Pleiotropic genes in psychiatry: Calcium channels and the stress-related FKBP5 gene in antidepressant resistance

The association of FKBP5 gene polymorphism with genetic susceptibility to depression and response to antidepressant treatment- a systematic review

BACKGROUND

Given the inconsistencies in current studies regarding the impact of FKBP5 gene polymorphisms on depression, arising from variations in study methods, subjects, and treatment strategies, this paper provides a comprehensive review of the relationship between FKBP5 gene polymorphisms and genetic susceptibility to depression, as well as their influence on response to antidepressant treatment.

METHODS

Electronic databases were searched up to April 11, 2023, for all literature in English and Chinese on depression, FKBP5 gene polymorphisms, and antidepressant treatment. Data extraction and quality assessment were performed for key study characteristics. Qualitative methods were used to synthesize the study results.

RESULTS

A total of 21 studies were included, with the majority exhibiting average to moderate quality. Six SNPs (rs3800373, rs1360780, rs9470080, rs4713916, rs9296158, rs9394309) were broadly implicated in susceptibility to depression, while rs1360780 and rs3800373 were linked to antidepressant treatment sensitivity. Additionally, rs1360780 was associated with adverse reactions to antidepressant drug treatment. However, these associations were largely unconfirmed in replication studies.

CONCLUSIONS

Depression is recognized as a polygenic genetic disorder, with multiple genes contributing, each exerting relatively small effects. Future studies should explore not only multiple gene interactions but also epigenetic changes. Presently, research on FKBP5 in affective disorders remains notably limited, highlighting the necessity for further investigations in this domain.

Susceptibility to Treatment-Resistant Depression Within Families

Importance

Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes.

Objective

To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data.

Design, Setting, and Participants

This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship.

Main Outcomes and Measures

Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality.

Results

This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings.

Conclusions and Relevance

To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.

Global Impacts of Western Diet and Its Effects on Metabolism and Health: A Narrative Review

The Western diet is a modern dietary pattern characterized by high intakes of pre-packaged foods, refined grains, red meat, processed meat, high-sugar drinks, candy, sweets, fried foods, conventionally raised animal products, high-fat dairy products, and high-fructose products. The present review aims to describe the effect of the Western pattern diet on the metabolism, inflammation, and antioxidant status; the impact on gut microbiota and mitochondrial fitness; the effect of on cardiovascular health, mental health, and cancer; and the sanitary cost of the Western diet. To achieve this goal, a consensus critical review was conducted using primary sources, such as scientific articles, and secondary sources, including bibliographic indexes, databases, and web pages. Scopus, Embase, Science Direct, Sports Discuss, ResearchGate, and the Web of Science were used to complete the assignment. MeSH-compliant keywords such "Western diet", "inflammation", "metabolic health", "metabolic fitness", "heart disease", "cancer", "oxidative stress", "mental health", and "metabolism" were used. The following exclusion criteria were applied: (i) studies with inappropriate or irrelevant topics, not germane to the review's primary focus; (ii) Ph.D. dissertations, proceedings of conferences, and unpublished studies. This information will allow for a better comprehension of this nutritional behavior and its effect on an individual's metabolism and health, as well as the impact on national sanitary systems. Finally, practical applications derived from this information are made.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD.

MATERIALS AND METHODS

Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery-Åsberg Depression Rating Scale (MADRS). Using peripheral blood samples, we performed RNA-sequencing, assessed DNA methylation using the Illumina EPIC array, and performed genotyping using the Illumina PsychArray. To address this rich multi-omic dataset with high dimensional features, we developed integrative Geneset-Embedded non-negative Matrix factorization (iGEM), a non-negative matrix factorization (NMF) based model, supplemented with auxiliary information regarding gene sets and gene-methylation relationships. In particular, we factorize the subjects by features (i.e., gene expression or DNA methylation) into subjects-by-factors and factors-by-features. We define the factors as the meta-phenotypes as they represent integrated composite scores of the molecular measurements for each subject.

RESULTS

Using our model, we identified a number of meta-phenotypes which were related to AD response. By integrating geneset information into the model, we were able to relate these meta-phenotypes to biological processes, including a meta-phenotype related to immune and inflammatory functions as well as other genes related to depression or AD response. The meta-phenotype identified several genes including immune interleukin 1 receptor like 1 (IL1RL1) and interleukin 5 receptor (IL5) subunit alpha (IL5RA), AKT/PIK3 pathway related phosphoinositide-3-kinase regulatory subunit 6 (PIK3R6), and sphingomyelin phosphodiesterase 3 (SMPD3), which has been identified as a target of AD treatment.

CONCLUSIONS

The derived meta-phenotypes and associated biological functions represent both biomarkers to predict response, as well as potential new treatment targets. Our method is applicable to other diseases with multi-omic data, and the software is open source and available on Github (https://github.com/li-lab-mcgill/iGEM).

Understanding treatment-resistant depression using "omics" techniques: A systematic review

BACKGROUND

Treatment-resistant depression (TRD) results in huge healthcare costs and poor patient clinical outcomes. Most studies have adopted a "candidate mechanism" approach to investigate TRD pathogenesis, however this is made more challenging due to the complex and heterogeneous nature of this condition. High-throughput "omics" technologies can provide a more holistic view and further insight into the underlying mechanisms involved in TRD development, expanding knowledge beyond already-identified mechanisms. This systematic review assessed the information from studies that examined TRD using hypothesis-free omics techniques.

METHODS

PubMed, MEDLINE, Embase, APA PsycInfo, Scopus and Web of Science databases were searched on July 2022. 37 human studies met the eligibility criteria, totalling 17,518 TRD patients, 571,402 healthy controls and 62,279 non-TRD depressed patients (including antidepressant responders and untreated MDD patients).

RESULTS

Significant findings were reported that implicate the role in TRD of various molecules, including polymorphisms, genes, mRNAs and microRNAs. The pathways most commonly reported by the identified studies were involved in immune system and inflammation, neuroplasticity, calcium signalling and neurotransmitters.

LIMITATIONS

Small sample sizes, variability in defining TRD, and heterogeneity in study design and methodology.

CONCLUSIONS

These findings provide insight into TRD pathophysiology, proposing future research directions for novel drug targets and potential biomarkers for clinical staging and response to antidepressants (citalopram/escitalopram in particular) and electroconvulsive therapy (ECT). Further validation is warranted in large prospective studies using standardised TRD criteria. A multi-omics and systems biology strategy with a collaborative effort will likely deliver robust findings for translation into the clinic.

Genetic Association Study and Machine Learning to Investigate Differences in Platelet Reactivity in Patients with Acute Ischemic Stroke Treated with Aspirin

Aspirin resistance (AR) is a pressing problem in current ischemic stroke care. Although the role of genetic variations is widely considered, the data still remain controversial. Our aim was to investigate the contribution of genetic features to laboratory AR measured through platelet aggregation with arachidonic acid (AA) and adenosine diphosphate (ADP) in ischemic stroke patients. A total of 461 patients were enrolled. Platelet aggregation was measured via light transmission aggregometry. Eighteen single-nucleotide polymorphisms (SNPs) in ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1 and PEAR1 genes and the intergenic 9p21.3 region were determined using low-density biochips. We found an association of rs1330344 in the PTGS1 gene with AR and AA-induced platelet aggregation. Rs4311994 in ADRA2A gene also affected AA-induced aggregation, and rs4523 in the TBXA2R gene and rs12041331 in the PEAR1 gene influenced ADP-induced aggregation. Furthermore, the effect of rs1062535 in the ITGA2 gene on NIHSS dynamics during 10 days of treatment was found. The best machine learning (ML) model for AR based on clinical and genetic factors was characterized by AUC = 0.665 and F1-score = 0.628. In conclusion, the association study showed that PTGS1, ADRA2A, TBXA2R and PEAR1 polymorphisms may affect laboratory AR. However, the ML model demonstrated the predominant influence of clinical features.

Polymorphisms of COMT and CREB1 are associated with treatment-resistant depression in a Chinese Han population

Genetic factors play a crucial role for the pathophysiology of treatment-resistant depression (TRD). It has been established that Catechol-O-methyltransferase (COMT) and cyclic amp-response element-binding protein (CREB) are associated with antidepressant response. The aim of this study was to explore the association between single nucleotide polymorphisms (SNPs) in COMT and CREB1 genes and TRD in a Chinese population. We recruited 181 patients with major depressive disorder (MDD) and 80 healthy controls, including 81 TRD patients. Depressive symptoms were assessed with the Hamilton Depression Rating Scale-17 (HDRS). Genotyping was performed using mass spectrometry. Genetic analyses were conducted by PLINK Software. The distribution of COMT SNP rs4818 allele and genotypes were significantly different between TRD and controls. Statistical differences in allele frequencies were observed between TRD and non-TRD groups, including rs11904814 and rs6740584 in CREB1 gene, rs4680 and rs4818 in COMT gene. There were differences in the distribution of HDRS total scores among different phenotypes of CREB1 rs11904814, CREB1 rs6740584, COMT rs4680 and rs4818. Gene-gene interaction effect of COMT-CREB1 (rs4680 × rs6740584) revealed significant epistasis in TRD. There findings indicate that COMT and CREB1 polymorphisms influence the risk of TRD and affect the severity of depressive symptoms of MDD.

