- Study protocol
- Open Access
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
BMC Medical Genomics volume 14, Article number: 253 (2021)
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.
Chronic pain is a major clinical problem in the United States, affecting 20% of adults, and it is one of the most common reasons that adults seek medical care . Despite evidence indicating that high doses of prescription opioids (POs) are linked to an increased risk of opioid-related overdose death , many chronic pain patients are treated with opioids. In the United States, over 14,000 people died from overdoses involving POs in 2019 , but amidst the COVID-19 pandemic, nonfatal overdoses and overdose deaths have increased by 50–76% [4,5,6,7]. Misuse of these drugs was previously estimated to cost health insurance companies up to $72.5 billion a year . Recent epidemiological estimates indicate that 2–5% of the United States population misuses POs . At Geisinger, an integrated health system with > 2 million patients, where this study is based, over 300,000 patients have been treated with POs. A previous study on a small sample of patients being treated with POs at Geisinger indicates that 13.2–41.3% meet the criteria for moderate to severe opioid use disorder (OUD) . The same study reports that depression, anxiety, illicit drug use, post-traumatic stress disorder, alcohol dependence, being under 65 years old, and patient-reported assessments of poor health are all associated with increased risk for OUD. In subsequent analyses of the Geisinger population, we find that patients treated with opioids for chronic non-progressive pain, who are enrolled in a contract-based medication management program, are much more likely to have characteristics of OUD determined via chart review, as well as comorbid conditions, such as depression and anxiety .
In addition to known clinical risk factors, there is also a strong genetic component to OUD and other substance use disorders . Genome-wide association studies (GWAS) are a powerful approach that uses common allelic variants to identify genes and implicate specific biological pathways in certain disease states. GWAS can also be helpful in predicting the risk of certain diseases in subgroups of the population . Previous GWAS analyses reveal certain genetic variation that may be linked to risk for developing OUD, including changes found in the coding regions of genes responsible for calcium and potassium channel function . The largest GWAS of OUD to date uncovered only 1 statistically significant genetic variation, a single nucleotide polymorphism (SNP) in OPRM1, the gene encoding the mu-opioid receptor . A major challenge for genetic studies of OUD is low statistical power due primarily to limited sample sizes and high phenotypic heterogeneity. Thus, one factor hindering discovery of genomic predictors of OUD is the ascertainment of well-characterized (i.e., deeply phenotyped) populations of individuals who are exposed to opioids without developing OUD, as well as those with confirmed OUD.
Longitudinal electronic health records (EHRs) are a digital version of a patient’s medical history and when harnessed for research, can provide real-world clinical data at population-scale. In health systems with a biobank, genotype data can be linked with existing clinical data to generate derived associations. EHR-derived phenotypes have advanced genomic discovery of major medical and psychiatric diseases [15,16,17], with the majority of derived phenotypes focusing on diagnostic codes, lab values, and medication data. One type of EHR data that has not been widely utilized for discovery is imaging data. For example, magnetic resonance images (MRIs) of the brain may further reveal important clinical insights about individuals using opioids. POs have been shown to cause structural changes in the brain after periods of use as brief as one month . Substance abuse also causes recognizable structural brain changes; however, there are few studies that look specifically at structural changes subsequent to PO abuse. One study of brain MRIs in opioid abusers specifically excluded patients with a pain diagnosis . Consequently, little is known about the structural and functional differences of chronic pain patients with and without OUD.
A more thorough understanding of the clinical and genetic risk factors for OUD is needed for genomic and neurobiological discovery, as well as to enable physicians to readily identify patients at high risk for OUD. Geisinger, with its large geographically stable population, research infrastructure, and status as an integrated health system, is ideal for a study that combines data from clinical, genomic, and patient-reported sources. Of primary relevance to studies of opioid use and abuse, Geisinger has a large chronic pain patient population, with over 30,000 patients currently receiving POs and over 300,000 with opioid exposure. Geisinger serves a primarily rural population (more than 12 Pennsylvania counties considered Appalachia) and has an existing biobank that holds specimens from nearly 200,000 patients with linked genetic sequence data, to date. The informed consent for the biobank allows for recontact of a highly engaged patient population: consent for the biobank protocol is > 85% . Thus, the Geisinger environment provides a unique opportunity for patient recruitment into a longitudinal study of opioid use, abuse, and OUD.
