Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry

Exploring potential working mechanisms of accelerated HF-rTMS in refractory major depression with a focus on locus coeruleus connectivity

BACKGROUND

This study investigates the effects of accelerated high-frequency repetitive transcranial magnetic stimulation (aHF-rTMS), applied to the left dorsolateral prefrontal cortex (DLPFC), on locus coeruleus (LC) functional connectivity in the treatment of refractory medication-resistant major depression (MRD).

METHODS

We studied 12 antidepressant-free refractory MRD patients, focusing on how aHF-rTMS affects the LC, a central component of the brain's noradrenergic system and key to mood regulation and stress response.

RESULTS

A stronger decrease in LC functional connectivity following aHF-rTMS treatment resulted in better clinical improvement. We observed such LC functional connectivity decreases with several brain regions, including the superior frontal gyrus, precentral gyrus, middle occipital gyrus, and cerebellum. Moreover, our exploratory analyses hint at a possible role for E-field modeling in forecasting clinical outcomes. Additional analyses suggest potential genetic and dopaminergic factors influencing changes in LC functional connectivity in relation to clinical response.

CONCLUSIONS

The findings of this study underscore the pivotal role of the LC in orchestrating higher cognitive functions through its extensive connections with the prefrontal cortices, facilitating decision-making, influencing attention, and addressing depressive rumination. Additionally, the observed enhancement in LC-(posterior) cerebellar connectivity not only highlights the cerebellum's role in moderating clinical outcomes through noradrenergic system modulation but also suggests its potential as a predictive marker for aHF-rTMS efficacy. These results reveal new insights into the neural mechanisms of refractory depression and suggest therapeutic targets for enhancing noradrenergic activity, thereby addressing both cognitive and psychomotor symptoms associated with the disorder.

Treatment-resistant depression: molecular mechanisms and management

Due to the heterogeneous nature of depression, the underlying etiological mechanisms greatly differ among individuals, and there are no known subtype-specific biomarkers to serve as precise targets for therapeutic efficacy. The extensive research efforts over the past decades have not yielded much success, and the currently used first-line conventional antidepressants are still ineffective for close to 66% of patients. Most clinicians use trial-and-error treatment approaches, which seem beneficial to only a fraction of patients, with some eventually developing treatment resistance. Here, we review evidence from both preclinical and clinical studies on the pathogenesis of depression and antidepressant treatment response. We also discuss the efficacy of the currently used pharmacological and non-pharmacological approaches, as well as the novel emerging therapies. The review reveals that the underlying mechanisms in the pathogenesis of depression and antidepressant response, are not specific, but rather involve an interplay between various neurotransmitter systems, inflammatory mediators, stress, HPA axis dysregulation, genetics, and other psycho-neurophysiological factors. None of the current depression hypotheses sufficiently accounts for the interactional mechanisms involved in both its etiology and treatment response, which could partly explain the limited success in discovering efficacious antidepressant treatment. Effective management of treatment-resistant depression (TRD) requires targeting several interactional mechanisms, using subtype-specific and/or personalized therapeutic modalities, which could, for example, include multi-target pharmacotherapies in augmentation with psychotherapy and/or other non-pharmacological approaches. Future research guided by interaction mechanisms hypotheses could provide more insights into potential etiologies of TRD, precision biomarker targets, and efficacious therapeutic modalities.

Comparative mortality risks in two independent bipolar cohorts

OBJECTIVES

To compare mortality rates in bipolar disorder with common causes of mortality.

METHODS

Observational data from the Prechter Longitudinal Study of Bipolar Disorder (PLS-BD) of 1128 participants including 281 controls was analyzed using logistical regression to quantify mortality rates in comparison with common comorbidities and causes of death. Outcome and treatment measures, including ASRM, GAD-7, PHQ-9 and medication use were used to stratify those with bipolar disorder (BD) that are alive or deceased. A larger cohort of 10,735 existing BD patients with 7,826 controls (no psychiatric diagnosis) from the University of Michigan Health (U-M Health) clinics was used as replication, observational secondary data analysis.

RESULTS

The mortality rates are significantly different between those with BD and controls in both PLS-BD and U-M Health. Those with BD and are deceased have a higher percentage of elevated depression measures but show no difference in mania or anxiety measures nor medication use patterns. In both cohorts, a diagnosis of BD increases the odds of mortality greater than history of smoking or being older than ≥ 60-years of age.

CONCLUSION

BD was found to increase odds of mortality significantly and beyond that of a history of smoking. This finding was replicated in an independent sample.

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

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

Brain-Derived Neurotrophic Factor (BDNF) as a Predictor of Treatment Response in Major Depressive Disorder (MDD): A Systematic Review

Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.

DNA Methylation Patterns in Relation to Acute Severity and Duration of Anxiety and Depression

Depression and anxiety are common mental disorders that often occur together. Stress is an important risk factor for both disorders, affecting pathophysiological processes through epigenetic changes that mediate gene-environment interactions. In this study, we explored two proposed models about the dynamic nature of DNA methylation in anxiety and depression: a stable change, in which DNA methylation accumulates over time as a function of the duration of clinical symptoms of anxiety and depression, or a flexible change, in which DNA methylation correlates with the acute severity of clinical symptoms. Symptom severity was assessed using clinical questionnaires for anxiety and depression (BDI-II, IDS-C, and HAM-A), and the current episode and the total lifetime symptom duration was obtained from patients' medical records. Peripheral blood DNA methylation levels were determined for the BDNF, COMT, and SLC6A4 genes. We found a significant negative correlation between COMT_1 amplicon methylation and acute symptom scores, with BDI-II (R(22) = 0.190, p = 0.033), IDS-C (R(22) = 0.199, p = 0.029), and HAM-A (R(22) = 0.231, p = 0.018) all showing a similar degree of correlation. Our results suggest that DNA methylation follows flexible dynamics, with methylation levels closely associated with acute clinical presentation rather than with the duration of anxiety and depression. These results provide important insights into the dynamic nature of DNA methylation in anxiety and affective disorders and contribute to our understanding of the complex interplay between stress, epigenetics, and individual phenotype.

