Relating addiction and psychiatric disorders

Prevalence and clustering of health behaviours and the association with socio-demographics and mental well-being in Dutch university students

The college years represent a vulnerable period for developing health-risk behaviours (e.g., physical inactivity/unhealthy eating habits/substance use/problematic internet use/insufficient sleep). This study examined current health behaviour levels (RQ1), health behaviour classes (RQ2) and between-class differences in socio-demographics (RQ3) and mental well-being (RQ4) among Dutch university students (n = 3771). Participants (Mage = 22.7 (SD = 4.3); 71.2% female/27.3% male/1.5% other) completed an online survey (Oct-Nov 2021). Descriptive statistics (RQ1), Latent Class Analysis (RQ2), and Kruskal-Wallis/Chi-square tests (RQ3-4) were used. RQ1: Prevalence rates suggest that a subsequent proportion of the student sample engages in health-risk behaviours. RQ2: Four classes were identified: class 1 (n = 862) "Licit substance use health-risk group", class 2 (n = 435) "Illicit and licit substance use health-risk group", class 3 (n = 1876) "Health-protective group" and class 4 (n = 598) "Non-substance use health-risk group". RQ3: Class 1 represents relatively more international students and students in a steady relationship. Class 2 represents relatively more older/male/(pre-)master students and students living with roommates/in a steady relationship/with more financial difficulty. Class 3 represents relatively more younger/female students and students living with family/with lower Body Mass Index (BMI)/less financial difficulty. Class 4 represents relatively more younger/non-Western/international/bachelor students and students living with children/single/part of LGBTIQ+ community/with higher BMI. RQ4: Class 3 has significantly higher mental well-being while class 4 has significantly lower mental well-being, relative to the other classes. Above findings provide new insights which can help educational institutes and governments better understand the clustering of students' health behaviours and between-class differences in socio-demographics and mental well-being.

Challenges of Treating ADHD with Comorbid Substance Use Disorder: Considerations for the Clinician

Adults with attention deficit/hyperactivity disorder (ADHD) often present psychiatric comorbidities and, in particular, substance use disorder (SUD). ADHD-SUD comorbidity is characterized by greater severity of both disorders, earlier age of onset, higher likelihood of polydrug-abuse and suicidal behaviors, more hospitalizations, and lower treatment adherence. At the present stage, research focused on the pharmacological management of ADHD with comorbid SUD in both adolescents and adults is still lacking. Furthermore, while the short-term effects of stimulants are well studied, less is known about the chronic effects of these drugs on dopamine signaling. Current available evidence is consistent in reporting that high doses of stimulant medications in ADHD-SUD subjects have a mild to moderate efficacy on ADHD symptoms. Some data suggest that pharmacological treatment with stimulants may be beneficial for both ADHD symptoms and comorbid cocaine or amphetamine use. However, in the long run, stimulant medications may have a potential risk for misuse. For the absence of potential misuse, atomoxetine is often recommended for ADHD with comorbid cocaine or amphetamine use disorder. However, its efficacy in reducing addictive behavior is not demonstrated. In subjects with other subtypes of SUD, both atomoxetine and stimulant drugs seem to have scarce impact on addictive behavior, despite the improvement in ADHD symptomatology. In this population, ADHD treatment should be combined with SUD-specific strategies.

Self-Medication of ADHD Symptoms: Does Caffeine Have a Role?

OBJECTIVE

Stimulants are the most effective treatment for Attention Deficit/ Hyperactivity disorder (ADHD). In addition, studies have shown that nicotine dependence in patients with ADHD is probably best explained by self-medication. The question is whether this is also true for caffeine use and caffeine dependence. The aim of our study was, therefore, to examine the relationship of ADHD symptoms, caffeine consumption, caffeine use disorder (CUD) and well-being. We hypothesized that those who have more ADHD symptoms and regularly consume caffeine have higher psychological well-being than those who have more ADHD symptoms, but do not consume caffeine.

METHODS

A general population sample (N = 2,259, 70.5% male, mean age 34.0) filled out the 10-item Caffeine Use Disorder Questionnaire (CUDQ), the Adult ADHD Self-report Scale (ASRS) and the WHO-5 Well-Being Index (WHO-5) and were asked about their caffeine consumption habits in an online survey.

RESULTS

There were no associations between ADHD and coffee, tea, energy drink or cola consumption or daily caffeine consumption. However, the results of the path analysis showed that the level of ADHD symptoms was positively associated with the level of CUD (β = 0.350) and negatively with the WHO-5 (β = -0.259).

CONCLUSIONS

Caffeine consumption was not associated with ADHD symptom severity and thus not likely to represent self-medication. On the contrary, caffeine use disorder severity is associated with more ADHD symptoms and both caffeine use disorder and ADHD are associated with lower well-being.