In this prospective study, we plan to identify 20,000 patients who have been prescribed opioid analgesic medications at least twice in their lifetime (over 10,000 enrolled, to date). Patients are eligible to participate if they are between the ages 18–75 and of European ancestry. We determined to only include patients of European ancestry given the characteristics of Geisinger’s population (~ 96% white) and to improve power to detect genetic signal (see Methods and Discussion for plans to replicate in more diverse patient populations). Patients who are not already enrolled in the biobank protocol are invited to participate when consented for this study. Using this study design, we harness the rich diversity of data captured in EHRs and combine this with prospective self-reported questionnaire data from opioid-exposed and opioid-using patients, thereby establishing a cohort of genotyped and deeply phenotyped patients with a range of opioid use, misuse, dependence, and addiction.
This is a prospective study utilizing standard questionnaires, chart review, genetic, and brain imaging data to determine possible clinical, genetic, and neuroanatomic traits that predispose an individual to opioid addiction.
Patients are recruited from the health system using a multi-pronged recruitment strategy that harnesses the clinical-research infrastructure at Geisinger (See Figs. 1 and 2). A list of eligible patients that meet inclusion, but not exclusion, criteria (below and in Fig. 1) are identified by a Geisinger data broker. These eligibility lists are then aligned with specific Geisinger clinic schedules on a recruitment dashboard, allowing research assistants to approach prospective recruits to explain the study during their regularly scheduled clinic visits. If patients are already enrolled in the Geisinger biobank, MyCode, they are reminded of their participation and then interested patients are additionally consented into GaPO. For patients not already enrolled in the biobank, the research assistant explains MyCode and GaPO and consent is obtained for both studies. In addition to in-person clinic-based recruitment, patients are enrolled via a digital recruitment arm of the study. For digital recruitment, patients already enrolled in MyCode are sent information on the study via the patient portal or e-mail, and consent and study participation are achieved virtually using REDCap.
Patients have received at least two opioid prescriptions over the course of their lifetimes
If < 3 prescriptions over a lifetime in the EHR prescription database, verbal confirmation from patient that the prescriptions were filled and multiple doses taken is required.
Reads, writes, speaks, and understands English
Self-identifies as European ancestry
Is currently enrolled in the Geisinger biobank (MyCode) project, or consents to enroll in MyCode
Has other severe debilitating disease which may interfere with assessment and response to opioid treatment for chronic pain (e.g., metastatic cancer, palliative end of life care)
Not of European ancestry
We recruit patients from multiple clinics across Geisinger’s geographic service area that have a large proportion of patients with opioid prescriptions. We use the recruitment dashboards described above for each clinic type, which includes the interventional pain setting and pharmacy clinics assisting with complex medication management. In order to enrich our sample for patients that have a confirmed diagnosis of OUD, we also recruit from Geisinger’s Addiction Medicine Clinics , which serve patients undergoing outpatient treatment for substance use disorders.
After providing informed consent to Geisinger’s biobank protocol (MyCode ) and GaPO, patients complete several questionnaires, taking ~ 20–30 min in total (or each?). The consent and questionnaires are completed using REDCap, either on an iPad at clinical appointments (with the help of a research assistant), or remotely, from a personal computer or other digital device. Questionnaires include: the Brief Risk Questionnaire (BRQ ), Fagerstrom Test for Nicotine Dependence (FTND ), Alcohol Use Disorder Identification Test (AUDIT ), Generalized Anxiety Disorder 7-Item (GAD-7 ), Patient Health Questionnaire-9 (PHQ-9 [26, 27]), and DSM Questions to determine OUD, amongst others (see Table 1).
After completion of the questionnaires, there is no further study-related patient contact. As part of the consent process, patients give permission to access their entire health record and give permission to link non-health record data (such as insurance claims, external prescription databases, etc.). Following enrollment, the study plan comprises several elements, including estimating an OUD risk score based on chart review criteria , extracting brain imaging using the clinical imaging database pipeline, genomic analyses, and assessment of phenotypic stability based on EHR review. More detailed analytic plans are described in the Data Analysis section, below.