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches

OBJECTIVE

To identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.

METHOD

Han Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. Variable importance was evaluated by random forest algorithm.

RESULT

RF with RFE outperformed the other models in our samples based on the demographic information and clinical features (AUC = 61.2%, 95%CI: 60.1-62.4%) / TPH2 CpGs features (AUC = 66.6%, 95%CI: 65.4-67.8%) / both clinical and TPH2 CpGs features (AUC = 72.9%, 95%CI: 71.8-74.0%).

CONCLUSION

The effects of TPH2 on the early-stage antidepressant response were explored by machine learning algorithms. On the basis of the baseline depression severity and TPH2 CpG sites, machine learning approaches can enhance our ability to predict the early-stage antidepressant response. Some potentially important predictors (e.g., TPH2-10-60 (rs2129575), TPH2-2-163 (rs11178998), age of first onset, age) in early-stage treatment response could be utilized in future fundamental research, drug development and clinical practice.

Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2,367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

Application of Pharmacogenomics Testing in a Community-based Facility

This study was designed to examine the use of pharmacogenomics (PGx) testing in a community-based facility, the adoption of the PGx recommendations by providers, and assess challenges and opportunities for pharmacists in using PGx testing in a real-world setting. This was a retrospective study involving chart reviews of 137 patients with mood disorders who underwent PGx testing between September 2017 and December 2017. Eighty-seven patients who met inclusion and exclusion criteria were analyzed to evaluate the impact of PGx testing on psychotropic medication treatment and to evaluate the PGx test process. PGx test results were used by providers to guide their therapeutic modifications based on the gene-drug interactions identified. Patient medication use increased from 125 to 190 (P < .001) prescriptions. Patient medication belonging to no gene-drug interaction significantly increased from 46.4% to 87.4% (P < .001), medications belonging to moderate and significant gene-drug interaction decreased from 32.8% to 7.9% (P < .001) and 11.2% to 2.1% (P = .012), respectively. 88.5% of patients' psychotropic medication treatment after PGx testing was consistent with the PGx test report recommendations. The PGx test lengths of time analysis indicated that patient follow-up exceeded the standard time set by guidelines at multiple steps in the test process. There are multiple opportunities for pharmacists to become involved in the PGx testing process to improve patient care.

Divergent Directionality of Immune Cell-Specific Protein Expression between Bipolar Lithium Responders and Non-Responders Revealed by Enhanced Flow Cytometry

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4⁺ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4⁺ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow cytometry procedure. Data were analyzed for differences in protein expression levels. Results: The intent-to-treat sample included 13 lithium-responders (12 blood samples before treatment and 9 after treatment) and 11 lithium-non-responders (11 blood samples before treatment and 4 after treatment). No significant differences in expression between the groups was observed prior to lithium treatment. After treatment, the majority of analytes increased expression in responders and decreased expression in non-responders. Significant increases were seen for PDEB4 and NR3C1 in responders. A significant decrease was seen for NR3C1 in non-responders. Conclusions: Lithium induced divergent directionality of protein expression depending on the whether the patient was a responder or non-responder, elucidating molecular characteristics of lithium responsiveness. A subsequent study with a larger sample size is warranted.

Comparative serum proteomic analysis of a selected protein panel in individuals with schizophrenia and bipolar disorder and the impact of genetic risk burden on serum proteomic profiles

The diagnostic criteria for schizophrenia (SCZ) and bipolar disorder (BD) are based on clinical assessments of symptoms. In this pilot study, we applied high-throughput antibody-based protein profiling to serum samples of healthy controls and individuals with SCZ and BD with the aim of identifying differentially expressed proteins in these disorders. Moreover, we explored the influence of polygenic burden for SCZ and BD on the serum levels of these proteins. Serum samples from 113 individuals with SCZ and 125 with BD from the PsyCourse Study and from 44 healthy controls were analyzed by using a set of 155 antibodies in an antibody-based assay targeting a selected panel of 95 proteins. For the cases, genotyping and imputation were conducted for DNA samples and SCZ and BD polygenic risk scores (PRS) were calculated. Univariate linear and logistic models were used for association analyses. The comparison between SCZ and BD revealed two serum proteins that were significantly elevated in BD after multiple testing adjustment: "complement C9" and "Interleukin 1 Receptor Accessory Protein". Moreover, the first principal component of variance in the proteomics dataset differed significantly between SCZ and BD. After multiple testing correction, SCZ-PRS, BD-PRS, and SCZ-vs-BD-PRS were not significantly associated with the levels of the individual proteins or the values of the proteome principal components indicating no detectable genetic effects. Overall, our findings contribute to the evidence suggesting that the analysis of circulating proteins could lead to the identification of distinctive biomarkers for SCZ and BD. Our investigation warrants replication in large-scale studies to confirm these findings.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

Hair cortisol level might be indicative for a 3PM approach towards suicide risk assessment in depression: comparative analysis of mentally stable and depressed individuals versus individuals after completing suicide

Depression and suicidal behavior are interrelated, stress-associated mental health conditions, each lacking biological verifiability. Concepts of predictive, preventive, and personalized medicine (3PM) are almost completely missing for both conditions but are of utmost importance. Prior research reported altered levels of the stress hormone cortisol in the scalp hair of depressed individuals, however, data on hair cortisol levels (HCL) for suicide completers (SC) are missing. Here, we aimed to identify differences in HCL between subject with depression (n = 20), SC (n = 45) and mentally stable control subjects (n = 12) to establish the usage of HCL as a new target for 3PM. HCL was measured in extracts of pulverized hair (1-cm and 3-cm hair segments) using ELISA. In 3-cm hair segments, an average increase in HCL for depressed patients (1.66 times higher; p = .011) and SC (5.46 times higher; p = 1.65 × 10⁻⁵) compared to that for controls was observed. Furthermore, the average HCL in SC was significantly increased compared to that in the depressed group (3.28 times higher; p = 1.4 × 10⁻⁵). A significant correlation between HCL in the 1-cm and the 3-cm hair segments, as well as a significant association between the severity of depressive symptoms and HCL (3-cm segment) was found. To conclude, findings of increased HCL in subjects with depression compared to that in controls were replicated and an additional increase in HCL was seen in SC in comparison to patients with depression. The usage of HCL for creating effective patient stratification and predictive approach followed by the targeted prevention and personalization of medical services needs to be validated in follow-up studies.

