Predictors of posttreatment drinking outcomes in patients with alcohol dependence

Classical blood biomarkers identify patients with higher risk for relapse 6 months after alcohol withdrawal treatment

This naturalistic study among patients with alcohol dependence examined whether routine blood biomarkers could help to identify patients with high risk for relapse after withdrawal treatment. In a longitudinal study with 6-month follow-up among 133 patients with alcohol dependence who received inpatient alcohol withdrawal treatment, we investigated the usefulness of routine blood biomarkers and clinical and sociodemographic factors for potential outcome prediction and risk stratification. Baseline routine blood biomarkers (gamma-glutamyl transferase [GGT], alanine aminotransferase [ALT/GPT], aspartate aminotransferase [AST/GOT], mean cell volume of erythrocytes [MCV]), and clinical and sociodemographic characteristics were recorded at admission. Standardized 6 months' follow-up assessed outcome variables continuous abstinence, days of continuous abstinence, daily alcohol consumption and current abstinence. The combined threshold criterion of an AST:ALT ratio > 1.00 and MCV > 90.0 fl helped to identify high-risk patients. They had lower abstinence rates (P = 0.001), higher rates of daily alcohol consumption (P < 0.001) and shorter periods of continuous abstinence (P = 0.027) compared with low-risk patients who did not meet the threshold criterion. Regression analysis confirmed our hypothesis that the combination criterion is an individual baseline variable that significantly predicted parts of the respective outcome variances. Routinely assessed indirect alcohol biomarkers help to identify patients with high risk for relapse after alcohol withdrawal treatment. Clinical decision algorithms to identify patients with high risk for relapse after alcohol withdrawal treatment could include classical blood biomarkers in addition to clinical and sociodemographic items.

Assessing the Predictive Validity of the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES) in Alaska Native and American Indian People

OBJECTIVES

The objective of this study was to examine the predictive validity of the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES) among Alaska Native and American Indian (ANAI) people with an alcohol use disorder.

METHODS

The sample was 170 ANAI adults with an alcohol use disorder living in Anchorage, Alaska who were part of a larger alcohol intervention study. The primary outcome of this study was alcohol use as measured by mean urinary ethyl glucuronide (EtG). EtG urine tests were collected at baseline and then up to twice a week for four weeks. We conducted bivariate linear regression analyses to evaluate associations between mean EtG value and each of the three SOCRATES subscales (Recognition, Ambivalence, and Taking Steps) and other covariates such as demographic characteristics, alcohol use history, and chemical dependency service utilization. We then performed multivariable linear regression modeling to examine these associations after adjusting for covariates.

RESULTS

After adjusting for covariates, mean EtG values were negatively associated with the Taking Steps (P = 0.017) and Recognition (P = 0.005) subscales of the SOCRATES among ANAI people living in Alaska. We did not find an association between mean EtG values and the Ambivalence subscale (P = 0.129) of the SOCRATES after adjusting for covariates.

CONCLUSIONS

Higher scores on the Taking Steps and Recognition subscales of the SOCRATES at baseline among ANAI people predicted lower mean EtG values. This study has important implications for communities and clinicians who need tools to assist ANAI clients in initiating behavior changes related to alcohol use.

Exploration of the Role of Serine Proteinase Inhibitor A3 in Alcohol Dependence Using Gene Expression Omnibus Database

Background: Alcohol dependence is an overall health-related challenge; however, the specific mechanisms underlying alcohol dependence remain unclear. Serine proteinase inhibitor A3 (SERPINA3) plays crucial roles in multiple human diseases; however, its role in alcohol dependence clinical practice has not been confirmed. Methods: We screened Gene Expression Omnibus (GEO) expression profiles, and identified differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks were generated using STRING and Cytoscape, and the key clustering module was identified using the MCODE plugin. SERPINA3-based target microRNA prediction was performed using online databases. Functional enrichment analysis was performed. Fifty-eight patients with alcohol dependence and 20 healthy controls were recruited. Clinical variables were collected and follow-up was conducted for 8 months for relapse. Results: SERPINA3 was identified as a DEG. ELANE and miR-137 were identified after PPI analysis. The enriched functions and pathways included acute inflammatory response, response to stress, immune response, and terpenoid backbone biosynthesis. SERPINA3 concentrations were significantly elevated in the alcohol dependence group than in healthy controls (P < 0.001). According to the median value of SERPINA3 expression level in alcohol dependence group, patients were divided into high SERPINA3 (≥2677.33 pg/ml, n = 29) and low SERPINA3 groups (<2677.33 pg/ml, n = 29). Binary logistic analysis indicated that IL-6 was statistically significant (P = 0.015) Kaplan-Meier survival analysis did not indicate any difference in event-free survival between patients with low and high SERPINA3 levels (P = 0.489) after 8 months of follow-up. Receiver characteristic curve analysis revealed that SERPINA3 had an area under the curve of 0.921 (P < 0.0001), with a sensitivity and specificity of 93.1 and 80.0%, respectively. Cox regression analysis revealed that aspartate transaminase level was a negative predictor of relapse (β = 0.003; hazard ratio = 1.003; P = 0.03). Conclusions: SERPINA3 level was remarkably elevated in patients with alcohol dependence than healthy controls, indicating that SERPINA3 is correlated with alcohol dependence. However, SERPINA3 may not be a potential predictive marker of relapse with patients in alcohol dependence.

