The Relationship of Early Sleep Improvement With Response to Pharmacotherapy in Unipolar Psychotic Depression.

BACKGROUND: Since insomnia and depression are interrelated, improved sleep early in antidepressant pharmacotherapy may predict a positive treatment outcome. We investigated whether early insomnia improvement (EII) predicted treatment outcome in psychotic depression (PD) and examined if there was an interaction effect between EII and treatment type to assess if findings were treatment-specific. METHODS: This study is a secondary analysis of a randomized trial comparing 7 weeks treatment with the antidepressants venlafaxine, imipramine and venlafaxine plus the antipsychotic quetiapine in PD ( n = 114). Early insomnia improvement, defined as ≥20% reduced insomnia after 2 weeks, was assessed by the Hamilton Rating Scale for Depression (HAM-D-17). Associations between EII and treatment outcome were examined using logistic regressions. Subsequently, we added interaction terms between EII and treatment type to assess interaction effects. The predictive value of EII was compared with early response on overall depression (≥20% reduced HAM-D-17 score after 2 weeks). RESULTS: EII was associated with response (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.7-23.4; P = <0.001), remission of depression (OR, 6.1; 95% CI, 1.6-22.3; P = 0.009), and remission of psychosis (OR, 4.1; 95% CI, 1.6-10.9; P = 0.004). We found no interaction effects between EII and treatment type on depression outcome. Early insomnia improvement and early response on overall depression had a comparable predictive ability for treatment outcome. CONCLUSIONS: Early insomnia improvement was associated with a positive outcome in pharmacotherapy of PD, regardless of the medication type. Future studies are needed to confirm our findings and to examine the generalizability of EII as predictor in treatment of depression.

Tricyclic antidepressants for major depressive disorder: a comprehensive evaluation of current practice in the Netherlands.

BACKGROUND: Traditionally tricyclic antidepressants (TCAs) have an important place in treatment of major depressive disorder (MDD). Today, often other antidepressant medications are considered as first step in the pharmacological treatment of MDD, mainly because they are associated with less adverse effects, whereby the position of TCAs appears unclear. In this study we aimed to examine the current practice of TCAs in treatment of unipolar MDD. METHODS: A mixed methods approach was applied. First, a selection of leading international and national guidelines was reviewed. Second, actual TCA prescription was examined by analyzing health records of 75 MDD patients treated with the TCAs nortriptyline, clomipramine or imipramine in different centers in the Netherlands. Third, promotors and barriers influencing the choice for TCAs and dosing strategies were explored using semi-structured interviews with 24 Dutch psychiatrists. RESULTS: Clinical practice guidelines were sometimes indirective and inconsistent with each other. Health records revealed that most patients (71%) attained therapeutic plasma concentrations within two months of TCA use. Patients who achieved therapeutic plasma concentrations reached them on average after 19.6 days (SD 10.9). Both health records and interviews indicated that therapeutic nortriptyline concentrations were attained faster compared to other TCAs. Various factors were identified influencing the choice for TCAs and dosing by psychiatrists. CONCLUSIONS: Guideline recommendations and clinical practice regarding TCA prescription for MDD vary. To increase consistency in clinical practice we recommend development of an up-to-date guideline integrating selection and dosing of TCAs, including the roles of therapeutic drug monitoring and pharmacogenetics. Such a guideline is currently lacking and would contribute to optimal TCA treatment, whereby efficacy and tolerability may be increased.

An economic model of the cost-utility of pre-emptive genetic testing to support pharmacotherapy in patients with major depression in primary care.

The pharmacokinetics of many antidepressants (tricyclic antidepressants (TCA) or selective serotonin re-uptake inhibitors (SSRI)) are influenced by the highly polymorphic CYP2D6 enzyme. Therefore, pharmacogenetics could play an important role in the treatment of depressive patients. The potential cost-utility of screening patients is however still unknown. Therefore, a Markov model was developed to compare the strategy of screening for CYP2D6 and subsequently adjust antidepressant treatment according to a patient's metabolizer profile of poor, extensive, or ultra metabolizer, with the strategy of no screening ('one size fits all' principle). Each week a patient had a probability of side effects, which was followed by dosage titration or treatment switching. After 6 weeks treatment effect was evaluated followed by treatment adjustments if necessary, with a total time horizon of the model of 12 weeks. The analysis was performed from a societal perspective. The strategy of screening compared with no screening resulted in incremental costs of €91 (95 percentiles: €39; €152) more expensive but also more effect with 0.001 quality adjusted life years (QALYs) (95 percentiles: 0.001; 0.002) gain. The incremental cost-effectiveness ratio (ICER) was therefore €77,406 per QALY gained, but varied between €22,500 and €377,500 depending on the price of screening and productivity losses. According to our model, we cannot unequivocally conclude that screening for CYP2D6 in primary care patients using antidepressants is be cost-effective, as the results are surrounded by large uncertainty. Therefore, information from ongoing studies should be used to reduce these uncertainties.

A probabilistic framework for predicting disease dynamics: A case study of psychotic depression.

Unsupervised learning is often used to obtain insight into the underlying structure of medical data, but it is not always clear how to use such structure in an effective way. In this paper, we propose a probabilistic framework for predicting disease dynamics guided by latent states. The framework is based on hidden Markov models and aims to facilitate the selection of hypotheses that might yield insight into the dynamics. We demonstrate this by using clinical trial data for psychotic depression treatment as a case study. The discovered latent structure and proposed outcome are then validated using standard depression criteria, and are shown to provide new insight into the heterogeneity of psychotic depression in terms of predictive symptoms for different interventions.

Decreased Cognitive Functioning After Electroconvulsive Therapy Is Related to Increased Hippocampal Volume: Exploring the Role of Brain Plasticity.

OBJECTIVE: Electroconvulsive therapy (ECT) is still the most effective treatment of severe and therapy-refractory major depressive disorder. Cognitive side effects are the major disadvantage of ECT. Cognitive deficits are generally temporary in nature and may be mediated by the hippocampus. Recent studies have shown a temporary increase in hippocampal volume and a temporary decrease in cognitive functioning post-ECT compared with pre-ECT. This study investigates whether these volumetric changes are related to changes in cognitive functioning after ECT. METHODS: Nineteen medication-free patients with treatment-resistant major depressive disorder underwent a whole-brain magnetic resonance imaging scan and a neuropsychological examination (including the Rey auditory verbal learning task, Wechsler Memory Scale Visual Reproduction, fluency, Trail Making Task) within 1 week before and within 1 week after the course of ECT. Electroconvulsive therapy was administered twice a week bitemporally with a brief pulse. A matched healthy control group (n = 18) received the same neuropsychological examination and at a similar interval to that of the patients. RESULTS: Hippocampal volumes increased significantly from pretreatment to posttreatment in patients. Mean performance on cognitive tasks declined, or remained stable, whereas performance in controls generally improved because of retesting effects. The increase in hippocampal volume was related to changes in cognitive performance, indicating that this increase co-occurred with a decrease in cognitive functioning. CONCLUSIONS: Our findings tentatively suggest that the temporal increase in hippocampal volume after treatment, which may result from neurotrophic processes and is thought to be crucial for the antidepressive effect, is also related to the temporary cognitive side effects of ECT.

Adaptive and maladaptive functioning in Kleefstra syndrome compared to other rare genetic disorders with intellectual disabilities.

Detailed neurobehavioural profiles are of major value for specific clinical management, but have remained underexposed in the population with intellectual disabilities (ID). This was traditionally classified based on IQ level only. Rapid advances in genetics enable etiology based stratification in the majority of patients, which reduces clinical heterogeneity. This paper illustrates that specific profiles can be obtained for rare syndromes with ID. Our main aim was to study (mal)adaptive functioning in Kleefstra Syndrome (KS) by comparing and contrasting our findings to three other subgroups: Koolen-de Vries Syndrome, GATAD2B-related syndrome, and a mixed control group of individuals with ID. In total, we studied 58 individuals (28 males, 30 females) with ID; 24 were diagnosed with KS, 13 with Koolen-de Vries Syndrome, 6 with the GATAD2B-related syndrome, and 15 individuals with undefined neurodevelopmental disorders. All individuals were examined with a Vineland Adaptive Behavior Scale, mini PAS-ADD interview, and an Autism Diagnostic Observation Schedule to obtain measures of adaptive and maladaptive functioning. Each of the three distinctive genetic disorders showed its own specific profile of adaptive and maladaptive functioning, while being contrasted mutually. However, when data of the subgroups altogether are contrasted to the data of KS, such differences could not be demonstrated. Based on our findings, specific management recommendations were discussed for each of the three syndromes. It is strongly suggested to consider the genetic origin in individuals with congenital neurodevelopmental disorders for individual based psychiatric and behavioral management.

Understanding disease processes by partitioned dynamic Bayesian networks.

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder.

Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory.

Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.

Machine learning for antidepressant treatment selection in depression.

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.

Transmural collaborative care model for the review of antipsychotics: a feasibility study of a complex intervention.

General practitioners (GPs) are often unaware of antipsychotic (AP)-induced cardiovascular risk (CVR) and therefore patients using atypical APs are not systematically monitored. We evaluated the feasibility of a complex intervention designed to review the use of APs and advise on CVR-lowering strategies in a transmural collaboration. A mixed methods prospective cohort study in three general practices in the Netherlands was conducted in 2021. The intervention comprised three steps: a digital information meeting, a multidisciplinary meeting, and a shared decision-making visit to the GP. We assessed patient recruitment and retention rates, advice given and adopted, and CVR with QRISK3 score and mental state with MHI-5 at baseline and three months post-intervention. GPs invited 57 of 146 eligible patients (39%), of whom 28 (19%) participated. The intervention was completed by 23 (82%) and follow-up by 18 participants (64%). At the multidisciplinary meeting, 22 (78%) patients were advised to change AP use. Other advice concerned medication (other than APs), lifestyle, monitoring, and psychotherapy. At 3-months post-intervention, 41% (28/68) of this advice was adopted. Our findings suggest that this complex intervention is feasible for evaluating health improvement in patients using AP in a trial.

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.

Measuring Adaptive Behavior in Patients with Mendelian Neurodevelopmental Disorders. Comparison of ABAS-3 and Dutch Vineland Scales.

Objective: Several instruments are available for measuring (aspects of) adaptive functioning, but knowledge is lacking about which is best to use to monitor patients with etiologically homogeneous neurodevelopmental disorders. In this study we compare the use of the Vineland-Z and ABAS-3 adaptive behavior scales in such a specific group. Method: Of patients with a molecularly confirmed diagnosis of Kleefstra syndrome, 34 were assessed with both the Vineland-Z and ABAS-3 of which 12 (35,3%) males and 22 (64,7%) females. Raw scores and developmental ages were calculated and a comparison between the instruments was done via correlation analysis. Results: Biological age ranged from 12 to 50 years old (median age of 23,1 ± 9,6 years). Pearson r correlation analyses show that the Vineland-Z and ABAS-3 assessments are highly interchangeable in this population. However, there are practical issues which require attention: (i) the use of ABAS-3 needs several versions to cover the whole adaptive spectrum, and (ii) the Vineland-Z discriminates more at the lower end of the adaptive functioning spectrum compared to the ABAS-3, but less at the higher end. An ideal instrument for this specific purpose is not yet available. Conclusions: We recommend that either the Vineland-Z, with modification of the dated items, the abridged version of the Vineland III, or a merge of the 0-4/517 ABAS-3 versions would work best to assess the entire spectrum of adaptive functioning adequately.

Effectiveness of Genotype-Specific Tricyclic Antidepressant Dosing in Patients With Major Depressive Disorder: A Randomized Clinical Trial.

Importance: Evidence of the clinical benefit of pharmacogenetics-informed treatment (PIT) with antidepressants is still limited. Especially for tricyclic antidepressants (TCAs), pharmacogenetics may be of interest because therapeutic plasma concentrations are well defined, identification of optimal dosing can be time consuming, and treatment is frequently accompanied by adverse effects. Objective: To determine whether PIT results in faster attainment of therapeutic TCA plasma concentrations compared with usual treatment in patients with unipolar major depressive disorder (MDD). Design, Setting, and Participants: This randomized clinical trial compared PIT with usual treatment among 111 patients at 4 centers in the Netherlands. Patients were treated with the TCAs nortriptyline, clomipramine, or imipramine, with clinical follow-up of 7 weeks. Patients were enrolled from June 1, 2018, to January 1, 2022. At inclusion, patients had unipolar nonpsychotic MDD (with a score of ≥19 on the 17-item Hamilton Rating Scale for Depression [HAMD-17]), were aged 18 to 65 years, and were eligible for TCA treatment. Main exclusion criteria were a bipolar or psychotic disorder, substance use disorder, pregnancy, interacting comedications, and concurrent use of psychotropic medications. Intervention: In the PIT group, the initial TCA dosage was based on CYP2D6 and CYP2C19 genotypes. The control group received usual treatment, which comprised the standard initial TCA dosage. Main Outcomes and Measures: The primary outcome was days until attainment of a therapeutic TCA plasma concentration. Secondary outcomes were severity of depressive symptoms (measured by HAMD-17 scores) and frequency and severity of adverse effects (measured by Frequency, Intensity, and Burden of Side Effects Rating scores). Results: Of 125 patients randomized, 111 (mean [SD] age, 41.7 [13.3] years; 69 [62.2%] female) were included in the analysis; of those, 56 were in the PIT group and 55 were in the control group. The PIT group reached therapeutic concentrations faster than the control group (mean [SD], 17.3 [11.2] vs 22.0 [10.2] days; Kaplan-Meier χ21 = 4.30; P = .04). No significant difference in reduction of depressive symptoms was observed. Linear mixed-model analyses showed that the interaction between group and time differed for the frequency (F6,125 = 4.03; P = .001), severity (F6,114 = 3.10; P = .008), and burden (F6,112 = 2.56; P = .02) of adverse effects, suggesting that adverse effects decreased relatively more for those receiving PIT. Conclusions and Relevance: In this randomized clinical trial, PIT resulted in faster attainment of therapeutic TCA concentrations, with potentially fewer and less severe adverse effects. No effect on depressive symptoms was observed. These findings indicate that pharmacogenetics-informed dosing of TCAs can be safely applied and may be useful in personalizing treatment for patients with MDD. Trial Registration: ClinicalTrials.gov Identifier: NCT03548675.

Multi-omics data integration methods and their applications in psychiatric disorders.

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Genome-wide association uncovers shared genetic effects among personality traits and mood states.

Measures of personality and psychological distress are correlated and exhibit genetic covariance. We conducted univariate genome-wide SNP (~2.5 million) and gene-based association analyses of these traits and examined the overlap in results across traits, including a prediction analysis of mood states using genetic polygenic scores for personality. Measures of neuroticism, extraversion, and symptoms of anxiety, depression, and general psychological distress were collected in eight European cohorts (n ranged 546-1,338; maximum total n = 6,268) whose mean age ranged from 55 to 79 years. Meta-analysis of the cohort results was performed, with follow-up associations of the top SNPs and genes investigated in independent cohorts (n = 527-6,032). Suggestive association (P = 8 × 10(-8)) of rs1079196 in the FHIT gene was observed with symptoms of anxiety. Other notable associations (P < 6.09 × 10(-6)) included SNPs in five genes for neuroticism (LCE3C, POLR3A, LMAN1L, ULK3, SCAMP2), KIAA0802 for extraversion, and NOS1 for general psychological distress. An association between symptoms of depression and rs7582472 (near to MGAT5 and NCKAP5) was replicated in two independent samples, but other replication findings were less consistent. Gene-based tests identified a significant locus on chromosome 15 (spanning five genes) associated with neuroticism which replicated (P < 0.05) in an independent cohort. Support for common genetic effects among personality and mood (particularly neuroticism and depressive symptoms) was found in terms of SNP association overlap and polygenic score prediction. The variance explained by individual SNPs was very small (up to 1%) confirming that there are no moderate/large effects of common SNPs on personality and related traits.

The Potential of Polygenic Risk Scores to Predict Antidepressant Treatment Response in Major Depression: A Systematic Review.

