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Opioid use disorder (OUD) affects approximately 21.9 million people worldwide. This study aims to determine the association between age of onset of opioid use and comorbid disorders, both physical and psychiatric, in patients receiving methadone maintenance treatment (MMT) for OUD. Understanding this association may inform clinical practice about important prognostic factors of patients on MMT, enabling clinicians to identify high-risk patients. This study includes data collected between June 2011 and August 2016 for the Genetics of Opioid Addiction research collaborative between McMaster University and the Canadian Addiction Treatment Centers. All patients were interviewed by trained health professionals using the Mini-International Neuropsychiatric Interview and case report forms. Physical comorbidities were verified using patients’ electronic medical records. A multi-variable logistic regression model was constructed to determine the strength of the association between age of onset of opioid use and the presence of physical or psychiatric comorbidity while adjusting for current age, sex, body mass index, methadone dose and smoking status. Data from 627 MMT patients with a mean age of 38.8 years (SD = 11.07) were analyzed. Individuals with an age of onset of opioid use younger than 18 years were found to be at higher odds for having a physical or psychiatric comorbid disorder compared to individuals with an age of onset of opioid use of 31 years or older (odds ratio 2.94, 95% confidence interval 1.20, 7.19, p= 0.02). A significant association was not found between the risk of having a comorbidity and an age of onset of opioid use between 18 and 25 years or 26 and 30 years, compared to an age of onset of opioid use of 31 years or older. Our study demonstrates that the younger one begins to use opioids, the greater their chance of having a physical or psychiatric co-morbidity. Understanding the risk posed by an earlier onset of opioid use for the later development of comorbid disorders informs clinical practice about important prognostic predictors and aids in the identification of high-risk patients.

The heritability of opioid use disorder has been widely investigated; however, the influence of specific genes on methadone treatment outcomes is not well understood. The association between response to methadone treatment and genes that are involved in substance use behaviors and reward mechanisms is poorly understood, despite evidence suggesting their contribution to opioid use disorder. The aim of this study was to investigate the effect of brain-derived neurotrophic factor (BDNF) and dopamine receptor D2 (DRD2) polymorphisms on continued opioid use among patients on methadone treatment for opioid use disorder. BDNF196G>A (rs6265) and DRD2-241A>G (rs1799978) genetic variants were examined in patients with opioid use disorder who were recruited from methadone treatment clinics across Southern Ontario, Canada. We collected demographic information, substance use history, blood for genetic analysis, and urine to measure opioid use. We used regression analysis to examine the association between continued opioid use and genetic variants, adjusting for age, sex, ethnicity, methadone dose, duration in treatment, and number of urine screens. Among 240 patients treated with methadone for opioid use disorder, 36.3 percent (n = 87) and 11.3 percent (n = 27) had at least one risk allele for rs6265and rs1799978, respectively. These genetic variants were not significantly associated with continued opioid use while on methadone maintenance treatment [rs6265: odds ratio (OR) = 1.37, 95 % confidence interval (CI) = 0.792, 2.371, p= 0.264; rs1799978: OR 1.27, 95 % CI 0.511, 3.182, p= 0.603]. Despite an association of BDNFrs6265and DRD2rs1799978with addictive behaviors, these variants were not associated with continued illicit opioid use in patients treated with methadone. Problematic use of opioids throughout treatment with methadone may be attributed to nongenetic factors or a polygenic effect requiring further exploration. Additional research should focus on investigating these findings in larger samples and different populations.

Abstract.Background:For decades we have understood the risk factors for suicide in the general population but have fallen short in understanding what distinguishes the risk for suicide among patients with serious psychiatric conditions. Aims:This prompted us to investigate risk factors for suicidal behavior among psychiatric inpatients. Method:We reviewed all psychiatric hospital admissions (2008–2011) to a centralized psychiatric hospital in Ontario, Canada. Using multivariable logistic regression we evaluated the association between potential risk factors and lifetime history of suicidal behavior, and constructed a model and clinical risk score to predict a history of this behavior. Results:The final risk prediction model for suicidal behavior among psychiatric patients (n= 2,597) included age (in three categories: 60–69 [OR= 0.74, 95% CI = 0.73–0.76], 70–79 [OR= 0.45, 95% CI = 0.44–0.46], 80+ [OR= 0.31, 95% CI = 0.30–.31]), substance use disorder (OR= 1.30, 95% CI = 1.27–1.32), mood disorder (OR= 1.49, 95% CI = 1.47–1.52), personality disorder (OR= 2.30, 95% CI = 2.25–2.36), psychiatric disorders due to general medical condition (OR= 0.52, 95% CI = 0.50–0.55), and schizophrenia (OR= 0.42, 95% CI = 0.41–0.43). The risk score constructed from the risk prediction model ranges from −9 (lowest risk, 0% predicted probability of suicidal behavior) to +5 (highest risk, 97% predicted probability). Conclusion:Risk estimation may help guide intensive screening and treatment efforts of psychiatric patients with high risk of suicidal behavior.

This narrative review aims to provide an overview of the impact of opioid dependence and the contribution of genetics to opioid dependence. Epidemiological data demonstrate that opioid dependence is a global trend with farreaching effects on the social, economic, and health care systems. A review of classical genetic studies of opioid use suggests significant heritability of drug use behavior, however the evidence from molecular genetic studies is inconclusive. Nonetheless, certain genetic variants are important to consider given their role in the pathophysiology of addictive behavior. We undertook a literature review to identify the current state of knowledge regarding the role of genes in opioid dependence. Determining the association of genetic markers could change the current understanding of the various factors contributing to opioid dependence and therefore may improve recognition of individuals at risk for the disorder and prevention and treatment strategies.