Personalized medicine in psychiatry: problems and promises

Towards precision dosing in psychiatry: Population pharmacokinetics meta-modelling of clozapine and lithium

BACKGROUND

Treatment optimization is mandatory in psychiatric diseases and the use of population pharmacokinetics (popPK) models through model informed precision dosing (MIPD) has the potential to improve patient medical care. In this perspective, meta-modelling methods could provide popPK models with improved predictive performances and most of covariates of interest. The aims of this study were to develop meta-models of clozapine and lithium, assess their predictability and propose optimized dosing regimens for both drugs.

METHODS

Two popPK models for each drug were retained to develop the meta-models. For clozapine, the model with the best predictive performances and gender as a covariate and one with smoking status were retained. For lithium, the model with the best predictive performances and fat-free mass as covariate and one with glomerular filtration rate were retained.

RESULTS

Both meta-models showed improved predictability compared to the original models. Clozapine meta-model simulations allowed us to propose dosing regimen according to gender and smoking status. Steady-state doses ranged from 375 to 725 mg/day for clozapine once daily, and from 350 to 650 mg/day for clozapine twice daily. Lithium meta-model simulations allowed us to propose dosing regimen according to weight, body mass index, gender and GFR. Our steady-state dose propositions ranged from 625 to 1125 mg/day for males, and from 375 to 750 mg/day for females.

CONCLUSION

Both meta-models met the acceptability criteria for use in clinical practice on all subpopulations of interest. Those models could be used in the perspective of MIPD for clozapine and lithium.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

A review of the WHO strategy on traditional, complementary, and integrative medicine from the perspective of academic consortia for integrative medicine and health

Despite important progress in modern medicine, widely regarded as an indispensable foundation of healthcare in all highly advanced nations and regions, not all patients respond well to available treatments in biomedicine alone. Additionally, there are concerns about side effects of many medications and interventions, the unsustainable cost of healthcare and the low resolution of chronic non-communicable diseases and mental disorders whose incidence has risen in the last decades. Besides, the chronic stress and burnout of many healthcare professionals impairs the therapeutic relationship. These circumstances call for a change in the current paradigm and practices of biomedicine healthcare. Most of the world population (80%) uses some form of traditional, complementary, and integrative medicine (T&CM), usually alongside biomedicine. Patients seem equally satisfied with biomedicine and T&CM, but in the field of T&CM there are also many challenges, such as unsupported claims for safety and/or efficacy, contamination of herbal medicines and problems with regulation and quality standards. As biomedicine and T&CM seem to have different strengths and weaknesses, integration of both approaches may be beneficial. Indeed, WHO has repeatedly called upon member states to work on the integration of T&CM into healthcare systems. Integrative medicine (IM) is an approach that offers a paradigm for doing so. It combines the best of both worlds (biomedicine and T&CM), based on evidence for efficacy and safety, adopting a holistic personalized approach, focused on health. In the last decades academic health centers are increasingly supportive of IM, as evidenced by the foundation of national academic consortia for integrative medicine in Brazil (2017), the Netherlands (2018), and Germany (2024) besides the pioneering American consortium (1998). However, the integration process is slow and sometimes met with criticism and even hostility. The WHO T&CM strategies (2002-2005 and 2014-2023) have provided incipient guidance on the integration process, but several challenges are yet to be addressed. This policy review proposes several possible solutions, including the establishment of a global matrix of academic consortia for IM, to update and extend the WHO T&CM strategy, that is currently under review.

On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia - Results from an Expert Consensus Following the Delphi Method

Introduction

Schizophrenia is a severe mental illness that usually begins in late adolescence or early adulthood. Current pharmacological treatments, while acceptably effective for many patients, are rarely clinically tailored or individualized. The lack of sufficient etiopathological knowledge of the disease, together with overall comparable effect sizes for efficacy between available antipsychotics and the absence of clinically actionable biomarkers, has hindered the advance of individualized medicine in the treatment of schizophrenia. Nevertheless, some degree of stratification based on clinical markers could guide treatment choices and help clinicians move toward individualized psychiatry. To this end, a panel of experts met to formally discuss the current approach to individualized treatment in schizophrenia and to define how treatment individualization could help improve clinical outcomes.

Methods

A task force of seven experts iteratively developed, evaluated, and refined questionnaire items, which were then evaluated using the Delphi method. Descriptive statistics were used to summarize and rank expert responses. Expert discussion, informed by the results of a scoping review on personalizing the pharmacologic treatment of adults and adolescents with schizophrenia, ultimately generated recommendations to guide individualized pharmacologic treatment in this population.

Results

There was substantial agreement among the expert group members, resulting in the following recommendations: 1) individualization of treatment requires consideration of the patient's diagnosis, clinical presentation, comorbidities, previous treatment response, drug tolerability, adherence patterns, and social factors; 2) patient preferences should be considered in a shared decision-making approach; 3) identified barriers to personalized care that need to be overcome include the lack of actionable biomarkers and mechanistic similarities between available treatments, but digital tools should be increasingly used to enhance individualized treatment.

Conclusion

Individualized care can help provide effective, tailored treatments based on an individual's clinical characteristics, disease trajectory, family and social environment, and goals and preferences.

Early trajectories and moderators of autistic language profiles: A longitudinal study in preschoolers

LAY ABSTRACT

Language development can greatly vary among autistic children. Children who struggle with language acquisition often face many challenges and experience lower quality of life. However, little is known about the early language trajectories of autistic preschoolers and their moderators. Autistic language can be stratified into three profiles. Language unimpaired experience little to no language difficulties; language impaired show significant difficulties in language; minimally verbal never develop functional language. In this study, we used a longitudinal sample of preschoolers with autism and with typical development (aged 1.5-5.7 years). We replicated the three language profiles through a data-driven approach. We also found that different factors modulated the language outcome within each group. For instance, non-verbal cognition at age 2.4 moderated the participants' attribution to each language profile. Moreover, early intervention moderated verbal outcome in the language impaired profile. In conclusion, we provided a detailed description of how autistic preschoolers acquire language, and what factors might influence their trajectories. Our findings could inspire more personalized intervention for early autistic language difficulties.

High-Anxious People Generalize Costly Pain-Related Avoidance Behavior More to Novel Safe Contexts Compared to Low-Anxious People

Pain-related avoidance is adaptive when there is a bodily threat, but when it generalizes to safe movements/situations, it may become disabling. Both subclinical anxiety-a vulnerability marker for chronic pain-and chronic pain are associated with excessive fear generalization to safe stimuli/situations. Previous research focused mainly on passive fear correlates (psychophysiological arousal and self-reports) leaving avoidance behavior poorly understood. Therefore, we tested whether high-anxious individuals generalize their pain-related avoidance behavior more to novel, safe contexts than low-anxious people. In a robotic-arm-reaching task, both groups (low vs high trait anxiety) performed 1 of 3 movements to reach a target. In the threat context (black background), a painful stimulus could be partly/completely prevented by performing more effortful trajectories (longer and more force needed); in the safe context (white background), no pain occurred. Generalization of avoidance was tested in 2 novel contexts (light/dark gray backgrounds). We assessed pain expectancy, pain-related fear, startle eyeblink responses for all trajectories, and avoidance behavior (ie, maximal deviation from shortest trajectory). Results indicated that differential fear and expectancy selectively generalized to the novel context resembling the original threat context in both groups. Interestingly and in contrast with the verbal reports, high-anxious participants avoided more in the novel context resembling the original safe context, but not in the 1 resembling the threat context. No generalization emerged in the startle data. Because excessive pain-related avoidance specifically may cause withdrawal from daily life activities, these findings suggest that high-anxious individuals may be vulnerable to developing chronic pain disability. PERSPECTIVE: This paper shows that high-anxious people do not overgeneralize pain-related fear and pain expectancy learned in a threat context more to novel, safe contexts than low-anxious individuals, but that they do avoid more in those contexts. These findings suggest that high-anxious individuals may be vulnerable to developing chronic pain disability.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

Biomarker-Guided Tailored Therapy in Major Depression

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Counterconditioning reduces contextual renewal in a novel context but not in the acquisition context

