Nortriptyline, a tricyclic antidepressant, has an important role in the pharmacotherapy of major depressive disorder (MDD). Individualized dosing approaches, such as pharmacogenetics-based and phenotype-based dosing, may enhance early achievement of therapeutic plasma concentrations, but their comparative accuracy has not been investigated. Our objective was to compare the accuracy of three nortriptyline dosing strategies: pharmacogenetics-based, phenotype-based, and standard dosing. Using pharmacokinetic modeling based on data from a randomized controlled trial, we assessed and compared the following dosing strategies: pharmacogenetics-based dosing depending on the cytochrome P-450 (CYP) 2D6 genotype, phenotype-based dosing determined by the plasma concentration measured after a single nortriptyline administration, and standard dosing (125 mg/day). A population pharmacokinetic model was developed to assess phenotype-based dosing recommendations. We evaluated the dosing strategies by comparing the number of participants with predicted therapeutic, subtherapeutic, and supratherapeutic plasma concentrations using Chi-squared (χ2) tests. Variability in plasma concentrations was assessed using F-tests. Both pharmacogenetics-based (χ2 (1) = 8.0, p = 0.01) and phenotype-based dosing (χ2 (1) = 5.3, p = 0.02) significantly increased the likelihood of achieving therapeutic plasma concentrations compared with standard dosing while reducing plasma concentration variability. No significant difference was found in the prediction of therapeutic concentrations between the two individualized dosing strategies (χ2 (1) = 0.33, p = 0.56). Pharmacogenetics-based and phenotype-based dosing demonstrate greater accuracy in predicting therapeutic nortriptyline plasma concentrations than standard dosing. Further research is warranted to explore the clinical application of model-informed precision dosing for nortriptyline and other psychotropic medications.

Background:It is well established that patients with severe mental illness and those treated with atypical antipsychotics (AAPs) are at an increased risk of cardiovascular disease. However, primary care currently lacks adequate monitoring of AAP usage, its effects, and the associated cardiovascular risk. We have developed TACTIC, a transmural collaborative care model for patients using AAPs prescribed by the general practitioner (GP) to address the issues of potential overtreatment with AAPs and undertreatment for cardiovascular risk. TACTIC comprises three steps: an informative video for patients, a multidisciplinary meeting, and a shared decision-making consultation with the GP. Objectives:To evaluate TACTIC's effectiveness on cardiovascular risk and mental health and its cost-effectiveness. Methods:We will conduct an incomplete stepped wedge cluster randomized trial in the Netherlands.40 GP-nurse clusters are randomized into four waves. Each cluster recruits adult patients (25-85 years), without prior diagnoses of dementia, delirium, or cardiovascular disease, for whom the GP prescribes AAPs. Every five months, a new wave starts with TACTIC. Measurements are taken before the intervention starts and every 5 months until the study concludes. Primary outcomes are cardiovascular risk and mental health as measured with the QRISK3 score and MHI5, respectively. The economic evaluation consists of two cost-utility analyses, one on the data collected alongside the trial and one based on a model extrapolating the trial data to a 10-year horizon. We will also evaluate the process of delivering TACTIC. Conclusion:This study will assess TACTIC's (cost)effectiveness and provide insights for successful delivery in general practice. Clinical trials registration:clinicaltrials.gov NCT05647980.

OBJECTIVES:To evaluate the cost-effectiveness of preemptive CYP2D6 and CYP2C19 genotype-informed tricyclic antidepressant dosing from a societal and a healthcare perspective. METHODS:A trial-based cost-effectiveness analysis was conducted with data from the Pharmacogenetics for Individualized Tricyclic Antidepressant dosing study. This multicenter randomized controlled trial (n = 111) compared pharmacogenetic-informed treatment (PIT) with treatment as usual. Quality-adjusted life-years (QALY) and costs were measured at 13 and 26 weeks. Prices were based on or indexed to 2022 tariffs. Single imputation nested in the bootstrap percentile method (using 5000 bootstrap replications) was performed to address missing data and to estimate uncertainty around cost-effectiveness outcomes. Incremental net monetary benefit (iNMB) was calculated based on a willingness to pay (WTP) of €50 000/QALY. RESULTS:Our data showed a marginal difference of -0.0125 QALYs (95% confidence interval [CI] -0.0404 to 0.0149, week 13) and 0.0012 QALYs (95% CI -0.0491 to 0.0574, week 26) for PIT versus treatment as usual. From the healthcare perspective, a cost saving of €148 (week 13) and €521 (26 week) was found for PIT. The societal perspective showed increased costs of €1300 (week 13) and €1704 (26 weeks) for PIT. The mean iNMB from a healthcare perspective was positive at 26 weeks, the other iNMBs (13 weeks and societal perspective) were negative. CONCLUSIONS:We observed marginal differences of QALYs in both the healthcare and societal perspective with cost savings from the healthcare perspective and additional cost from the societal perspective. These mixed results warrant more long-term observational studies to determine whether preemptive genotyping in tricyclic antidepressant dosing will be cost-effective.

