Genome-wide association studies of antidepressant class response and treatment-resistant depression

Epigenome-Wide DNA Methylation in Unipolar Depression: Predictive Biomarker of Antidepressant Treatment Response?

BACKGROUND

Despite the well-documented efficacy of antidepressant agents for the treatment of major depressive disorder (MDD), initial treatment nonresponse rates are high. Recent years have seen an increase in research into predictive biomarkers toward improving diagnosis and individualized treatment. Among those, epigenetic mechanisms such as DNA methylation constitute promising candidate markers in predicting antidepressant treatment response in MDD. The present study sought to address epigenome-wide DNA methylation as a predictor of antidepressant treatment response in the largest sample to date of patients with MDD.

METHODS

Epigenome-wide DNA methylation was analyzed using the Infinium MethylationEPIC BeadChip in peripheral blood of n = 230 Caucasian patients with MDD receiving 6-week antidepressant treatment in a naturalistic in-patient setting as well as in a subsample of n = 107 patients primarily receiving continuous treatment with serotonin reuptake inhibitors or serotonin and norepinephrine reuptake inhibitors. Treatment response was assessed by means of the Hamilton Depression Scale.

RESULTS

No genome-wide significant hits were observed. Suggestive (P < 1E-5) epigenome-wide evidence was discerned for altered DNA methylation at 6 CpG sites (LOC102724467, LOC100506023, RSPO2, SAG, IL16, PRKCI) to predict response to naturalistic antidepressant treatment. In patients treated with serotonin reuptake inhibitors or serotonin and norepinephrine reuptake inhibitors, differential DNA methylation at 11 CpGs, for example, mapping to the TIMP2, VDAC1, or SORL1 genes, was suggestively associated with treatment response.

CONCLUSIONS

The present results provide preliminary evidence for altered DNA methylation patterns to be associated with antidepressant treatment response in MDD. Provided significant replication in independent and larger samples, the present findings might in the future aid in clinical decision-making toward more individualized and thus more efficacious treatments of MDD.

A genome-wide association study of escitalopram treatment outcomes in patients with major depressive disorder

Major depressive disorder (MDD) is a common psychological condition, the consequences of which, such as suicide, can be severe. Escitalopram, a selective serotonin reuptake inhibitor, is a commonly used antidepressant in clinics. However, more than one-third of patients with MDD do not respond to this drug. Gene polymorphism may affect the efficacy of escitalopram, but the genetic architecture of the antidepressant response in patients with MDD remains unclear. We perform a genome-wide association study (GWAS) of the genetic effect on the outcome of escitalopram in patients with MDD. A total of 203 patients with MDD and 176 healthy control (HC) adults were recruited from Beijing Anding Hospital. Patients received 12 weeks of antidepressant treatment with escitalopram. The Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) or Hamilton depression scale (HAMD) were used to evaluate the severity of depression symptoms at the baseline and the end of 2 and 12 weeks of treatment. A total of 140 variants in MDD patients were identified by GWAS to have genome-wide significance (p < 5e - 8) compared with HCs. Similarly, 189 and 18 variants were identified to be associated with QIDS-SR and HAMD score changes in patients after antidepressant treatment (p < 1e - 5), including rs12602361, rs72799048, rs16842235, and rs2518256. In the two weeks QIDS-SR score study, the gene-level association for these variants and gene set enrichment analyses implicate the enrichment of genes involved in the synaptic plasticity process and nervous system development. Our results implicate the predictive capacity of the effect of escitalopram treatment, supporting a link between genetic basis and remission of depression.

Shared polygenic susceptibility to treatment response in severe affective and psychotic disorders: Evidence from GWAS data sets

While schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) genetically correlate, the pleiotropy underlying response/resistance to drugs used in these disorders has not been investigated. The aim of this study is to analyze the genetic relationship between treatment-resistant schizophrenia (TRS), response to lithium in BD (respLi) and response to antidepressants in MDD (respAD) using the conditional/conjunctional false discovery rate (cond/conjFDR) methodology, based on the hypothesis that shared mechanisms related to a common psychopathology factor underlie these phenotypes. A cross-trait polygenic enrichment for TRS conditioned on associations with respLi was observed. The conjFDR analysis identified rs11631065 (chr15:66654304) as a shared locus between them. One of the genes at this locus is MAP2K1, previously reported as associated with TRS after conditioning on body mass index genome-wide association study (GWAS). The set of genes at TRS-respLi conjFDR < 0.95 showed enrichment in response to psychotropic drugs in severe mental disorders from GWAS Catalog as well as in neurodevelopment and synaptic pathways. In conclusion, our study constitutes the first evidence of a transdiagnostic genetic signal associated with response to different pharmacological treatments in psychotic and affective disorders. It is necessary to confirm these results when larger GWAS of these phenotypes are available.