Effects of substance misuse and current family history of substance use disorder on brain structure in adolescents and young adults with attention-deficit/hyperactivity disorder

BACKGROUND

Alterations in brain structure in attention-deficit/hyperactivity disorder (ADHD) show considerable overlap with those observed in substance use disorder (SUD). These overlapping structural alterations in ADHD and SUD might be explained by family history (FH-trait) effects of SUD, and/or substance misuse (state) effects. Our aim was to investigate effects of 1) current parental SUD (SUD-FH) and 2) recent substance misuse (SM) on brain structure in a cohort of ADHD patients and controls.

DESIGN

Cortical thickness and subcortical volumes were measured using structural MRI. We compared ADHD subjects and controls with or without SUD-FH (aim 1) and additionally explored differences between SUD-FH- and SUD-FH + subjects with one versus two parents with SUD. We also compared ADHD groups with and without SM (ADHD + SM and ADHD-only, respectively) and controls (aim 2).

FINDINGS

There was no association between SUD-FH and brain structure. Exploratory analysis on SUD-FH showed decreased IFG thickness (p = 0.032) and nucleus accumbens (NAcc) volume (p = 0.017) in subjects with two versus one SUD parent, regardless of ADHD. ADHD + SM showed decreased inferior frontal gyrus (IFG) thickness compared to controls (pars opercularis p = 0.025, pars orbitalis p = 0.010, pars triangularis p = 0.049), while no difference was found between ADHD-only and either ADHD + SM or controls.

CONCLUSIONS

Despite negative findings in the primary trait-analysis, exploratory trait-analysis on SUD-FH loading suggested potential SUD trait-effects on IFG thickness and NAcc volume. Substance misuse state effects in ADHD were linked to lower IFG thickness. Future studies should confirm these findings and investigate their clinical relevance, including the functional consequences of decreased IFG thickness.

Investigating genetic correlation and causality between nicotine dependence and ADHD in a broader psychiatric context

People with attention-deficit/hyperactivity disorder (ADHD) or other psychiatric disorders show high rates of nicotine dependence (ND). This comorbidity might be (partly) explained by shared genetic factors. Genetic correlations between ND and ADHD (or other psychiatric disorders) have not yet been estimated. A significant genetic correlation might indicate genetic overlap, but could also reflect a causal relationship. In the present study we investigated the genetic correlation (with LD score regression analyses) between ND and ADHD, as well as between ND and other major psychiatric conditions (major depressive disorder, schizophrenia, anxiety, bipolar disorder, autism spectrum, anorexia nervosa, and antisocial behavior) based on the summary statistics of large Genome Wide Association studies. We explored the causal nature of the relationship between ND and ADHD using two-sample Mendelian randomization. We found a high genetic correlation between ND and ADHD (r_g  = .53, p = 1.85 × 10^-13 ), and to a lesser extent also between ND-major depressive disorder (r_g  = .42, p = 3.6 × 10^-11 ) and ND-schizophrenia (r_g  = .18, p = 1.1 × 10^-3 ). We did not find evidence for a causal relationship from liability for ADHD to ND (which could be due to a lack of power). The strong genetic correlations might reflect different phenotypic manifestations of (partly) shared underlying genetic vulnerabilities. Combined with the lack of evidence for a causal relationship from liability for ADHD to ND, these findings stress the importance to further investigate the underlying genetic vulnerability explaining co-morbidity in psychiatric disorders.

The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder

BACKGROUND

Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base.

METHODS

We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder.

RESULTS

We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents.

CONCLUSIONS

Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.

Treatment of Adolescents with Concurrent Substance Use Disorder and Attention-Deficit/Hyperactivity Disorder: A Systematic Review

Childhood attention-deficit/hyperactivity disorder (ADHD) is a risk factor for the development of substance abuse and substance use disorders (SUD) in adolescence and (early) adulthood. ADHD and SUD also frequently co-occur in treatment-seeking adolescents, which complicates diagnosis and treatment, and is associated with poor treatment outcomes. In this study, we provide a systematic review of controlled studies on the effectiveness of pharmacological, psychosocial, and complementary treatments of ADHD in adolescents with and without comorbid SUD. In addition, we review the longitudinal association between pharmacotherapy for childhood ADHD and the development of SUD in adolescence and early adulthood. We conducted a systematic review of the research literature published since 2000 using Medline, PsycINFO, and the Cochrane Database of Systematic Reviews databases to select randomized clinical trials, observational studies, and meta-analyses. The quality of the evidence from each study was rated using the SIGN grading system. Based on the limited evidence available, strong clinical recommendations are not justified, but provisionally, we conclude that stimulant treatment in children with ADHD may prevent the development of SUD in adolescence or young adulthood, that high-dose stimulant treatment could be an effective treatment for adolescents with ADHD and SUD comorbidity, that cognitive behavior therapy might have a small beneficial effect in these patients, and that alternative treatments are probably not effective. More studies are needed to draw definitive conclusions that will allow for strong clinical recommendations.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review

BACKGROUND

Poor mental health has consistently been associated with substance use (smoking, alcohol drinking, cannabis use, and consumption of caffeinated drinks). To properly inform public health policy it is crucial to understand the mechanisms underlying these associations, and most importantly, whether or not they are causal.

