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Introduction: The co-occurrence of mental disorder symptoms is common in people who use substances. It is unclear whether patterns of comorbidity in a population-based sample of adults are consistent with prior work. The study goal was to identify this comorbidity structure and evaluate its stability over time. Methods: Using Waves 1, 2, and 3 of the Population Assessment of Tobacco and Health Study, this study applied latent class analysis to identify comorbid substance use, internalizing and externalizing mental disorder symptoms, and their stability over time. Results: A four-class solution was identified for Wave 1 as: (1) low symptom (N = 23,571, 72.9%), (2) internalizing (N = 4,098, 12.7%), (3) externalizing (N = 2,691, 8.3%), and (4) comorbid (N = 1,960, 6.1%). Similar latent profiles emerged across the three waves specifically where the low symptom class was largest (65.5% to 72.9%), and the comorbid class was smallest (6.1% to 8.2%). However, the composition of the classes changed in Wave 3 with low comorbid (N = 5,400, 20.6%) and substance use (N = 1,524, 5.8%) classes emerging. Overall, when individuals transitioned from preceding to subsequent wave, they typically transitioned into the low symptom class. Conclusion: The comorbidity structure of substance use behaviors, including cigarette and e-cigarette use, and mental disorder symptoms in a population-based sample of U.S. adults was characterized by four classes. Psychiatric comorbidity may vary in severity within a population and by specific sociodemographic factors. When comparing the latent classes over three years of data, the results suggest that the comorbidity structure may change as participants age. [ABSTRACT FROM AUTHOR]

Background and aimsDisentangling the putative impact of cannabis on brain morphology from other comorbid substance use is critical. After controlling for the effects of nicotine, alcohol and multi-substance use, this study aimed to determine whether frequent cannabis use is associated with significantly smaller subcortical grey matter volumes.DesignExploratory analyses using mixed linear models, one per region of interest (ROI), were performed whereby individual differences in volume (outcome) at seven subcortical ROIs were regressed onto cannabis and comorbid substance use (predictors).SettingTwo large population-based twin samples from the United States and Australia.ParticipantsA total of 622 young Australian adults [66% female; μage  = 25.9, standard deviation SD) = 3.6] and 474 middle-aged US males (μage  = 56.1SD = 2.6 ) of predominately Anglo-Saxon ancestry with complete substance use and imaging data. Subjects with a history of stroke or traumatic brain injury were excluded.MeasurementsMagnetic resonance imaging (MRI) and volumetric segmentation methods were used to estimate volume in seven subcortical ROIs: thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens. Substance use measurements included maximum nicotine and alcohol use, total life-time multi-substance use, maximum cannabis use in the young adults and regular cannabis use in the middle-aged males.FindingsAfter correcting for multiple testing (P = 0.007), cannabis use was unrelated to any subcortical ROI. However, maximum nicotine use was associated with significantly smaller thalamus volumes in middle-aged males.ConclusionsIn exploratory analyses based on young adult and middle-aged samples, normal variation in cannabis use is unrelated statistically to individual differences in brain morphology as measured by subcortical volume.

The estimation of cortical thickness is in part dependent on the degree of contrast in T1 signal intensity between white matter and gray matter along the cortical mantle. The ratio of white matter to gray matter signal (WM/GM contrast) has been found to vary as a function of age and Alzheimer's disease status, suggesting a biological component to what might otherwise be labeled as a nuisance variable. The aim of the present study was to determine if measures of WM/GM contrast are genetically influenced, as well as the degree to which this phenotype may be related to the genetic and environment determinants of cortical thickness. Participants were 514 male twins (130 monozygotic, 97 dizygotic pairs, and 60 unpaired individuals) from the Vietnam Era Twin Study of Aging. Ages ranged from 51 to 59 years. Measures of WM/GM contrast and cortical thickness were derived for 66 cortical regions of interest (ROI) using FreeSurfer-based methods. Univariate and bivariate twin analyses were used in order to estimate the heritability of WM/GM contrast, as well as the degree of shared genetic and environmental variance between WM/GM contrast and cortical thickness. WM/GM contrast was found to be significantly heritable in the majority of ROIs. The average heritability across individual ROIs was highest in the occipital lobe (.50), and lowest in the cingulate cortex (.24). Significant phenotypic correlations between WM/GM contrast and cortical thickness were observed for most of the ROIs. The majority of the phenotypic correlations were negative, ranging from ?.11 to ?.54. Of the 66 associations, only 17 significant genetic correlations were found, ranging from ?.16 to ?.34, indicating small amounts of shared genetic variance. The majority of the phenotypic correlations were accounted for by small unique environmental effects common between WM/GM contrast and cortical thickness. These findings demonstrate that like cortical thickness, WM/GM contrast is a genetically influenced brain structure phenotype. The lack of significant genetic correlations with cortical thickness suggests that this measure potentially represents a unique source of genetic variance, one that has yet to be explored by the field of imaging genetics.

