Evidence map›Paper›PMID 39354046›Full record

ArticleScientific reports2024

A genome-wide Association study of the Count of Codeine prescriptions.

Wenyu Song, Max Lam, Ruize Liu, Aurélien Simona, Scott G Weiner, Richard D Urman, Kenneth J Mukamal, Adam Wright, David W Bates

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Wenyu SongDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA. wsong@bwh.harvard.edu.
Max LamStanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Ruize LiuStanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Aurélien SimonaDivision of Clinical Pharmacology and Toxicology, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland.
Scott G WeinerDepartment of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Richard D UrmanDepartment of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Kenneth J MukamalDepartment of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Adam WrightDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
David W BatesDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.

Funding

Using a Novel Comprehensive Linked Dataset to Determine Early Predictors of Opioid OverdoseR01DA044167 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI WEINER, SCOTT GORDON · 2018 to 2022
$2.6M
Linking Genetic and Clinical Data to Optimize Surgical Opioid Analgesic Prescribing and Predict Risks of Opioid-Related Adverse Drug EventsK01DA059572 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI Wenyu Song · 2024 to 2026
$581k
NIDA NIH HHS 1K01DA059572-01NIDA NIH HHS K01 DA059572NIDA NIH HHS R01 DA044167
6 · The paper itself

Abstract

Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.

Indexed as

Analgesics, OpioidCodeineElectronic Health RecordsGenome-Wide Association StudyAdultAgedDrug PrescriptionsFemaleHumansMaleMiddle AgedOpioid-Related DisordersPhenotypePolymorphism, Single NucleotideAnalgesics, OpioidCodeineElectronic health recordGenome-wide association studyMedication use phenotypeOpioid prescription phenotypeOpioid use disorderPolygenic risk score

Identifiers

PMID39354046
PMCPMC11445378

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Registered trials

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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.