Grant-Funded Lab Projects
Characterizing the Microbiome-Gut-Brain Axis in Individuals with Alcohol Use Disorder The human gut contains trillions of microbes, called the gut microbiome. Each person has a unique network of microbes in their gut. Some of these microbes are beneficial, while others are harmful. The gut microbiome communicates with the brain in a bidirectional manner, meaning that the gut communicates with the brain and the brain communicates with the gut. This pattern of communication is called the gut microbiome brain axis. Recently, preclinical (animal) recent has shown that chronic alcohol use can change the gut microbiome in rodents. People with an alcohol use disorder may also have an altered gut microbiome. This project seeks to characterize the gut microbiome brain axis in people with an alcohol use disorder and people without an alcohol use disorder. To do this work, we collect fecal samples, blood samples, and questionnaire data. We also collect functional magnetic resonance imaging (fMRI) data to understand investigate the brain.
The specific aims of this project are: (1) to identify the gut microbiota discriminating individuals with AUD from controls; (2) to evaluate the relationship between the gut microbiome and AUD phenomenology; and (3) to test the relationship between gut microbiota and a brain- based biomarker for AUD. The successful completion of this study will provide the first data linking the microbiome-gut-brain axis to AUD in a clinical sample. Next we will use this data to develop special treatments that target harmful gut microbiota to help people with an alcohol use disorder.
Identifying treatment responders in medication trials for AUD using machine learning approaches Alcohol use disorder (AUD), as defined in DSM-5, represents a highly prevalent, costly, and often untreated condition in the United States. Pharmacotherapy offers a promising avenue for treating AUD and for improving clinical outcomes for this debilitating disorder. While developing novel medications to treat AUD remains a high priority research area, there remain major opportunities to further elucidate clinical response in completed medication trials. To that end, a key question in randomized clinical trials (RCTs) is which patients respond to a given pharmacotherapy. Identifying treatment responders provides major opportunities to advance clinical care for AUD by personalizing medication practices on the bases of variables/predictors of good clinical response. For example, while the effect size for medications such as naltrexone is deemed small-to-moderate, a host of studies over the past decade have shown that its effect size may be considerably larger for certain subgroups of patients. Towards advancing precision medicine for AUD and leveraging data from a host of carefully conducted RCTs for AUD, this R03 application seeks to conduct secondary data analysis. Specifically, we propose to analyze data from four RCTs conducted by the NIAAA Clinical Investigations Group (NCIG). These state-of-the-art RCTs for AUD have tested the following pharmacotherapies: (a) quetiapine, (b) Levetiracetam XR (Keppra XR®), (c) Varenicline (Chantix®), and (d) HORIZANT® (Gabapentin Enacarbil) Extended-Release. In this R03 application, we propose to use a machine learning approach to identify treatment responders in the NCIG RCTs. Machine learning represents a highly promising and underutilized data analytic strategy in the field of AUD treatment response. Machine learning models prioritize the ability to predict future outcomes over creating perfectly fitting models for the data at hand. This results in models which are more generalizable to future observations, which fits well with our goal of identifying responders in RCTs. Leveraging data from these pivotal RCTs through secondary data analysis and using novel analytic methods, namely machine learning, provides a cost-effective approach to identifying AUD pharmacotherapy responders.