Association between CYP metabolizer phenotypes and selective serotonin reuptake inhibitors induced weight gain: a retrospective cohort study

BACKGROUND

Prescription medications such as selective serotonin reuptake inhibitors (SSRIs), commonly used to treat depression, are associated with weight gain. The role of pharmacogenomics in predicting SSRI-induced weight gain is unclear.

METHODS

In this retrospective cohort study from participants in the Mayo Clinic RIGHT study who were prescribed citalopram, paroxetine, sertraline, or fluoxetine, our aim was to evaluate the association of metabolizer phenotype and total body weight after 6 months of SSRIs initiation. We evaluated the metabolizer phenotypes (poor/intermediate, normal, and rapid/ultra-rapid) of the cytochromes P450 enzymes genes: CYP2C9, CYP2C19, and CYP2D6 known to influence the metabolism of SSRI medications: CYP2C19 for citalopram, CYP2D6 for paroxetine, CYP2D6 and CYP2C19 for sertraline, and CYP2D6 and CYP2C9 fluoxetine. In addition, we assessed the association of metabolizer phenotype and total body weight change at six months following SSRI prescription using parametric analysis of covariance adjusted for baseline body weight and multivariate regression models.

RESULTS

CYP2C19 poor/intermediate metabolizers prescribed citalopram gained significantly more weight than normal or rapid/ultra-rapid metabolizers at 6 months (TBWG %: 2.6 [95% CI 1.3-4.1] vs. 0.4 [95% CI -0.5 - 1.3] vs. -0.1 [-95% CI -1.5-1.1]; p = 0.001). No significant differences in weight outcomes at six months of treatment with paroxetine, sertraline, or fluoxetine were observed by metabolizer status.

CONCLUSIONS

Weight gain observed with citalopram may be mediated by CYP2C19 metabolizer status.

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.

Biomarkers as predictors of treatment response to tricyclic antidepressants in major depressive disorder: A systematic review

Tricyclic antidepressants (TCAs) are frequently prescribed in case of non-response to first-line antidepressants in Major Depressive Disorder (MDD). Treatment of MDD often entails a trial-and-error process of finding a suitable antidepressant and its appropriate dose. Nowadays, a shift is seen towards a more personalized treatment strategy in MDD to increase treatment efficacy. One of these strategies involves the use of biomarkers for the prediction of antidepressant treatment response. We aimed to summarize biomarkers for prediction of TCA specific (i.e. per agent, not for the TCA as a drug class) treatment response in unipolar nonpsychotic MDD. We performed a systematic search in PubMed and MEDLINE. After full-text screening, 36 papers were included. Seven genetic biomarkers were identified for nortriptyline treatment response. For desipramine, we identified two biomarkers; one genetic and one nongenetic. Three nongenetic biomarkers were identified for imipramine. None of these biomarkers were replicated. Quality assessment demonstrated that biomarker studies vary in endpoint definitions and frequently lack power calculations. None of the biomarkers can be confirmed as a predictor for TCA treatment response. Despite the necessity for TCA treatment optimization, biomarker studies reporting drug-specific results for TCAs are limited and adequate replication studies are lacking. Moreover, biomarker studies generally use small sample sizes. To move forward, larger cohorts, pooled data or biomarkers combined with other clinical characteristics should be used to improve predictive power.

Antidepressant Treatment and Manic Switch in Bipolar I Disorder: A Clinical and Molecular Genetic Study

Affective switch is an important clinical issue when treating bipolar disorder. Though commonly seen in clinical practice, the benefits of prescribing antidepressants for bipolar depression are still controversial. To date, there have been few genetic studies and no genome-wide association study (GWAS), focusing on manic switch following bipolar depression. This study aims to investigate the effects of individual genomics and antidepressant medication on the risk of manic switch in bipolar I disorder (BPI). A total of 1004 patients with BPI who had at least one depressive episode with complete data on antidepressant treatment and outcome were included. Clinical assessment of mania and depression was performed by trained psychiatric nurses and psychiatrists using the Chinese version of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN), and the diagnosis of BPI was made according to DSM-IV criteria. Manic switch was defined as a manic episode occurring within eight weeks of remission from an acute depressive episode. The age at first depressive episode of the study patients was 30.7 years (SD 12.5) and 56% of all patients were female. GWAS was carried out in a discovery group of 746 patients, followed by replication in an independent group of 255 patients. The top SNP rs10262219 on chromosome 7 showed the strongest allelic association with manic switch (p = 2.21 × 10−7) in GWAS, which was however not significantly replicated. Antidepressant treatment significantly (odds ratio 1.7; 95% CI 1.3−2.2; p < 0.001) increased the risk of manic switch. In logistic regression analysis, the CC genotype of rs10262219 (odds ratio 3.0; 95% CI 1.7−5.2) and antidepressant treatment (odds ratio 2.3; 95% CI 1.4−3.7) significantly increased the risk of manic switch with a joint effect (odds ratio 5.9; 95% CI 3.7−9.4). In conclusion, antidepressant medication and rs10262219 variants jointly increased the risk of manic switch after bipolar depression.