Machine Learning: An Overview and Applications in Pharmacogenetics

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC−MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit.

Prognosis and Improved Outcomes in Major Depression: A Review

(Reprinted from Transl Psychiatry. 2019 Apr 3; 9(1):127. Open access; is licensed under a Creative Commons Attribution 4.0 International License).

Genetic variants associated with psychotic symptoms across psychiatric disorders

BACKGROUND

Recent evidence suggests that psychiatric symptoms share a common genetic liability across diagnostic categories. The present study investigated the effects of variants within previously identified relevant genes on specific symptom clusters, independently from the diagnosis.

METHODS

1550 subjects affected by Schizophrenia (SCZ), Major Depressive Disorder or Bipolar Disorder were included. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Hamilton Depression Rating Scale (HDRS). Principal component analysis and a further clinical refinement were used to define symptom clusters. Clusters scores were tested for association with 46 genetic variants within nine genes previously linked to one or more major psychiatric disorders by large genome wide association studies (ANK3, CACNA1C, CACNB2, FKBP5, FZD3, GRM7, ITIH3, SYNE1, TCF4). Exploratory analyses were performed in each disorder separately to further elucidate the SNPs effects.

RESULTS

five PANSS clusters (Negative; Impulsiveness; Cognitive; Psychotic; Depressive) and four HDRS clusters (Core Depressive; Somatic; Psychotic-like; Insomnia) were identified. CACNA1C rs11615998 was associated with HDRS Psychotic cluster in the whole sample. In the SCZ sample, CACNA1C rs11062296 was associated with PANSS Impulsiveness cluster and CACNA1C rs2238062 was associated with PANSS negative cluster.

DISCUSSION

CACNA1C rs11615998 was associated with psychotic symptoms (C-allele carriers have decreased psychotic-risk) independently from the diagnosis, in line with the evidence of a cross disorder effect of many risk variants. This gene was previously associated with SCZ and cross-disorder liability to psychiatric disorders. Our findings confirmed that deep phenotyping is pivotal to clarify the role of genetic variants on symptoms patterns.

FKBP5 Gene Variants May Modulate Depressive Features in Bipolar Disorder

BACKGROUND

Previous evidence suggested the possible association of FK506 binding protein 5 (FKBP5) gene variants in bipolar disorder (BPD).

OBJECTIVE

Given the need of refinement of the findings obtained in large but poorly phenotyped samples, this study investigated the possible role of variants within FKBP5 in a small but deeply phenotyped BPD sample.

METHODS

A sample (N = 131) of bipolar patients were investigated with 10 polymorphisms within the FKBP5 gene. A control sample (N = 65) was also used for the analyses. Treatment response and remission of symptoms were evaluated using of the Hamilton Depression Rating Scale (HDRS), Hamilton Anxiety Rating Scale (HARS), and Young Mania Rating Scale (YMRS). The same analyses were also performed on the depressive subsample of BPD (D.BPD).

RESULTS

rs3800373 was associated with disorder risk in the depressive BPD subsample with the G allele being more frequent in subjects with a D.BPD phenotype. This was the only association that survived statistical correction.

CONCLUSIONS

rs3800373 FKBP5 may increase the risk of developing predominantly depressed BPD, probably through the creation of an enhancer consensus sequence in the 3'UTR of the gene, thus potentially increasing its expression. This finding seems to be partially supported by literature data, which evidenced increased levels of FKBP5 in psychiatric subjects.

Can Machine Learning help us in dealing with treatment resistant depression? A review

BACKGROUND

About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive disorder is still unknown. Therefore, Machine Learning (ML) techniques emerged in the last years as interesting approaches to deal with such complex problems.

METHODS

We performed a bibliographic search on Pubmed, Google Scholar and Medline of clinical, imaging, genetic and EEG ML classification studies on treatment-responding depression and TRD as well as studies trying to predict response to a specific treatment in already established TRD. The inclusion criteria were met by eleven studies. Seven focused on the definition of predictors of TRD onset while four attempted to predict the response to specific treatments in TRD.