Data elements and survey instruments
In 2007, Geisinger adopted an opt-in biobank protocol (MyCode, ), with most patients consented in-person by a research consenter in the context of regularly scheduled clinical appointments. To date, > 280,000 patients provided consent for the biobank, which captures DNA from blood samples obtained during standard clinical patient blood draw procedures. In 2014, Geisinger Health System and Regeneron Pharmaceuticals partnered to launch the DiscovEHR project . The goal of this project is to use DNA samples from the MyCode participants to obtain genomic information from individuals and link it to their clinical data to better understand the genetic basis of diseases. Genetic data for the GaPO study is made available through MyCode/DiscovEHR, including whole exome sequencing (WES) and Human OmniExpressExome (HOEE) genotype data. To date, > 200,000 samples have been collected from consented patients and > 185,000 have been genotyped and sequenced from the MyCode/DiscovEHR cohort.
In addition to DNA, patients  provide self-report data on survey questions and validated surveys and  give permission to access other existing and future data within their health record. Please see descriptions below, as well as Tables 1 and 2.
As described in the Study Design section above, we capture several self-report questionnaires from enrolled patients as a quantitative estimate of several traits. See Table 1 for complete descriptions and details and Additional File 1 for survey questions that are not part of standardized assessments.
Electronic health record (EHR) data
A variety of data types are available within a patient’s EHR. As part of the consent process, patients agree to allow access to their health record for the duration of study enrollment. This allows for completion of chart review on all available data from years before the patient enrolls, as well as longitudinal chart review and data export for months and years following the enrollment date. For the types of information that are available in EHRs, please see more comprehensive reviews [23, 29]. Although the entire EHR is available for the current study, we describe the most relevant variables that are captured in the Geisinger EHR, and focus on any Geisinger-specific programs and data resources. See Table 2 for complete description and details regarding EHR variables.
We will conduct statistical analyses of phenotype, genetic, and neuroimaging data (both as separate and integrated datasets?). These will include Genome-, Phenome-, and Exome-Wide Analyses to discover genes and clinical phenotypes associated with OUD risk. Analyses will also include regression and multivariate analyses to assess differences in brain structure over a continuum of opioid abuse risk, as determined by a quantitative PO addiction score (see Chart Review, below). Statistical analyses will also combine all of the measures below in an effort to identify a comprehensive and reliable set of risk factors for OUD.
Chart Review. Enrolled patients’ entire EHR will be reviewed using a rubric-based procedure  to determine a quantitative DSM-based OUD severity score.
EHR data analysis. EHR data from discrete fields will be exported from all enrolled patients’ charts. Common comorbid diagnoses will be determined using cluster and correlation analyses. Patient groups and or case/control status may also be determined using ICD codes for OUD.
Patient-reported questionnaire data. Patient-reported questionnaire data will be scored according to standard procedures for each assessment. Summary scores will be compared between various groups (e.g. those with and without an OUD diagnosis) and used as covariates in genomic and brain imaging analyses, described below.
Genomic analyses. Various genetic analyses of data will be performed to identify specific DNA sequence changes that are associated with OUD. Methodologies related to DNA sample preparation, sequencing, sequence alignment, variant identification, genotype assignment, and quality control (QC) steps will be carried out as described in Dewey et al. . For Illumina HOEE genotyping data, SNPs will be called using standard methods in Illumina GenomeStudio. For genotype imputation, genotypes from the HOEE genotyping array will be imputed using the University of Michigan human imputation server (https://imputationserver.sph.umich.edu/). Imputed data will be cleaned using standard QC methods.
GWAS will be performed by running genotyped, imputed, or WES variants (MAF>1%) against quantitative measures of OUD. The primary analysis will use a mixed linear model to assess the relationship between OUD severity score and SNPs coded additively with respect to the number of minor alleles. Models will be controlled for any ancestry differences using principal components (PCs). To understand possible confounds, covariates, including biological sex, BMI, age, FTND score, AUDIT score, PHQ-9 score, and GAD-7 score will be included in models. Functional enrichment analysis and mapping regulatory variation will be followed up using eQTL approaches using publicly available genet expression databases.