Pharmacogenomics-Guided Pharmacotherapy in Patients with Major Depressive Disorder or Bipolar Disorder Affected by Treatment-Resistant Depressive Episodes: A Long-Term Follow-up Study

Treatment-resistant depression (TRD) reduces affected patients’ quality of life and leads to important social health care costs. Pharmacogenomics-guided treatment (PGT) may be effective in the cure of TRD. The main aim of this study was to evaluate the clinical changes after PGT in patients with TRD (two or more recent failed psychopharmacological trials) affected by bipolar disorder (BD) or major depressive disorder (MDD) compared to a control group with treatment as usual (TAU). We based the PGT on assessing different gene polymorphisms involved in the pharmacodynamics and pharmacokinetics of drugs. We analyzed, with a repeated-measure ANOVA, the changes between the baseline and a 6 month follow-up of the efficacy index assessed through the Clinical Global Impression (CGI) scale, and depressive symptoms through the Hamilton Depression Rating Scale (HDRS). The PGT sample included 53 patients (26 BD and 27 MDD), and the TAU group included 52 patients (31 BD and 21 MDD). We found a significant within-subject effect of treatment time on symptoms and efficacy index for the whole sample, with significant improvements in the efficacy index (F = 8.544; partial η² = 0.077, p < 0.004) and clinical global impression of severity of illness (F = 6.818; partial η² = 0.062, p < 0.01) in the PGT vs. the TAU group. We also found a significantly better follow-up response (χ² = 5.479; p = 0.019) and remission (χ² = 10.351; p = 0.001) rates in the PGT vs. the TAU group. PGT may be an important option for the long-term treatment of patients with TRD affected by mood disorders, providing information that can better define drug treatment strategies and increase therapeutic improvement.

Circulating hsa-let-7e-5p and hsa-miR-125a-5p as Possible Biomarkers in the Diagnosis of Major Depression and Bipolar Disorders

Background. Evidence shows that microRNAs (miRNAs) could play a key role in the homeostasis and development of major depressive disorder and bipolar disorder. The present study is aimed at investigating the changes in circulating miRNA expression profiles in a plasma of patients suffering from major depressive disorder (MDD) and bipolar disorder (BD) to distinguish and evaluate these molecules as biomarkers for mood disorders. Methods. A study enrolled a total of 184 subjects: 74 controls, 84 MDD patients, and 26 BD patients. Small RNA sequencing revealed 11 deregulated circulating miRNAs in MDD and BD plasma, of which expression of 5, hsa-miR-139-3p, miRNAs hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-125a-5p, and hsa-miR-483-5p, were further verified using qPCR. miRNA gene expression data was evaluated alongside the data from clinical assessment questionnaires. Results. hsa-let-7e-5p and hsa-miR-125a-5p were both confirmed upregulated: 0.75-fold and 0.25-fold, respectively, in the MDD group as well as 1.36-fold and 0.68-fold in the BD group. Receiver operating curve (ROC) analysis showed mediocre diagnostic sensitivity and specificity of both hsa-let-7e-5p and hsa-miR-125a-5p with approximate area under the curve (AOC) of 0.66. ROC analysis of combined miRNA and clinical assessment data showed that hsa-let-7e-5p and hsa-miR-125a-5p testing could improve MDD and BD diagnostic accuracy by approximately 10%. Conclusions. Circulating hsa-let-7e-5 and hsa-miR-125a-5p could serve as additional peripheral biomarkers for mood disorders; however, suicidal ideation remains the major diagnostic factor for MDD and BD.

DNA Genotyping Based on Isothermal Amplification and Colorimetric Detection by Consumer Electronics Devices

The point-of-care testing of DNA biomarkers requires compact biosensing systems and consumer electronic technologies provide fascinating opportunities. Their portability, mass-produced components, and high-performance readout capabilities are the main advantages for the development of novel bioanalytical methods.This chapter describes the detection of single nucleotide polymorphisms (SNP) through methods based on user-friendly optical devices (e.g., USB digital microscope, flatbed scanner, smartphone, and DVD drive). Loop mediated isothermal amplification (LAMP) enables the required discrimination of each specific variant prior to the optical reading. In the first method, products are directly hybridized to the allele-specific probes attached to plastic chips in an array format. The second method, allele-specific primers are used, enabling the direct end-point detection based a colorimetric dyer and a microfluidic chamber chip. In both approaches, devices are employed for chip scanning.A representative application to the genotyping of a clinically relevant SNP from human samples is provided, showing the excellent features achieved. Consumer electronic devices are able to register sensitive precise measurements in terms of signal-to-noise ratios, image resolution, and scan-to-scan reproducibility. The integrated DNA-based method lead a low detection limit (100 genomic DNA copies), reproducible (variation <15%), high specificity (genotypes validated by reference method), and cheap assays (<10 €/test). The underlying challenge is the reliable implementation into minimal-specialized clinical laboratories, incorporating additional advantages, such as user-friendly interface, low cost, and connectivity for telemedicine needs.

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

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Baseline measures alone not able to predict escitalopram response above default.

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This poor baseline performance contradicts results from smaller studies.

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Accuracy improved using change in functional connectivity from baseline to week 2.

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Measures of early change following treatment may be crucial for accurate prediction.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

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.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Chronic Lithium Treatment Affects Anxious Behaviors and theExpression of Serotonergic Genes in Midbrain Raphe Nuclei of Defeated Male Mice

There is experimental evidence that chronic social defeat stress is accompanied by the development of an anxiety, development of a depression-like state, and downregulation of serotonergic genes in midbrain raphe nuclei of male mice. Our study was aimed at investigating the effects of chronic lithium chloride (LiCl) administration on anxiety behavior and the expression of serotonergic genes in midbrain raphe nuclei of the affected mice. A pronounced anxiety-like state in male mice was induced by chronic social defeat stress in daily agonistic interactions. After 6 days of this stress, defeated mice were chronically treated with saline or LiCl (100 mg/kg, i.p., 2 weeks) during the continuing agonistic interactions. Anxiety was assessed by behavioral tests. RT-PCR was used to determine Tph2, Htr1a, Htr5b, and Slc6a4 mRNA expression. The results revealed anxiolytic-like effects of LiCl on social communication in the partition test and anxiogenic-like effects in both elevated plus-maze and social interaction tests. Chronic LiCl treatment upregulated serotonergic genes in midbrain raphe nuclei. Thus, LiCl effects depend on the treatment mode, psycho-emotional state of the animal, and experimental context (tests). It is assumed that increased expression of serotonergic genes is accompanied by serotonergic system activation and, as a side effect, by higher anxiety.

Be Fit, Be Sharp, Be Well: The Case for Exercise as a Treatment for Cognitive Impairment in Late-life Depression

OBJECTIVE

To lay out the argument that exercise impacts neurobiological targets common to both mood and cognitive functioning, and thus more research should be conducted on its use as an alternative or adjunctive treatment for cognitive impairment in late-life depression (LLD).

METHOD

This narrative review summarizes the literature on cognitive impairment in LLD, describes the structural and functional brain changes and neurochemical changes that are linked to both cognitive impairment and mood disruption, and explains how exercise targets these same neurobiological changes and can thus provide an alternative or adjunctive treatment for cognitive impairment in LLD.

RESULTS

Cognitive impairment is common in LLD and predicts recurrence of depression, poor response to antidepressant treatment, and overall disability. Traditional depression treatment with medication, psychotherapy, or both, is not effective in fully reversing cognitive impairment for most depressed older adults. Physical exercise is an ideal treatment candidate based on evidence that it 1) is an effective treatment for depression, 2) enhances cognitive functioning in normal aging and in other patient populations, and 3) targets many of the neurobiological mechanisms that underlie mood and cognitive functioning. Results of the limited existing clinical trials of exercise for cognitive impairment in depression are mixed but overall support this contention.

CONCLUSIONS

Although limited, existing evidence suggests exercise may be a viable alternative or adjunctive treatment to address cognitive impairment in LLD, and thus more research in this area is warranted. Moving forward, additional research is needed in large, diverse samples to translate the growing research findings into clinical practice.

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.

Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments

Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.

The Genetics of Response to and Side Effects of Lithium Treatment in Bipolar Disorder: Future Research Perspectives

Although the mood stabilizer lithium is a first-line treatment in bipolar disorder, a substantial number of patients do not benefit from it and experience side effects. No clinical tool is available for predicting lithium response or the occurrence of side effects in everyday clinical practice. Multiple genetic research efforts have been performed in this field because lithium response and side effects are considered to be multifactorial endophenotypes. Available results from linkage and segregation, candidate-gene, and genome-wide association studies indicate a role of genetic factors in determining response and side effects. For example, candidate-gene studies often report GSK3β, brain-derived neurotrophic factor, and SLC6A4 as being involved in lithium response, and the latest genome-wide association study found a genome-wide significant association of treatment response with a locus on chromosome 21 coding for two long non-coding RNAs. Although research results are promising, they are limited mainly by a lack of replicability and, despite the collaboration of consortia, insufficient sample sizes. The need for larger sample sizes and "multi-omics" approaches is apparent, and such approaches are crucial for choosing the best treatment options for patients with bipolar disorder. In this article, we delineate the mechanisms of action of lithium and summarize the results of genetic research on lithium response and side effects.

Pharmacogenetic/Pharmacogenomic Tests for Treatment Prediction in Depression

Genetic factors play a significant but complex role in antidepressant (AD) response and tolerability. During recent years, there is growing enthusiasm in the promise of pharmacogenetic/pharmacogenomic (PGx) tools for optimizing and personalizing treatment outcomes for patients with major depressive disorder (MDD). The influence of pharmacokinetic and pharmacodynamic genes on response and tolerability has been investigated, including those encoding the cytochrome P450 superfamily, P-glycoprotein, monoaminergic transporters and receptors, intracellular signal transduction pathways, and the stress hormone system. Genome-wide association studies are also identifying new genetic variants associated with AD response phenotypes, which, combined with methods such as polygenic risk scores (PRS), is opening up new avenues for novel personalized treatment approaches for MDD. This chapter describes the basic concepts in PGx of AD response, reviews the major pharmacokinetic and pharmacodynamic genes involved in AD outcome, discusses PRS as a promising approach for predicting AD efficacy and tolerability, and addresses key challenges to the development and application of PGx tests.

Programmed cell death 4 as an endogenous suppressor of BDNF translation is involved in stress-induced depression

Brain-derived neurotrophic factor (BDNF) is a growth factor that plays vital roles in the neuron survival, growth, and neuroplasticity. Alteration to BDNF expression is associated with major depressive disorder. However, the BDNF translational machinery in depression remains unknown. Herein, we pointed that Pdcd4, a suppressor oncogene, acted as an endogenous inhibitor for the translation of BDNF, and selectively repressed the translation of BDNF splice variant IIc mRNA in an eIF4A-dependent manner. Chronic restraint stress (CRS) up-regulated Pdcd4 expression in hippocampus via decreasing mTORC1-mediated proteasomes degradation pathway, which resulted in the reduction of BDNF protein expression. Moreover, over-expression of Pdcd4 in the hippocampus triggered spontaneous depression-like behaviors under the non-stressed conditions in mice, while systemic or neuron-specific knockout of Pdcd4 reverses CRS-induced depression-like behaviors. Importantly, administration of Pdcd4 siRNA or an interfering peptide that interrupts the Pdcd4-eIF4A complex substantially promoted BDNF expression and rescued the behavioral disorders which were caused by CRS. Overall, we have discovered a previously unrecognized role of Pdcd4 in controlling BDNF mRNA translation, and provided a new method that boosting BDNF expression through blocking the function of Pdcd4 in depression, indicating that Pdcd4 might be a new potential target for depressive disorder therapy.

How Can Drug Metabolism and Transporter Genetics Inform Psychotropic Prescribing?