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.

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.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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.

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 χ 2 1 = 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 ( F 6,125 = 4.03; P = .001), severity ( F 6,114 = 3.10; P = .008), and burden ( F 6,112 = 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

Abstract 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.

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 0-4/5-17 ABAS-3 versions would work best to assess the entire spectrum of adaptive functioning adequately.

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.

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.

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.

The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30-39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access. Trail registration: https://www.trialregister.nl/trial/7955.

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.

The increasing presence of genetic neurodevelopmental disorders (NDDs) results in greater demands for counseling. Many studies focus on the characteristics of patients, but less on family functioning. The aim of this study is to objectify parental stress and to study its relationship with child characteristics and environmental factors across several syndromes. 56 individuals with NDD participated: 24 with Kleefstra Syndrome, 13 with Koolen-de Vries Syndrome, and 19 with other rare (mono) genetic disorders. Parents were asked to complete the General Functioning subscale of the Family Assessment Device (FAD-GF), the Child Behavioral Checklist, and a questionnaire about demographic parental data. 25.5% of the families scored above the cut-off for pathological stress (>2.17). The mean FAD–GF score was 1.84. There was no significant difference between mean FAD-score of the subgroups (p=0,70). (Para)medical counselors should address this high amount of parental stress during counseling and consider these genetic syndromes as complex chronical illnesses.

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 1/4 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.

BACKGROUND:Psychomotor Retardation is a key symptom of Major Depressive Disorder. According to the literature its presence may affect the prognosis of treatment. Aim of the present study is to investigate the prognostic role of Psychomotor Retardation in patients with unipolar Psychotic Depression who are under antidepressant treatment.METHODS:The Salpetriere Retardation Rating Scale was administered at baseline and after 6 weeks to 122 patients with unipolar Psychotic Depression who were randomly allocated to treatment with imipramine, venlafaxine or venlafaxine plus quetiapine. We studied the effects of Psychomotor Retardation on both depression and psychosis related outcome measures.RESULTS:73% of the patients had Psychomotor Retardation at baseline against 35% after six weeks of treatment. The presence of Psychomotor Retardation predicted lower depression remission rates in addition to a higher persistence of delusions. After six weeks of treatment, venlafaxine was associated with higher levels of Psychomotor Retardation compared to imipramine and venlafaxine plus quetiapine.CONCLUSIONS:Our data confirm that Psychomotor Retardation is a severity marker of unipolar Psychotic Depression. It is highly prevalent and predicts lower effectivity of antidepressant psychopharmacological treatment.

Medical students’ attitudes towards patients with addiction and towards other chronic conditions might be comparable. Positive attitudes towards such conditions can be developed during medical education. This study evaluated medical students’ attitudes towards patients with addiction, dementia, and diabetes, and how such attitudes change during training. This is an observational prospective cohort study. Participants ( N = 100) were students who recruited from Primary and Community Care training, which consisted of theoretical courses and a clinical rotation. The Medical Condition Regard Scale was used to measure the attitudes and a repeated-measures analysis was performed to compare them. Before training, students’ attitudes towards patients with addiction were more negative than towards dementia and diabetes. The attitudes towards addiction remained stable during the entire training, whereas the attitudes towards dementia and diabetes remained stable during the theoretical courses but decreased after the clinical rotation. These findings emphasize a need to pay attention to students’ attitude development towards patients with chronic diseases, addiction in particular, during medical training.