Polygenic risk score (PRS)
An important benefit of GWAS is to predict the relative genetic risk that individuals may have to develop a particular disease. Knowledge of this risk can then be used for prevention, diagnosis, prognosis, and treatment of a disease. To estimate the genetic liability for OUD in individual patients, we will use a polygenic risk score (PRS) approach, summarizing the genetic effects among an ensemble of markers across the genome.
Rare variant analyses
Although GWAS will identify common variants (MAF > 1%) contributing to OUD risk, it is important to consider how rare coding (or even non-coding) variants and/or CNVs (MAF < 1%) within genes lying closest to GWAS hits may contribute to OUD risk. Rare SNPs within the same gene will be binned and then analyzed using a variety of aggregate techniques, including burden tests and kernel-based association methods (e.g., SKAT; ).
Brain imaging analyses
Available MRI data are extracted from the clinical picture archiving system and de-identified through a Geisinger data broker using existing Geisinger protocols. Briefly, patients with available MRI data are identified based on corresponding CPT codes for MRI of the head/neck. Image accession numbers are de-identified and corresponding image headers stripped of protected health information. Images are uploaded to a research picture archiving system and linked with corresponding patient data using study IDs. Images are run through a quality control process and gray matter, white matter, and cerebrospinal fluid volumes extracted using commonly used and available neuroimaging software. Output volumes can be used in a series of analyses relating brain volumetrics to phenotypic and genomic data.
The study design described here demonstrates the utility of harnessing several real-world clinical resources and the translational research infrastructure within an integrated health system, Geisinger, for the purposes of scientific discovery. The primary goal of our current work is to use these resources to discover novel risk genes associated with OUD, but this study design can also be useful as a model for understanding comorbidities or other complex diseases. The combination of EHR data capture along with patient reported information expands the potential of this data source for deep, high-throughput phenotyping, enabling the identification of thousands of patients with well-characterized opioid use history in a relatively short time frame.
Previous research in OUD predominantly focused on patients who used illicit opioids and are in a treatment setting. Many intermediate opioid phenotypes are lost when opioid use behaviors are condensed to define individuals as either cases or controls. EHR data offer the opportunity to better understand the full continuum of opioid phenotypes, ranging from exposure to addiction. The integrated nature of Geisinger’s health system, including embedding addiction medicine clinics into a whole-patient treatment model, also allows for an unique opportunity to understand the spectrum of opioid using patient phenotypes, including those in the context of chronic pain treatment, as well as those in an active treatment setting with a confirmed diagnosis.
One of the primary goals of this effort is to capture deep phenotyping for use in genetic analyses. Although diagnostic codes from EHRs have been used extensively for genetic discovery of many medical diseases [15,16,17], the use of EHR data for case/control definition of psychiatric disease is still evolving. One challenge regarding patient characterization within this study is that of phenotype stability and case–control status. Previous GWAS of OUD used a range of control definitions, including family-based characterization involving interviews , populations characterized by opioid misuse ; as well as definitions that require at least one documented opioid prescription . OUD case characterization is a less daunting challenge; OUD is a lifetime diagnosis, so every individual with an OUD treatment history can be considered a true case. The real challenge will be validation of OUD diagnoses in patients not in treatment. These patients may lack an OUD diagnosis in the EHR, based on ICD codes and/or treatment at a substance use disorder clinic. Previous epidemiological work within Geisinger has demonstrated that many people have OUD based on chart review and/or patient interview, even when that diagnosis has not been formally conferred [10, 11]. Patients with pre-existing nicotine use disorder, anxiety, and/or mood disorders tend to have greater numbers of OUD symptoms following opioid exposure and/or treatment of non-progressive pain with POs [6, 7], for review, see . Thus, when a genomic analysis is completed, there may be patients with opioid exposure that have not yet developed OUD, but due to other factors, are at particularly high risk. Given the complexities of defining such opioid-using ‘controls’, the use of more sophisticated multivariate modeling, such as Genomic structural equation modeling  that can account for these comorbidities and risk factors may be best for uncovering genetic differences associated with OUD.