Many genetic variants in drug metabolizing enzymes and transporters have been shown to be relevant for treating psychiatric disorders. Associations are strong enough to feature on drug labels and for prescribing guidelines based on such data. A range of commercial tests are available; however, there is variability in included genetic variants, methodology, and interpretation. We herein provide relevant background for understanding clinical associations with specific variants, other factors that are relevant to consider when interpreting such data (such as age, gender, drug-drug interactions), and summarize the data relevant to clinical utility of pharmacogenetic testing in psychiatry and the available prescribing guidelines. We also highlight areas for future research focus in this field.

Increasing engagement in pharmacology and pharmacogenetics education using games and online resources: The PharmacoloGenius mobile app

Mobile applications represent useful instruments to convey information and engage the users even during traveling, thanks to the wide diffusion of smartphones, tablets, smartwatches, and similar devices. As such, they have high potential as learning tools that can act complementary to traditional teaching approaches. In the field of pharmacology, mobile applications are increasingly being used to improve adherence of patients or to help them report suspect adverse drug reactions. However, they have been scarcely applied to pharmacology education. In this article, we present PharmacoloGenius, a free Android mobile application integrating resources useful for students as well as healthcare professionals or researchers to expand knowledge on pharmacological topics. We gave particular emphasis to pharmacogenetics, as it is a fundamental tool to achieve personalized treatment. The application offers original games such as pharmacological trivia based on textbooks or special "journal club" trivia based on research articles conveying the state of the art on specific topics. Additionally, the app offers a curated list of online resources to study pharmacology and pharmacogenetics (e.g., free online courses, videos, and databases) as well as updated news on conferences, grants, and opportunities for pharmacologists. In conclusion, PharmacoloGenius aims to be a useful instrument for people interested in expanding their knowledge on pharmacology in an engaging way.

Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review

BACKGROUND

Psychiatric genetics has had limited success in translational efforts. A thorough understanding of the present state of translation in this field will be useful in the facilitation and assessment of future translational progress.

PURPOSE

A narrative literature review was conducted. Combinations of 3 groups of terms were searched in EBSCOhost, Google Scholar, and PubMed. The review occurred in multiple steps, including abstract collection, inclusion/exclusion criteria review, coding, and analysis of included papers.

RESULTS

One hundred and fourteen articles were analyzed for the narrative review. Across those, 4 bottlenecks were noted that, if addressed, may provide insights and help improve and increase translation in the field of psychiatric genetics. These 4 bottlenecks are emphasizing linear translational frameworks, relying on molecular genomic findings, prioritizing certain psychiatric disorders, and publishing more reviews than experiments.

CONCLUSIONS

These entwined bottlenecks are examined with one another. Awareness of these bottlenecks can inform stakeholders who work to translate and/or utilize psychiatric genetic information. Potential solutions include utilizing nonlinear translational frameworks as well as a wider array of psychiatric genetic information (e.g., family history and gene-environment interplay) in this area of research, expanding which psychiatric disorders are considered for translation, and when possible, conducting original research. Researchers are urged to consider how their research is translational in the context of the frameworks, genetic information, and psychiatric disorders discussed in this review. At a broader level, these efforts should be supported with translational efforts in funding and policy shifts.

MULTIOMIC PATTERNS IN BODY FLUIDS: TECHNOLOGICAL CHALLENGE WITH A GREAT POTENTIAL TO IMPLEMENT THE ADVANCED PARADIGM OF 3P MEDICINE

Liquid biopsy (LB) is defined as a sample of any of body fluids (blood, saliva, tear fluid, urine, sweat, amniotic, cerebrospinal and pleural fluids, cervicovaginal secretion, and wound efflux, amongst others), which can be ex vivo analysed to detect and quantity the target(s) of interest. LB represents diagnostic approach relevant for organ-specific changes and systemic health conditions including both manifested diseases and their prestages such as suboptimal health. Further, experts emphasise that DNA-based analysis alone does not provide sufficient information for optimal diagnostics and effective treatments. Consequently, of great scientific and clinical utility are molecular patterns detected by hybrid technologies such as metabolomic tools and molecular imaging. Future proposed strategies utilise multiomic pillars (generally genome, tanscriptome, proteome, metabolome, epigenome, radiome, and microbiome), system-biological approach, and multivariable algorithms for diagnostic, prognostic, and therapeutic purposes. Current article analyses pros and cons of the mass spectrometry-based technologies, provides eminent examples of a success story "from discovery to clinical application," and demonstrates a "road-map" for the technology-driven paradigm change from reactive to predictive, preventive and personalised medical services as the medicine of the future benefiting the patient and healthcare at large. © 2019 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Targeting the endocannabinoid system: a predictive, preventive, and personalized medicine-directed approach to the management of brain pathologies

Cannabis-inspired medical products are garnering increasing attention from the scientific community, general public, and health policy makers. A plethora of scientific literature demonstrates intricate engagement of the endocannabinoid system with human immunology, psychology, developmental processes, neuronal plasticity, signal transduction, and metabolic regulation. Despite the therapeutic potential, the adverse psychoactive effects and historical stigma, cannabinoids have limited widespread clinical application. Therefore, it is plausible to weigh carefully the beneficial effects of cannabinoids against the potential adverse impacts for every individual. This is where the concept of "personalized medicine" as a promising approach for disease prediction and prevention may take into the account. The goal of this review is to provide an outline of the endocannabinoid system, including endocannabinoid metabolizing pathways, and will progress to a more in-depth discussion of the therapeutic interventions by endocannabinoids in various neurological disorders.

Response to fluoxetine in children and adolescents: a weighted gene co-expression network analysis of peripheral blood

The inconclusive and non-replicated results of pharmacogenetic studies of antidepressant response could be related to the lack of acknowledgement of its mechanism of action. In this scenario, gene expression studies provide and interesting framework to reveal new candidate genes for pharmacogenetic studies or peripheral biomarkers of fluoxetine response. We propose a system biology approach to analyse changes in gene expression induced by eight weeks of treatment with fluoxetine in peripheral blood. 21 naïve child and adolescents participated in the present study. Our analysis include the identification of gene co-expression modules, using Weighted Gene Co-expression Network Analysis (WGCNA), followed by protein-protein interaction (PPi) network construction coupled with functional annotation. Our results revealed two modules of co-expression genes related to fluoxetine treatment. The constructed networks from these modules were enriched for biological processes related to cellular and metabolic processes, cell communication, immune system processes, cell death, response to stimulus and neurogenesis. Some of these processes, such as immune system, replicated previous findings in the literature, whereas, neurogenesis, a mechanism proposed to be involved in fluoxetine response, had been identified for first time using peripheral tissues. In conclusion, our study identifies several biological processes in relation to fluoxetine treatment in peripheral blood, offer new candidate genes for pharmacogenetic studies and valuable markers for peripheral moderator biomarkers discovery.

The black sheep of the family- whole-exome sequencing in family of lithium response discordant bipolar monozygotic twins

Twin studies are among the most promising strategies for studying heritable disorders, including bipolar disorder (BD). The aim of the present study was to identify distinguishing genes between monozygotic (MZ) twins with different BD phenotype and compare them to their non-affected siblings. Whole-exome sequencing (WES) can identify rare and structural variants that could detect the polygenetic burden of complex disorders. WES was performed on a family composed of two MZ twins with BD, their unaffected brother and unaffected parents. The twins have a discordant response to lithium and distinct course of illness. Following WES, six genes of particular interest emerged: Neurofibromin type 1 (NF1), Biorientation of chromosomes in cell division 1 (BOD1), Golgi-associated gamma adaptin ear-containing ARF binding protein 3 (GGA3), Disrupted in schizophrenia 1 (DISC1), Neuromedin U receptor 2 (NMUR2), and Huntingtin interacting protein 1-related (HIP1R). Interestingly, many of these influence glutamatergic pathways and thus the findings may have therapeutical implications. These results may provide important insights to unveil genetic underpinnings of BD and the response to lithium.

Pathway and network embedding methods for prioritizing psychiatric drugs

One in five Americans experience mental illness, and roughly 75% of psychiatric prescriptions do not successfully treat the patient's condition. Extensive evidence implicates genetic factors and signaling disruption in the pathophysiology of these diseases. Changes in transcription often underlie this molecular pathway dysregulation; individual patient transcriptional data can improve the efficacy of diagnosis and treatment. Recent large-scale genomic studies have uncovered shared genetic modules across multiple psychiatric disorders - providing an opportunity for an integrated multi-disease approach for diagnosis. Moreover, network-based models informed by gene expression can represent pathological biological mechanisms and suggest new genes for diagnosis and treatment. Here, we use patient gene expression data from multiple studies to classify psychiatric diseases, integrate knowledge from expert-curated databases and publicly available experimental data to create augmented disease-specific gene sets, and use these to recommend disease-relevant drugs. From Gene Expression Omnibus, we extract expression data from 145 cases of schizophrenia, 82 cases of bipolar disorder, 190 cases of major depressive disorder, and 307 shared controls. We use pathway-based approaches to predict psychiatric disease diagnosis with a random forest model (78% accuracy) and derive important features to augment available drug and disease signatures. Using protein-protein-interaction networks and embedding-based methods, we build a pipeline to prioritize treatments for psychiatric diseases that achieves a 3.4-fold improvement over a background model. Thus, we demonstrate that gene-expression-derived pathway features can diagnose psychiatric diseases and that molecular insights derived from this classification task can inform treatment prioritization for psychiatric diseases.

Alzheimer's Disease and Neurotransmission Gene Variants: Focus on Their Effects on Psychiatric Comorbidities and Inflammatory Parameters

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder accounting for 60-70% of dementia cases. Genetic origin accounts for 49-79% of disease risk. This paper aims to investigate the association of 17 polymorphisms within 7 genes involved in neurotransmission (COMT, HTR2A, PPP3CC, RORA, SIGMAR1, SIRT1, and SORBS3) and AD.

METHODS

A Greek and an Italian sample were investigated, for a total of 156 AD subjects and 301 healthy controls. Exploratory analyses on psychosis and depression comorbidities were performed, as well as on other available clinical and serological parameters.

RESULTS

AD was associated with rs4680 within the COMT gene in the total sample. Trends of association were found in the 2 subsamples. Some nominal associations were found for the depressive phenotype. rs10997871 and rs10997875 within SIRT1 were nominally associated with depression in the total sample and in the Greek subsample. rs174696 within COMT was associated with depression comorbidity in the Italian subsample.

DISCUSSION

Our data support the role of COMT, and particularly of rs4680, in the pathogenesis of AD. Furthermore, the SIRT1 gene seems to modulate depressive symptomatology in the AD population.

Expression alteration of microRNAs in Nucleus Accumbens is associated with chronic stress and antidepressant treatment in rats

BACKGROUND

Nucleus Accumbens (NAc) is a vital brain region for the process of reward and stress, whereas microRNA plays a crucial role in depression pathology. However, the abnormality of NAc miRNA expression during the stress-induced depression and antidepressant treatment, as well as its biological significance, are still unknown.

METHODS

We performed the small RNA-sequencing in NAc of rats from three groups: control, chronic unpredictable mild stress (CUMS), and CUMS with an antidepressant, Escitalopram. We applied an integrative pipeline for analyzing the miRNA expression alternation in different model groups, including differential expression analysis, co-expression analysis, as well as a subsequent pathway/network analysis to discover both miRNA alteration pattern and its biological significance.