RESULTS

The results showed that it seems possible to classify between responders MDD and TRD with good accuracies based on clinical variables. Moreover, some studies reported the possibility of using EEG measures to predict response to different pharmacological and non-pharmacological treatments in established TRD.

LIMITATIONS

The definition of TRD, the selection of variables together with ML algorithms and pipelines varies across the studies, ultimately determining the unfeasibility to implement these models in clinical practice.

CONCLUSIONS

The findings suggest that ML could be a valid approach to increase our understanding of TRD and to better classify and stratify this disorder, which may ultimately help clinicians in the assessment of major depressive disorders.

Association Analysis of 14 Candidate Gene Polymorphism with Depression and Stress among Gestational Diabetes Mellitus

The association of candidate genes and psychological symptoms of depression, anxiety, and stress among women with gestational diabetes mellitus (GDM) in Malaysia was determined in this study, followed by the determination of their odds of getting psychological symptoms, adjusted for socio-demographical background, maternal, and clinical characteristics. Single nucleotide polymorphisms (SNPs) recorded a significant association between SNP of EPHX2 (rs17466684) and depression symptoms (AOR = 7.854, 95% CI = 1.330–46.360) and stress symptoms (AOR = 7.664, 95% CI = 1.579–37.197). Associations were also observed between stress symptoms and SNP of OXTR (rs53576) and (AOR = 2.981, 95% CI = 1.058–8.402) and SNP of NRG1 (rs2919375) (AOR = 9.894, 95% CI = 1.159–84.427). The SNP of EPHX2 (rs17466684) gene polymorphism is associated with depression symptoms among Malaysian women with GDM. SNP of EPHX2 (rs17466684), OXTR (rs53576) and NRG1 (rs2919375) are also associated with stress symptoms.

Neurovascular glucocorticoid receptors and glucocorticoids: implications in health, neurological disorders and drug therapy

Glucocorticoid receptors (GRs) are ubiquitous transcription factors widely studied for their role in controlling events related to inflammation, stress and homeostasis. Recently, GRs have reemerged as crucial targets of investigation in neurological disorders, with a focus on pharmacological strategies to direct complex mechanistic GR regulation and improve therapy. In the brain, GRs control functions necessary for neurovascular integrity, including responses to stress, neurological changes mediated by the hypothalamic-pituitary-adrenal axis and brain-specific responses to corticosteroids. Therefore, this review will examine GR regulation at the neurovascular interface in normal and pathological conditions, pharmacological GR modulation and glucocorticoid insensitivity in neurological disorders.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Genetic Associations between Voltage-Gated Calcium Channels and Psychiatric Disorders

Psychiatric disorders are mental, behavioral or emotional disorders. These conditions are prevalent, one in four adults suffer from any type of psychiatric disorders world-wide. It has always been observed that psychiatric disorders have a genetic component, however, new methods to sequence full genomes of large cohorts have identified with high precision genetic risk loci for these conditions. Psychiatric disorders include, but are not limited to, bipolar disorder, schizophrenia, autism spectrum disorder, anxiety disorders, major depressive disorder, and attention-deficit and hyperactivity disorder. Several risk loci for psychiatric disorders fall within genes that encode for voltage-gated calcium channels (CaVs). Calcium entering through CaVs is crucial for multiple neuronal processes. In this review, we will summarize recent findings that link CaVs and their auxiliary subunits to psychiatric disorders. First, we will provide a general overview of CaVs structure, classification, function, expression and pharmacology. Next, we will summarize tools to study risk loci associated with psychiatric disorders. We will examine functional studies of risk variations in CaV genes when available. Finally, we will review pharmacological evidence of the use of CaV modulators to treat psychiatric disorders. Our review will be of interest for those studying pathophysiological aspects of CaVs.

Results of the European Group for the Study of Resistant Depression (GSRD) - basis for further research and clinical practice

