The Munich vulnerability study on affective disorders: microstructure of sleep in high-risk subjects

Vulnerability markers for affective disorders have focused on stress hormone regulation and sleep. Among rapid eye movement (REM) sleep, increased REM pressure and elevated REM density are promising candidates for vulnerability markers. Regarding nonREM sleep, a deficit in amount of and latency until slow wave sleep during the first half of the night is a characteristic for depression. To further elucidate whether changes in the microstructure of sleep may serve as vulnerability markers we investigated the premorbid sleep composition in 21 healthy high-risk proband (HRPs) with a positive family history for affective disorders and compared HRPs with a control group of healthy subjects (HCs) without personal and family history for psychiatric disorders. The sleep electroencephalogram (EEG) was conventionally scored and submitted to a quantitative EEG analysis. The main difference in sleep characteristics between HRPs and HCs was an abnormally increased REM density. Differences in the spectral composition of sleep EEG were restricted to an increased power in the sigma frequency range. Since the HRP group comprised six unrelated and 15 related subjects we controlled for sibling effects. We could replicate the increased REM density in the group of HRPs whereas elevated power in the low sigma frequencies persisted only with approaching significance. The present study further supports elevated REM density as putative vulnerability marker for affective disorders. However, sleep EEG in our group of HRPs did not show slow wave sleep abnormalities. Ongoing follow up investigations of HRPs will clarify whether the observed increase in sigma EEG activity during nonREM sleep is of clinical relevance with respect to the likelihood to develop an affective disorder

Psychopathological Profiles in Transsexuals and the Challenge of Their Special Status among the Sexes

OBJECTIVE:Investigating psychopathological profiles of transsexuals raises a very basic methodological question: are control groups, which represent the biological or the phenotypic sex, most suited for an optimal evaluation of psychopathology of transsexuals? METHOD:Male-to-female (MtF) (n=52) and female-to-male transsexuals (FtM) (n=32), receiving cross-sex hormone treatment, were compared with age matched healthy subjects of the same genetic sex (n=178) and with the same phenotypic sex (n=178) by means of the Symptom Check List-90-Revisited instrument (SCL-90-R). We performed analyses of covariance (ANCOVA) to test for group and sex effects. Furthermore, we used a profile analysis to determine if psychopathological symptom profiles of transsexuals more closely resemble genotypic sex or phenotypic sex controls. RESULTS:Transsexual patients reported more symptoms of psychopathological distress than did healthy control subjects in all subscales of the SCL-90-R (all p<0.001), regardless of whether they were compared with phenotype or genotype matched controls. Depressive symptoms were more pronounced in MtF than in FtM (SCL-90-R score 0.85 vs. 0.45, p = 0.001). We could demonstrate that FtM primarily reflect the psychopathological profile of biological males rather than that of biological females (r = 0.945), while MtF showed a slightly higher profile similarity with biological females than with biological males (r = 0.698 vs. r = 0.685). CONCLUSION:Our findings suggest that phenotypic sex matched controls are potentially more appropriate for comparison with the psychopathology of transsexual patients than are genetic sex matched controls

Genomewide Association Scan of Suicidal Thoughts and Behaviour in Major Depression

© the authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Suicidal behaviour can be conceptualised as a continuum from suicidal ideation, to suicidal attempts to completed suicide. In this study we identify genes contributing to suicidal behaviour in the depression study RADIANT. Methodology/Principal Findings: A quantitative suicidality score was composed of two items from the SCAN interview. In addition, the 251 depression cases with a history of serious suicide attempts were classified to form a discrete trait. The quantitative trait was correlated with younger onset of depression and number of episodes of depression, but not with gender. A genome-wide association study of 2,023 depression cases was performed to identify genes that may contribute to suicidal behaviour. Two Munich depression studies were used as replication cohorts to test the most strongly associated SNPs. No SNP was associated at genome-wide significance level. For the quantitative trait, evidence of association was detected at GFRA1, a receptor for the neurotrophin GDRA (p = 2e-06). For the discrete trait of suicide attempt, SNPs in KIAA1244 and RGS18 attained p-values of ,5e-6. None of these SNPs showed evidence for replication in the additional cohorts tested. Candidate gene analysis provided some support for a polymorphism in NTRK2, which was previously associated with suicidality. Conclusions/Significance: This study provides a genome-wide assessment of possible genetic contribution to suicidal behaviour in depression but indicates a genetic architecture of multiple genes with small effects. Large cohorts will be required to dissect this further

Genetic Contribution to Alcohol Dependence: Investigation of a Heterogeneous German Sample of Individuals with Alcohol Dependence, Chronic Alcoholic Pancreatitis, and Alcohol-Related Cirrhosis