Genetic determinants of antidepressant and antipsychotic drug response

Today, more than 90% of inpatients hospitalized with Major Depression or Schizophrenia are treated with psychotropic drugs. Since none of the treatment options is causal, response rates are modest and the course of recovery is very heterogeneous. Genetic studies on the etiology and pathogenesis of major psychiatric disorders over the past decades have been largely unsuccessful. Likewise, genetic studies to predict response to psychopharmacological treatment have also not been particularly successful. In this project we have recruited 902 inpatients with ICD-10 diagnoses of schizophrenic ("F2 patients") or depressive disorders ("F3 patients"). The study assessed today's acute inpatient treatment regimens with up to 8 repeated measurements regarding the time course of recovery and adverse side effects. The genotyping included 100 candidate genes with genotypic patterns computed from 549 Single Nucleotide Polymorphisms (SNPs). To predict response to psychopharmacological treatment, we relied on a multidimensional approach to analyzing genetic diversity in combination with multilayer Neural Nets (NNs). Central to this new method were the "gene vectors" that (1) assessed the multidimensional genotypic patterns observed with genes; and (2) evaluated the correlations between genes. By means of these methods, we searched for combinations of multidimensional genotypic patterns that were characteristic of treatment responders while being rare among non-responders. The chosen method of approach provided a powerful technique to detail the complex structures of SNP data that are not detectable by conventional association methods. Molecular-genetic NNs enabled correct classification of 100% "non-responders", along with 94.7% correctly classified "responders" among the F2 patients, and 82.6% correctly classified "responders" among the F3 patients. The F2 and F3 classifiers were not disjoint but showed an overlap of 29.6% and 35.7% between the diagnostic groups, thus indicating that clinical diagnoses may not constitute etiologic entities. Our results suggested that patients may have an unspecific physical-genetic disposition that enables, facilitates, impedes or prevents recovery from major psychiatric disorders by setting various thresholds for exogenous triggers that initiate improvement ("recovery disposition"). Even though this disposition is not causally linked to recovery, it can nonetheless be clinically used in the sense of a "surrogate". Indeed, clinicians are also interested in reliable tools that can "do the job", despite the fact that etiology and pathogenesis of the treated disorders remain unknown.

Drug classes affecting intracranial aneurysm risk: Genetic correlation and Mendelian randomization

INTRODUCTION

There is no non-invasive treatment to prevent aneurysmal subarachnoid hemorrhage (ASAH) caused by intracranial aneurysm (IA) rupture. We aimed to identify drug classes that may affect liability to IA using a genetic approach.

PATIENTS AND METHODS

Using genome-wide association summary statistics we calculated genetic correlation between unruptured IA (N = 2140 cases), ASAH (N = 5140) or the combined group, and liability to drug usage from 23 drug classes (N up to 320,000) independent of the risk factor high blood pressure. Next, we evaluated the causality and therapeutic potential of correlated drug classes using three different Mendelian randomization frameworks.

RESULTS

Correlations with IA were found for antidepressants, paracetamol, acetylsalicylic acid, opioids, beta-blockers, and peptic ulcer and gastro-esophageal reflux disease drugs. MR showed no evidence that genetically predicted usage of these drug classes caused IA. Genetically predicted high responders to antidepressant drugs were at higher risk of IA (odds ratio [OR] = 1.61, 95% confidence interval (CI) = 1.09-2.39, p = 0.018) and ASAH (OR = 1.68, 95% CI = 1.07-2.65, p = 0.024) if they used antidepressant drugs. This effect was absent in non-users. For beta-blockers, additional analyses showed that this effect was not independent of blood pressure after all. A complex and likely pleiotropic relationship was found between genetic liability to chronic multisite pain, pain medication usage (paracetamol, acetylsalicylic acid, and opioids), and IA.

CONCLUSIONS

We did not find drugs decreasing liability to IA and ASAH but found that antidepressant drugs may increase liability. We observed pleiotropic relationships between IA and other drug classes and indications. Our results improve understanding of pathogenic mechanisms underlying IA.

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits

OBJECTIVE

Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD.

METHODS

Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks.

RESULTS

Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications.

CONCLUSIONS

This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Human pluripotent stem cells as a translational toolkit in psychedelic research in vitro

Psychedelics, recognized for their impact on perception, are resurging as promising treatments with rapid onset for mood and substance use disorders. Despite increasing evidence from clinical trials, questions persist about the cellular and molecular mechanisms and their precise correlation with treatment outcomes. Murine neurons and immortalized non-neural cell lines harboring overexpressed constructs have shed light on neuroplastic changes mediated by the serotonin 2A receptor (5-HT2AR) as the primary mechanism. However, limitations exist in capturing human- and disease-specific traits. Here, we discuss current accomplishments and prospects for incorporating human pluripotent stem cells (PSCs) to complement these models. PSCs can differentiate into various brain cell types, mirroring endogenous expression patterns and cell identities to recreate disease phenotypes. Brain organoids derived from PSCs resemble cell diversity and patterning, while region-specific organoids simulate circuit-level phenotypes. PSC-based models hold significant promise to illuminate the cellular and molecular substrates of psychedelic-induced phenotypic recovery in neuropsychiatric disorders.

