ArticleThe pharmacogenomics journal2021
Identification of a sex-stratified genetic algorithm for opioid addiction risk.
Article in The pharmacogenomics journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Genetic variation in BDNF is associated with opioid use disorder severity.Human genetics · 2026Article
- Review
- Tailored Therapies in Addiction Medicine: Redefining Opioid Use Disorder Treatment with Precision Medicine.Journal of personalized medicine · 2025Review
- Opioid use disorder risk alleles in self-reported assigned African American/Afro-Caribbean and European biogeographical genetic ancestry groups and in males and females.The pharmacogenomics journal · 2024Article
- The dopamine 3 receptor as a candidate biomarker and therapeutic for opioid use disorder.Addiction biology · 2024Article
- Pharmacogenetic landscape of pain management variants among Mediterranean populations.Frontiers in pharmacology · 2024Article
- Scoping review of associations between cytochrome P450 3A4/5 single nucleotide polymorphisms and risk factors for fentanyl overdose.PharmacogenomicsArticle
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The opioid epidemic has had a devastating impact on our country, with wide-ranging effects on healthcare, corrections, employment, and social systems. Programs have been put in place for monitoring prescriptions, initiating and expanding medications for opioid use disorder, and harm reduction (i.e., naloxone distribution, needle exchanges). However, opportunities for personalization of opioid therapy based on addiction risk have been limited. The goal of the present study was to develop an objective risk assessment algorithm based on genetic markers that are correlated with opioid use disorder (OUD). A total of 180 single-nucleotide polymorphisms (SNPs) were tested in patients with and without OUD. SNPs selected for testing were associated with opioid metabolism and drug reward pathways based on previous studies. Of the 394 patients recruited, 200 had OUD and 194 served as controls without OUD but with prior opioid exposure. Logistic regression analyses stratified by sex identified ten unique SNPs in females and nine unique SNPs in males that were significantly associated with OUD. A Genetics Opioid Risk Score (GenORs) was calculated by counting the number of OUD risk-associated SNPs/genotypes for each patient. To evaluate the discrimination of the GenORs, a receiver operating characteristic (ROC) curve for each sex was generated and determined to be sensitive and specific. This represents the first published example of a sex-based genetic risk score with potential to predict OUD, and the first OUD algorithm to include opioid-associated pharmacokinetic genes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.