Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use

Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

A causal relationship between gut microbiota and subcortical brain structures contributes to the microbiota-gut-brain axis: a Mendelian randomization study

A correlation between gut microbiota and brain structure, referring to as a component of the gut-brain axis, has been observed in observational studies. However, the causality of this relationship and its specific bacterial taxa remains uncertain. To reveal the causal effects of gut microbiota on subcortical brain volume, we applied Mendelian randomization (MR) studies in this study. Genome-wide association study data were obtained from the MiBioGen Consortium (n = 18,340) and the Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium (n = 13,170). The primary estimate was obtained utilizing the inverse-variance weighted, while heterogeneity and pleiotropy were assessed using the Cochrane Q statistic, MR Pleiotropy RESidual Sum and Outlier, and MR-Egger intercept. Our findings provide strong evidence that a higher abundance of the genus Parasutterella is causally correlated with a decrease in intracranial volume (β = -30,921.33, 95% CI -46,671.78 to -15,170.88, P = 1.19 × 10-4), and the genus FamilyXIIIUCG001 is associated with a decrease in thalamus volume (β = -141.96, 95% CI: -214.81 to -69.12, P = 1.0× 10-4). This MR study offers novel perspectives on the intricate interplay between the gut microbiota and subcortical brain volume, thereby lending some support to the existence of the microbiota-gut-brain axis.

The interaction effects of age, APOE and common environmental risk factors on human brain structure

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

The potential role of gray matter volume differences in the association between smoking and depression: A narrative review

Tobacco use and major depression are both leading contributors to the global burden of disease and are also highly comorbid. Previous research indicates bi-directional causality between tobacco use and depression, but the mechanisms that underlie this causality are unclear, especially for the causality from tobacco use to depression. Here we narratively review the available evidence for a potential causal role of gray matter volume in the association. We summarize the findings of large existing neuroimaging meta-analyses, studies in UK Biobank, and the Enhancing NeuroImaging Genetics through MetaAnalysis (ENIGMA) consortium and assess the overlap in implicated brain areas. In addition, we review two types of methods that allow us more insight into the causal nature of associations between brain volume and depression/smoking: longitudinal studies and Mendelian Randomization studies. While the available evidence suggests overlap in the alterations in brain volumes implicated in tobacco use and depression, there is a lack of research examining the underlying pathophysiology. We conclude with recommendations on (genetically-informed) causal inference methods useful for studying these associations.

Investigating the Relationship Between Smoking Behavior and Global Brain Volume

Background

Previous studies have shown that brain volume is negatively associated with cigarette smoking, but there is an ongoing debate about whether smoking causes lowered brain volume or a lower brain volume is a risk factor for smoking. We address this debate through multiple methods that evaluate directionality: Bradford Hill's criteria, which are commonly used to understand a causal relationship in epidemiological studies, and mediation analysis.

Methods

In 32,094 participants of European descent from the UK Biobank dataset, we examined the relationship between a history of daily smoking and brain volumes, as well as an association of genetic risk score to ever smoking with brain volume.

Results

A history of daily smoking was strongly associated with decreased brain volume, and a history of heavier smoking was associated with a greater decrease in brain volume. The strongest association was between total gray matter volume and a history of daily smoking (effect size = -2964 mm3, p = 2.04 × 10-16), and there was a dose-response relationship with more pack years smoked associated with a greater decrease in brain volume. A polygenic risk score for smoking initiation was strongly associated with a history of daily smoking (effect size = 0.05, p = 4.20 × 10-84), but only modestly associated with total gray matter volume (effect size = -424 mm3, p = .01). Mediation analysis indicated that a history of daily smoking mediated the relationship between the smoking initiation polygenic risk score and total gray matter volume.

Conclusions

A history of daily smoking is strongly associated with a decreased total brain volume.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Association of smoking with brain gray and white matter volume: a Mendelian randomization study

BACKGROUND

Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association.

METHODS

Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect.

RESULTS

Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, -0.100; 95% CI, -0.156 to -0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division.

CONCLUSIONS

This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.

Mendelian randomization analyses identify bidirectional causal relationships of obesity with psychiatric disorders

BACKGROUND

Obesity have been showed to be strongly associated with psychiatric disorders, but the exact causality and the direction of the relationship remain inconclusive. Thus, we aimed to identify the causal associations between obesity and psychiatric disorders using two-sample Mendelian randomization (MR).

METHODS

Single-nucleotide polymorphisms associated with obesity, including body mass index (BMI), waist-hip ratio (WHR), and waist-hip ratio adjusted for BMI (WHRadjBMI), were extracted from a genome-wide association study of 694,649 European ancestry from the GIANT consortium. Summary level data for 10 psychiatric disorders were obtained from the Psychiatric Genomics Consortium. Inverse-variance weighted (IVW) method was used as the primary analysis, while several sensitivity analyses were applied to evaluate heterogeneity and pleiotropy.

RESULTS

The main MR results suggested higher BMI or WHR was positively causally associated with an increased risk of attention deficit hyperactivity disorder (ADHD), anorexia nervosa (AN), post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and Alzheimer's disease (ALZ), but negatively causally associated with an increased risk of obsessive-compulsive disorder (OCD) and schizophrenia. For the reverse direction, ADHD and MDD were associated with an increased risk of obesity, but schizophrenia and ALZ were associated with a decreased risk of obesity.

CONCLUSION

Our findings support evidence of causal relationships between obesity and ADHD, MDD, PTSD, ALZ, SCZ, AN, and OCD, and confirmed the bidirectional causal relationships between obesity and ADHD, MDD, SCZ, and ALZ.

Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies

Alcohol use is a growing global health concern and economic burden. Alcohol involvement (i.e., initiation, use, problematic use, alcohol use disorder) has been reliably associated with broad spectrum grey matter differences in cross-sectional studies. These findings have been largely interpreted as reflecting alcohol-induced atrophy. However, emerging data suggest that brain structure differences also represent pre-existing vulnerability factors for alcohol involvement. Here, we review evidence from human studies with designs (i.e., family-based, genomic, longitudinal) that allow them to assess the plausibility that these correlates reflect predispositional risk factors and/or causal consequences of alcohol involvement. These studies provide convergent evidence that grey matter correlates of alcohol involvement largely reflect predisposing risk factors, with some evidence for potential alcohol-induced atrophy. These conclusions highlight the importance of study designs that can provide causal clues to cross-sectional observations. An integrative model may best account for these data, in which predisposition to alcohol use affects brain development, effects which may then be compounded by the neurotoxic consequences of heavy alcohol use.

Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: A pooled analysis in the European Lifebrain consortium

BACKGROUND

Lifestyle-related risk factors, such as obesity, physical inactivity, short sleep, smoking and alcohol use, have been associated with low hippocampal and total grey matter volumes (GMV). However, these risk factors have mostly been assessed as separate factors, leaving it unknown if variance explained by these factors is overlapping or additive. We investigated associations of five lifestyle-related factors separately and cumulatively with hippocampal and total GMV, pooled across eight European cohorts.

METHODS

We included 3838 participants aged 18-90 years from eight cohorts of the European Lifebrain consortium. Using individual person data, we performed cross-sectional meta-analyses on associations of presence of lifestyle-related risk factors separately (overweight/obesity, physical inactivity, short sleep, smoking, high alcohol use) as well as a cumulative unhealthy lifestyle score (counting the number of present lifestyle-related risk factors) with FreeSurfer-derived hippocampal volume and total GMV. Lifestyle-related risk factors were defined according to public health guidelines.

RESULTS

High alcohol use was associated with lower hippocampal volume (r = -0.10, p = 0.021), and overweight/obesity with lower total GMV (r = -0.09, p = 0.001). Other lifestyle-related risk factors were not significantly associated with hippocampal volume or GMV. The cumulative unhealthy lifestyle score was negatively associated with total GMV (r = -0.08, p = 0.001), but not hippocampal volume (r = -0.01, p = 0.625).

CONCLUSIONS

This large pooled study confirmed the negative association of some lifestyle-related risk factors with hippocampal volume and GMV, although with small effect sizes. Lifestyle factors should not be seen in isolation as there is evidence that having multiple unhealthy lifestyle factors is associated with a linear reduction in overall brain volume.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2-74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0-71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank

BACKGROUND AND AIMS

Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

DESIGN

Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis.

SETTING

United Kingdom.

PARTICIPANTS

The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28].

MEASUREMENTS

Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers.

FINDINGS

The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results.

CONCLUSIONS

There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.

Causal inference on neuroimaging data with Mendelian randomisation

While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.

Association Between Brain Structure and Alcohol Use Behaviors in Adults: A Mendelian Randomization and Multiomics Study

IMPORTANCE

Past studies have identified associations between brain macrostructure and alcohol use behaviors. However, identifying directional associations between these phenotypes is difficult due to the limitations of observational studies.

OBJECTIVE

To use mendelian randomization (MR) to identify directional associations between brain structure and alcohol use and elucidate the transcriptomic and cellular underpinnings of identified associations.

DESIGN, SETTING, AND PARTICIPANTS

The main source data comprised summary statistics from population-based and case-control genome-wide association studies (GWAS) of neuroimaging, behavioral, and clinical phenotypes (N = 763 874). Using these data, bidirectional and multivariable MR was performed analyzing associations between brain macrostructure and alcohol use. Downstream transcriptome-wide association studies (TWAS) and cell-type enrichment analyses investigated the biology underlying identified associations. The study approach was data driven and did not test any a priori hypotheses. Data were analyzed August 2021 to May 2022.

MAIN OUTCOMES AND MEASURES

Brain structure phenotypes (global cortical thickness [GCT] and global cortical surface area [GCSA] in 33 709 individuals and left-right subcortical volumes in 19 629 individuals) and alcohol use behaviors (alcoholic drinks per week [DPW] in 537 349 individuals, binge drinking frequency in 143 685 individuals, and alcohol use disorder in 8845 individuals vs 20 657 control individuals [total of 29 502]).

RESULTS

The main bidirectional MR analyses were performed in samples totaling 763 874 individuals, among whom more than 94% were of European ancestry, 52% to 54% were female, and the mean cohort ages were 40 to 63 years. Negative associations were identified between genetically predicted GCT and binge drinking (β, -2.52; 95% CI, -4.13 to -0.91) and DPW (β, -0.88; 95% CI, -1.37 to -0.40) at a false discovery rate (FDR) of 0.05. These associations remained significant in multivariable MR models that accounted for neuropsychiatric phenotypes, substance use, trauma, and neurodegeneration. TWAS of GCT and alcohol use behaviors identified 5 genes at the 17q21.31 locus oppositely associated with GCT and binge drinking or DPW (FDR = 0.05). Cell-type enrichment analyses implicated glutamatergic cortical neurons in alcohol use behaviors.

CONCLUSIONS AND RELEVANCE

The findings in this study show that the associations between GCT and alcohol use may reflect a predispositional influence of GCT and that 17q21.31 genes and glutamatergic cortical neurons may play a role in this association. While replication studies are needed, these findings should enhance the understanding of associations between brain structure and alcohol use.