A genetic risk score to predict treatment nonresponse in psychotic depression

Psychotic depression is a severe and difficult-to-treat subtype of major depressive disorder for which higher rates of treatment-resistant depression were found. Studies have been performed aiming to predict treatment-resistant depression or treatment nonresponse. However, most of these studies excluded patients with psychotic depression. We created a genetic risk score (GRS) based on a large treatment-resistant depression genome-wide association study. We tested whether this GRS was associated with nonresponse, nonremission and the number of prior adequate antidepressant trials in patients with a psychotic depression. Using data from a randomized clinical trial with patients with a psychotic depression (n = 122), we created GRS deciles and calculated positive prediction values (PPV), negative predictive values (NPV) and odds ratios (OR). Nonresponse and nonremission were assessed after 7 weeks of treatment with venlafaxine, imipramine or venlafaxine plus quetiapine. The GRS was negatively correlated with treatment response (r = -0.32, p = 0.0023, n = 88) and remission (r = -0.31, p = 0.0037, n = 88), but was not correlated with the number of prior adequate antidepressant trials. For patients with a GRS in the top 10%, we observed a PPV of 100%, a NPV of 73.7% and an OR of 52.4 (p = 0.00072, n = 88) for nonresponse. For nonremission, a PPV of 100%, a NPV of 51.9% and an OR of 21.3 (p = 0.036, n = 88) was observed for patients with a GRS in the top 10%. Overall, an increased risk for nonresponse and nonremission was seen in patients with GRSs in the top 40%. Our results suggest that a treatment-resistant depression GRS is predictive of treatment nonresponse and nonremission in psychotic depression.

The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review

Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.

Preclinical models of treatment-resistant depression: challenges and perspectives

Treatment-resistant depression (TRD) is a subgroup of major depressive disorder in which the use of classical antidepressant treatments fails to achieve satisfactory treatment results. Although there are various definitions and grading models for TRD, common criteria for assessing TRD have still not been established. However, a common feature of any TRD model is the lack of response to at least two attempts at antidepressant pharmacotherapy. The causes of TRD are not known; nevertheless, it is estimated that even 60% of TRD patients are so-called pseudo-TRD patients, in which multiple biological factors, e.g., gender, age, and hormonal disturbances are concomitant with depression and involved in antidepressant drug resistance. Whereas the phenomenon of TRD is a complex disorder difficult to diagnose and successfully treat, the search for new treatment strategies is a significant challenge of modern pharmacology. It seems that despite the complexity of the TRD phenomenon, some useful animal models of TRD meet the construct, the face, and the predictive validity criteria. Based on the literature and our own experiences, we will discuss the utility of animals exposed to the stress paradigm (chronic mild stress, CMS), and the Wistar Kyoto rat strain representing an endogenous model of TRD. In this review, we will focus on reviewing research on existing and novel therapies for TRD, including ketamine, deep brain stimulation (DBS), and psychedelic drugs in the context of preclinical studies in representative animal models of TRD.

Association between NTRK2 Polymorphisms, Hippocampal Volumes and Treatment Resistance in Major Depressive Disorder

Despite the increasing availability of antidepressant drugs, a high rate of patients with major depression (MDD) does not respond to pharmacological treatments. Brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling is thought to influence antidepressant efficacy and hippocampal volumes, robust predictors of treatment resistance. We therefore hypothesized the possible role of BDNF and neurotrophic receptor tyrosine kinase 2 (NTRK2)-related polymorphisms in affecting both hippocampal volumes and treatment resistance in MDD. A total of 121 MDD inpatients underwent 3T structural MRI scanning and blood sampling to obtain genotype information. General linear models and binary logistic regressions were employed to test the effect of genetic variations related to BDNF and NTRK2 on bilateral hippocampal volumes and treatment resistance, respectively. Finally, the possible mediating role of hippocampal volumes on the relationship between genetic markers and treatment response was investigated. A significant association between one NTRK2 polymorphism with hippocampal volumes and antidepressant response was found, with significant indirect effects. Our results highlight a possible mechanistic explanation of antidepressant action, possibly contributing to the understanding of MDD pathophysiology.

Polygenic risk scores of lithium response and treatment resistance in major depressive disorder

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate the genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) and non-TRD within ~4500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores (PRS) of antidepressants and lithium response for individuals with MDD and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1778 ECT-treated MDD cases, nearly all (94%) used antidepressants before their first ECT and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We did not observe a significant difference in the mean PRS of antidepressant response between TRD and non-TRD; however, we found that TRD cases had a significantly higher PRS of lithium response compared to non-TRD cases (OR = 1.10-1.12 under various definitions). The results support the evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.

Disease progression to bipolar disorder among adolescents and young adults with antidepressant-resistant and antidepressant-responsive depression: Does antidepressant class matter?

Studies have demonstrated a positive relationship between antidepressant resistance and the progression of bipolar disorder. However, the influence of antidepressant classes such as selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs) in this context has yet to be investigated. A total of 5,285 adolescents and young adults with antidepressant-resistant depression and 21,140 with antidepressant-responsive depression were recruited in the present study. The antidepressant-resistant depression group was divided into two subgroups: only resistant to SSRIs (n = 2,242, 42.4%) and additionally resistant to non-SSRIs (n = 3,043, 57.6%) groups. The status of bipolar disorder progression was monitored from the date of depression diagnosis to the end of 2011. Patients with antidepressant-resistant depression were more likely to develop bipolar disorder during the follow-up (hazard ratio [HR]: 2.88, 95% confidence interval [CI]: 2.67-3.09) than those with antidepressant-responsive depression. Furthermore, the group that was additionally resistant to non-SSRIs were at the highest risk of bipolar disorder (HR: 3.02, 95% CI: 2.76-3.29), followed by the group that was only resistant to SSRIs (2.70, 2.44-2.98). Adolescents and young adults with antidepressant-resistant depression, especially those who responded poorly to both SSRIs and SNRIs, were at increased risk of subsequent bipolar disorder compared with those with antidepressant-responsive depression. Further studies are warranted to elucidate the molecular pathomechanisms underlying the resistance to SSRIs and SNRIs and subsequent bipolar disorder.

The Impact of the CYP2D6 and CYP1A2 Gene Polymorphisms on Response to Duloxetine in Patients with Major Depression

Depression is a global mental health concern, and personalized treatment approaches are needed to optimize its management. This study aimed to investigate the influence of the CYP2D6 and CYP1A2 gene polymorphisms on the efficacy of duloxetine in reducing depressive and anxiety symptoms. A sample of 100 outpatients with major depression, who initiated monotherapy with duloxetine, were followed up. Polymorphisms in the CYP2D6 and CYP1A2 genes were assessed. The severity of depressive and anxiety symptoms was recorded using standardized scales. Adverse drug reactions (ADRs) were analyzed. Statistical analyses, including linear regression, were conducted to examine the relationships between genetic polymorphisms, clinical variables, and treatment outcomes. Patients with higher values of the duloxetine metabolic index (DMI) for CYP2D6, indicating a faster metabolism, achieved a greater reduction in anxiety symptoms. The occurrence of ADRs was associated with a lower reduction in anxiety symptoms. However, no significant associations were found between studied gene polymorphisms and reduction in depressive symptoms. No significant effects of the DMI for CYP1A2 were found. Patients with a slower metabolism may experience less benefit from duloxetine therapy in terms of anxiety symptom reduction. Personalizing treatment based on the CYP2D6 and CYP1A2 gene polymorphisms can enhance the effectiveness of antidepressant therapy and improve patient outcomes.

Exome-wide association study of treatment-resistant depression suggests novel treatment targets

Treatment-resistant depression (TRD) is a severe form of major depressive disorder (MDD) with substantial public health impact and poor treatment outcome. Treatment outcome in MDD is significantly heritable, but genome-wide association studies have failed to identify replicable common marker alleles, suggesting a potential role for uncommon variants. Here we investigated the hypothesis that uncommon, putatively functional genetic variants are associated with TRD. Whole-exome sequencing data was obtained from 182 TRD cases and 2021 psychiatrically healthy controls. After quality control, the remaining 149 TRD cases and 1976 controls were analyzed with tests designed to detect excess burdens of uncommon variants. At the gene level, 5 genes, ZNF248, PRKRA, PYHIN1, SLC7A8, and STK19 each carried exome-wide significant excess burdens of variants in TRD cases (q < 0.05). Analysis of 41 pre-selected gene sets suggested an excess of uncommon, functional variants among genes involved in lithium response. Among the genes identified in previous TRD studies, ZDHHC3 was also significant in this sample after multiple test correction. ZNF248 and STK19 are involved in transcriptional regulation, PHYIN1 and PRKRA are involved in immune response, SLC7A8 is associated with thyroid hormone transporter activity, and ZDHHC3 regulates synaptic clustering of GABA and glutamate receptors. These results implicate uncommon, functional alleles in TRD and suggest promising novel targets for future research.

CircGNB1 drives osteoarthritis pathogenesis by inducing oxidative stress in chondrocytes

BACKGROUND

Circular RNAs (circRNAs) have risen to prominence as important regulators of biological processes. This study investigated whether circGNB1 functions as a competitive endogenous RNA to regulate the pathological process of oxidative stress in age-related osteoarthritis (OA).

METHODS

The relationship between circGNB1 expression and oxidative stress/OA severity was determined in cartilages from OA patients at different ages. The biological roles of circGNB1 in oxidative stress and OA progression, and its downstream targets were determined using gain- and loss-of-function experiments in various biochemical assays in human chondrocytes (HCs). The in vivo effects of circGNB1 overexpression and knockdown were also determined using a destabilization of the medial meniscus (DMM) mouse model.

RESULTS

Increased circGNB1 expression was detected in HCs under oxidative and inflammatory stress and in the cartilage of older individuals. Mechanistically, circGNB1 sponged miR-152-3p and thus blocked its interaction with its downstream mRNA target, ring finger protein 219 (RNF219), which in turn stabilized caveolin-1 (CAV1) by preventing its ubiquitination at the K47 residue. CircGNB1 inhibited IL-10 signalling by antagonizing miR-152-3p-mediated RNF219 and CAV1 inhibition. Consequently, circGNB1 overexpression promoted OA progression by enhancing catabolic factor expression and oxidative stress and by suppressing anabolic genes in vitro and in vivo. Furthermore, circGNB1 knockdown alleviated the severity of OA, whereas circGNB1 overexpression had the opposite effect in a DMM mouse model of OA.

CONCLUSION

CircGNB1 regulated oxidative stress and OA progression via the miR-152-3p/RNF219/CAV1 axis. Modulating circGNB1 could be an effective strategy for treating OA.

Depression among Patients with an Implanted Left Ventricular Assist Device: Uncovering Pathophysiological Mechanisms and Implications for Patient Care

Depression is a common and devastating mental illness associated with increased morbidity and mortality, partially due to elevated rates of suicidal attempts and death. Select patients with end-stage heart failure on a waiting-list for a donor heart undergo left ventricular assist device (LVAD) implantation. The LVAD provides a circulatory flow of oxygenated blood to the body, mimicking heart functionality by operating on a mechanical technique. LVAD improves functional capacity and survivability among patients with end-stage heart failure. However, accumulating data suggests that LVAD recipients suffer from an increased incidence of depression and suicide attempts. There is scarce knowledge regarding the pathological mechanism and appropriate treatment approach for depressed LVAD patients. This article summarizes the current evidence on the association between LVAD implantation and occurrence of depression, suggesting possible pathological mechanisms underlying the device-associated depression and reviewing the current treatment strategies. The summarized data underscores the need for a rigorous pre-(LVAD)-implantation psychiatric evaluation, continued post-implantation mental health assessment, and administration of antidepressant treatment as necessary.

Migraine and treatment-resistant depression

Migraine and major depressive disorders (MDD) or treatment resistant depression (TRD) represent a significant global burden and are often comorbid, further complicating diagnosis and treatment. Epidemiological studies have demonstrated a bidirectional relationship between migraine and MDD/TRD, with patients suffering from one disorder exhibiting a heightened risk of developing the other. This association is believed to result from shared genetic factors, neurotransmitter dysregulation, inflammation, hormonal alteration, and other conditions comorbid with both disorders. Emerging evidence suggests that therapeutics targeting common pathways in both disorders may be beneficial for comorbid patients. Novel therapeutics for migraine or MDD/TRD, such as calcitonin gene-related peptide (CGRP)-targeting therapy, onabotulinumtoxinA, ketamine/esketamine, vagus nerve stimulation or transcranial magnetic stimulation, may be helpful in selected patients with comorbid migraine-MDD/TRD. Nevertheless, continued efforts are needed to improve early detection and intervention, to better understand the complex interplay between genetic, environmental, and psychosocial factors contributing to this comorbidity, to identify novel therapeutic targets, and ultimately, to alleviate the disease burden caused by this comorbidity.

Investigating genetic overlap between antidepressant and lithium response and treatment resistance in major depressive disorder

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) within ~ 4 500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores of antidepressant and lithium response for individuals with MDD, and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1 778 ECT-treated MDD cases, nearly all (94%) used antidepressants before first ECT, and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We found that TRD cases tend to have lower genetic load of antidepressant response than non-TRD, although the difference was not significant; furthermore, TRD cases had significantly higher genetic load of lithium response (OR = 1.10-1.12 under different definitions). The results support evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.

Interferon signaling and hypercytokinemia-related gene expression in the blood of antidepressant non-responders

Only 50% of patients with depression respond to the first antidepressant drug administered. Thus, biomarkers for prediction of antidepressant responses are needed, as predicting which patients will not respond to antidepressants can optimize selection of alternative therapies. We aimed to identify biomarkers that could predict antidepressant responsiveness using a novel data-driven approach based on statistical pattern recognition. We retrospectively divided patients with major depressive disorder into antidepressant responder and non-responder groups. Comprehensive gene expression analysis was performed using peripheral blood without narrowing the genes. We designed a classifier according to our own discrete Bayes decision rule that can handle categorical data. Nineteen genes showed differential expression in the antidepressant non-responder group (n = 15) compared to the antidepressant responder group (n = 15). In the training sample of 30 individuals, eight candidate genes had significantly altered expression according to quantitative real-time polymerase chain reaction. The expression of these genes was examined in an independent test sample of antidepressant responders (n = 22) and non-responders (n = 12). Using the discrete Bayes classifier with the HERC5, IFI6, and IFI44 genes identified in the training set yielded 85% discrimination accuracy for antidepressant responsiveness in the 34 test samples. Pathway analysis of the RNA sequencing data for antidepressant responsiveness identified that hypercytokinemia- and interferon-related genes were increased in non-responders. Disease and biofunction analysis identified changes in genes related to inflammatory and infectious diseases, including coronavirus disease. These results strongly suggest an association between antidepressant responsiveness and inflammation, which may be useful for future treatment strategies for depression.

The ERICH3 rs11580409 polymorphism is associated with 6-month antidepressant response in depressed patients

INTRODUCTION

Major Depressive Disorder (MDD) is the current leading cause of disability worldwide. The effect of its main treatment option, antidepressant drugs (AD), is influenced by genetic and metabolic factors. The ERICH3 rs11580409(A > C) genetic polymorphism was identified as a factor influencing serotonin (5HT) levels in a pharmacometabolomics-informed genome-wide association study. It was also associated with response following AD treatment in several cohorts of depressed patients.

OBJECTIVE

Our aim was to analyze the association of the ERICH3 rs11580409(A > C) genetic polymorphism with response following AD treatment and plasma 5HT levels in METADAP, a cohort of 6-month AD-treated depressed patients.

METHODS

Clinical (n = 377) and metabolic (n = 150) data were obtained at baseline and after 3 (M3) and 6 months (M6) of treatment. Linear mixed-effects models and generalized logistic mixed-effects models were used to assess the association of the rs11580409 polymorphism with the Hamilton Depression Rating Scale (HDRS) score, response and remission rates, and plasma 5HT levels.

RESULTS

The interaction between the ERICH3 rs11580409 polymorphism and time was an overall significant factor in mixed-effects models of the HDRS score (F_3,870 = 3.35, P = 0.019). At M6, CC homozygotes had a significantly lower HDRS score compared to A allele carriers (coefficient = -3.50, 95%CI [-6.00--0.99], P = 0.019). No association between rs11580409 and 5HT levels was observed.

CONCLUSION

Our results suggest an association of rs11580409 with response following long-term AD treatment. The rs11580409 genetic polymorphism may be a useful biomarker for treatment response in major depression.

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.

Polygenic heterogeneity in antidepressant treatment and placebo response

The genetic architecture of antidepressant response is poorly understood. Polygenic risk scores (PRS), exploration of placebo response and the use of sub-scales might provide insights. Here, we investigate the association between PRSs for relevant complex traits and response to vortioxetine treatment and placebo using clinical scales, including sub-scales and self-reported assessments. We collected a clinical test sample of Major Depressive Disorder (MDD) patients treated with vortioxetine (N = 907) or placebo (N = 455) from seven randomized, double-blind, clinical trials. In parallel, we obtained data from an observational web-based study of vortioxetine-treated patients (N = 642) with self-reported response. PRSs for antidepressant response, psychiatric disorders, and symptom traits were derived using summary statistics from well-powered genome-wide association studies (GWAS). Association tests were performed between the PRSs and treatment response in each of the two test samples and empirical p-values were evaluated. In the clinical test sample, no PRSs were significantly associated with response to vortioxetine treatment or placebo following Bonferroni correction. However, clinically assessed treatment response PRS was nominally associated with vortioxetine treatment and placebo response given by several secondary outcome scales (improvement on HAM-A, HAM-A Psychic Anxiety sub-scale, CPFQ &amp; PDQ), (P ≤ 0.026). Further, higher subjective well-being PRS (P ≤ 0.033) and lower depression PRS (P = 0.01) were nominally associated with higher placebo response. In the self-reported test sample, higher schizophrenia PRS was significantly associated with poorer self-reported response (P = 0.0001). The identified PRSs explain a low proportion of the variance (1.2-5.3%) in placebo and treatment response. Although the results were limited, we believe that PRS associations bear unredeemed potential as a predictor for treatment response, as more well-powered and phenotypically similar GWAS bases become available.

The Cannabis-Induced Epigenetic Regulation of Genes Associated with Major Depressive Disorder

The prevalence of depression is increasing worldwide, as is the number of people suffering from treatment-resistant depression; these patients constitute 30% of those treated. Unfortunately, there have not been significant advances in the treatment of this disorder in the past few decades. Exposure to cannabis and cannabis-derived compounds impacts depression symptomatology in different ways, with evidence indicating that cannabidiol has antidepressant effects; there have been mixed results with medical cannabis. Even though the exact molecular mechanisms of the action underlying changes in depression symptomatology upon exposure to cannabis and cannabis-derived compounds are still unknown, there is strong evidence that these agents have a widespread impact on epigenetic regulation. We hypothesized that exposure to cannabis or cannabis-derived compounds changes the DNA methylation levels of genes associated with depression. To test this hypothesis, we first performed a literature search to identify genes that are differentially methylated upon exposure to cannabis and cannabis-derived compounds, as reported in methylome-wide association studies. We next checked whether genes residing in loci associated with depression, as identified in the largest currently available genome-wide association study of depression, were reported to be epigenetically regulated by cannabis or cannabis-related compounds. Multiple genes residing in loci associated with depression were found to be epigenetically regulated by exposure to cannabis or cannabis-derived compounds. This epigenomic regulation of depression-associated genes by cannabis or cannabis-derived compounds was reported across diverse organisms, tissues, and developmental stages and occurred in genes crucial for neuronal development, functioning, survival, and synapse functioning, as well as in genes previously implicated in other mental disorders.

The pharmacogenomics of selective serotonin reuptake inhibitors

Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.

Genophenotypic Factors and Pharmacogenomics in Adverse Drug Reactions

Adverse drug reactions (ADRs) rank as one of the top 10 leading causes of death and illness in developed countries. ADRs show differential features depending upon genotype, age, sex, race, pathology, drug category, route of administration, and drug–drug interactions. Pharmacogenomics (PGx) provides the physician effective clues for optimizing drug efficacy and safety in major problems of health such as cardiovascular disease and associated disorders, cancer and brain disorders. Important aspects to be considered are also the impact of immunopharmacogenomics in cutaneous ADRs as well as the influence of genomic factors associated with COVID-19 and vaccination strategies. Major limitations for the routine use of PGx procedures for ADRs prevention are the lack of education and training in physicians and pharmacists, poor characterization of drug-related PGx, unspecific biomarkers of drug efficacy and toxicity, cost-effectiveness, administrative problems in health organizations, and insufficient regulation for the generalized use of PGx in the clinical setting. The implementation of PGx requires: (i) education of physicians and all other parties involved in the use and benefits of PGx; (ii) prospective studies to demonstrate the benefits of PGx genotyping; (iii) standardization of PGx procedures and development of clinical guidelines; (iv) NGS and microarrays to cover genes with high PGx potential; and (v) new regulations for PGx-related drug development and PGx drug labelling.

Personality, symptom, and demographic correlates of perceived efficacy of selective serotonin reuptake inhibitor monotherapy among current users with low mood: A data-driven approach

BACKGROUND

Selective serotonin reuptake inhibitors (SSRIs) are often the first-line treatment option for depressive symptoms, however their efficacy varies across patients. Identifying predictors of response to SSRIs could facilitate personalised treatment of depression and improve treatment outcomes. The aim of this study was to develop a data-driven formulation of demographic, personality, and symptom-level factors associated with subjective response to SSRI treatment.

METHODS

Participants were recruited online and data were collected retrospectively through an extensive digital mental health questionnaire. Extreme gradient boosting classification with nested cross-validation was used to identify factors distinguishing between individuals with low (n=37) and high (n=111) perceived benefit from SSRI treatment.

RESULTS

The algorithm demonstrated a good predictive performance (test AUC=.88±.07). Positive affectivity was the strongest predictor of response to SSRIs and a major confounder of the remaining associations. After controlling for positive affectivity, as well as current wellbeing, severity of current depressive symptoms, and multicollinearity, only low positive affectivity, chronic pain, sleep problems, and unemployment remained significantly associated with diminished subjective response to SSRIs.

LIMITATIONS

This was an exploratory analysis of data collected at a single time point, for a study which had a different primary aim. Therefore, the results may not reflect causal relationships, and require validation in future prospective studies. Furthermore, the data were self-reported by internet users, which could affect integrity of the dataset and limit generalisability of the results.

CONCLUSIONS

Our findings suggest that demographic, personality, and symptom data may offer a potential cost-effective and efficient framework for SSRI treatment outcome prediction.

Novel drug developmental strategies for treatment-resistant depression

Major depressive disorder is a leading cause of disability worldwide. Because conventional therapies are ineffective in many patients, novel strategies are needed to overcome treatment‐resistant depression (TRD). Limiting factors of successful drug development in the last decades were the lack of (1) knowledge of pathophysiology, (2) translational animal models and (3) objective diagnostic biomarkers. Here, we review novel drug targets and drug candidates currently investigated in Phase I–III clinical trials. The most promising approaches are inhibition of glutamatergic neurotransmission by NMDA and mGlu₅ receptor antagonists, modulation of the opioidergic system by κ receptor antagonists, and hallucinogenic tryptamine derivates. The only registered drug for TRD is the NMDA receptor antagonist, S‐ketamine, but add‐on therapies with second‐generation antipsychotics, certain nutritive, anti‐inflammatory and neuroprotective agents seem to be effective. Currently, there is an intense research focus on large‐scale, high‐throughput omics and neuroimaging studies. These results might provide new insights into molecular mechanisms and potential novel therapeutic strategies.

Understanding genetic risk factors for common side effects of antidepressant medications

Background

Major depression is one of the most disabling health conditions internationally. In recent years, new generation antidepressant medicines have become very widely prescribed. While these medicines are efficacious, side effects are common and frequently result in discontinuation of treatment. Compared with specific pharmacological properties of the different medications, the relevance of individual vulnerability is understudied.

Methods

We used data from the Australian Genetics of Depression Study to gain insights into the aetiology and genetic risk factors to antidepressant side effects. To this end, we employed structural equation modelling, polygenic risk scoring and regressions.

Results

Here we show that participants reporting a specific side effect for one antidepressant are more likely to report the same side effect for other antidepressants, suggesting the presence of shared individual or pharmacological factors. Polygenic risk scores (PRS) for depression associated with side effects that overlapped with depressive symptoms, including suicidality and anxiety. Body Mass Index PRS are strongly associated with weight gain from all medications. PRS for headaches are associated with headaches from sertraline. Insomnia PRS show some evidence of predicting insomnia from amitriptyline and escitalopram.

Conclusions

Our results suggest a set of common factors underlying the risk for antidepressant side effects. These factors seem to be partly explained by genetic liability related to depression severity and the nature of the side effect. Future studies on the genetic aetiology of side effects will enable insights into their underlying mechanisms and the possibility of risk stratification and prophylaxis strategies.

Antidepressants are commonly prescribed medications, but adverse side effects are cause for treatment discontinuation. We analysed data from a large group of adults who have taken antidepressants to understand why some people experience specific side effects. Our results suggest that a person’s genetic characteristics play a role. For example, participants genetically predisposed to a higher body mass index were more likely to report weight gain from antidepressants. These results open up the possibility of predicting adverse side effects as we increase our knowledge on the genetics of related complex traits. Future studies can focus on performing large-scale genetic studies of antidepressant side effects to gain further insights into the mechanisms underlying antidepressant side effects and to identify genetic markers of side effects that could be used in the clinic.

Campos et al. study the genetic aetiology of antidepressant side effects. Using data from the Australian Genetics of Depression study, the authors show that polygenic risk scores for traits such as BMI, insomnia and headaches have a shared genetic basis with side effects to commonly used antidepressant drugs.

Genetic and clinical characteristics of treatment-resistant depression using primary care records in two UK cohorts

Treatment-resistant depression (TRD) is a major contributor to the disability caused by major depressive disorder (MDD). Primary care electronic health records provide an easily accessible approach to investigate TRD clinical and genetic characteristics. MDD defined from primary care records in UK Biobank (UKB) and EXCEED studies was compared with other measures of depression and tested for association with MDD polygenic risk score (PRS). Using prescribing records, TRD was defined from at least two switches between antidepressant drugs, each prescribed for at least 6 weeks. Clinical-demographic characteristics, SNP-based heritability (h²_SNP) and genetic overlap with psychiatric and non-psychiatric traits were compared in TRD and non-TRD MDD cases. In 230,096 and 8926 UKB and EXCEED participants with primary care data, respectively, the prevalence of MDD was 8.7% and 14.2%, of which 13.2% and 13.5% was TRD, respectively. In both cohorts, MDD defined from primary care records was strongly associated with MDD PRS, and in UKB it showed overlap of 71-88% with other MDD definitions. In UKB, TRD vs healthy controls and non-TRD vs healthy controls h²_SNP was comparable (0.25 [SE = 0.04] and 0.19 [SE = 0.02], respectively). TRD vs non-TRD was positively associated with the PRS of attention deficit hyperactivity disorder, with lower socio-economic status, obesity, higher neuroticism and other unfavourable clinical characteristics. This study demonstrated that MDD and TRD can be reliably defined using primary care records and provides the first large scale population assessment of the genetic, clinical and demographic characteristics of TRD.

Prospects for personalization of depression treatment with genome sequencing

The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient's condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.