Meta-analysis of CYP2C19 and CYP2D6 metabolic activity on antidepressant response from 13 clinical studies using genotype imputation.

Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. The association of CYP2C19 and CYP2D6 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR=1.46, 95% CI [1.03, 2.06], p=0.033, heterogeneity I2=0%, subgroup difference p=0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19 and CYP2D6, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. CYP2D6 structural variants cannot be imputed from genotype data, limiting inference of pharmacogenetic effects. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.

Changes in psychiatric services dynamics during the COVID-19 pandemic: Recognizing the need for resources shift.

With the significant impact of COVID-19 pandemic on the health, and the functioning of health care system, it has become increasingly important to understand changes in the ways health services were utilized and the factors influencing it. Drop in psychiatric admissions was seen during the pandemic, but also an increase in acute hospitalizations and emergency visits. Our aim was to analyze changes in out- and in-patient services utilization in the largest Croatian psychiatric institution during the first year of the pandemic, observed through the lens of the stringency index, and compare it to the pre-pandemic year. Along with an overall drop in hospitalizations, but a unit-specific rise in hospitalization, we have observed a non-significant overall drop in regular outpatient visits, and a significant drop coinciding with strictest epidemiological measures. There was also a significant increase in emergency visits coinciding with epidemiological measures that failed to return to pre-pandemic values, pointing to an expected significant and prolonged burden on emergency services. Simultaneous analysis of changing dynamics of mental health care service utilization during the pandemic helps us identify specific points of increased burden, and help us plan for early and flexible resources shift in order to adequately respond to evolving challenges.

Identifying the Common Genetic Basis of Antidepressant Response.

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Methodology for clinical genotyping of CYP2D6 and CYP2C19.

Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.

Difference of Symptoms Networks in Early and Late Phase Schizophrenia; A Cross-Sectional Network Analysis.

OBJECTIVE: The functional remission or recovery of schizophrenia patients is a challenging task which relies on pharmacotherapy but also on the timing of psychotherapy and other therapeutic interventions. The study aimed to assess the difference in strength and structure of symptoms networks between early and late phase schizophrenia. Our secondary objective was to check whether the overall, positive, negative, and general symptoms severity change over the course of treatment and disorder. METHODS: This nested cross-sectional analysis combined the samples from two studies performed during 2014-2016 at University Psychiatric Hospital Vrapce, Zagreb, Croatia on the consecutive sample of men 30-60 years old diagnosed with schizophrenia, 85 of them in the early (≤5 years from diagnosis), and 143 in the late phase of the illness. The study was funded by the project: "Biomarkers in schizophrenia - integration of complementary methods in longitudinal follow up of FEP patients". RESULTS: Median (IQR) age of the participant in the early phase was 36 (32-45) years and in the late phase 44 (38-49) years. Patients in the early phase had significantly higher odds for being in the symptomatic remission compared to the patients in the late-phase schizophrenia (OR=2.11; 95% CI 1.09-4.09) and had 10% less pronounced negative symptoms. The global strength, density, and structure of the symptoms network were not significantly different between the two study groups. CONCLUSIONS: Negative symptoms severity change with the course of illness and differ from the early to the late phase of schizophrenia. However, the overall network of psychotic symptoms is relatively stable, and overall strengths or density and the partial relationship between particular symptoms do not change significantly. The observed worsening of negative symptoms is probably at least partially caused by the lack of clear guidelines and effective treatment options aimed specifically toward negative symptoms.

Choline elevation in amygdala region at recovery indicates longer survival without depressive episode: a magnetic resonance spectroscopy study.

RATIONALE: Depression, with variable longitudinal patterns, recurs in one third of patients. We lack useful predictors of its course/outcome, and proton magnetic resonance spectroscopy (1H-MRS) of brain metabolites is an underused research modality in finding outcome correlates. OBJECTIVES: To determine if brain metabolite levels/changes in the amygdala region observed early in the recovery phase indicate depression recurrence risk in patients receiving maintenance therapy. METHODS: Forty-eight patients on stable-dose antidepressant (AD) maintenance therapy were analyzed from recovery onset until (i) recurrence of depression or (ii) start of AD discontinuation. Two 1H-MRS scans (6 months apart) were performed with a focus on amygdala at the beginning of recovery. N-acetylaspartate (NAA), choline-containing metabolites (Cho), and Glx (glutamine/glutamate and GABA) were evaluated with regard to time without recurrence, and risks were assessed by Cox proportional hazard modeling. RESULTS: Twenty patients had depression recurrence, and 23 patients reached AD discontinuation. General linear model repeated measures analysis displayed three-way interaction of measurement time, metabolite level, and recurrence on maintenance therapy, in a multivariate test, Wilks' lambda = 0.857, F(2,40) = 3.348, p = 0.045. Cho levels at the beginning of recovery and subsequent changes convey the highest risk for earlier recurrence. Patients experiencing higher amygdala Cho after recovery are at a significantly lower risk for depression recurrence (hazard ratio = 0.32; 95% confidence interval 0.13-0.77). CONCLUSION: Cho levels/changes in the amygdala early in the recovery phase correlate with clinical outcome. In the absence of major NAA fluctuations, changes in Cho and Glx may suggest a shift towards reduction in (previously increased) glutamatergic neurotransmission. Investigation of a larger sample with greater sampling frequency is needed to confirm the possible predictive role of metabolite changes in the amygdala region early in the recovery phase.

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients.

The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

A functional variant in the serotonin receptor 7 gene (HTR7), rs7905446, is associated with good response to SSRIs in bipolar and unipolar depression.

Predicting antidepressant response has been a clinical challenge for mood disorder. Although several genome-wide association studies have suggested a number of genetic variants to be associated with antidepressant response, the sample sizes are small and the results are difficult to replicate. Previous animal studies have shown that knockout of the serotonin receptor 7 gene (HTR7) resulted in an antidepressant-like phenotype, suggesting it was important to antidepressant action. In this report, in the first stage, we used a cost-effective pooled-sequencing strategy to sequence the entire HTR7 gene and its regulatory regions to investigate the association of common variants in HTR7 and clinical response to four selective serotonin reuptake inhibitors (SSRIs: citalopram, paroxetine, fluoxetine and sertraline) in a retrospective cohort mainly consisting of subjects with bipolar disorder (n = 359). We found 80 single-nucleotide polymorphisms (SNPs) with false discovery rate < 0.05 associated with response to paroxetine. Among the significant SNPs, rs7905446 (T/G), which is located at the promoter region, also showed nominal significance (P < 0.05) in fluoxetine group. GG/TG genotypes for rs7905446 and female gender were associated with better response to two SSRIs (paroxetine and fluoxetine). In the second stage, we replicated this association in two independent prospective samples of SSRI-treated patients with major depressive disorder: the MARS (n = 253, P = 0.0169) and GENDEP studies (n = 432, P = 0.008). The GG/TG genotypes were consistently associated with response in all three samples. Functional study of rs7905446 showed greater activity of the G allele in regulating expression of HTR7. The G allele displayed higher luciferase activity in two neuronal-related cell lines, and estrogen treatment decreased the activity of only the G allele. Electrophoretic mobility shift assay suggested that the G allele interacted with CCAAT/enhancer-binding protein beta transcription factor (TF), while the T allele did not show any interaction with any TFs. Our results provided novel pharmacogenomic evidence to support the role of HTR7 in association with antidepressant response.

Trajectories of Suicidal Ideation During 12 Weeks of Escitalopram or Nortriptyline Antidepressant Treatment Among 811 Patients With Major Depressive Disorder.

BACKGROUND: Suicidal ideation is a frequent and difficult-to-treat clinical challenge among patients with major depressive disorder (MDD). However, little is known regarding the differential development during antidepressant treatment and whether some patients may suffer from persistent suicidal ideation. METHODS: Among 811 patients with Schedules for Clinical Assessment in Neuropsychiatry (SCAN)-verified MDD from 2004-2007 assessed weekly for 12 weeks of escitalopram or nortriptyline antidepressant treatment, we applied item response theory to integrate a suicidality score based on 3 rating scales. We performed latent growth mixture modeling analysis to empirically identify trajectories. Multinomial logistic regression analyses estimated associations with potential predictors. RESULTS: We identified 5 distinct classes of suicidal ideation. The Persistent-low class (53.7%) showed no suicidal ideation whereas the Persistent-high class (9.8%) had high suicidal ideation throughout 12 weeks. Two classes showed a fluctuating course: the Fluctuating class (5.2%) ended at a low level of suicidal ideation, whereas the Slow-response-relapse class (4.8%) initially responded slowly but then experienced a large increase to a high level of suicidal ideation after 12 weeks. The Fast-response class (26.5%) had a high baseline severity similar to the Persistent-high class but responded quickly within a few weeks and remained at a low level. Previous suicide attempts and higher mood symptom severity were associated with worse suicidal ideation trajectories, whereas living with a partner showed a trend toward better response. CONCLUSION: Approximately 1 of 5 patients with MDD showed high or fluctuating suicidal ideation despite antidepressant treatment. Studies should investigate whether suicidal ideation may persist for longer periods and more targeted treatment possibilities. TRIAL REGISTRATION: ISRCTN​​ identifier: ISRCTN03693000​​​​.

Neuroimaging research in posttraumatic stress disorder - Focus on amygdala, hippocampus and prefrontal cortex.

Neuroimaging research reflects the complexity of post-traumatic stress disorder and shares some common difficulties of post-traumatic stress disorder research, such as the different classifications of the disorder over time, changes in diagnostic criteria, and extensive comorbidities, as well as precisely delineated and prevailing genetic and environmental determinants in the development of the disorder and its clinical manifestations. Synthesis of neuroimaging findings in an effort to clarify causes, clinical manifestations, and consequences of the disorder is complicated by a variety of applied technical approaches in different brain regions, differences in symptom dimensions in a study population, and typically small sample sizes, with the interplay of all of these consequently bringing about divergent results. Furthermore, combinations of the aforementioned issues serve to weaken any comprehensive meta-analytic approach. In this review, we focus on recent neuroimaging studies and those performed on larger samples, with particular emphasis on research concerning the amygdala, hippocampus, and prefrontal cortex, as these are the brain regions postulated by the core research to play a prominent role in the pathophysiology of post-traumatic stress disorder. Additionally, we review the guidelines for future research and list a number of new intersectional and cross-sectional approaches in the area of neuroimaging. We conclude that future neuroimaging research in post-traumatic stress disorder will certainly benefit from a higher integration with genetic research, better profiling of control groups, and a greater involvement of the neuroimaging genetics approach and from larger collaborative studies.

Choline and N-acetyl aspartate levels in the dorsolateral prefrontal cortex at the beginning of the recovery phase as markers of increased risk for depressive episode recurrence under different duration of maintenance therapy and after it: a retrospective cohort study.

AIM: To evaluate the relationship between the dynamics of proton magnetic resonance spectroscopy (1H-MRS) brain metabolite levels at the beginning of the recovery phase of the index depressive episode and the time to the recurrence of depression. METHODS: This retrospective cohort study analyzed the changes in N-acetyl aspartate (NAA), choline (Cho), and glutamate-glutamine in 48 patients with recurrent depression treated with maintenance antidepressant monotherapy at a stable dose. 1H-MRS was performed at the start of the recovery phase and 6 months later. 1H-MRS parameters, index episode descriptors, and depressive disorder course were analyzed by Cox proportional hazards model. RESULTS: NAA and Cho decrease six months after the beginning of the recovery period were time-independent risk factors for depressive episode recurrence. Hazard ratio associated with NAA decrease was 2.02 (95% confidence interval 1.06-3.84) and that associated with Cho decrease was 2.06 (95% confidence interval 1.02-4.17). These changes were not related to symptoms severity, as Montgomery-Asberg Depression Scale score remained generally unchanged (mean -0.01; standard deviation 1.6) over the first 6 months of recovery. CONCLUSION: Patients receiving maintenance antidepressant therapy after recovery who experience a decrease in NAA or Cho levels early in the recovery phase have a double risk of depressive episode recurrence. Sustained NAA and Cho levels at the beginning of the recovery phase may indicate increased brain resilience conferred by antidepressant therapy, while NAA and Cho decrease may indicate only the trait-related temporal effect of therapy in another stratum of patients.

Genes associated with anhedonia: a new analysis in a large clinical trial (GENDEP).

A key feature of major depressive disorder (MDD) is anhedonia, which is a predictor of response to antidepressant treatment. In order to shed light on its genetic underpinnings, we conducted a genome-wide association study (GWAS) followed by investigation of biological pathway enrichment using an anhedonia dimension for 759 patients with MDD in the GENDEP study. The GWAS identified 18 SNPs associated at genome-wide significance with the top one being an intronic SNP (rs9392549) in PRPF4B (pre-mRNA processing factor 4B) located on chromosome 6 (P = 2.07 × 10-9) while gene-set enrichment analysis returned one gene ontology term, axon cargo transport (GO: 0008088) with a nominally significant P value (1.15 × 10-5). Furthermore, our exploratory analysis yielded some interesting, albeit not statistically significant genetic correlation with Parkinson's Disease and nucleus accumbens gray matter. In addition, polygenic risk scores (PRSs) generated from our association analysis were found to be able to predict treatment efficacy of the antidepressants in this study. In conclusion, we found some markers significantly associated with anhedonia, and some suggestive findings of related pathways and biological functions, which could be further investigated in other studies.

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: Meta-analysis of data from genome-wide association studies.

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects. CYP2C19 metabolic phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was assessed by standardized percentage symptom improvement and by remission. Side effect data were available at weeks 2-4, 6 and 9 in three samples. A fixed-effects meta-analysis was performed using EM as the reference group. Analysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD = 0.43, CI = 0.19-0.66) and higher remission rates (OR = 1.55, CI = 1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR = 1.26, CI = 1.08-1.47), neurological (OR = 1.28, CI = 1.07-1.53) and sexual side effects (OR = 1.52, CI = 1.23-1.87; week 6 values were similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups. CYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs being relatively rare among Caucasians (∼2%).

Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables.

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.

Association between C-reactive protein (CRP) with depression symptom severity and specific depressive symptoms in major depression.

INTRODUCTION: Population-based studies have associated inflammation, particularly higher C-reactive protein (CRP), with depressive severity, but clinical trials in major depressive disorder were rather non-specific without examining the role of gender. We aimed to investigate the association between CRP and overall depression severity including specific depressive symptoms and to examine potential gender differences. METHODS: We included 231 individuals with major depressive disorder from the Genome-Based Therapeutics Drugs for Depression (GENDEP) study. At baseline, we assessed high-sensitivity CRP levels and psychopathology with the Montgomery Aasberg Depression Rating Scale (MADRS). We performed linear regression analyses to investigate the association between baseline CRP levels with overall MADRS severity and specific symptoms at baseline and adjusted for age, gender, anti-inflammatory and psychotropic drug treatment, body mass index, smoking, inflammatory diseases, and recruitment center. RESULTS: Higher CRP levels were significantly associated with greater overall MADRS symptom severity (p=0.02), which was significant among women (p=0.02) but not among men (p=0.68). Among women, higher CRP was associated with increased severity on observed mood, cognitive symptoms, interest-activity, and suicidality, but we found no significant associations among men. Interaction analyses showed no significant gender differences on the overall MADRS score or specific symptoms. DISCUSSION: Our results support the sickness syndrome theory suggesting that chronic low-grade inflammation may be associated with a subtype of depression. The potential gender differences in psychopathology may be explained by biological and/or psychosocial factors, e.g. differential modulation of immune responses by sex hormones. Clinical studies should investigate symptom-specific and/or gender-specific treatment guided by peripheral inflammatory markers.