MRI analysis has contributed substantially to our understanding of brain development, aging, and cognitive processing. Studies of brain structure that examine group-level differences tend to utilize prospective research recruitment strategies, requiring substantial time and money to assemble large population cohorts. Conversely, health care systems amass large collections of MRIs as part of routine patient care, but clinically-ascertained imaging tends to only be used for small cohort or case studies. To our knowledge, this study represents the first to extract thousands of brain MRIs from the EHR and use these to better understand opioid use and OUD. Although MRIs from clinical care are limited to brain structure measurements, there have been previous findings of altered brain structure from chronic pain  as well as use of opioids [18, 19]. There is also a body of work indicating evidence for structure–function relationships in the brain (for reviews, see [35, 36]), including our own work examining structural markers in the orbitofrontal cortex [37, 38], a brain region thought to be associated with risk for substance use disorders [39,40,41], Individual MRI metrics, such as a measurement of brain volume in a given region of cortex, can then be integrated statistically with phenotype and genetic data to evaluate the involvement of specific neuroanatomy in mediating relationships between genotype and phenotype (for review of approaches, see ).
The Geisinger health system translational research infrastructure is unique in many ways, contributing to the potential of the current study. Namely, Geisinger is one of a few integrated health systems in the United States, serving a largely rural population with a very low outmigration rate. Geisinger is also the primary health system serving patients within its geographical service area. Thus, there is incredibly dense data capture for large portions of a given patient’s lifetime. With Geisinger’s health insurance plan and integration of national prescribing data, a complete picture of a patient’s health and treatment course can be captured from existing resources. Other US health systems with large biobanks (e.g. Vanderbilt University Medical Center’s BioVU (https://www.vumc.org/dbmi/biovu), Massachusetts General Brigham Biobank (https://biobank.massgeneralbrigham.org)) are located in urban geographical regions and treat patients from a more diverse and geographically wide patient base. Certainly, other health system biobanks offer other distinct and meaningful data capture that is not present in the Geisinger population (e.g. more racially diverse populations, see Limitations, below). In addition to the distinct features of Geisinger, there are challenges across all biobanks with shifting clinical diagnosis and prescribing trends in all U.S. health systems, given ongoing efforts to limit opioid prescribing and increased recognition, identification, and treatment of patients with OUD [43,44,45].
There are several limitations of the type of data captured as part of this study. This sample will be limited to those that use the health care system. In addition, our patients that are identified as having OUD within the context of addiction medicine treatment will be composed of treatment-seeking patients, which is a small subsample of patients with OUD . Although our recruitment sample is quite large (currently 10,000 people), this sample size is still very small relative to the estimated size needed for well-powered GWAS analyses. For this reason, and based on the population characteristics at Geisinger, we prioritized patients with European American ancestry to maximize statistical power. Prescription OUD and the ongoing epidemic stemming from prescription opioid abuse is also more prevalent in European American populations. Given the limited racial diversity of our proposed sample, we aim to participate in multi-site endeavors that are outside of the immediate scope of this funded work to better understand this phenotype and any genetic findings in more diverse populations.
Another limitation exists in the challenges of EHR phenotyping and cohort effects that are present in all health system biobanks. One salient example is our own observation that diagnostic practices surrounding OUD diagnoses have dramatically changed over the course of the past few years at Geisinger. As recently as 5 years ago, the stigma and lack of physician education surrounding OUD stemming from Pos given during routine clinical care resulted in very few individuals being diagnosed. There were several Geisinger system-wide initiatives to reduce stigma, increase patient identification, and reduce opioid prescribing that may have accelerated changes in the health system. Further, one could argue that the most severe disease exists in patients who never present for treatment, either because the treatments as currently available require patient compliance and participation, or because a number of the most ill will die before entering treatment. Efforts and associated funding to reach and characterize the most severely impacted of the OUD spectrum is a pressing need for future research.