The present study investigated the genetic contribution to alcohol dependence (AD) using genome-wide association data from three German samples. These comprised patients with: (i) AD; (ii) chronic alcoholic pancreatitis (ACP); and (iii) alcohol-related liver cirrhosis (ALC). Single marker, gene-based, and pathway analyses were conducted. A significant association was detected for the ADH1B locus in a gene-based approach (puncorrected = 1.2 × 10−6; pcorrected = 0.020). This was driven by the AD subsample. No association with ADH1B was found in the combined ACP + ALC sample. On first inspection, this seems surprising, since ADH1B is a robustly replicated risk gene for AD and may therefore be expected to be associated also with subgroups of AD patients. The negative finding in the ACP + ALC sample, however, may reflect genetic stratification as well as random fluctuation of allele frequencies in the cases and controls, demonstrating the importance of large samples in which the phenotype is well assessed

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response

Chronotype is associated with psychological well-being depending on the composition of the study sample

Past studies examining the effect of chronotype and social jetlag on psychological well-being have been inconsistent so far. Here, we recruited participants from the general population and enquired about their natural sleeping behavior, sleep quality, depressive symptoms, and perceived stress. Partial correlations were computed between sleep variables and indicators of psychological well-being, controlling for age and sex. Less sleep during work days was found a good indicator for impairments in psychological well-being. In exploratory follow-up analyses, the same correlations were calculated within groups of early, intermediate, and late chronotype. We observed that the composition of the sample in terms of chronotype influenced whether associations between sleep variables and psychological well-being could be observed, a finding that is advised to be taken into account in future studies.Peer Reviewe

Long-Term Outcome after Lithium Augmentation in Unipolar Depression: Focus on HPA System Activity

Background: Lithium augmentation is a first-line strategy for depressed patients resistant to antidepressive therapy, but little is known about patients’ subsequent long-term course or outcome predictors. We investigated long-term outcomes of unipolar depressed patients who had participated in a study on the effects of lithium augmentation on the hypothalamic-pituitary-adrenocortical system using the combined dexamethasone/corticotrophin-releasing hormone (DEX/CRH) test. Methods: Twelve to 28 months (mean 18.6 ± 4.6 months) after lithium augmentation, 23 patients were assessed with a standardized interview, of which 18 patients had complete DEX/CRH test results. Relapse was diagnosed by DSM-IV criteria (Structured Clinical Interview for DSM-IV; SCID I). Results: Only 11 patients (48%) had a favorable follow-up, defined as absence of major depressive episodes during the observation period. Patients with a favorable and an unfavorable course did not differ in clinical or sociodemographic parameters, endocrinological results or continuation of lithium. However, fewer previous depressive episodes tended to correlate (p = 0.09) with a favorable course. Conclusion: Results from studies using the DEX/CRH test to predict relapse in depressed patients treated with antidepressants were not replicated for lithium augmentation. Our finding could reflect the elevation of DEX/CRH results by lithium, independent of clinical course. Limitations of the study are its small sample size, the heterogeneous clinical baseline conditions and the lack of lithium serum levels. The fact that lithium continuation did not predict the course might be related to the difference between the efficacy of lithium in controlled studies and its effectiveness in naturalistic settings.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich

Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models

The identification of generalizable treatment response classes (TRC[s]) in major depressive disorder (MDD) would facilitate comparisons across studies and the development of treatment prediction algorithms. Here, we investigated whether such stable TRCs can be identified and predicted by clinical baseline items. We analyzed data from an observational MDD cohort (Munich Antidepressant Response Signature [MARS] study, N = 1017), treated individually by psychopharmacological and psychotherapeutic means, and a multicenter, partially randomized clinical/pharmacogenomic study (Genome-based Therapeutic Drugs for Depression [GENDEP], N = 809). Symptoms were evaluated up to week 16 (or discharge) in MARS and week 12 in GENDEP. Clustering was performed on 809 MARS patients (discovery sample) using a mixed model with the integrated completed likelihood criterion for the assessment of cluster stability, and validated through a distinct MARS validation sample and GENDEP. A random forest algorithm was used to identify prediction patterns based on 50 clinical baseline items. From the clustering of the MARS discovery sample, seven TRCs emerged ranging from fast and complete response (average 4.9 weeks until discharge, 94% remitted patients) to slow and incomplete response (10% remitted patients at week 16). These proved stable representations of treatment response dynamics in both the MARS and the GENDEP validation sample. TRCs were strongly associated with established response markers, particularly the rate of remitted patients at discharge. TRCs were predictable from clinical items, particularly personality items, life events, episode duration, and specific psychopathological features. Prediction accuracy improved significantly when cluster-derived slopes were modelled instead of individual slopes. In conclusion, model-based clustering identified distinct and clinically meaningful treatment response classes in MDD that proved robust with regard to capturing response profiles of differently designed studies. Response classes were predictable from clinical baseline characteristics. Conceptually, model-based clustering is translatable to any outcome measure and could advance the large-scale integration of studies on treatment efficacy or the neurobiology of treatment response

Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning

Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%;significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments