Exploration of Medicine

Special Issue Topic

The Biological Basis of Substance Use Disorders

Submission Deadline: January 01, 2023

Guest Editor

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

About the Special Issue

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

Call for Papers

Published Articles

Open Access

Original Article

Genome-wide association study of phenotypes measuring progression from first cocaine or opioid use to dependence reveals novel risk genes
Aim: Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few g [...] Read more.

Aim:

Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few genetic risk loci have been identified for either opioid or cocaine dependence (OD or CD, respectively). A genome-wide association study of OD and CD related phenotypes reflecting the time between first self-reported use of these substances and a first DSM-IV dependence diagnosis was conducted.

Methods:

Cox proportional hazards regression in a discovery sample of 6,188 African-Americans (AAs) and 6,835 European-Americans (EAs) participants in a genetic study of multiple substance dependence phenotypes were used to test for association between genetic variants and these outcomes. The top findings were tested for replication in two independent cohorts.

Results:

In the discovery sample, three independent regions containing variants associated with time to dependence at P < 5 × 10⁻⁸ were identified, one (rs61835088 = 1.03 × 10⁻⁸) for cocaine in the combined EA-AA meta-analysis in the gene FAM78B on chromosome 1, and two for opioids in the AA portion of the sample in intergenic regions of chromosomes 4 (rs4860439, P = 1.37 × 10⁻⁸) and 9 (rs7032521, P = 3.30 × 10⁻⁸). After meta-analysis with data from the replication cohorts, the signal at rs61835088 improved (HR = 0.87, P = 3.71 × 10⁻⁹ and an intergenic SNP on chromosome 21 (rs2825295, HR = 1.14, P = 2.57 × 10⁻⁸) that missed the significance threshold in the AA discovery sample became genome-wide significant (GWS) for CD.

Conclusions:

Although the two GWS variants are not in genes with obvious links to SUD biology and have modest effect sizes, they are statistically robust and show evidence for association in independent samples. These results may point to novel pathways contributing to disease progression and highlight the utility of related phenotypes to better understand the genetics of SUDs.

Richard Sherva ... Lindsay A. Farrer

Published: February 28, 2021 Explor Med. 2021;2:60–73
DOI: https://doi.org/10.37349/emed.2021.00032

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Aim:

Substance use disorders (SUD) result in substantial morbidity and mortality worldwide. Opioids, and to a lesser extent cocaine, contribute to a large percentage of this health burden. Despite their high heritability, few genetic risk loci have been identified for either opioid or cocaine dependence (OD or CD, respectively). A genome-wide association study of OD and CD related phenotypes reflecting the time between first self-reported use of these substances and a first DSM-IV dependence diagnosis was conducted.

Methods:

Cox proportional hazards regression in a discovery sample of 6,188 African-Americans (AAs) and 6,835 European-Americans (EAs) participants in a genetic study of multiple substance dependence phenotypes were used to test for association between genetic variants and these outcomes. The top findings were tested for replication in two independent cohorts.

Results:

In the discovery sample, three independent regions containing variants associated with time to dependence at P < 5 × 10⁻⁸ were identified, one (rs61835088 = 1.03 × 10⁻⁸) for cocaine in the combined EA-AA meta-analysis in the gene FAM78B on chromosome 1, and two for opioids in the AA portion of the sample in intergenic regions of chromosomes 4 (rs4860439, P = 1.37 × 10⁻⁸) and 9 (rs7032521, P = 3.30 × 10⁻⁸). After meta-analysis with data from the replication cohorts, the signal at rs61835088 improved (HR = 0.87, P = 3.71 × 10⁻⁹ and an intergenic SNP on chromosome 21 (rs2825295, HR = 1.14, P = 2.57 × 10⁻⁸) that missed the significance threshold in the AA discovery sample became genome-wide significant (GWS) for CD.

Conclusions:

Although the two GWS variants are not in genes with obvious links to SUD biology and have modest effect sizes, they are statistically robust and show evidence for association in independent samples. These results may point to novel pathways contributing to disease progression and highlight the utility of related phenotypes to better understand the genetics of SUDs.

Open Access

Original Article

A machine learning approach to identify correlates of current e-cigarette use in Canada
Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully iden [...] Read more.

Aim:

Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm.

Methods:

This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy.

Results:

Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking.

Conclusions:

People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.

Rui Fu ... Michael Chaiton

Published: February 28, 2021 Explor Med. 2021;2:74–85
DOI: https://doi.org/10.37349/emed.2021.00033

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Aim:

Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm.

Methods:

This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy.

Results:

Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking.

Conclusions:

People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.

Open Access

Original Article

Placental OPRM1 DNA methylation and associations with neonatal opioid withdrawal syndrome, a pilot study
Aim: 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 [...] Read more.

Aim:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

Elisha M. Wachman ... Huiping Zhang

Published: June 29, 2020 Explor Med. 2020;1:124–135
DOI: https://doi.org/10.37349/emed.2020.00009

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Aim:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

Open Access

Original Article

Identifying factors associated with opioid cessation in a biracial sample using machine learning
Aim: 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. Methods: We employed multiple machine [...] Read more.

Aim:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

Jiayi W. Cox ... Lindsay A. Farrer

Published: February 29, 2020 Explor Med. 2020;1:27–41
DOI: https://doi.org/10.37349/emed.2020.00003

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Times Cited: 1

Aim:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

Guest Editor

About the Special Issue

Call for Papers

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