METHODS

In this pre-registered systematic review, we assessed the evidence for causal relationships between mental health and substance use from Mendelian randomization (MR) studies, following PRISMA. We rated the quality of included studies using a scoring system that incorporates important indices of quality, such as the quality of phenotype measurement, instrument strength, and use of sensitivity methods.

RESULTS

Sixty-three studies were included for qualitative synthesis. The final quality rating was '-' for 16 studies, '- +' for 37 studies, and '+'for 10 studies. There was robust evidence that higher educational attainment decreases smoking and that there is a bi-directional, increasing relationship between smoking and (symptoms of) mental disorders. Another robust finding was that higher educational attainment increases alcohol use frequency, but decreases binge-drinking and alcohol use problems, and that mental disorders causally lead to more alcohol drinking without evidence for the reverse.

CONCLUSIONS

The current MR literature increases our understanding of the relationship between mental health and substance use. Bi-directional causal relationships are indicated, especially for smoking, providing further incentive to strengthen public health efforts to decrease substance use. Future MR studies should make use of large(r) samples in combination with detailed phenotypes, a wide range of sensitivity methods, and triangulate with other research methods.

An integrative systems-based analysis of substance use: eQTL-informed gene-based tests, gene networks, and biological mechanisms

Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to substance use, however, the functional genes and biological mechanisms remain poorly understood. The discovery and characterization of risk genes can be facilitated by the integration of genome-wide association data and gene expression data across biologically relevant tissues and/or cell types to identify genes whose expression is altered by DNA sequence variation (expression quantitative trait loci; eQTLs). The integration of gene expression data can be extended to the study of genetic co-expression, under the biologically valid assumption that genes form co-expression networks to influence the manifestation of a disease or trait. Here, we integrate genome-wide association data with gene expression data from 13 brain tissues to identify candidate risk genes for 8 substance use phenotypes. We then test for the enrichment of candidate risk genes within tissue-specific gene co-expression networks to identify modules (or groups) of functionally related genes whose dysregulation is associated with variation in substance use. We identified eight gene modules in brain that were enriched with gene-based association signals for substance use phenotypes. For example, a single module of 40 co-expressed genes was enriched with gene-based associations for drinks per week and biological pathways involved in GABA synthesis, release, reuptake and degradation. Our study demonstrates the utility of eQTL and gene co-expression analysis to uncover novel biological mechanisms for substance use traits.

Genomic relationships across psychiatric disorders including substance use disorders

BACKGROUND

A recent study investigated the genetic associations and latent genetic structure among eight psychiatric disorders using findings from genome-wide association studies (GWASs). No data from substance use disorders were included, while these represent an important category of mental disorders and could influence the latent genetic structure. We extended the original paper by recreating the genetic relationship matrix, graph, and latent genetic factor structure, including additional data from substance use disorders.

METHODS

We used GWAS summary statistics of 11 psychiatric disorders, including alcohol dependence, nicotine dependence, and cannabis use disorder. We estimated genetic correlations between all traits in Linkage Disequilibrium-Score Regression. A graph was created to illustrate the network of genetic correlations. We then used the genetic relationships to model a latent genetic factor structure.

RESULTS

Alcohol and nicotine dependence showed significant genetic correlations with several other psychiatric disorders, including ADHD, schizophrenia, and major depression. Cannabis use disorder was only significantly associated with ADHD. The addition of substance use disorders resulted in some changes in the latent structure of the factor model when compared to the original model including eight disorders. All substance use disorders contributed mostly to Factor 3, a heterogeneous factor with also loadings from ADHD, major depression, Autism Spectrum Disorders, and Tourette Syndrome.

CONCLUSIONS

Alcohol and nicotine dependence show widespread genetic correlations with other psychiatric disorders. Including substance use disorders in the factor analysis results in some changes in the underlying genetic factor structure. Given the instability of such models, identified structures should be interpreted with caution.

From apathy to addiction: Insights from neurology and psychiatry

The tendency to engage in addictive behaviors has long been tied to the actions of the dopamine system. Early theories were based on the fact that all addictive drugs and behaviors (such as gambling) increase dopamine levels in the striatum, and the evidence that dopamine signaled reward or reward prediction error. However, with a changing emphasis of addiction away from purely pharmacological models that emphasize tolerance and withdrawal, towards one of behavioral dyscontrol, is there still a place for abnormal dopamine signaling in addiction? Here we recast the dopamine theory of addiction based on the idea that tonic dopamine may index a continuous phenotype that goes from apathy to impulsivity and compulsivity. Higher tonic dopamine signaling would make individuals vulnerable to drug reinforcement and cue-induced craving. We relate this to computational models of dopamine signaling, and review clinical and neuroimaging evidence from Parkinson's Disease, schizophrenia and bipolar disorder in support of this model.