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. 2025 Jan 2;8(1):e2453913.
doi: 10.1001/jamanetworkopen.2024.53913.

Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder

Christal N Davis  1   2 , Zeal Jinwala  1   2 , Alexander S Hatoum  3 , Sylvanus Toikumo  1   2 , Arpana Agrawal  4 , Christopher T Rentsch  5   6   7 , Howard J Edenberg  8 , James W Baurley  9 , Emily E Hartwell  1   2 , Richard C Crist  1   2 , Joshua C Gray  10 , Amy C Justice  5   6 , Joel Gelernter  5   11   12 , Rachel L Kember  1   2 , Henry R Kranzler  1   2 ; VA Million Veteran Program
Collaborators, Affiliations

Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder

Christal N Davis et al. JAMA Netw Open. .

Abstract

Importance: Recently, the US Food and Drug Administration gave premarketing approval to an algorithm based on its purported ability to identify individuals at genetic risk for opioid use disorder (OUD). However, the clinical utility of the candidate genetic variants included in the algorithm has not been independently demonstrated.

Objective: To assess the utility of 15 genetic variants from an algorithm intended to predict OUD risk.

Design, setting, and participants: This case-control study examined the association of 15 candidate genetic variants with risk of OUD using electronic health record data from December 20, 1992, to September 30, 2022. Electronic health record data, including pharmacy records, were accrued from participants in the Million Veteran Program across the US with opioid exposure (n = 452 664). Cases with OUD were identified using International Classification of Diseases, Ninth Revision, or International Classification of Diseases, Tenth Revision, diagnostic codes, and controls were individuals with no OUD diagnosis.

Exposures: Number of risk alleles present across 15 candidate genetic variants.

Main outcome and measures: Performance of 15 genetic variants for identifying OUD risk assessed via logistic regression and machine learning models.

Results: A total of 452 664 individuals with opioid exposure (including 33 669 with OUD) had a mean (SD) age of 61.15 (13.37) years, and 90.46% were male; the sample was ancestrally diverse (with individuals of genetically inferred European, African, and admixed American ancestries). Using Nagelkerke R2, collectively, the 15 candidate genes accounted for 0.40% of variation in OUD risk. In comparison, age and sex alone accounted for 3.27% of the variation. The ensemble machine learning. The ensemble machine learning model using the 15 variants as predictive factors correctly classified 52.83% (95% CI, 52.07%-53.59%) of individuals in an independent testing sample.

Conclusions and relevance: Results of this study suggest that the candidate genetic variants included in the approved algorithm do not meet reasonable standards of efficacy in identifying OUD risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of both false-positive and false-negative findings. More clinically useful models are needed to identify individuals at risk of developing OUD.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Hatoum reported having a patent pending for T-020507, a multi-omics algorithm for testing neurological and psychiatric pharmaceutical efficacy. Dr Baurley reported being the owner of BioRealm LLC, a data science service company, and having patent US11610144B2 for biosignature discovery for substance use disorder using statistical learning issued. Dr Gelernter reported having patent 10 900 082 for genotype-guided dosing of opioid agonists issued and receiving payment for editorial work for Complex Psychiatry. Dr Kranzler reported receiving personal fees from Altimmune Inc, Clearmind Medicine Inc, Dicerna Pharmaceuticals, Entheon Biomedical Corp, Eli Lilly and Company, Sophrosyne Pharmaceuticals, Sobrera Pharma, and the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative and grant funding from Alkermes PLC outside the submitted work; and having patent 10 900 082 issued. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Percentage of Variance in Opioid Use Disorder (OUD) Case-Control Status Explained by Combined Single Nucleotide Variant Regression Models
PCs indicates principal components.
Figure 2.
Figure 2.. Area Under the Receiver Operating Characteristic Curves Estimating Opioid Use Disorder Case-Control Status
Short-term opioid exposure indicates 4 to 30 days. Linear SVM indicates linear support vector machine model. The diagonal line represents a classifier model that predicts at chance levels.
Figure 3.
Figure 3.. Area Under the Receiver Operating Characteristic Curves of Models Predicting Genetically Inferred Ancestry From 15 Candidate Single Nucleotide Polymorphisms
Genetically inferred ancestry was based on genetic similarity to global superpopulations defined by the 1000 Genomes Project (European, African, and Admixed American). Linear SVM indicates linear support vector machine model. The diagonal line represents a classifier model that predicts at chance levels.
See this image and copyright information in PMC

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