As extinction is a context-dependent form of learning, conditioned responses tend to return when the conditioned stimulus (CS) is encountered outside the extinction context, known as contextual renewal. Counterconditioning is a technique that may lead to a more persistent reduction of the conditioned response. However, the effects of aversive-to-appetitive counterconditioning on contextual renewal in rodent studies are mixed. Further, research in humans is sparse, particularly direct statistical comparisons between counterconditioning and standard extinction techniques within the same study. Using a causal associative learning framework (the allergist task) implemented online, we compared the effectiveness of counterconditioning to standard extinction in preventing the renewal of judgements on the allergic properties of different food items (CSs). In a between-subjects design, 328 participants first learned that particular food items (CSs) lead to an allergic reaction in a specific restaurant (context A). Next, one CS was extinguished (no allergic reaction) while another CS was counterconditioned (positive outcome) in restaurant B. Causal judgements of the allergic properties of food items occurred in either the response acquisition context (ABA group, N = 112), the response reduction context where extinction and counterconditioning had occurred (ABB group, N = 107), or a novel context (ABC group, N = 109). Results showed that counterconditioning, compared to extinction, diminished the renewal of causal judgements to the CS in a novel context (ABC group). Still, casual judgements returned for both counter-conditioned and extinguished CSs in the response acquisition context (ABA group). Counterconditioning and extinction were similarly effective at preventing recovery of causal judgements in the response reduction context (ABB group); however, only in context B did participants choose the counter-conditioned CS as less likely to cause an allergic reaction in comparison to the extinguished CS. These findings indicate scenarios in which counterconditioning is more effective than standard extinction at diminishing the return of threat associations, with implications for improving the generalization of safety learning.

Major Depressive Disorder and Oxidative Stress: A Review of Peripheral and Genetic Biomarkers According to Clinical Characteristics and Disease Stages

Major depressive disorder (MDD) is currently the main cause of disability worldwide, but its pathophysiology remains largely unknown, especially given its high heterogeneity in terms of clinical phenotypes and biological characteristics. Accordingly, its management is still poor. Increasing evidence suggests that oxidative stress, measured on various matrices such as serum, plasma or erythrocytes, has a critical role in MDD. The aim of this narrative review is to identify serum, plasma and erythrocyte biomarkers of oxidative stress in MDD patients according to disease stage and clinical features. Sixty-three articles referenced on PubMed and Embase between 1 January 1991, and 31 December 2022, were included. Modifications to antioxidant enzymes (mainly glutathione peroxidase and superoxide dismutase) in MDD were highlighted. Non-enzymatic antioxidants (mainly uric acid) were decreased in depressed patients compared to healthy controls. These changes were associated with an increase in reactive oxygen species. Therefore, increased oxidative damage products (principally malondialdehyde, protein carbonyl content and 8-hydroxy-2'-deoxyguanosine) were present in MDD patients. Specific modifications could be identified according to disease stages and clinical features. Interestingly, antidepressant treatment corrected these changes. Accordingly, in patients in remission from depression, oxidative stress markers were globally normalized. This narrative review suggests the particular interest of oxidative stress biomarkers for MDD care that may contribute to the heterogeneity of the disease and provide the opportunity to find new therapeutic targets.

Plasma acetyl-l-carnitine and l-carnitine in major depressive episodes: a case-control study before and after treatment

BACKGROUND

Major depressive disorder (MDD) is the main cause of disability worldwide, its outcome is poor, and its underlying mechanisms deserve a better understanding. Recently, peripheral acetyl-l-carnitine (ALC) has been shown to be lower in patients with major depressive episodes (MDEs) than in controls. l-Carnitine is involved in mitochondrial function and ALC is its short-chain acetyl-ester. Our first aim was to compare the plasma levels of l-carnitine and ALC, and the l-carnitine/ALC ratio in patients with a current MDE and healthy controls (HCs). Our second aim was to assess their changes after antidepressant treatment.

METHODS

l-Carnitine and ALC levels and the carnitine/ALC ratio were measured in 460 patients with an MDE in a context of MDD and in 893 HCs. Depressed patients were re-assessed after 3 and 6 months of antidepressant treatment for biology and clinical outcome.

