Predictors and outcomes in primary depression care (POKAL) – a research training group develops an innovative approach to collaborative care

BACKGROUND: The interdisciplinary research training group (POKAL) aims to improve care for patients with depression and multimorbidity in primary care. POKAL includes nine projects within the framework of the Chronic Care Model (CCM). In addition, POKAL will train young (mental) health professionals in research competences within primary care settings. POKAL will address specific challenges in diagnosis (reliability of diagnosis, ignoring suicidal risks), in treatment (insufficient patient involvement, highly fragmented care and inappropriate long-time anti-depressive medication) and in implementation of innovations (insufficient guideline adherence, use of irrelevant patient outcomes, ignoring relevant context factors) in primary depression care. METHODS: In 2021 POKAL started with a first group of 16 trainees in general practice (GPs), pharmacy, psychology, public health, informatics, etc. The program is scheduled for at least 6 years, so a second group of trainees starting in 2024 will also have three years of research-time. Experienced principal investigators (PIs) supervise all trainees in their specific projects. All projects refer to the CCM and focus on the diagnostic, therapeutic, and implementation challenges. RESULTS: The first cohort of the POKAL research training group will develop and test new depression-specific diagnostics (hermeneutical strategies, predicting models, screening for suicidal ideation), treatment (primary-care based psycho-education, modulating factors in depression monitoring, strategies of de-prescribing) and implementation in primary care (guideline implementation, use of patient-assessed data, identification of relevant context factors). Based on those results the second cohort of trainees and their PIs will run two major trials to proof innovations in primary care-based a) diagnostics and b) treatment for depression. CONCLUSION: The research and training programme POKAL aims to provide appropriate approaches for depression diagnosis and treatment in primary care

Increased d-amino acid oxidase expression in the bilateral hippocampal CA4 of schizophrenic patients: a post-mortem study

An important risk gene in schizophrenia is d-amino acid oxidase (DAAO). To establish if expression of DAAO is altered in cortical, hippocampal or thalamic regions of schizophrenia patients, we measured gene expression of DAAO in a post-mortem study of elderly patients with schizophrenia and non-affected controls in both hemispheres differentiating between gray and white matter. We compared cerebral post-mortem samples (granular frontal cortex BA9, middle frontal cortex BA46, superior temporal cortex BA22, entorhinal cortex BA28, sensoric cortex BA1–3, hippocampus (CA4), mediodorsal nucleus of the thalamus) from 10 schizophrenia patients to 13 normal subjects investigating gene expression of DAAO in the gray and white matter of both hemispheres of the above-mentioned brain regions by in situ-hybridization. We found increased expression of DAAO-mRNA in the hippocampal CA4 of schizophrenic patients. Compared to the control group, both hemispheres of the hippocampus of schizophrenic patients showed an increased expression of 46% (right, P = 0.013) and 54% (left, P = 0.019), respectively. None of the other regions examined showed statistically significant differences in DAAO expression. This post-mortem study demonstrated increased gene expression of DAAO in the left and right hippocampus of schizophrenia patients. This increased expression could be responsible for a decrease in local d-serine levels leading to a NMDA-receptor hypofunction that is hypothesized to play a major role in the pathophysiology of schizophrenia. However, our study group was small and results should be verified using larger samples

Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium’s possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response — defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10−12, R2 = 1.9%) and continuous (P = 6.4 × 10−9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22–5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10−4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life

Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research