Predicting relapse after alcohol use disorder treatment in a high-risk cohort: The roles of anhedonia and smoking

On average, two-thirds of individuals treated for alcohol use disorder (AUD) relapse within six months. There is a critical need to identify modifiable risk factors associated with relapse that can be addressed during AUD treatment. Candidate factors include mood disorders and cigarette smoking, which frequently co-occur with AUD. We predicted that co-occurrence of mood disorders, cigarette smoking, and other modifiable conditions will predict relapse within six months of AUD treatment. Ninety-five Veterans, 23-91 years old, completed assessments of multiple characteristics including demographic information, co-occurring psychiatric disorders, and medical conditions during residential treatment for AUD. Participants' alcohol consumption was monitored over six months after participation. Logistic regression was used to determine if, mood disorders, cigarette smoking status, alcohol consumption, educational level, and comorbid general medical conditions are associated with relapse after AUD treatment. Sixty-nine percent of Veterans (n = 66) relapsed within six months of study while 31% remained abstinent (n = 29). While education, comorbid general medical conditions, and mood disorder diagnoses were not predictors of relapse, Veterans with greater symptoms of anhedonia, active smokers, and fewer days of abstinence prior to treatment showed significantly greater odds for relapse within six months. Anhedonia and cigarette smoking are modifiable risk factors, and effective treatment of underlying anhedonic symptoms and implementation of smoking cessation concurrent with AUD-focused interventions may decrease risk of relapse.

Alcohol use disorder relapse factors: A systematic review

A relapsing-remitting course is very common in patients with an Alcohol Use Disorder (AUD). Understanding the determinants associated with alcohol resumption remains a formidable task. This paper examines relapse determinants based on a systematic review of recent alcohol literature (2000-2019). Relevant databases were consulted for articles that contained information about specific relapse determinants and reported statistical significance of each relapse determinant in predicting relapse. Relapse was broadly defined based on the characterization in the included articles. From the initial identified 4613 papers, a total of 321 articles were included. Results encompass multiple relapse determinants, which were ordered according to biopsychosocial and spiritual categories, and presented, using a descriptive methodology. Psychiatric co-morbidity, AUD severity, craving, use of other substances, health and social factors were consistently significantly associated with AUD relapse. Conversely, supportive social network factors, self efficacy, and factors related to purpose and meaning in life, were protective against AUD relapse. Despite heterogeneity in different methods, measures, and sample characteristics, these findings may contribute to a better therapeutic understanding in which specific factors are associated with relapse and those that prevent relapse. Such factors may have a role in a personalized medicine framework to improve patient outcomes.

Frequency and Predictors of Alcohol-Related Outcomes Following Alcohol Residential Rehabilitation Programs: A 12-Month Follow-Up Study

Excessive use of alcohol has been identified as a major risk factor for diseases, injury conditions and increased mortality. The aims of this study were to estimate the frequency of success (abstinence and no alcohol related hospitalization) at 6- and 12-month follow-up after hospital discharge, and to identify the predictors of success. In 2009, a total of 1040 patients at their first admission in one of the 12 Residential Alcohol Abuse Rehabilitation Units (RAARUs) participating in the CORRAL (COordinamento of Residenzialità Riabilitative ALcologiche) project were included in the study. Several socio-demographic and clinical variables, and the number of treatments' strategies during the rehabilitation were collected. Information on alcohol abstinence and no alcohol related hospitalization was assessed through a phone interview using a health worker-administered structured questionnaire at six and 12 months after discharge. An inverse probability weighted, repeated measures Poisson regression model with robust variance was applied to estimate the association between patients' characteristics and the study's outcomes, accounting for non-responders status. The frequencies of abstinence and non-alcohol related hospitalization were 68.38% and 90.73% at six months, respectively, and 68.65% and 87.6% at 12 months, respectively. Patients that were already abstainers in the month before RAARUs' admission have an increased probability of being abstainers after discharge (relative risk: RR 1.20, 95% confidence interval: 95%CI 1.08⁻1.33) and of having an alcohol related hospitalization at 12 months. Subjects undergoing more than four treatment strategies (RR 1.19; 95% CI 1.01⁻1.40) had a higher abstinence probability and lower probability of no alcohol related hospitalizations after 12 months. Finally, patients with dual diagnosis (co-occurrence of alcohol abuse/dependence and psychiatric disorders) have a decreased probability of not being hospitalized for alcohol-related problems (RR 0.95; 95% CI 0.91⁻0.99). The results of this study suggest that specific attention should be paid to the intensity of treatment, with particular regard to a multidisciplinary rehabilitation in order to respond to the complexity of alcohol dependent patients.

Predicting the future relapse of alcohol-dependent patients from structural and functional brain images

In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.