RESULTS

As compared to HC, depressed patients had lower ALC levels (p < 0.00001), higher l-carnitine levels (p < 0.00001) and higher l-carnitine/ALC ratios (p < 0.00001). ALC levels increased [coefficient: 0.18; 95% confidence interval (CI) 0.12-0.24; p < 0.00001], and l-carnitine levels (coefficient: -0.58; 95% CI -0.75 to -0.41; p < 0.00001) and l-carnitine/ALC ratios (coefficient: -0.41; 95% CI -0.47 to -0.34; p < 0.00001), decreased after treatment. These parameters were completely restored after 6 months of antidepressant. Moreover, the baseline l-carnitine/ALC ratio predicted remission after 3 months of treatment (odds ratio = 1.14; 95% CI 1.03-1.27; p = 0.015).

CONCLUSIONS

Our data suggest a decreased mitochondrial metabolism of l-carnitine into ALC during MDE. This decreased mitochondrial metabolism is restored after a 6-month antidepressant treatment. Moreover, the magnitude of mitochondrial dysfunction may predict remission after 3 months of antidepressant treatment. New strategies targeting mitochondria should be explored to improve treatments of MDD.

Interpersonal attunement in social interactions: from collective psychophysiology to inter-personalized psychiatry and beyond

In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and enactivism to dynamical systems, active inference and machine learning. To this end, we define interpersonal attunement as a set of multi-scale processes of building up and materializing social expectations-put simply, anticipating and interacting with others and ourselves. While cultivating and negotiating common ground, via communication and culture-building activities, are indispensable for the survival of the individual, the relevant multi-scale mechanisms have been largely considered in isolation. Here, collective psychophysiology, we argue, can lend itself to the fine-tuned analysis of social interactions, without neglecting the individual. On the other hand, an interpersonal mismatch of expectations can lead to a breakdown of communication and social isolation known to negatively affect mental health. In this regard, we review psychopathology in terms of interpersonal misattunement, conceptualizing psychiatric disorders as disorders of social interaction, to describe how individual mental health is inextricably linked to social interaction. By doing so, we foresee avenues for an inter-personalized psychiatry, which moves from a static spectrum of disorders to a dynamic relational space, focusing on how the multi-faceted processes of social interaction can help to promote mental health. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Psychotherapies and digital interventions for OCD in adults: What do we know, what do we need still to explore?

BACKGROUND

Despite significant advances in the understanding and treatment of obsessive compulsive disorder (OCD), current treatment options are limited in terms of efficacy for symptom remission. Thus, assessing the potential role of iterative or alternate psychotherapies is important. Also, the potential role of digital technologies to enhance the accessibility of these therapies, should not be underestimated. We also need to embrace the idea of a more personalized treatment choice, being cognisant of clinical, genetic and neuroimaging predictors of treatment response.

PROCEDURES

Non-systematic review of current literature on emerging psychological and digital therapies for OCD, as well as of potential biomarkers of treatment response.

FINDINGS

A number of 'third wave' therapies (e.g., Acceptance and Commitment Therapy, Mindfulness-Based Cognitive Therapy) have an emerging and encouraging evidence base in OCD. Other approaches entail employment of elements of other psychotherapies such as Dialectical Behaviour Therapy; or trauma-focussed therapies such as Eye Movement Desensitisation and Reprocessing, and Imagery Rescripting and Narrative Therapy. Further strategies include Danger Ideation Reduction Therapy and Habit Reversal. For these latter approaches, large-scale randomised controlled trials are largely lacking, and the precise role of these therapies in treating people with OCD, remains to be clarified. A concentrated 4-day program (the Bergen program) has shown promising short- and long-term results. Exercise, music, and art therapy have not been adequately tested in people with OCD, but may have an adjunctive role. Digital technologies are being actively investigated for enhancing reach and efficacy of psychological therapies for OCD. Biomarkers, including genetic and neuroimaging, are starting to point to a future with more 'personalised medicine informed' treatment strategizing for OCD.