Here, we describe a study that recruits a large patient population using a combination of research and clinical infrastructure within one integrated health system, Geisinger. With this study, we aim to provide a platform for clinical and genetic discovery related to opioid use and abuse. To capture similar information within populations using other health system biobanks, it will be critical to close existing gaps in relevant data capture. We and others have shown that PO data are valuable for identifying patients at high risk for developing OUD [11, 14, 47,48,49,50]. However, these studies can only be completed in distinct populations where prescription drug histories are captured with relatively high density. For example, outside of the current study, other work has used cohorts such as military veterans, who receive all of their care within the same infrastructure [14, 50] or have drawn from large, population-based cohorts of medically insured adults . Prescription drug information is captured at the state level, with most states maintaining a prescription drug monitoring program for clinical use. National programs also exist; for example, Surescripts maintains an e-prescribing database that captures dispensed drug information from most retail pharmacies across the United States. To facilitate identification of individuals who have a high likelihood of being OUD cases, the approved use of state-wide and national prescription drug monitoring programs for research purposes is a necessary step that will enable scientific discovery and dramatically improve patient identification and treatment.
Availability of data and materials
At the end of the study, data captured as part of GaPO will be deidentified and deposited in public repositories. Data requests can also be made by contacting the corresponding author, Vanessa Troiani, by email: email@example.com.
Genetics and Prescription Opioid
Opioid Use Disorder
Single Nucleotide Polymorphism
Genome Wide Association Study
Electronic Health Record
Magnetic Resonance Imaging
Brief Risk Questionnaire
Fagerstrom Test of Nicotine Dependence
Alcohol Use Disorders Identification Test
Generalized Anxiety Disorder
Patient Health Questionnaire
Diagnostic and Statistical Manual of Mental Disorders
Polygenic Risk Score
Zelaya CE, Dahlhamer JM, Lucas JW, Connor EM. Chronic pain and high-impact chronic pain among US adults, 2019. 2020.
Baumblatt JAG, Wiedeman C, Dunn JR, Schaffner W, Paulozzi LJ, Jones TF. High-risk use by patients prescribed opioids for pain and its role in overdose deaths. JAMA Intern Med. 2014;174(5):796–801.
Wide-ranging online data for epidemiologic research (WONDER). Atlanta, GA: CDC, National Center for Health Statistics; 2020. Available at http://wonder.cdc.gov. [Internet]. 2020. Available from: http://wonder.cdc.gov
Appa A, Rodda LN, Cawley C, Zevin B, Coffin PO, Gandhi M, et al. Drug Overdose Deaths Before and After Shelter-in-Place Orders During the COVID-19 Pandemic in San Francisco. JAMA Netw Open. 2021;4(5):e2110452.
Currie JM, Schnell MK, Schwandt H, Zhang J. Trends in drug overdose mortality in ohio during the first 7 months of the COVID-19 pandemic. JAMA Netw Open. 2021;4(4):e217112.
Glober N, Mohler G, Huynh P, Arkins T, O’Donnell D, Carter J, et al. Impact of COVID-19 pandemic on drug overdoses in indianapolis. J Urban Health. 2020;97(6):802–7.
Ochalek TA, Cumpston KL, Wills BK, Gal TS, Moeller FG. Nonfatal opioid overdoses at an urban emergency department during the COVID-19 pandemic. JAMA. 2020;324(16):1673.
Prevention (CDC C for DC and. Vital signs: overdoses of prescription opioid pain relievers—United States, 1999–2008. MMWR Morb Mortal Wkly Rep. 2011;60(43):1487–92.
Substance Abuse and Mental Health Services Administration. (2020). Key substance use and mental health indicators in the United States: Results from the 2019 National Survey on Drug Use and Health (HHS Publication No. PEP20-07-01-001, NSDUH Series H-55). Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Retrieved from https://www.samhsa.gov/data/.
Boscarino J, Hoffman S, Han J. Opioid-use disorder among patients on long-term opioid therapy: impact of final DSM-5 diagnostic criteria on prevalence and correlates. Subst Abuse Rehabil. 2015;83.
Palumbo SA, Adamson KM, Krishnamurthy S, Manoharan S, Beiler D, Seiwell A, et al. Assessment of probable opioid use disorder using electronic health record documentation. JAMA Netw Open. 2020;3(9):e2015909–e2015909.
