Genetics and prescription opioid use (GaPO): study design for consenting a cohort from an existing biobank to identify clinical and genetic factors influencing prescription opioid use and abuse.


Journal

BMC medical genomics
ISSN: 1755-8794
Titre abrégé: BMC Med Genomics
Pays: England
ID NLM: 101319628

Informations de publication

Date de publication:
26 10 2021
Historique:
received: 07 07 2021
accepted: 15 10 2021
entrez: 27 10 2021
pubmed: 28 10 2021
medline: 3 3 2022
Statut: epublish

Résumé

Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered. We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records. We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.

Sections du résumé

BACKGROUND
Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered.
METHODS
We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records.
DISCUSSION
We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.

Identifiants

pubmed: 34702274
doi: 10.1186/s12920-021-01100-z
pii: 10.1186/s12920-021-01100-z
pmc: PMC8547564
doi:

Substances chimiques

Analgesics, Opioid 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

253

Subventions

Organisme : NIDA NIH HHS
ID : R01 DA044015
Pays : United States
Organisme : NIDA NIH HHS
ID : DA044015
Pays : United States

Informations de copyright

© 2021. The Author(s).

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Auteurs

Vanessa Troiani (V)

Geisinger Clinic, Geisinger, Danville, PA, USA. vtroiani@geisinger.edu.
Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA. vtroiani@geisinger.edu.
Neuroscience Institute, Geisinger, Danville, PA, USA. vtroiani@geisinger.edu.
Department of Basic Sciences, Geisinger Commonwealth School of Medicine, Scranton, PA, USA. vtroiani@geisinger.edu.

Richard C Crist (RC)

Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Glenn A Doyle (GA)

Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Thomas N Ferraro (TN)

Center for Neurobiology and Behavior, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ, USA.

Donielle Beiler (D)

Geisinger Clinic, Geisinger, Danville, PA, USA.

Stephanie Ranck (S)

Geisinger Clinic, Geisinger, Danville, PA, USA.

Kortney McBryan (K)

Geisinger Clinic, Geisinger, Danville, PA, USA.

Margaret A Jarvis (MA)

Geisinger Clinic, Geisinger, Danville, PA, USA.

Jordan S Barbour (JS)

Geisinger Clinic, Geisinger, Danville, PA, USA.

John J Han (JJ)

Department of Pain Medicine, Geisinger Medical Center, Danville, PA, USA.

Ryan J Ness (RJ)

Department of Pain Medicine, Geisinger Medical Center, Danville, PA, USA.

Wade H Berrettini (WH)

Geisinger Clinic, Geisinger, Danville, PA, USA.
Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ, USA.

Janet D Robishaw (JD)

Department of Biomedical Science, Schmidt College of Medicine of Florida Atlantic University, Boca Raton, FL, USA.

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Classifications MeSH