BACKGROUND: Understanding the genetic underpinnings of antidepressant treatment response in unipolar major depressive disorder (MDD) can be useful in identifying patients at risk for poor treatment response or treatment resistant depression. A polygenic risk score (PRS) is a useful tool to explore genetic liability of a complex trait such as antidepressant treatment response. Here, we review studies that use PRSs to examine genetic overlap between any trait and antidepressant treatment response in unipolar MDD. METHODS: A systematic search of literature was conducted in PubMed, Embase, and PsycINFO. Our search included studies examining associations between PRSs of psychiatric as well as non-psychiatric traits and antidepressant treatment response in patients with unipolar MDD. A quality assessment of the included studies was performed. RESULTS: In total, eleven articles were included which contained PRSs for 30 traits. Studies varied in sample size and endpoints used for antidepressant treatment response. Overall, PRSs for attention-deficit hyperactivity disorder, the personality trait openness, coronary artery disease, obesity, and stroke have been associated with antidepressant treatment response in patients with unipolar MDD. LIMITATIONS: The endpoints used by included studies differed significantly, therefore it was not possible to perform a meta-analysis. CONCLUSIONS: Associations between a PRS and antidepressant treatment response have been reported for a number of traits in patients with unipolar MDD. PRSs could be informative to predict antidepressant treatment response in this population, given advances in the field. Most importantly, there is a need for larger study cohorts and the use of standardized outcome measures.

Cardiovascular risk management in patients with severe mental illness or taking antipsychotics: A qualitative study on barriers and facilitators among dutch general practitioners.

BACKGROUND: Patients with severe mental illness (SMI) or receiving treatment with antipsychotics (APs) have an increased risk of cardiovascular disease. Cardiovascular risk management (CVRM) increasingly depends on general practitioners (GPs) because of the shift of mental healthcare from secondary to primary care and the surge of off-label AP prescriptions. Nevertheless, the uptake of patients with SMI/APs in CVRM programmes in Dutch primary care is low. OBJECTIVES: To explore which barriers and facilitators GPs foresee when including and treating patients with SMI or using APs in an existing CVRM programme. METHODS: In 2019, we conducted a qualitative study among 13 Dutch GPs. During individual in-depth, semi-structured interviews a computer-generated list of eligible patients who lacked annual cardiovascular risk (CVR) screening guided the interview. Data was analysed thematically. RESULTS: The main barriers identified were: (i) underestimation of patient CVR and ambivalence to apply risk-lowering strategies such as smoking cessation, (ii) disproportionate burden on GPs in deprived areas, (iii) poor information exchange between GPs and psychiatrists, and (iv) scepticism about patient compliance, especially those with more complex conditions. The main facilitators included: (i) support of GPs through a computer-generated list of eligible patients and (ii) involvement of family or carers. CONCLUSION: This study displays a range of barriers and facilitators anticipated by GPs. These indicate the preconditions required to remove barriers and facilitate GPs, namely adequate recommendations in practice guidelines, improved consultation opportunities with psychiatrists, practical advice to support patient adherence and incentives for practices in deprived areas.

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.

Correction: Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context.

[This corrects the article DOI: 10.1371/journal.pone.0260952.].

Association of the neutrophil to lymphocyte ratio and white blood cell count with response to pharmacotherapy in unipolar psychotic depression: An exploratory analysis.

Background: Low-grade inflammation occurs in a subgroup of patients with Major Depressive Disorder (MDD) and may be associated with response to antidepressant medications. The Neutrophil to Lymphocyte Ratio (NLR) and total White Blood cell Count (WBC) are markers of systemic inflammation which have not been investigated as predictors for outcome to pharmacotherapy in unipolar depression yet. Moreover, the association between inflammation and treatment response has not been studied in unipolar Psychotic Depression (PD). We conducted an exploratory analysis to examine the prognostic significance of NLR and WBC in pharmacotherapy of PD. Methods: Baseline NLR and WBC were examined in their association with response to seven weeks of treatment with antidepressants (venlafaxine or imipramine) and the combination of an antidepressant with an antipsychotic (venlafaxine plus quetiapine) in 87 patients with PD. Logistic regression models were adjusted for age, gender, Body Mass Index (BMI), depression severity, duration of the current episode and number of previous depressive episodes. Secondary outcomes were remission of depression and disappearance of psychotic symptoms. Results: Higher NLR was associated with increased response to pharmacotherapy (Exp(B) 1.66, 95 % CI 1.03-2.66, p = 0.036), but not with remission of depression or disappearance of psychotic symptoms. WBC was not associated with any of the outcome measures. Conclusion: NLR may be a novel, inexpensive and widely available biomarker associated with response to pharmacotherapy in PD. The association between white blood cell measures and treatment outcome should be further investigated for different types of antidepressants in PD and in non-psychotic MDD.