Supplemental Material for The Association of Life Stress With Substance Use Symptoms: A Network Analysis and Replication

The prevalence of substance use and substance use disorders (SUDs) and the co-occurrence of SUDs with other mental health disorders peaks in emerging adulthood. This review examines prevalence as a function of gender, race/ethnicity, historical trends, and geographic regions across both the US and Western world. Prospective predictors reviewed include the effects of early life stress, parental factors (including parental use, support, and parenting skills), peer affiliations, internalizing and externalizing behaviors, educational attainment, personality, and timing of pubertal development. Concurrent predictors include assumption of adult roles and college attendance, stress associated with life events, changes in personality, and laws and taxation. Also reviewed are consequences of use, including neurological changes. The peak in prevalence across emerging adulthood may be due to several factors, including freedom from constraint, increased peer pressure, less than optimal decision-making skills, high disinhibition, and increased stress during this developmental period.

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Early Life Stress and Substance Use Disorders: Underlying Neurobiology and Pathways to Adverse Outcomes

Adversity and Resilience Science ◽

10.1007/s42844-020-00005-7 ◽

2020 ◽

Vol 1 (1) ◽

pp. 29-47 ◽

Cited By ~ 3

Author(s):

Dylan Kirsch ◽

Charles M. Nemeroff ◽

Elizabeth T. C. Lippard

Keyword(s):

Substance Use ◽

Substance Use Disorders ◽

Life Stress ◽

Early Life ◽

Early Life Stress ◽

Adverse Outcomes

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Substance use and abuse, COVID-19-related distress, and disregard for social distancing: A network analysis

Addictive Behaviors ◽

10.1016/j.addbeh.2020.106754 ◽

2021 ◽

Vol 114 ◽

pp. 106754

Author(s):

Steven Taylor ◽

Michelle M. Paluszek ◽

Geoffrey S. Rachor ◽

Dean McKay ◽

Gordon J.G. Asmundson

Keyword(s):

Substance Use ◽

Network Analysis ◽

Social Distancing ◽

Substance Use And Abuse

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Researching Substance Use & Recovery Using Social Network Analysis

10.4135/9781473999046 ◽

2017 ◽

Keyword(s):

Substance Use ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis

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Early Life Stress as a Risk Factor for Substance use Disorders: Clinical and Neurobiological Substrates

Indian Journal of Psychological Medicine ◽

10.4103/0253-7176.150816 ◽

2015 ◽

Vol 37 (1) ◽

pp. 36-41 ◽

Cited By ~ 4

Author(s):

Sajoy Purathumuriyil Varghese ◽

Janitza L. Montalvo-Ortiz ◽

John G. Csernansky ◽

Rodney I. Eiger ◽

Amy A. Herrold ◽

...

Keyword(s):

Substance Use ◽

Risk Factor ◽

Substance Use Disorders ◽

Life Stress ◽

Early Life ◽

Early Life Stress ◽

Neurobiological Substrates

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DNA methylation of the glucocorticoid receptor gene predicts substance use in adolescence: longitudinal data from over 1000 young individuals

Translational Psychiatry ◽

10.1038/s41398-021-01601-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Elena Raffetti ◽

Philippe Anastasios Melas ◽

Anton Jonatan Landgren ◽

Filip Andersson ◽

Yvonne Forsell ◽

...

Keyword(s):

Dna Methylation ◽

Substance Use ◽

Glucocorticoid Receptor ◽

Life Stress ◽

Early Life ◽

Predictor Variable ◽

Early Life Stress ◽

Glucocorticoid Receptor Gene ◽

Self Reports ◽

Middle Adolescence

AbstractEarly life stress has been linked to increased methylation of the Nuclear Receptor Subfamily 3 Group C Member 1 (NR3C1) gene, which codes for the glucocorticoid receptor. Moreover, early life stress has been associated with substance use initiation at a younger age, a risk factor for developing substance use disorders. However, no studies to date have investigated whether NR3C1 methylation can predict substance use in young individuals. This study included adolescents 13–14 years of age that reported no history of substance use at baseline, (N = 1041; males = 46%). Participants contributed saliva DNA samples and were followed in middle adolescence as part of KUPOL, a prospective cohort study of 7th-grade students in Sweden. Outcome variables were self-reports of (i) recent use, (ii) lifetime use, and (iii) use duration of (a) alcohol, (b) tobacco products, (c) cannabis, or (d) any substance. Outcomes were measured annually for three consecutive years. The predictor variable was DNA methylation at the exon 1 F locus of NR3C1. Risk and rate ratios were calculated as measures of association, with or without adjustment for internalizing symptoms and parental psychiatric disorders. For a subset of individuals (N = 320), there were also morning and afternoon salivary cortisol measurements available that were analyzed in relation to NR3C1 methylation levels. Baseline NR3C1 hypermethylation associated with future self-reports of recent use and use duration of any substance, before and after adjustment for potential confounders. The overall estimates were attenuated when considering lifetime use. Sex-stratified analyses revealed the strongest association for cigarette use in males. Cortisol analyses revealed associations between NR3C1 methylation and morning cortisol levels. Findings from this study suggest that saliva NR3C1 hypermethylation can predict substance use in middle adolescence. Additional longitudinal studies are warranted to confirm these findings.

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Agent-Based Models for Assessing Complex Statistical Models: An Example Evaluating Selection and Social Influence Estimates from SIENA

10.31235/osf.io/98fp4 ◽

2019 ◽

Author(s):

Sebastian Daza ◽

L. Kurt Kreuger

Keyword(s):

Social Sciences ◽

Substance Use ◽

Network Analysis ◽

Social Influence ◽

Empirical Research ◽

Statistical Models ◽

Analysis Method ◽

Agent Based Models ◽

Agent Based ◽

Highly Sensitive

Although Agent-based models (ABM) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study is complex. As an example, we create an ABM to evaluate the estimation of selection and influence effects by SIENA, a stochastic actor-oriented model proposed by Tom A. B. Snijders and colleagues. It is a prominent network analysis method that has gained popularity during the last 10 years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have. The results from our analysis show that selection and influence are estimated by SIENA asymmetrically, and that with very simple assumptions, we can generate data where selection estimates are highly sensitive to mis-specification, suggesting caution when interpreting SIENA analyses.

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