Symptoms of major depression: Their stability, familiality, and prediction by genetic, temperamental, and childhood environmental risk factors

Background: Psychiatry has long sought to develop biological diagnostic subtypes based on symptomatic differences. This effort assumes that symptoms reflect, with good fidelity, underlying etiological processes. We address this question for major depression (MD). Methods: We examine, in twins from a population-based registry, similarity in symptom endorsement in individuals meeting criteria for last-year MD at separate interview waves and in concordant twin pairs. Among individuals with MD, we explore the impact of genetic-temperamental and child adversity risk factors on individual reported symptoms. Aggregated criteria do not separate insomnia from hypersomnia, weight gain from loss, etc. while disaggregated criteria do. Results: In twins with MD at two different waves, the mean tetrachoric correlations (+/- SEM) for aggregated and disaggregated DSM-IV A criteria were, respectively, + 0.31 +/- 0.06 and + 0.34 +/- 0.03. In monozygotic (MZ) and dizygotic (DZ) twin pairs concordant for last-year MD, the mean tetrachoric correlations for aggregated and disaggregated criteria were, respectively, + 0.33 +/- 0.07 and + 0.43 +/- 0.04, and + 0.05 +/- 0.08 and + 0.07 +/- 0.04. In individuals meeting MD criteria, neuroticism predicted the most MD symptoms (10), followed by childhood sexual abuse (8), low parental warmth (6), and genetic risk (4). Conclusions: The correlations for individual depressive symptoms over multiple episodes and within MZ twins concordant for MD are modest suggesting the important role of transient influences. The multidetermination of individual symptoms was further evidenced by their prediction by personality and exposure to early life adversities. The multiple factors influencing symptomatic presentation inMDmay contribute to our difficulties in isolating clinical depressive subtypes with distinct pathophysiologies

Evaluation of a new trauma-related drinking to cope measure: Latent structure and heritability

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) commonly co-occur, share latent genetic risk, and are associated with many negative public health outcomes. Via a self-medication framework, trauma-related drinking to cope (TRD), an unexplored phenotype to date, may help explain why these two disorders co-occur, thus serving as an essential target for treatment and prevention efforts. This study sought to create a novel measure of TRD and to investigate its indirect influences on the association between PTSD and AUD, as well as its potential shared molecular genetic risk with PTSD in a genetically-informative study of college students. A sample of 1,896 undergraduate students with a history of trauma and alcohol use provided genotypic data and completed an online assessment battery. The psychometric properties of TRD and how it relates to relevant constructs were examined using descriptive statistics and structural equation modeling. Results of a correlated multiple mediator model indicated that, while accounting for the effects of generalized drinking motives, TRD partially mediated the relation between PTSD and alcohol use problems (β = 0.213, p \u3c .001), consistent with the self-medication hypothesis, and that this relationship was stronger for males (β = 0.804, p \u3c .001) than for females (β = 0.463, p \u3c .001). Results were substantiated using longitudinal data. Genotypic analyses to be presented will include univariate genome wide complex trait analyses (GCTA) to establish SNP-based heritability associated with TRD and PTSD, separately, as well as bivariate GCTA to examine potential overlap in heritability between TRD and PTSD.https://scholarscompass.vcu.edu/gradposters/1047/thumbnail.jp

The structure of the symptoms of major depression:Factor analysis of a lifetime worst episode of depressive symptoms in a large general population sample

Background: A range of depressive symptoms may occur during an episode of major depression (MD). Do these symptoms describe a single disorder liability or different symptom dimensions? This study investigates the structure and clinical relevance of an expanded set of depressive symptoms in a large general population sample. Methods: We studied 43,431 subjects from the Dutch Lifelines Cohort Study who participated in an online survey assessing the 9 symptom criteria of MD (DSM-IV-TR) and additional depressive symptoms during their worst lifetime episode of depressive symptoms lasting two weeks or more. Exploratory factor analyses were performed on expanded sets of 9, 14, and 24 depressive symptoms. The clinical relevance of the identified symptom dimensions was analyzed in confirmatory factor analyses including ten external validators. Results: A single dimension adequately accounted for the covariation among the 9 DSM-criteria, but multiple dimensions were needed to describe the 14 and 24 depressive symptoms. Five dimensions described the structure underlying the 24 depressive symptoms. Three cognitive affective symptom dimensions were mainly associated with risk factors for MD. Two somatic dimensions –appetite/weight problems and sleep problems—were mainly associated with BMI and age, respectively. Limitations: Respondents of our online survey tended to be more often female, older, and more highly educated than non-respondents. Conclusions: Different symptom dimensions described the structure of depressive symptoms during a lifetime worst episode in a general population sample. These symptom dimensions resembled those reported in a large clinical sample of Han-Chinese women with recurrent MD, suggesting robustness of the syndrome of MD

