Abstract
Growing evidence suggests that prescription opioid use affects depression and anxiety disorders; however, observational studies are subject to confounding, making causal inference and determining the direction of these associations difficult. To investigate the potential bidirectional associations between the genetic liability for prescription opioid and other nonopioid pain medications and both major depressive disorder (MDD) and anxiety and stress-related disorders (ASRD) using genetically based methods. We performed 2-sample mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) to assess potential associations of self-reported prescription opioid and nonopioid analgesics, including nonsteroidal anti-inflammatories (NSAIDs) and acetaminophen-like derivatives use with MDD and ASRD. The GWAS data were derived from participants of predominantly European ancestry included in observational cohorts. Data were analyzed February 20, 2020, to May 4, 2020. Major depressive disorder, ASRD, and self-reported pain medications (opioids, NSAIDs, anilides, and salicylic acid). The GWAS data were derived from participants of predominantly European ancestry included in the population-based UK Biobank and Lundbeck Foundation Initiative for Integrative Psychiatric Research studies: approximately 54% of the initial UK Biobank sample and 55.6% of the Lundbeck Foundation Initiative for Integrative Psychiatric Research sample selected for the ASRD GWAS were women. In a combined sample size of 737 473 study participants, single-variable MR showed that genetic liability for increased prescription opioid use was associated with increased risk of both MDD (odds ratio [OR] per unit increase in log odds opioid use, 1.14; 95% CI, 1.06-1.22; P < .001) and ASRD (OR, 1.24; 95% CI, 1.07-1.44; P = .004). Using multivariable MR, these opioid use estimates remained after accounting for other nonopioid pain medications (MDD OR, 1.14; 95% CI, 1.04-1.25; P = .005; ASRD OR, 1.30; 95% CI, 1.08-1.46; P = .006), and in separate models, accounting for comorbid pain conditions. Bidirectional analyses showed that genetic liability for MDD but not ASRD was associated with increased prescription opioid use risk (OR, 1.18; 95% CI, 1.08-1.30; P < .001). These estimates were generally consistent across single-variable and multivariable inverse variance-weighted (MV-IVW) and MR-Egger sensitivity analyses. Pleiotropy-robust methods did not indicate bias in any MV-IVW estimates. The findings of this mendelian randomization analysis suggest evidence for potential causal associations between the genetic liability for increased prescription opioid use and the risk for MDD and ASRD. While replication studies are necessary, these findings may inform prevention and intervention strategies directed toward the opioid epidemic and depression.
Highlights
IMPORTANCE Growing evidence suggests that prescription opioid use affects depression and anxiety disorders; observational studies are subject to confounding, making causal inference and determining the direction of these associations difficult
The genome-wide association studies (GWAS) data were derived from participants of predominantly European ancestry included in the population-based UK Biobank and Lundbeck Foundation Initiative for Integrative Psychiatric Research studies: approximately 54% of the initial UK Biobank sample and 55.6% of the Lundbeck Foundation Initiative for Integrative Psychiatric Research sample selected for the ASRD GWAS were women
In a combined sample size of 737 473 study participants, single-variable mendelian randomization (MR) showed that genetic liability for increased prescription opioid use was associated with increased risk of both major depressive disorder (MDD) and ASRD (OR, 1.24; 95% CI, 1.07-1.44; P = .004)
Summary
Study Design and Data Sources A detailed description of the methods used in this study is provided in the eMethods in the Supplement. We used publicly available summary statistics from 3 GWAS sources of predominantly European ancestry (Figure 1; eTable 1 in the Supplement). Because all analyses are based on publicly available summary data, no ethical approval from an institutional review board was required for this study. Data Sets We used summary statistics from the first medication use casecontrol GWAS conducted among UKB study participants to generate genetic instruments for opioid and nonopioid pain medications.[28] Pain medication categories were classified by active ingredient using the Anatomical Therapeutic Chemical Classification System and assigned to 23 categories by active ingredient, including opioids (eg, morphine, oxycodone, codeine, fentanyl, pethidine, and tramadol), NSAIDs, anilides, and salicylic acid products (eTable 2 in the Supplement). MV IVW No of SNVs. OR (95% CI) 1.14 (1.04-1.25).
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