CONCLUSIONS

There are a number of potential psychological options for the treatment of people with OCD who do not respond adequately to exposure/response prevention or cognitive behaviour therapy. Adjunctive exercise, music, and art therapy might be useful, albeit the evidence base for these is very small. Consideration should be given to different ways of delivering such interventions, including group-based, concentrated, inpatient, or with outreach, where appropriate. Digital technologies are an emerging field with a number of potential applications for aiding the treatment of OCD. Biomarkers for treatment response determination have much potential capacity and deserve further empirical testing.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

Effect of individualized occupational therapy on social functioning in patients with schizophrenia: A five-year follow-up of a randomized controlled trial

This study aimed to evaluate the long-term effects of adding individualized occupational therapy (IOT) to a three-months group occupational therapy (GOT) on social functioning in inpatients with schizophrenia or schizoaffective disorder at a follow-up investigation five-years after discharge. Initially, patients were randomly assigned to GOT + IOT or GOT alone, with 102 patients, 48 in GOT + IOT and 54 in GOT alone, completing the five years follow-up. The primary outcome was change in social functioning assessed by the Social Functioning Scale (SFS) from baseline to five-year follow-up. Other outcomes included Brief Assessment of Cognition in Schizophrenia (BACS), Schizophrenia Cognition Rating Scale (SCoRS), Intrinsic Motivation Inventory (IMI), Global Assessment of Functioning (GAF), Positive and Negative Syndrome Scale (PANSS), and Client Satisfaction Questionnaire-8 (CSQ-8). There were significant improvements for the GOT + IOT group over GOT in the SFS total score, which could be explained by improvements in withdrawal/social engagement, interpersonal communication, pro-social activities, recreation, and independence-competence. Multiple regression analysis showed that the period from hospitalization to commencing occupational therapy, type of occupational therapy, BACS motor speed, BACS executive function, and IMI interest/enjoyment were significantly associated with SFS total score. Our findings suggest that adding IOT to GOT may improve the long-term outcome on social functioning in schizophrenic patients. However, the long time period between intervention and follow-up and the unavailability of treatment information during the follow-up period has to be mentioned as a limiting factor of this study.

Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive

Importance

To make progress toward precision psychiatry, it is crucial to move beyond case-control studies and instead capture individual variations and interpret them in the context of a normal range of biological systems.

Objective

To evaluate whether baseline deviations from a normative reference range in subcortical volumes are better predictors of antipsychotic treatment response than raw volumes in patients with first-episode psychosis (FEP) who were naive to antipsychotic medication.

Design, Setting, and Participants

In this prospective longitudinal study, patients with first-episode psychosis who were referred from different clinical settings (emergency department, inpatient units, and outpatient clinics) at the University of Alabama at Birmingham were included. A total of 286 patients were screened, 114 consented, 104 enrolled in the treatment trial, and 85 completed the trial. Patients were observed for 16 weeks. Controls were matched by age and sex. Data were collected between June 2016 and July 2021, and data were analyzed from August 2021 to June 2022.

Interventions

Risperidone on a flexible dosing scheme for 16 weeks. There was an option to switch to aripiprazole for excessive adverse effects.

Main Outcomes and Measures

The main outcome of this study was to evaluate, in patients with FEP who were naive to antipsychotic medication, the association of baseline raw volumes and volume deviations in subcortical brain regions with response to antipsychotic medication. Raw brain volumes or volume deviation changes after treatment were not examined.

Results

Of 190 included participants, 111 (58.4%) were male, and the mean (SD) age was 23.7 (5.5) years. Volumes and deviations were quantified in 98 patients with FEP, and data from 92 controls were used as comparison for case-control contrasts and reference curve calibration. In case-control contrasts, patients with FEP had lower raw thalamus (P = .002; F = 9.63; df = 1), hippocampus (P = .009; F = 17.23; df = 1), amygdala (P = .01; F = 6.55; df = 1), ventral diencephalon (P = .03; F = 4.84; df = 1), and brainstem volumes (P = .004; F = 8.39; df = 1). Of 98 patients, 36 patients with FEP (36%) displayed extreme deviations. Associations with treatment response significantly differed between raw volume and deviation measures in the caudate (z = -2.17; P = .03) and putamen (z = -2.15; P = .03).

Conclusions and Relevance

These data suggest that normative modeling allows capture of interindividual heterogeneity of regional brain volumes in patients with FEP and characterize structural pathology in a clinically relevant fashion. This holds promise for progress in precision medicine in psychiatry, where group-level studies have failed to derive reliable maps of structural pathology.