Introduction: Genetic factors impact alcohol consumption and use disorder (AUD), with large-scale genome-wide association studies (GWAS) identifying numerous associated variants. Aggregate genetic methods in combination with important environmental factors (e.g., interpersonal trauma [IPT]) can be applied to expand our understanding of the ways by which genetic and environmental variables work together to influence alcohol consumption and disordered use. The present study aimed to detail the relationships between genome-wide polygenic scores (PGS) for alcohol phenotypes (i.e., alcohol consumption and AUD status) and IPT exposure as well as the interaction between them across ancestry. Methods: Data were drawn from the Spit for Science (S4S) study, a US college student population, where participants reported on IPT exposure prior to college and alcohol consumption and problems during college (N = 9,006; ancestry: 21.3% African [AFR], 12.5% Admixed Americas [AMR], 9.6% East Asian [EAS], 48.1% European [EUR], 8.6% South Asian [SAS]). Two trans-ancestry PGS were constructed, one for alcohol consumption and another for AUD, using largescale GWAS summary statistics from multiple ancestries weighted using PRS-CSx. Regression models were applied to test for the presence of associations between alcohol-PGS and IPT main and interaction effects. Results: In the meta-analysis across ancestry groups, IPT exposure and PGS were significantly associated with alcohol consumption (ßIPT = 0.31, PIPT = 0.0002; ßPGS = 0.09, PPGS = 0.004) and AUD (ORIPT = 1.12, PIPT = 3.5 × 10-8; ORPGS = 1.02, PPGS = 0.002). No statistically significant interactions were detected between IPT and sex nor between IPT and PGS. When inspecting ancestry specific results, the alcohol consumption-PGS and AUD-PGS were only statistically significant in the EUR ancestry group (ßPGS = 0.09, PPGS = 0.04; ORPGS = 1.02, PPGS = 0.022, respectively). Discussion: IPT exposure prior to college was strongly associated with alcohol outcomes in this college-age sample, which could be used as a preventative measure to identify students at high risk for problematic alcohol use. Additionally, results add to developing evidence of polygenic score association in meta-analyzed samples, highlighting the importance of continued efforts to increase ancestral representation in genetic studies and inclusive analytic approaches to increase the generalizability of results from genetic association studies. [ABSTRACT FROM AUTHOR]

Background and Aims: Electronic cigarette use has escalated rapidly in recent years, particularly among youth. Little is known about the genetic influences on e‐cigarette use. This study aimed to determine whether genetic risk for regular use of combustible cigarettes or for number of cigarettes smoked per day confers risk for ever e‐cigarette use or frequency of e‐cigarette use. Design, Setting, Participants, and Measurements: We used data from 9541 young adults from the Spit for Science longitudinal cohort study (2011–2019). Polygenic scores (PGS) of regular combustible cigarette use (PGS‐RCU) and cigarettes per day (PGS‐CPD) were constructed using summary statistics from the two largest available genome‐wide association study (GWAS) meta‐analysis of European ancestry and East Asian ancestry of combustible cigarette use and used to test whether the PGS of RCU or CPD predicted lifetime e‐cigarette use and frequency of past 30‐day e‐cigarette use in a diverse sample of young adults of African (AFR), Admixed American (AMR), East Asian (EAS), European (EUR), and South Asian (SAS) ancestry. Findings: The PGS‐RCU was associated with lifetime e‐cigarette use in the EUR sample (OR = 1.27, 95% CI = 1.19–1.36, P = 7.53 × 10−12), but not in the other subsamples (ps > 0.12). This association remained significant after excluding regular combustible cigarette smokers (OR = 1.21, 95% CI = 1.12–1.31, P = 3.36 × 10−6). There was no statistically significant association between PGS‐CPD and lifetime e‐cigarette use and neither the PGS‐RCU nor the PGS‐CPD were associated with frequency of e‐cigarette use in the past 30 days in any of the subsamples. Conclusions: Genetic factors associated with regular combustible cigarette use appear to be associated with ever e‐cigarette use in young adults. We did not find evidence for shared genetic factors influencing heaviness of use of combustible cigarettes and current e‐cigarette use frequency. [ABSTRACT FROM AUTHOR]

