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Background and Aims: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)‐derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. Design and Setting: Cross‐sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14–22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17–22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22–37 years). Cases: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. Measurements Graph theory metrics of segregation and integration were used to summarize SCN. Findings Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = −0.029, P = 0.002], lower modularity (AUC = −0.14, P = 0.004), lower average shortest path length (AUC = −0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = −0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. Conclusion: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN‐derived metrics to detect brain‐related psychopathology. [ABSTRACT FROM AUTHOR]

Chronic alcohol consumption leads to myocardial injury, ventricle dilation, and cardiac dysfunction, which is defined as alcoholic cardiomyopathy (ACM). To explore the induced myocardial injury and underlying mechanism of ACM, the Liber-DeCarli liquid diet was used to establish an animal model of ACM and histopathology, echocardiography, molecular biology, and metabolomics were employed. Hematoxylin-eosin and Masson's trichrome staining revealed disordered myocardial structure and local fibrosis in the ACM group. Echocardiography revealed thinning wall and dilation of the left ventricle and decreased cardiac function in the ACM group, with increased serum levels of brain natriuretic peptide (BNP) and expression of myocardial BNP mRNA measured through enzyme-linked immunosorbent assay and real-time quantitative polymerase chain reaction (PCR), respectively. Through metabolomic analysis of myocardium specimens, 297 differentially expressed metabolites were identified which were involved in KEGG pathways related to the biosynthesis of unsaturated fatty acids, vitamin digestion and absorption, oxidative phosphorylation, pentose phosphate, and purine and pyrimidine metabolism. The present study demonstrated chronic alcohol consumption caused disordered cardiomyocyte structure, thinning and dilation of the left ventricle, and decreased cardiac function. Metabolomic analysis of myocardium specimens and KEGG enrichment analysis further demonstrated that several differentially expressed metabolites and pathways were involved in the ACM group, which suggests potential causes of myocardial injury due to chronic alcohol exposure and provides insight for further research elucidating the underlying mechanisms of ACM. [ABSTRACT FROM AUTHOR]

Alcohol use disorder is characterised by disrupted reward learning, underpinned by dysfunctional cortico-striatal reward pathways, although relatively little is known about the biology of reward processing in populations who engage in risky alcohol use. Cues that trigger reward anticipation can be categorized according to their learnt valence (i.e., positive vs. negative outcomes) and motivational salience (i.e., incentive vs. neutral cues). Separating EEG signals associated with these dimensions is challenging because of their inherent collinearity, but the recent application of machine learning methods to single EEG trials affords a solution. Here, the Alcohol Use Disorders Identification Test (AUDIT) was used to quantify risky alcohol use, with participants split into high alcohol (HA) (n = 22, mean AUDIT score: 13.82) and low alcohol (LA) (n = 22, mean AUDIT score: 5.77) groups. We applied machine learning multivariate single-trial classification to the electroencephalography (EEG) data collected during reward anticipation. The LA group demonstrated significant valence discrimination in the early stages of reward anticipation within the cue-P3 time window (400-550 ms), whereas the HA group was insensitive to valence within this time window. Notably, the LA, but not the HA group demonstrated a relationship between single-trial variability in the early valence component and reaction times for gain and loss trials. This study evidences disrupted hypoactive valence sensitivity in the HA group, revealing potential neurophysiological markers for risky drinking behaviours which place individuals at-risk of adverse health events. [ABSTRACT FROM AUTHOR]

Chronic alcohol exposure can cause myocardial degenerative diseases, manifested as cardiac insufficiency, arrhythmia, etc. These are defined as alcoholic cardiomyopathy (ACM). Alcohol-mediated myocardial injury has previously been studied through metabolomics, and it has been proved to be involved in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway concerning unsaturated fatty acids biosynthesis and oxidative phosphorylation, which tentatively explored the mechanism of ACM induced by chronic drinking. To further study alcohol-induced myocardial injury, myocardial specimens from a previously successfully established mouse model of ACM were subjected to histological, echocardiographic, and proteomic analyses, and validated by real-time quantitative polymerase chain reaction (qPCR). Results of histopathology and echocardiography showed the hypertrophy of cardiomyocytes, the dilation of ventricles, and decreased cardiac function. Proteomic results, available via ProteomeXchange with identifier PXD032949, revealed 56 differentially expressed proteins (DEPs) were identified, which have the potential to be involved in the KEGG pathway related to fatty acid biosynthesis disorders, lipid metabolism disorders, oxidative stress, and, ultimately, in the development of dilated cardiomyopathy (DCM). The present study further elucidates the underlying effects of myocardial injury due to chronic alcohol intake, laying a foundation for further studies to clarify the potential mechanisms of ACM. [ABSTRACT FROM AUTHOR]

Background and aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy‐drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol. Design Cross‐sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics. Setting and participants: A total of 745 adults with AD and 979 non‐dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)–Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe. Measurements Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency). Findings The younger AD adults had lower network segregation and higher integration relative to non‐dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area‐under‐the‐curve (AUC) difference = −0.0142, 95% confidence interval (CI) = −0.1333, 0.0092; P‐value = 0.017], clustering coefficient (AUC difference = −0.0164, 95% CI = −0.1456, 0.0043; P‐value = 0.008) and local efficiency (AUC difference = −0.0141, 95% CI = −0.0097, 0.0034; P‐value = 0.010), as well as lower average shortest path length (AUC difference = −0.0405, 95% CI = −0.0392, 0.0096; P‐value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = −0.0011, 0.0043; P‐value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = −0.0131, 95% CI = −0.1304, 0.0033; P‐value = 0.024), lower average shortest path length (AUC difference = −0.0362, 95% CI = −0.0334, 0.0118; P‐value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = −0.0011, 0.0038; P‐value = 0.048). Conclusions: Cross‐sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy‐drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre‐existing risk factor for problematic drinking. [ABSTRACT FROM AUTHOR]

Impulsivity is a multidimensional construct that is related to different aspects of alcohol use, abuse, and dependence. Inhibitory control, one facet of impulsivity, can be assayed using the stop-signal task (SST) and quantified behaviorally via the stop-signal reaction time (SSRT) and electrophysiologically using event-related potentials (ERPs). Research on the relationship between alcohol use and SSRTs, and between alcohol use and inhibitory-control ERPs, is mixed. Here, adult alcohol users (n = 79), with a wide range of scores on the Alcohol Use Disorders Identification Test (AUDIT), completed the SST under electroencephalography (EEG) (70% of participants had AUDIT total scores greater than or equal to 8). Other measures, including demographic, self-report, and task-based measures of impulsivity, personality, and psychological factors, were also recorded. A machine-learning method with penalized linear regression was used to correlate individual differences in alcohol use with impulsivity measures. Four separate models were tested, with out-of-sample validation used to quantify performance. ERPs alone statistically predicted alcohol use (cross-validated r = 0.28), with both early and late ERP components contributing to the model (larger N2, but smaller P3, amplitude). Behavioral data from a wide range of impulsivity measures were also associated with alcohol use (r = 0.37). SSRT was a relatively weak statistical predictor, whereas the Stroop interference effect was relatively strong. The addition of nonimpulsivity behavioral measures did not improve the correlation (r = 0.34) and was similar when ERPs were combined with non-ERP data (r = 0.29). These findings show that inhibitory control ERPs are robustly correlated individual differences in alcohol use. [ABSTRACT FROM AUTHOR]