Error-related activity of the sensorimotor network contributes to the prediction of response to cognitive-behavioral therapy in obsessive-compulsive disorder

BACKGROUND

Although cognitive behavioral therapy is a highly effective treatment for obsessive-compulsive disorder (OCD), yielding large symptom reductions on the group level, individual treatment response varies considerably. Identification of treatment response predictors may provide important information for maximizing individual treatment response and thus achieving efficient treatment resource allocation. Here, we investigated the predictive value of previously identified biomarkers of OCD, namely the error-related activity of the supplementary motor area (SMA) and the sensorimotor network (SMN, postcentral gyrus/precuneus).

METHODS

Seventy-two participants with a primary diagnosis of OCD underwent functional magnetic resonance imaging (fMRI) scanning while performing a flanker task prior to receiving routine-care CBT.

RESULTS

Error-related BOLD response of the SMN significantly contributed to the prediction of treatment response beyond the variance accounted for by clinical and sociodemographic variables. Stronger error-related SMN activity at baseline was associated with a higher likelihood of treatment response.

CONCLUSIONS

The present results illustrate that the inclusion of error-related SMN activity can significantly increase treatment response prediction quality in OCD. Stronger error-related activity of the SMN may reflect the ability to activate symptom-relevant processing networks and may thus facilitate response to exposure-based CBT interventions.

Associations between depression, domain-specific physical activity, and BMI among US adults: NHANES 2011-2014 cross-sectional data

Background

Physical activity is associated with depression. However, benefits of physical activity on depression may differ for specific domains of physical activity (i.e., leisure-time, work, and travel). Moreover, the relationship between physical activity and depression could also differ for people in different Body Mass Index (BMI) categories. This study investigated the relationship between domain-specific physical activity and BMI with depression, and the moderation effects of BMI on the relationship between domain physical activity and depression.

Methods

Complex survey data from the NHANES 2011-2014 was used (N=10,047). Depression was measured using the Patient Health Questionnaire (PHQ-9). Participants reported physical activity minutes in each domain using the Global Physical Activity Questionnaire. Demographic characteristics were self-reported. Weight and height were objectively measured and used for calculating BMI. Survey procedures were used to account for complex survey design. As two survey cycles were used, sampling weights were re-calculated and used for analyses. Taylor series linearisation was chosen as a variance estimation method.

Results

Participants who engaged in ≥150 minutes/week of total moderate-vigorous physical activity (MVPA) (adjusted B = 0.83, 95% CI [0.50, 1.16]) and leisure-time MVPA (adjusted B = 0.84, 95% CI [0.57, 1.11]) experienced lower levels of depression compared to those engaging in <150 MVPA minutes/week. Work and travel-related physical activity were not associated with depression. Overweight (adjusted B = -0.40, 95% CI [-0.76, -0.04]) and underweight/normal weight participants (adjusted B = -0.60, 95%CI [-0.96, -0.25]) experienced less depressive symptoms compared to obese participants. BMI did not moderate the relationship between domain-specific physical activity and depression.

Conclusions

Interventions that focus on leisure-time physical activity appear to be best suited to improve depression, however, this needs to be confirmed in purposefully designed intervention studies. Future studies may also examine ways to improve the effectiveness of work and travel physical activity for reducing depression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14037-4.

Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.

Neural correlates of emotional reactivity predict response to cognitive-behavioral therapy in obsessive-compulsive disorder

BACKGROUND

Examining predictive biomarkers to identify individuals who will likely benefit from a specific treatment is important for the development of targeted interventions. The late positive potential (LPP) is a neural marker of attention and elaborated stimulus processing, and increased LPP responses to negative stimuli are characteristic of pathological anxiety. The present study investigated whether LPP reactivity would prospectively predict response to cognitive-behavioral therapy (CBT), the first-line treatment for obsessive-compulsive disorder (OCD).

METHODS

To this end, the LPP in response to negative as compared to neutral pictures was examined in 45 patients with OCD, who underwent CBT in a naturalistic outpatient setting. LPP amplitudes were used as predictors of symptom reduction after CBT.