Introduction: As the use of electronic cigarette (EC) continues to rise in the United States, especially among adolescents and young adults, it is necessary to better understand the factors associated with EC initiation. Specifically, it is unclear how genetic and environmental contributions influence the initiation of EC. Furthermore, the degree to which genetic and environmental influences are shared between EC initiation and conventional cigarette (CC) initiation is unknown.Methods: A sample of young adult twins ages 15-20 (N = 858 individuals; 421 complete twin pairs) was used to estimate the genetic and environmental influences on the liability of initiation unique to EC and CC as well as the degree to which these factors are shared between the two. Approximately 24% of participants initiated the use of EC, 19% initiated the use of CC, and 11% initiated the dual use.Results: Combined contributions of additive genetic and shared environmental influences were significant for CC (ACC = 0.19 [95% confidence interval {CI} = 0-0.79], p = 0.57; CCC = 0.42 [95% CI = 0-0.70], p = 0.13) and EC (AEC = 0.25 [95% CI = 0-0.83, p = 0.44; CEC = 0.42 [95% CI = 0-0.73], p = 0.12), whereas unique environmental influences were significant (ECC = 0.39 [95% CI = 0.18-0.57], p < 0.001; EEC = 0.32 [95% CI = 0.14-0.56], p < 0.001). Results also demonstrated a significant overlap of the unique environmental (rE = 0.87, p < 0.001) and familial influences contributing to correlation between the two phenotypes in the bivariate analysis.Conclusions: These preliminary results suggest that both genes and environmental influences are potential drivers of EC initiation among adolescents and young adults.Implications: This article is the first to use a sample of twin to estimate the contributions of genetic and environmental influences toward EC initiation and estimate the potential for overlapping influences with CC initiation. This study has implications for future debate about the etiology of EC and CC use with respect to potential overlapping genetic and environmental influences. [ABSTRACT FROM AUTHOR]

Background: Measuring and estimating alcohol consumption (AC) is important for individual health, public health, and Societal benefits. While self-report and diagnostic interviews are commonly used, incorporating biological-based indices can offer a complementary approach., Methods: We evaluate machine learning (ML) based predictions of AC using blood and urine-derived biomarkers. This research has been conducted using the UK Biobank (UKB) Resource. In addition to the prediction of the number of alcoholic Drinks Per Week (DPW), four other related phenotypes were predicted for performance comparison. Five ML models were assessed including LASSO, Ridge regression, Gradient Boosting Machines (GBM), Model Boosting (MBOOST), and Extreme Gradient Boosting (XGBOOST)., Results: All five ML methods achieved moderate prediction of DPW (r 2 =0.304-0.356) with biomarkers significantly increasing prediction above using only known covariates and liver enzymes (r 2 =0.105). XGBOOST achieved the best prediction performance (r 2 =0.356, MAE=5.214) at the expense of increasing model complexity and training resources compared to other ML methods. All ML models were able to accurately predict if subjects were heavy drinkers (DPW>8 for women and DPW>15 for men) and produced explainable models that highlighted the role of biomarkers in predicting DPW. While phenotype correlations were similar across methods, XGBOOST produced similar heritability estimates for observed (h 2 =0.064) and predicted (h 2 =0.077) DPW. The estimated genetic correlation between observed and predicted DPW was 0.877., Conclusions: Predicting AC from ML-based biological measures provides an opportunity to identify individuals at increased risk of heavy AC, thereby offering complementary avenue for risk assessment beyond self-report, screening instruments, or structured interviews, which have some known biases. In addition, explainable AI tools identified a constellation of biomarkers associated with AC.