superior frontal context, and cortical thickness and surface area. We use three methods that use summary statistics of genome-wide association studies to investigate genome-wide and local genetic overlap (genomic structural equation modeling, local analysis of (co)variant association), as well as causal associations (Mendelian randomization).
Results
While we find causal effects of smoking on brain volume in different brain areas, and with psychiatric disorders, brain volume did not seem to mediate the effect of smoking on psychiatric disorders.
Conclusions
While these findings are limited by characteristics of the included summary statistics (e.g. sample size), we conclude that brain volume of these areas is unlikely to explain a substantial part of any effect of smoking on psychiatric disorders. Nevertheless, genetic methods are valuable tools for exploring other potential mechanisms, such as brain functional connectivity, foregoing the need to collect all phenotypes in one dataset
There is growing evidence that smoking increases the risk of developing psychiatric disorders, but the underlying mechanisms are largely unknown. We examine brain structure as a potential pathway between smoking and psychiatric disease liability.
Methods
We test associations between smoking (initiation, cigarettes per day, cessation, lifetime use) and depression, bipolar disorder, and schizophrenia, with and without correcting for volume of the amygdala, hippocampus, lateral and medial orbitofrontal cortex, superior frontal context, and cortical thickness and surface area. We use three methods that use summary statistics of genome-wide association studies to investigate genome-wide and local genetic overlap (genomic structural equation modeling, local analysis of (co)variant association), as well as causal associations (Mendelian randomization).
Results
While we find causal effects of smoking on brain volume in different brain areas, and with psychiatric disorders, brain volume did not seem to mediate the effect of smoking on psychiatric disorders.
Conclusions
While these findings are limited by characteristics of the included summary statistics (e.g. sample size), we conclude that brain volume of these areas is unlikely to explain a substantial part of any effect of smoking on psychiatric disorders. Nevertheless, genetic methods are valuable tools for exploring other potential mechanisms, such as brain functional connectivity, foregoing the need to collect all phenotypes in one dataset
| Original language | English |
|---|---|
| Article number | E171 |
| Journal | Psychological Medicine |
| Volume | 55 |
| DOIs | |
| Publication status | Published - 24 Jun 2025 |
Bibliographical note
Publisher Copyright:© The Author(s), 2025. Published by Cambridge University Press.
Research Groups and Themes
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