Data mining algorithm predicts a range of adverse outcomes in major depression

Background: Course of illness in major depression (MD) is highly varied, which might lead to both under- and overtreatment if clinicians adhere to a 'one-size-fits-all' approach. Novel opportunities in data mining could lead to prediction models that can assist clinicians in treatment decisions tailored to the individual patient. This study assesses the performance of a previously developed data mining algorithm to predict future episodes of MD based on clinical information in new data. Methods: We applied a prediction model utilizing baseline clinical characteristics in subjects who reported lifetime MD to two independent test samples (total n = 4226). We assessed the model's performance to predict future episodes of MD, anxiety disorders, and disability during follow-up (1–9 years after baseline). In addition, we compared its prediction performance with well-known risk factors for a severe course of illness. Results: Our model consistently predicted future episodes of MD in both test samples (AUC 0.68–0.73, modest prediction). Equally accurately, it predicted episodes of generalized anxiety disorder, panic disorder and disability (AUC 0.65–0.78). Our model predicted these outcomes more accurately than risk factors for a severe course of illness such as family history of MD and lifetime traumas. Limitations: Prediction accuracy might be different for specific subgroups, such as hospitalized patients or patients with a different cultural background. Conclusions: Our prediction model consistently predicted a range of adverse outcomes in MD across two independent test samples derived from studies in different subpopulations, countries, using different measurement procedures. This replication study holds promise for application in clinical practice

Genetic and Environmental Structure of DSM-IV Criteria for Antisocial Personality Disorder: A Twin Study

Results from previous studies on DSM-IV and DSM-5 Antisocial Personality Disorder (ASPD) have suggested that the construct is etiologically multidimensional. To our knowledge, however, the structure of genetic and environmental influences in ASPD has not been examined using an appropriate range of biometric models and diagnostic interviews. The 7 ASPD criteria (section A) were assessed in a population-based sample of 2794 Norwegian twins by a structured interview for DSM-IV personality disorders. Exploratory analyses were conducted at the phenotypic level. Multivariate biometric models, including both independent and common pathways, were compared. A single phenotypic factor was found, and the best-fitting biometric model was a single-factor common pathway model, with common-factor heritability of 51% (95% CI 40–67%). In other words, both genetic and environmental correlations between the ASPD criteria could be accounted for by a single common latent variable. The findings support the validity of ASPD as a unidimensional diagnostic construct

Genetic Risks to Nicotine Dependence Predict Negative Mood and Affect in Current Non-Smokers

Nicotine is the psychoactive agent involved in nicotine dependence. However, nicotine as a drug and its effects on human psychology are largely under-investigated in genetic studies. In this study, we recruited 208 current non-smokers to evaluate the effect of nicotine and its relationship to genetic risks to nicotine dependence. Exploratory and confirmatory factor analyses, as well as measurement invariance testing, were conducted to evaluate the latent factor structures of the POMS, PANAS and DEN questionnaires across 3 nicotine doses. Structural models were used to examine the effects of nicotine and their relationship to genetic risks of nicotine dependence. We found that nicotine administration led to the change of both measurement construct and factor means, indicating the causal effect of nicotine on the psychological responses. The genotypes of rs588765 predicted the scores of the DEN Confused and Dizzy factors (p = 0.0003 and 0.001 respectively) and rs16969968 and rs588765 were associated with the PANAS Nervous factor (p = 0.006 and 0.007 respectively). Our study suggested that genetic risk of nicotine dependence is associated with acute psychological responses. The integration of psychometric analyses and dose effects could be a powerful approach for genetic study of nicotine dependence

Genetic Risks to Nicotine Dependence Predict Negative Mood and Affect in Current Non-Smokers

Nicotine is the psychoactive agent involved in nicotine dependence. However, nicotine as a drug and its effects on human psychology are largely under-investigated in genetic studies. In this study, we recruited 208 current non-smokers to evaluate the effect of nicotine and its relationship to genetic risks to nicotine dependence. Exploratory and confirmatory factor analyses, as well as measurement invariance testing, were conducted to evaluate the latent factor structures of the POMS, PANAS and DEN questionnaires across 3 nicotine doses. Structural models were used to examine the effects of nicotine and their relationship to genetic risks of nicotine dependence. We found that nicotine administration led to the change of both measurement construct and factor means, indicating the causal effect of nicotine on the psychological responses. The genotypes of rs588765 predicted the scores of the DEN Confused and Dizzy factors (p = 0.0003 and 0.001 respectively) and rs16969968 and rs588765 were associated with the PANAS Nervous factor (p = 0.006 and 0.007 respectively). Our study suggested that genetic risk of nicotine dependence is associated with acute psychological responses. The integration of psychometric analyses and dose effects could be a powerful approach for genetic study of nicotine dependence

Intermediate stable states in substance use

Many people across the world use potentially addictive legal and illegal substances, but evidence suggests that not all use leads to heavy use and dependence, as some substances are used moderately for long periods of time. Here, we empirically examine, the stability of and transitions between three substance use states: zero-use, moderate use, and heavy use. We investigate two large datasets from the US and the Netherlands on yearly usage and change of alcohol, nicotine, and cannabis. Results, which we make available through an extensive interactive tool, suggests that there are stable moderate use states, even after meeting criteria for a positive diagnosis of substance abuse or dependency, for both alcohol and cannabis use. Moderate use of tobacco, however, was rare. We discuss implications of recognizing three states rather than two states as a modeling target, in which the moderate use state can both act as an intervention target or as a gateway between zero use and heavy use