Gelernter J, Kranzler HR, Sherva R, Koesterer R, Almasy L, Zhao H, et al. Genome-wide association study of opioid dependence: multiple associations mapped to calcium and potassium pathways. Biol Psychiatry. 2014;76(1):66–74.
Wray NR, Goddard ME, Visscher PM. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 2007;17(10):1520–8.
Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, et al. Association of OPRM1 functional coding variant with opioid use disorder: a genome-wide association study. JAMA Psychiat. 2020;77(10):1072–80.
Saadatagah S, Jose M, Dikilitas O, Alhabi L, Miller AA, Fan X, et al. Genetic basis of hypercholesterolemia in adults. NPJ Genomic Med. 2021;6(1):1–7.
Levin MG, Klarin D, Walker VM, Gill D, Lynch J, Hellwege JN, et al. Association between genetic variation in blood pressure and increased lifetime risk of peripheral artery disease. Arterioscler Thromb Vasc Biol. 2021;41(6):2027–34.
Sanchez-Roige S, Cox NJ, Johnson EO, Hancock DB, Davis LK. Alcohol and cigarette smoking consumption as genetic proxies for alcohol misuse and nicotine dependence. Drug Alcohol Depend. 2021;221:108612.
Younger JW, Chu LF, D’Arcy NT, Trott KE, Jastrzab LE, Mackey SC. Prescription opioid analgesics rapidly change the human brain. PAIN®. 2011;152(8):1803–10.
Upadhyay J, Maleki N, Potter J, Elman I, Rudrauf D, Knudsen J, et al. Alterations in brain structure and functional connectivity in prescription opioid-dependent patients. Brain. 2010;133(7):2098–114.
Carey DJ, Fetterolf SN, Davis FD, Faucett WA, Kirchner HL, Mirshahi U, et al. The Geisinger MyCode community health initiative: an electronic health record–linked biobank for precision medicine research. Genet Med. 2016;18(9):906–13.
Barbour JS, Jarvis MA, Withers DJ. How Geisinger dramatically reduced deaths from opioid use disorder. NEJM Catal Innov Care Deliv. 2020;1(2).
Jones T, Lookatch S, Moore T. Validation of a new risk assessment tool: the Brief Risk Questionnaire. J Opioid Manag. 2015;11(2):171–83.
Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom K-O. The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991;86(9):1119–27.
Saunders JB, Aasland OG, Babor TF, De La Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction. 1993;88(6):791–804.
Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092.
Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.
Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann. 2002;32(9):509–15.
Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB, Fetterolf SN, et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science. 2016;354(6319):aaf6814.
Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory: Table 1. J Am Med Inform Assoc. 2013;20(e2):e226–31.
Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82–93.
Nelson EC, Agrawal A, Heath AC, Bogdan R, Sherva R, Zhang B, et al. Evidence of CNIH3 involvement in opioid dependence. Mol Psychiatry. 2016;21(5):608–14.
Amari E, Rehm J, Goldner E, Fischer B. Nonmedical prescription opioid use and mental health and pain comorbidities: a narrative review. Can J Psychiatry. 2011;56(8):495–502.
Grotzinger AD, Rhemtulla M, de Vlaming R, Ritchie SJ, Mallard TT, Hill WD, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019;3(5):513–25.
Borsook D, Erpelding N, Becerra L. Losses and gains: chronic pain and altered brain morphology. Expert Rev Neurother. 2013;13(11):1221–34.
Suárez LE, Markello RD, Betzel RF, Misic B. linking structure and function in macroscale brain networks. Trends Cogn Sci. 2020;24(4):302–15.
Alexander-Bloch A, Giedd JN, Bullmore E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci. 2013;14(5):322–36.
Troiani V, Patti MA, Adamson K. The use of the orbitofrontal H-sulcus as a reference frame for value signals. Eur J Neurosci. 2020;51(9):1928–43.
Patti MA, Wochele S, Hu Y, Regier PS, Childress AR, Troiani V. Orbitofrontal sulcogyral morphology in patients with cocaine use disorder. Psychiatry Res Neuroimaging. 2020;305:111174.
Schoenbaum G, Shaham Y. The role of orbitofrontal cortex in drug addiction: a review of preclinical studies. Biol Psychiatry. 2008;63(3):256–62.
