Neuroendocrine Response to Exogenous Ghrelin Administration, Combined With Alcohol, in Heavy-Drinking Individuals: Findings From a Randomized, Double-Blind, Placebo-Controlled Human Laboratory Study.

BACKGROUND: Accumulating evidence has established a role for the orexigenic hormone ghrelin in alcohol-seeking behaviors. Accordingly, the ghrelin system may represent a potential pharmacotherapeutic target for alcohol use disorder. Ghrelin modulates several neuroendocrine pathways, such as appetitive, metabolic, and stress-related hormones, which are particularly relevant in the context of alcohol use. The goal of the present study was to provide a comprehensive assessment of neuroendocrine response to exogenous ghrelin administration, combined with alcohol, in heavy-drinking individuals. METHODS: This was a randomized, crossover, double-blind, placebo-controlled human laboratory study, which included 2 experimental alcohol administration paradigms: i.v. alcohol self-administration and i.v. alcohol clamp. Each paradigm consisted of 2 counterbalanced sessions of i.v. ghrelin or placebo administration. Repeated blood samples were collected during each session, and peripheral concentrations of the following hormones were measured: leptin, glucagon-like peptide-1, pancreatic polypeptide, gastric inhibitory peptide, insulin, insulin-like growth factor-1, cortisol, prolactin, and aldosterone. RESULTS: Despite some statistical differences, findings were consistent across the 2 alcohol administration paradigms: i.v. ghrelin, compared to placebo, increased blood concentrations of glucagon-like peptide-1, pancreatic polypeptide, cortisol, and prolactin, both acutely and during the whole session. Lower levels of leptin and higher levels of aldosterone were also found during the ghrelin vs placebo session. CONCLUSION: These findings, gathered from a clinically relevant sample of heavy-drinking individuals with alcohol use disorder, provide a deeper insight into the complex interplay between ghrelin and appetitive, metabolic, and stress-related neuroendocrine pathways in the context of alcohol use.

Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status.

OBJECTIVE: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment. METHOD: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADT's were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. RESULTS: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. INTERPRETATION: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying < 7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. FUNDING: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT). RESEARCH IN CONTEXT: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. At the level of clinical practice, distinguishing between treatment- and nontreatment-seekers may help facilitate a targeted treatment approach. Previous efforts to understand the differences between these populations of individuals with AUD leveraged the multidimensional data collected in clinical research settings for AUD that are not well suited to traditional regression methods.Added Value of This Study: Alternating decision trees are well suited to deep-phenotyping data collected in clinical research settings as this approach handles nonparametric, skewed, and missing data whose relationships are nonlinear. This approach has proved to be superior in some cases to conventional clinical methods to solve diagnostic problems in medicine. We used a decision tree classifier to understand treatment- and non-treatment seeking group differences. The decision tree classifier approach chose a subset of factors arranged in an alternating decision tree that best predicts a given outcome. Assuming that the input measures are clinically relevant, the alternating decision tree that is generated has clinical value. Unlike other machine learning approaches, in addition to its predictive value, the nodes in the tree and their arrangement in a hierarchy have clinical utility. With the "if-then" logic of the tree, the clinician can learn what features become important and which recede in importance as the logic of the tree is followed. The decision tree classifier approach reduced 178 characterization measures (both categorical and continuous) in multiple domains to a decision tree comprised of 10 measures that together best classified subjects by treatment seeking status (yes/no).Implications After All the Available Evidence: We leveraged a large data set comprised of 178 clinical measures and using the decision tree approach, we have reduced these to a subset of 10 measures that accurately classified individuals with alcohol dependence by treatment utilization. From this analysis, drinking behavior variables and depression measures are strong treatment seeking predictors. Having identified a cluster of factors that predicts treatment seeking, we can assess the influence of these factors directly on the clinical study outcome measures themselves. In clinical practice these factors can be separate targets for treatment. In clinical research, the group differences my directly influence research outcomes for treatment of AUD.