Objectives: The overview outlines two decades of research from the European Group for the Study of Resistant Depression (GSRD) that fundamentally impacted evidence-based algorithms for diagnostics and psychopharmacotherapy of treatment-resistant depression (TRD). Methods: The GSRD staging model characterising response, non-response and resistance to antidepressant (AD) treatment was applied to 2762 patients in eight European countries. Results: In case of non-response, dose escalation and switching between different AD classes did not show superiority over continuation of original AD treatment. Predictors for TRD were symptom severity, duration of the current major depressive episode (MDE), suicidality, psychotic and melancholic features, comorbid anxiety and personality disorders, add-on treatment, non-response to the first AD, adverse effects, high occupational level, recurrent disease course, previous hospitalisations, positive family history of MDD, early age of onset and novel associations of single nucleoid polymorphisms (SNPs) within the PPP3CC, ST8SIA2, CHL1, GAP43 and ITGB3 genes and gene pathways associated with neuroplasticity, intracellular signalling and chromatin silencing. A prediction model reaching accuracy of above 0.7 highlighted symptom severity, suicidality, comorbid anxiety and lifetime MDEs as the most informative predictors for TRD. Applying machine-learning algorithms, a signature of three SNPs of the BDNF, PPP3CC and HTR2A genes and lacking melancholia predicted treatment response. Conclusions: The GSRD findings offer a unique and balanced perspective on TRD representing foundation for further research elaborating on specific clinical and genetic hypotheses and treatment strategies within appropriate study-designs, especially interaction-based models and randomized controlled trials.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

CA, Kasper S. Prognosis and improved outcomes in major depression: a review

Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. This literature review sought to investigate factors closely linked to outcome and summarize existing and novel strategies for improvement. The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. Potential biomarkers have been explored, including hippocampal volumes, neuronal activity of the anterior cingulate cortex, and levels of brain-derived neurotrophic factor (BDNF) and central and peripheral inflammatory markers (e.g., translocator protein (TSPO), interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor alpha (TNFα)). However, their integration into routine clinical care has not yet been fully elucidated, and more research is needed in this regard. Genetic findings suggest that testing for CYP450 isoenzyme activity may improve treatment outcomes. Strategies such as managing risk factors, improving clinical trial methodology, and designing structured step-by-step treatments are also beneficial. Finally, drawing on existing guidelines, we outline a sequential treatment optimization paradigm for selecting first-, second-, and third-line treatments for acute and chronically ill patients. Well-established treatments such as electroconvulsive therapy (ECT) are clinically relevant for treatment-resistant populations, and novel transcranial stimulation methods such as theta-burst stimulation (TBS) and magnetic seizure therapy (MST) have shown promising results. Novel rapid-acting antidepressants, such as ketamine, may also constitute a paradigm shift in treatment optimization for MDD.

Somato-Dendritic Regulation of Raphe Serotonin Neurons; A Key to Antidepressant Action

Several lines of evidence implicate serotonin (5-hydroxytryptamine, 5-HT)in regulating personality traits and mood control. Serotonergic neurons are classically thought to be tonic regular-firing, “clock-like” neurons. Neurotransmission by serotonin is tightly regulated by the serotonin transporter (SERT) and by autoreceptors (serotonin receptors expressed by serotonin neurons) through negative feedback inhibition at the cell bodies and dendrites (5-HT_1A receptors) of the dorsal raphe nuclei or at the axon terminals (5-HT_1B receptors). In dorsal raphe neurons, the release of serotonin from vesicles in the soma, dendrites, and/or axonal varicosities is independent of classical synapses and can be induced by neuron depolarization, by the stimulation of L-type calcium channels, by activation of glutamatergic receptors, and/or by activation of 5-HT₂ receptors. The resulting serotonin release displays a slow kinetic and a large diffusion. This process called volume transmission may ultimately affect the rate of discharge of serotonergic neurons, and their tonic activity. The therapeutic effects induced by serotonin-selective reuptake inhibitor (SSRI) antidepressants are initially triggered by blocking SERT but rely on consequences of chronic exposure, i.e., a selective desensitization of somatodendritic 5-HT_1A autoreceptors. Agonist stimulation of 5-HT_2B receptors mimicked behavioral and neurogenic SSRI actions, and increased extracellular serotonin in dorsal raphe. By contrast, a lack of effects of SSRIs was observed in the absence of 5-HT_2B receptors (knockout-KO), even restricted to serotonergic neurons (Htr2b^5-HTKO mice). The absence of 5-HT_2B receptors in serotonergic neurons is associated with a higher 5-HT_1A-autoreceptor reactivity and thus a lower firing activity of these neurons. In agreement, mice with overexpression of 5-HT_1A autoreceptor show decreased neuronal activity and increased depression-like behavior that is resistant to SSRI treatment. We propose thus that the serotonergic tone results from the opposite control exerted by somatodendritic (Gi-coupled) 5-HT_1A and (Gq-coupled) 5-HT_2B receptors on dorsal raphe neurons. Therefore, 5-HT_2B receptors may contribute to SSRI therapeutic effects by their positive regulation of adult raphe serotonergic neurons. Deciphering the molecular mechanism controlling extrasynaptic release of serotonin, and how autoreceptors interact in regulating the tonic activity of serotonergic neurons, is critical to fully understand the therapeutic effect of SSRIs.

