Background: Religiosity is a protective factor against many health problems, including alcohol use disorders (AUD). Studies suggest that religiosity has greater buffering effects on mental health problems among US Blacks and Hispanics than Whites. However, whether race/ethnic differences exist in the associations of religiosity, alcohol consumption and AUD is unclear. Method: Using 2004–2005 NESARC data (analytic n = 21 965), we examined the relationship of public religiosity (i.e. frequency of service attendance, religious social group size), and intrinsic religiosity (i.e. importance of religious/spiritual beliefs) to frequency of alcohol use and DSM-IV AUD in non-Hispanic (NH) Blacks, Hispanics and NH Whites, and whether associations differed by self-identified race/ethnicity. Results: Only public religiosity was related to AUD. Frequency of religious service attendance was inversely associated with AUD (NH Whites β : −0.103, p < 0.001; NH Blacks β : −0.115, p < 0.001; Hispanics β : −0.096, p < 0.001). This association was more robust for NH Blacks as compared with NH Whites and Hispanics (interaction β : 0.025, p < 0.001). Among NH Whites, higher intrinsic religiosity was inversely associated with alcohol use frequency ( β : −0.143, p < 0.001). These effects were more robust among NH Whites (interaction ( β : 0.072, p < 0.033) than for NH Blacks ( β : −0.080, p > 0.05) or Hispanics ( β : −0.002, p > 0.05). Conclusions: US adults reporting greater public religiosity were at lower risk for AUD. Public religiosity may be particularly important among NH Blacks, while intrinsic religiosity may be particularly important among NH Whites, and among Hispanics who frequently attend religious services. Findings may be explained by variation in drinking-related norms observed among these groups generally, and in the context of specific religious institutions.

Identification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understand the biology of alcohol use disorder (AUD) and drinks per week (DPW). Multi-omics integration approaches have shown potential for fine mapping complex loci to obtain biological insights to disease mechanisms. In this study, we use multi-omics approaches, to fine-map AUD and DPW associations at single SNP resolution to demonstrate that rs56030824 on chromosome 11 significantly reduces SPI1 mRNA expression in myeloid cells and lowers risk for AUD and DPW. Our analysis also identifies MAPT as a candidate causal gene specifically associated with DPW. Genes prioritized in this study show overlap with causal genes associated with neurodegenerative disorders. Multi-omics integration analyses highlight, genetic similarities and differences between alcohol intake and disordered drinking, suggesting molecular heterogeneity that might inform future targeted functional and cross-species studies.

Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior–posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.