Alcohol Use Disorder Clinical Trial
Official title:
Investigating the Mu:Kappa Opioid Receptor Imbalance in Alcohol Use Disorder
The primary objective of this multimodal positron emission tomography (PET) study is to use PET brain imaging to measure both MOR (Mu-Opioid receptors) and KOR (kappa-opioid receptors) in participants with alcohol use disorder (AUD) and to quantify the relationships between MOR and KOR, separately and jointly, to key clinical outcomes (e.g., craving, mood, withdrawal, time to lapse) during a quit attempt.
Primary Objective The primary objective of this multimodal positron emission tomography (PET) study is to use PET brain imaging to measure both MOR (Mu-Opioid receptors) and KOR (kappa-opioid receptors) in participants with alcohol use disorder (AUD) and to quantify the relationships between MOR and KOR, separately and jointly, to key clinical outcomes (e.g., craving, mood, withdrawal, time to lapse) during a quit attempt. Investigators will achieve this goal by completing the following aims: Aim 1: To determine whether participants with AUD in early abstinence (up to 6 days) have altered MOR and KOR availability compared to healthy subjects. Investigators propose to recruit 50 people with AUD (DSM-5 diagnosis) and 50 age- and sex-matched controls to each participate in one [11C]CFN and one [11C]PKAB PET scan. Participants with AUD will participate either 1) in a medically supervised inpatient unit, the Clinical Neuroscience Research Unit (CNRU), for ~6 days, or 2) will stop drinking on an outpatient basis with daily meetings for ~6 days. Measures of craving (including a cue reactivity task), mood, withdrawal, and drinking outcomes will be collected. Hypothesis: participants with AUD will have significantly higher MOR in ventral striatum, but lower KOR in amygdala and whole striatum compared to control participants. Aim 2: To relate measures of MOR and KOR availability to clinical outcomes during early and late abstinence. Early abstinence (~6 days) will be followed by an outpatient quit attempt for an additional 3 weeks supported by contingency management. Measures of craving, mood, withdrawal, and drinking outcomes will be collected throughout the study. Hypothesis: Taken one at a time, MOR availability in ventral striatum as well as KOR in the amygdala and whole striatum will each be significantly associated with abstinence-induced craving, mood, withdrawal, and time to lapse in participants with AUD. Aim 3: To maximize use of neuroimaging data via machine-learning-based clustering to identify biomarkers of the joint (MOR, KOR) dataset that characterize clinical outcomes in AUD during a quit attempt. Primary. The investigators will determine whether clusters of AUD patients based on features of the joint (MOR, KOR) data are significantly associated with distinct clinical outcomes (e.g., time to lapse) during a quit attempt. Secondary. Investigators will test the classification accuracy of our clustering approach. Hypotheses: Clustering of joint (MOR, KOR) data will yield clusters of AUD patients with significantly different clinical responses to a quit attempt. Clusters will be more compact (distinct) than if based on demographics or a single type of PET image, alone. A classification scheme based on (MOR, KOR) data will assign individual patients to pre-defined clusters with high classification accuracy, that is, to the cluster that best reflects their clinical outcomes. Aim 4: To determine whether MOR and KOR availability normalizes over the course of abstinence. Subjects who do not drop out of the study in the first 3 weeks may return for one [11C]CFN, one [11C]PKAB PET scan, and one MRI during weeks 3-6 of the study. This will allow us to gain information on the changes in the MOR and KOR systems that occur during a quit attempt. Investigators hypothesize a reduction in MOR and increase in KOR within subject in participants who are able to maintain abstinence. Secondary Objective (if applicable) The secondary objective of this study is to evaluate the predictive ability of the clustering method, i.e., its potential for personalized prediction of individual clinical outcomes. For any 'new' subject not used for the initial clustering, investigators will assign it to a cluster by a K-nearest neighbor approach. ;
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