Dr. Richard M. Sherva E-Mail
Boston University School of Medicine, Boston, MA, USA
Research Keywords: psychiatric genetics; substance use disorders; alzheimer’s disease; statistical genetics
Substance use disorders, ranging from nicotine addiction to the current opiate epidemic, are a profound public health burden. Although human and animal studies have made advances towards understanding the biological systems driving substance use, misuse, addiction, and recovery, effective prevention and treatment strategies are still lacking. A better understanding of the psychological and neurological factors driving addiction risk, including its underlying genes and regulatory mechanisms, may lead to additional targets for and a more personalized approach to treatment.
Keywords: substance use disorders; addiction; personalized medicine; psychiatric genetics; neurology; pharmacokinetics
Epigenetic variation of DNA methylation of the mu-opioid receptor gene (OPRM1) has been identified in the blood and saliva of individuals with opioid use disorder (OUD) and infants with neonatal opioid withdrawal syndrome (NOWS). It is unknown whether epigenetic variation in OPRM1 exists within placental tissue in women with OUD and whether it is associated with NOWS outcomes. In this pilot study, the authors aimed to 1) examine the association between placental OPRM1 DNA methylation levels and NOWS outcomes, and 2) compare OPRM1 methylation levels in opioid-exposed versus non-exposed control placentas.
Placental tissue was collected from eligible opioid (n = 64) and control (n = 29) women after delivery. Placental DNA was isolated and methylation levels at six cytosine-phosphate-guanine (CpG) sites within the OPRM1 promoter were quantified. Methylation levels were evaluated for associations with infant NOWS outcome measures: need for pharmacologic treatment, length of hospital stay (LOS), morphine treatment days, and treatment with two medications. Regression models were created and adjusted for clinical co-variates. Methylation levels between opioid and controls placentas were also compared.
The primary opioid exposures were methadone and buprenorphine. Forty-nine (76.6%) of the opioid-exposed infants required pharmacologic treatment, 10 (15.6%) two medications, and average LOS for all opioid-exposed infants was 16.5 (standard deviation 9.7) days. There were no significant associations between OPRM1 DNA methylation levels in the six CpG sites and any NOWS outcome measures. No significant differences were found in methylation levels between the opioid and control samples.
No significant associations were found between OPRM1 placental DNA methylation levels and NOWS severity in this pilot cohort. In addition, no significant differences were seen in OPRM1 methylation in opioid versus control placentas. Future association studies examining methylation levels on a genome-wide level are warranted.
Racial disparities in opioid use disorder (OUD) management exist, however, and there is limited research on factors that influence opioid cessation in different population groups.
We employed multiple machine learning prediction algorithms least absolute shrinkage and selection operator, random forest, deep neural network, and support vector machine to assess factors associated with ceasing opioid use in a sample of 1,192 African Americans (AAs) and 2,557 individuals of European ancestry (EAs) who met Diagnostic and Statistical Manual of Mental Disorders, 5th Edition criteria for OUD. Values for nearly 4,000 variables reflecting demographics, alcohol and other drug use, general health, non-drug use behaviors, and diagnoses for other psychiatric disorders, were obtained for each participant from the Semi-Structured Assessment for Drug Dependence and Alcoholism, a detailed semi-structured interview.
Support vector machine models performed marginally better on average than other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression considered the 83 most highly ranked variables across all methods and models and identified less recent cocaine use (AAs: odds ratio (OR) = 1.82, P = 9.19 × 10−5; EAs: OR = 1.91, P = 3.30 × 10−15), shorter duration of opioid use (AAs: OR = 0.55, P = 5.78 × 10−6; EAs: OR = 0.69, P = 3.01 × 10−7), and older age (AAs: OR = 2.44, P = 1.41 × 10−12; EAs: OR = 2.00, P = 5.74 × 10−9) as the strongest independent predictors of opioid cessation in both AAs and EAs. Attending self-help groups for OUD was also an independent predictor (P < 0.05) in both population groups, while less gambling severity (OR = 0.80, P = 3.32 × 10−2) was specific to AAs and post-traumatic stress disorder recovery (OR = 1.93, P = 7.88 × 10−5), recent antisocial behaviors (OR = 0.64, P = 2.69 × 10−3), and atheism (OR = 1.45, P = 1.34 × 10−2) were specific to EAs. Factors related to drug use comprised about half of the significant independent predictors in both AAs and EAs, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.
These proof-of-concept findings provide avenues for hypothesis-driven analysis, and will lead to further research on strategies to improve OUD management in EAs and AAs.