RESULT

A total of 423 miRNAs were included in analysis.18/8 differential expressing (DE) miRNA (adjusted p < 0.05, |log2FC| > 1) were observed in controls Vs. depression/depression Vs. treatment, 2 of which are overlapping. 78% (14/18) of these miRNAs showed opposite trends of alteration in stress and treatment. Two micro RNA, miR-10b-5p and miR-214-3p, appeared to be hubs in the regulation networks and also among the top findings in both differential analyses. Using co-expression analysis, we found a functional module that strongly correlated with stress (R = 0.96, P = 0.003), and another functional module with a moderate correlation with anhedonia (R = 0.89, P = 0.02). We also found that predicted targets of these miRNAs were significantly enriched in the Ras signaling pathway, which is associated with both depression, anhedonia, and antidepressant treatment.

CONCLUSION

Escitalopram treatment can significantly reverse NAc miRNA abnormality induced by chronic stress. However, the novel miRNA alteration that is absent in stress pathology also emerges, which means that antidepressant treatment is unlikely to bring miRNA expression back to the same level as the controls. Also, the Ras-signaling pathway may be involved in explaining the depression disease etiology, the clinical symptom, and treatment response of stress-induced depression.

INPP1 up-regulation by miR-27a contributes to the growth, migration and invasion of human cervical cancer

Inositol polyphosphate‐1‐phosphatase (INPP1) is an enzyme that is responsible for glycolysis and lipid metabolism. Here, we discovered that INPP1 expression was up‐regulated in CC tissues compared to that in adjacent normal tissues by RT‐qPCR. Inositol polyphosphate‐1‐phosphatase overexpression promoted and INPP1 knockdown suppressed cell viability, cellular migration/invasion and EMT in CC cells. To explore the mechanism of dysregulation, INPP1 was predicted to be a target of miR‐27a, and a pmiRGLO dual‐luciferase reporter assay showed that miR‐27a bound to the 3′ UTR of INPP1. RT‐qPCR revealed that miR‐27a was also up‐regulated and had a positive correlation with INPP1 expression in CC tissues. Furthermore, shR‐INPP1 could favour the malignant phenotype reversion induced by miR‐27a, suggesting that miR‐27a up‐regulates INPP1 to promote tumorigenic activities. Altogether, our findings show that the up‐regulation of INPP1 by miR‐27a contributes to tumorigenic activities and may provide a potential biomarker for CC.

Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative

Background

Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need.

Structure

The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24 months and an independent panel will assess and classify each participants’ response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence.

Conclusions

The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.

Electronic supplementary material

The online version of this article (10.1186/s40345-019-0156-x) contains supplementary material, which is available to authorized users.

Precision medicine: a call for increased pharmacogenomic education

Precision medicine is an emerging model of care where providers consider patients' genetic profiles, lifestyles and environments to offer more precise therapy. The potential of precision medicine is boundless as interdisciplinary teams utilize genetic technologies to improve patient outcomes. The integration of precision medicine into healthcare faces many barriers, including a lack of standardization and reimbursement concerns. This article argues that increased pharmacogenetics education and system-wide implementation is necessary to overcome some of these challenges. Extensive expansion of pharmacogenomics education is a step toward producing knowledgeable clinicians who are poised to apply its methodology and champion for patient-centered care.

Management of perinatal depression with non-drug interventions

Perinatal depression is a common disorder that has been associated with serious risks to mother and child. Recently, screening for depression in pregnant and postpartum women has increased, as has the development of new psychotherapy and non-drug treatment modalities. Matching patients to treatments can be challenging, and although research into personalized treatment of major depression in the general population has increased, no published guidelines focus on personalized treatment approaches to perinatal depression. In particular, guidelines on non-drug treatments are lacking. This review summarizes the evidence on personalized non-drug treatment of perinatal depression, how to incorporate patients' preferences, novel treatments under investigation, and the potential role of biomarkers in matching patients to treatment. The review provides recommendations for future research in personalized care of perinatal depression.

Innate immunity and oral microbiome: a personalized, predictive, and preventive approach to the management of oral diseases

Three recent advances in immunology, genetics, and microbiology have ushered in a new era in the continued efforts to better understand and treat oral diseases, moving ever closer to the three Ps of modern healthcare: personalized, predictive, and preventive medicine (PPPM). The discovery of now 15 subtypes of innate lymphoid cells, the refinement of DNA sequencing, and culture-independent characterization of the entire microbial community begin to reveal this complex adaptive network. All these advances warrant a systematic review as they have changed and will continue to change dental medicine. We will update dental professionals on these advances as related to oral diseases and associated pathologies in other organ systems such as premature labor, arthrosclerosis, and cancer. The five objectives are:Introduce the concept of microbiota and microbiomeExplain how we study microbiota and microbiomeDescribe the types and functions of innate lymphoid cellsInventory the unique demands of the oral cavityProvide a heuristic model to integrate the aboveConclusions and expert recommendations.

Pharmacological treatment of adult bipolar disorder

We summarize evidence supporting contemporary pharmacological treatment of phases of BD, including: mania, depression, and long-term recurrences, emphasizing findings from randomized, controlled trials (RCTs). Effective treatment of acute or dysphoric mania is provided by modern antipsychotics, some anticonvulsants (divalproex and carbamazepine), and lithium salts. Treatment of BD-depression remains unsatisfactory but includes some modern antipsychotics (particularly lurasidone, olanzapine + fluoxetine, and quetiapine) and the anticonvulsant lamotrigine; value and safety of antidepressants remain controversial. Long-term prophylactic treatment relies on lithium, off-label use of valproate, and growing use of modern antipsychotics. Lithium has unique evidence of antisuicide effects. Methods of evaluating treatments for BD rely heavily on meta-analysis, which is convenient but with important limitations. Underdeveloped treatment for BD-depression may reflect an assumption that effects of antidepressants are similar in BD as in unipolar major depressive disorder. Effective prophylaxis of BD is limited by the efficacy of available treatments and incomplete adherence owing to adverse effects, costs, and lack of ongoing symptoms. Long-term treatment of BD also is limited by access to, and support of expert, comprehensive clinical programs. Pursuit of improved, rationally designed pharmacological treatments for BD, as for most psychiatric disorders, is fundamentally limited by lack of coherent pathophysiology or etiology.

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (P_T) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.