Pharmacological Treatment of Alcohol Cravings

Oral Administration of Alcohol-Tolerant Lactic Acid Bacteria Alleviates Blood Alcohol Concentration and Ethanol-Induced Liver Damage in Rodents

Excessive alcohol consumption can have serious negative consequences on health, including addiction, liver damage, and other long-term effects. The causes of hangovers include dehydration, alcohol and alcohol metabolite toxicity, and nutrient deficiency due to absorption disorders. Additionally, alcohol consumption can slow reaction times, making it more difficult to rapidly respond to situations that require quick thinking. Exposure to a large amount of ethanol can also negatively affect a person's righting reflex and balance. In this study, we evaluated the potential of lactic acid bacteria (LAB) to alleviate alcohol-induced effects and behavioral responses. Two LAB strains isolated from kimchi, Levilactobacillus brevis WiKim0168 and Leuconostoc mesenteroides WiKim0172, were selected for their ethanol tolerance and potential to alleviate hangover symptoms. Enzyme activity assays for alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) were then conducted to evaluate the role of these bacteria in alcohol metabolism. Through in vitro and in vivo studies, these strains were assessed for their ability to reduce blood alcohol concentrations and protect against alcohol-induced liver damage. The results indicated that these LAB strains possess significant ethanol tolerance and elevate ADH and ALDH activities. LAB administration remarkably reduced blood alcohol levels in rats after excessive alcohol consumption. Moreover, the LAB strains showed hepatoprotective effects and enhanced behavioral outcomes, highlighting their potential as probiotics for counteracting the adverse effects of alcohol consumption. These findings support the development of functional foods incorporating LAB strains that can mediate behavioral improvements following alcohol intake.

DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health

Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.

Relationship between serum vitamin D and thyroid hormone profiles in male patients with alcohol dependence

BACKGROUND

Alcohol dependence (AD) results in several medical problems including vitamin D deficiency and thyroid dysfunction. However, the relationship between these two complications remains unclear. The aim of the present study was to explore the relationship between serum vitamin D and thyroid hormone profiles in male patients with AD.

METHODS

A total of 117 male patients with AD were enrolled. Vitamin D deficiency was defined as serum concentrations of the main circulating vitamin D, 25-hydroxy vitamin D [25(OH)D], below 50 nmol/L. The AD patients were divided into two groups accordingly: 46 patients with normal vitamin D levels (normal group) and 71 patients with vitamin D deficiency (deficiency group). The levels of thyroid hormone profiles including total triiodothyronine 3 (TT3), total thyroxine 4 (TT4), thyroid stimulating hormone (TSH), free triiodothyronine (fT3), and free thyroxine (fT4) between the two groups were compared. Correlation between the serum levels of 25(OH)D and thyroid hormone profiles was evaluated using simple correlation (Pearson's correlation) and multivariable analysis using linear regression models.

RESULTS

The prevalence of vitamin D deficiency in male patients with AD is 60.7% (71/117; 95% confidence interval: 51.6-69.1%). Moreover, the serum levels of TT3 (t = -2.682, p = 0.009), TT4 (t = -2.033, p = 0.044), fT3 (t = -2.986, p = 0.003), and fT4 (t = -2.558, p = 0.012) in deficiency group were significantly higher than those in normal group. Post hoc power analyses showed that the power for fT3 was sufficient (power > 0.80). Furthermore, univariate analysis showed that the serum vitamin D levels were negatively correlated with the TT3 (r = -0.189, p = 0.044), fT3 (r = -0.350, p < 0.001), and fT4 (r = -0.198, p = 0.033) levels, while multivariate analysis indicated that only fT3 was independently related to the serum levels of vitamin D in male patients with AD.

CONCLUSIONS

These results suggested that the serum vitamin D levels may be associated with fT3 in male patients with AD.