Smoking and the risk for bipolar disorder: causal evidence from a bidirectional Mendelian randomization study

Vermeulen, J.; Wootton, R.; Treur, J.; Sallis, H.; Jones, H.; Zammit, S.; van den Brink, W.; Goodwin, G.; de Haan, L.; Munafo, M.

doi:10.1101/522268

bioRxiv · 10.1101/522268

Smoking and the risk for bipolar disorder: causal evidence from a bidirectional Mendelian randomization study

Vermeulen, J.·Wootton, R.·Treur, J.·Sallis, H.·Jones, H.·Zammit, S.·van den Brink, W.·Goodwin, G.·de Haan, L.·Munafo, M.

View original source ↗Export metadata ↓

Abstract

ImportanceThere is increasing evidence that smoking is a risk factor for severe mental illness, including bipolar disorder. Conversely, patients with bipolar disorder might smoke more (often) as a result of the psychiatric disorder. Research that investigates causality and the direction of the relationship between smoking and bipolar disorder has been lacking.\n\nObjectiveTo investigate the direction and causal nature of the relationship between smoking and bipolar disorder we conducted a bidirectional Mendelian randomization (MR) study.\n\nData sourcesPublicly available summary statistics from genome-wide association studies on bipolar disorder, smoking initiation, smoking heaviness, smoking cessation and lifetime smoking (i.e., a compound measure of heaviness, duration and cessation). We applied multiple analytical methods with different, orthogonal assumptions to triangulate results, including inverse-variance weighted (IVW), MR-Egger or Egger SIMEX, weighted median, weighted mode, and Steiger filtered analyses.\n\nResultsAcross different methods of MR, consistent evidence was found for a positive effect of smoking on the odds of bipolar disorder (smoking initiation ORIVW=1.46, 95% CI=1.28-1.66, P=1.44x10-8, lifetime smoking ORIVW=1.72, 95% CI=1.29-2.28, P=1.8x10-4). The MR analyses of the liability of bipolar disorder on smoking provided no clear evidence of a strong causal effect (smoking initiation betaIVW= -0.008, 95% CI= -0.026-0.009, P=0.35; P=0.94; smoking heaviness betaIVW=0.028, 95% CI= 0.003-0.053, P=2.9x10-2; smoking cessation ORIVW= 0.997, 95% CI= 0.981-1.014, P=0.74; lifetime smoking betaIVW= -0.001, 95%CI=-0.022-0.020, P=0.95).\n\nConclusionsThese findings suggest that smoking initiation and lifetime smoking are likely to be a causal risk factor for developing bipolar disorder. We found some evidence that liability to smoking increased smoking heaviness. Given that smoking is a modifiable risk factor, these findings further support investment into smoking prevention and treatment in order to reduce mental health problems in future generations.

Source connections

Published article ↗

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Vermeulen, J., Wootton, R., Treur, J., Sallis, H., Jones, H., Zammit, S., van den Brink, W., Goodwin, G., de Haan, L., Munafo, M.. 2019-01-17. Smoking and the risk for bipolar disorder: causal evidence from a bidirectional Mendelian randomization study. https://doi.org/10.1101/522268

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