O’Brien JW, Hill SY. Neural predictors of substance use disorders in Young adulthood. Psychiatry Res Neuroimaging. 2017;268:22–6.
Luijten M, Schellekens AF, Kühn S, Machielse MWJ, Sescousse G. Disruption of reward processing in addiction: an image-based meta-analysis of functional magnetic resonance imaging studies. JAMA Psychiat. 2017;74(4):387.
Bogdan R, Salmeron BJ, Carey CE, Agrawal A, Calhoun VD, Garavan H, et al. Imaging genetics and genomics in psychiatry: a critical review of progress and potential. Biol Psychiatry. 2017;82(3):165–75.
Gleber R, Vilke GM, Castillo EM, Brennan J, Oyama L, Coyne CJ. Trends in emergency physician opioid prescribing practices during the United States opioid crisis. Am J Emerg Med. 2020;38(4):735–40.
Ball SJ, Simpson K, Zhang J, Marsden J, Heidari K, Moran WP, et al. High-Risk Opioid Prescribing Trends: Prescription Drug Monitoring Program Data From 2010 to 2018. J Public Health Manag Pract [Internet]. 2020 Sep 9 [cited 2021 Jul 5]; Publish Ahead of Print. Available from: https://journals.lww.com/https://doi.org/10.1097/PHH.0000000000001203
Sutherland TN, Wunsch H, Pinto R, Newcomb C, Brensinger C, Gaskins L, et al. Association of the 2016 US Centers for Disease Control and Prevention Opioid Prescribing Guideline With Changes in Opioid Dispensing After Surgery. JAMA Netw Open. 2021;4(6):e2111826.
Jones CM, McCance-Katz EF. Co-occurring substance use and mental disorders among adults with opioid use disorder. Drug Alcohol Depend. 2019;197:78–82.
Chua K-P, Brummett CM, Conti RM, Bohnert A. Association of opioid prescribing patterns with prescription opioid overdose in adolescents and young adults. JAMA Pediatr. 2020;174(2):141–8.
Brummett CM, Waljee JF, Goesling J, Moser S, Lin P, Englesbe MJ, et al. New Persistent Opioid Use After Minor and Major Surgical Procedures in US Adults. JAMA Surg. 2017;152(6):e170504.
Santosa KB, Hu H-M, Brummett CM, Olsen MA, Englesbe MJ, Williams EA, et al. New persistent opioid use among older patients following surgery: a Medicare claims analysis. Surgery. 2020;167(4):732–42.
Rentsch CT, Edelman EJ, Justice AC, Marshall BDL, Xu K, Smith AH, et al. Patterns and correlates of prescription opioid receipt among US veterans: a national, 18-year observational cohort study. AIDS Behav. 2019;23(12):3340–9.
Posner K, Brown GK, Stanley B, Brent DA, Yershova KV, Oquendo MA, et al. The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am J Psychiatry. 2011;168(12):1266–77.
We thank the patients that make this important work possible, as well as the research assistants and study staff.
This study is funded by the National Institutes of Health (R01DA044015 to VT, WBH, and JDR) and the State of Pennsylvania (WBH). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Ethics approval and consent to participate
Ethical approval for the study has been obtained from the Geisinger internal review board (IRB; study number 2017-0190). All participants receive a thorough explanation of the study consent form and are given the opportunity to ask questions prior to and during enrollment. All participants are asked to complete a digital consent form during enrollment. Participants are free to withdraw from the study at any time. Participants are reimbursed $10 for their time and effort, which was paid for using funds from the NIH grant, R01DA044015, and from a grant from the Pennsylvania Department of Health. A Certificate of Confidentiality has been obtained from the National Institutes of Health for this study. Identifiable data from the study will not be shared with participants’ doctors. All patient data are stored on a HIPAA-compliant portal. All study staff interacting with potential or enrolled participants received training on human subjects research.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Troiani, V., Crist, R.C., Doyle, G.A. et al. 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. BMC Med Genomics 14, 253 (2021). https://doi.org/10.1186/s12920-021-01100-z
- Opioid use disorder (OUD)
- Substance misuse