leveraging artificial intelligence and multi-omics data to predict opioid addiction

Conditions: Opioid Use Disorder; Addiction, Opioid

Summary: the primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (oud). the following will be tested: (1) modus (measuring risk for opioid use disorder using snps), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (snp) that utilizes machine learning to determine an individual's risk; and (2) microud (microbiome for opioid use disorder), which will be a novel microbiome prediction panel for oud risk. modus and microud will be developed using existing public datasets with genomic and microbiome data (e.g., all of us, human microbiome project). during development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the university of california, san diego, veteran affairs of san diego, and veteran affairs of palo alto (each site with separate irb). the hypothesis is that modus and microud will have high predictive potential for identifying high risk patients for oud.

the primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (oud). the following will be tested: (1) modus (measuring risk for opioid use disorder using snps), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (snp) that utilizes machine learning to determine an individual's risk; and (2) microud (microbiome for opioid use disorder), which will be a novel microbiome prediction panel for oud risk. modus and microud will be developed using existing public datasets with genomic and microbiome data (e.g., all of us, human microbiome project). during development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the university of california, san diego, veteran affairs of san diego, and veteran affairs of palo alto (each site with separate irb). the hypothesis is that modus and microud will have high predictive potential for identifying high risk patients for oud. specific aim 1. validate a novel genomic predictive panel assay - termed modus - in a prospective observational study that aims to recruit 300 subjects (\~200 from ucsd and va san diego) with a history of oud. this genomic panel will be developed separately but then validated on the study population. healthy control data will be used from a publicly-available de-identified genomic dataset (all of us research program) . specific aim 2. validate a novel microbiome predictive panel assay - termed microud - in a prospective observational study that aims to recruit 300 subjects (\~200 from ucsd and va san diego) with a history of oud. this microbiome panel will be developed separately but then validated on the study population. healthy control data will be used from a publicly-available de-identified microbiome dataset (human microbiome project).

Eligibility criteria:
inclusion criteria: * diagnosis of oud (active or in remission) defined by the dsm-5 criteria * age ≥ 18 years old exclusion criteria: * inability to participate independently with the study (i.e. dementia) * chronic opioid use that is not consistent with a diagnosis of oud * patients that are pregnant * children * institutionalized individuals * non-english speaking subjects as there are several surveys without appropriate translation and with sensitive information (e.g., questions about mental health and history of drug use) that is required to complete the study.

Keywords: genomics artificial intelligence microbiome opioid use disorder opioid addiction