International Union of Basic and Clinical Pharmacology CIV: The Neurobiology of Treatment-resistant Depression: From Antidepressant Classifications to Novel Pharmacological Targets

Major depressive disorder is one of the most prevalent and life-threatening forms of mental illnesses and a major cause of morbidity worldwide. Currently available antidepressants are effective for most patients, although around 30% are considered treatment resistant (TRD), a condition that is associated with a significant impairment of cognitive function and poor quality of life. In this respect, the identification of the molecular mechanisms contributing to TRD represents an essential step for the design of novel and more efficacious drugs able to modify the clinical course of this disorder and increase remission rates in clinical practice. New insights into the neurobiology of TRD have shed light on the role of a number of different mechanisms, including the glutamatergic system, immune/inflammatory systems, neurotrophin function, and epigenetics. Advances in drug discovery processes in TRD have also influenced the classification of antidepressant drugs and novel classifications are available, such as the neuroscience-based nomenclature that can incorporate such advances in drug development for TRD. This review aims to provide an up-to-date description of key mechanisms in TRD and describe current therapeutic strategies for TRD before examining novel approaches that may ultimately address important neurobiological mechanisms not targeted by currently available antidepressants. All in all, we suggest that drug targeting different neurobiological systems should be able to restore normal function but must also promote resilience to reduce the long-term vulnerability to recurrent depressive episodes.

The Genetics of Treatment-Resistant Depression: A Critical Review and Future Perspectives

BACKGROUND

One-third of depressed patients develop treatment-resistant depression with the related sequelae in terms of poor functionality and worse prognosis. Solid evidence suggests that genetic variants are potentially valid predictors of antidepressant efficacy and could be used to provide personalized treatments.

METHODS

The present review summarizes genetic findings of treatment-resistant depression including results from candidate gene studies and genome-wide association studies. The limitations of these approaches are discussed, and suggestions to improve the design of future studies are provided.

RESULTS

Most studies used the candidate gene approach, and few genes showed replicated associations with treatment-resistant depression and/or evidence obtained through complementary approaches (e.g., gene expression studies). These genes included GRIK4, BDNF, SLC6A4, and KCNK2, but confirmatory evidence in large cohorts was often lacking. Genome-wide association studies did not identify any genome-wide significant association at variant level, but pathways including genes modulating actin cytoskeleton, neural plasticity, and neurogenesis may be associated with treatment-resistant depression, in line with results obtained by genome-wide association studies of antidepressant response. The improvement of aggregated tests (e.g., polygenic risk scores), possibly using variant/gene prioritization criteria, the increase in the covering of genetic variants, and the incorporation of clinical-demographic predictors of treatment-resistant depression are proposed as possible strategies to improve future pharmacogenomic studies.

CONCLUSIONS

Genetic biomarkers to identify patients with higher risk of treatment-resistant depression or to guide treatment in these patients are not available yet. Methodological improvements of future studies could lead to the identification of genetic biomarkers with clinical validity.

Design of FRET Probes for SNP RS1006737, Related to Mood Disorder

Background:

Several studies have shown that the Single Nucleotide Polymorphism (SNP) in the CACAN1C gene, rs1006737, is related to different mood disorder illnesses, such as bipolar disorder and schizophrenia. Current day molecular procedures for allele detection of this gene can be very expensive and time consuming. Hence, a sensitive and specific molecular procedure for detecting these mutations in a large number of subjects is desirable, especially for research groups who have no complex laboratory equipment.

Objective:

The possibility of using a Fluorescence Resonance Energy Transfer (FRET) probe was evaluated by means of bioinformatic tools, designed for forecasting the molecular behavior of DNA probes used in the research field or for laboratory analysis methods.

Method:

In this study we used the DINAMelt Web Server to predict the Tms of FRET oligo in the presence of the A and/or G allele in rs1006737. The PCR primers were designed by using oligo 4 and oligo 6 primer analysis software,

Results:

The molecular probe described in this study detected a Tm difference of 5-6°C between alleles A and G in rs1006737, which also showed good discrimination for a heterozygous profile for this genomic region.

Conclusion:

Although in silico studies represent a relatively new avenue of inquiry, they have now started to be used to predict how a molecular probe interacts with its biological target, reducing the time and costs of molecular test tuning. The results of this study seem promising for further laboratory tests on allele detection in rs1006737 region.