METHODS: A cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups.
RESULTS: The OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81-1.0).
CONCLUSIONS: The applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.
Methods and Results: The study population was the postmortem cases of Asian population ranging from 16 to 75 years old in which blood and/or urine samples sent for alcohol and/or drug of abuse (DoA) analysis in year 2016 at our centre. Out of 434 cases, 54 from each group of positive and negative alcohol and/or DoA. Postmortem findings of lungs and postmortem CT scan urinary bladder volume (UBV) were recorded. Statistical significant correlation was obtained between urinary bladder distension on postmortem CT scan and cases with positive alcohol detection. However, the sensitivity was relatively low at 51.7%, whereas the specificity was 75% at the cut-off point. Low sensitivity and specificity at around 52.7% were obtained for pulmonary edema related to alcohol/DoA. This showed that UBV alone or pulmonary edema alone was not really a good indicator for alcohol or DoA intoxication. However, combination of both indicators provided higher sensitivity (73.3%) although specificity was lowered to 53.8%.
Conclusion: The findings of postmortem CT scan bladder distension and pulmonary edema could possibly identify intoxication cases but not conclusive.
METHODS: Higher education students from China, Ireland, Malaysia, South Korea, Taiwan, the Netherlands, and the United States were enrolled in a cross-sectional study from April to May 2020, which was during the beginning of the COVID-19 pandemic for most participants. An online survey, using validated tools, was distributed to assess perceived stress, dietary behaviors, alcohol misuse, sleep quality and duration, and resilience.
RESULTS: 2254 students completed the study. Results indicated that sleep quality mediated the relationship between perceived stress and dietary behaviors as well as the relationship between perceived stress and alcohol misuse. Further, increased resilience reduced the strength of the relationship between perceived stress and dietary behaviors but not alcohol misuse.
CONCLUSION: Based on these results, higher education students are likely to benefit from sleep education and resilience training, especially during stressful events.
METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality.
RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality.
CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.
DESIGN: A randomized, double-blind, placebo-controlled trial was conducted among incarcerated individuals with HIV and AUDs transitioning to the community from 2010 through 2016.
METHODS: Eligible participants (N = 100) were randomized 2:1 to receive 6 monthly injections of XR-NTX (n = 67) or placebo (n = 33) starting at release and continued for 6 months. The primary and secondary outcomes were the proportion that maintained or improved VS at <200 and <50 copies per milliliter from baseline to 6 months, respectively, using an intention-to-treat analysis.
RESULTS: Participants allocated to XR-NTX improved VS from baseline to 6 months for <200 copies per milliliter (48.0%-64.2%, P = 0.024) and for <50 copies per milliliter (31.0%-56.7%, P = 0.001), whereas the placebo group did not (<200 copies/mL: 64%-42.4%, P = 0.070; <50 copies/mL: 42.0%-30.3%, P = 0.292). XR-NTX participants were more likely to achieve VS than the placebo group at 6 months (<200 copies/mL: 64.2% vs. 42.4%; P = 0.041; <50 copies/mL: 56.7% vs. 30.3%; P = 0.015). XR-NTX independently predicted VS [<200 copies/mL: adjusted odds ratio (aOR) = 2.68, 95% confidence interval (CI) = 1.01 to 7.09, P = 0.047; <50 copies/mL: aOR = 4.54; 95% CI = 1.43 to 14.43, P = 0.009] as did receipt of ≥3 injections (<200 copies/mL: aOR = 3.26; 95% CI = 1.26 to 8.47, P = 0.010; <50 copies/mL: aOR = 6.34; 95% CI = 2.08 to 19.29, P = 0.001). Reductions in alcohol consumption (aOR = 1.43, 95% CI = 1.03 to 1.98, P = 0.033) and white race (aOR = 5.37, 95% CI = 1.08 to 27.72, P = 0.040) also predicted VS at <50 copies per milliliter.
CONCLUSIONS: XR-NTX improves or maintains VS after release to the community for incarcerated people living with HIV and AUDs.
METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.
RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.
CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.