RESULTS

We found that higher LPP amplitudes to negative relative to neutral stimuli were predictive of lower self-reported OCD symptoms after completion of CBT, controlling for pre-treatment symptoms. Further, LPP reactivity was negatively correlated with self-reported habitual use of suppression in everyday life.

LIMITATIONS

Some participants had already begun treatment at the time of study participation. Overall, results need further replication in larger samples and standardized therapy settings.

CONCLUSIONS

The current findings suggest that patients with increased emotional reactivity benefit more from CBT, possibly through less avoidance of anxiety-provoking stimuli during exposure with response prevention, a crucial component in CBT for OCD. Although its clinical utility still needs to be evaluated further, the LPP constitutes a promising candidate as a prognostic marker for CBT response in OCD.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis

BACKGROUND

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

METHODS

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

RESULTS

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

CONCLUSION

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Clinical Approaches to Late-Onset Psychosis

Psychosis can include schizophrenia, mood disorders with psychotic features, delusional disorder, active delirium, and neurodegenerative disorders accompanied by various psychotic symptoms. Late-onset psychosis requires careful intervention due to the greater associated risks of secondary psychosis; higher morbidity and mortality rates than early-onset psychosis; and complicated treatment considerations due to the higher incidence of adverse effects, even with the black box warning against antipsychotics. Pharmacological treatment, including antipsychotics, should be carefully initiated with the lowest dosage for short-term efficacy and monitoring of adverse side effects. Further research involving larger samples, more trials with different countries working in consortia, and unified operational definitions for diagnosis will help elaborate the clinical characteristics of late-onset psychosis and lead to the development of treatment approaches.

The GG genotype of the serotonin 4 receptor genetic polymorphism, rs1345697, is associated with lower remission rates after antidepressant treatment: Findings from the METADAP cohort

BACKGROUND

Pharmacological studies have yielded valuable insights into the role of the serotonin 4 receptor (HTR4) in major depressive episodes (MDE) and response to antidepressant drugs (AD). A genetic association has been shown between HTR4 and susceptibility to mood disorders. Our study aims at assessing the association between the HTR4 genetic polymorphism, rs1345697, and improvement in depressive symptoms and remission after antidepressant treatment in MDE patients.

METHODS

492 depressed patients from the METADAP cohort were treated prospectively for 6 months with ADs. The clinical outcomes according to HTR4 rs1345697 were compared after 1 (M1), 3 (M3), and 6 (M6) months of treatment. Mixed-effects logistic regression and adjusted linear models assessed the association between rs1345697 and 17-item Hamilton Depression Rating Scale (HDRS) score improvement and response/remission.

RESULTS

Over the 6 months of treatment, mixed-effects regressions showed lower improvements in HDRS scores (Coefficient=1.52; Confident Interval (CI) 95% [0.37-2.67]; p = 0.009) and lower remission rates (Odds Ratio=2.0; CI95% [1.0-4.1]; p = 0.05) in GG homozygous patients as compared to allele A carriers.

LIMITATIONS

The major limitations of our study are the uncertainty of the rs1345697 effect on HTR4 function, the substantial drop-out rate, and the fact that analysis is not based on randomization between polymorphism groups.

CONCLUSIONS

In our study, patients who were homozygous carriers of the variant G of the HTR4 rs1345697 had lower depressive symptoms improvement and 2-fold lower remission rates after antidepressant treatment as compared to allele A carriers. Randomization study should be done to confirm these results.

Improving the Assessment Process of Family Functioning in Adult Bipolar Disorders: A PRISMA Systematic Review

In order to determine family functioning in the treatment of adults with bipolar disorders, guidelines are needed regarding the way family functioning may be assessed. The present systematic review aims to investigate how family functioning is assessed in this context. Following PRISMA guidelines, a total of 29 studies were reviewed. Results showed that although there was no consensual family functioning assessment across studies, 27 studies (93%) relied on self-report questionnaires, 12 studies (41%) relied on one family member as an informant (adult with bipolar disorder or other) and the adult considered was mostly a woman in the acute phase of bipolar I disorder. Significant heterogeneity was observed in the assessment of family functioning. Methodological considerations regarding the assessment of family functioning are discussed.

Evaluating the evidence for sex differences: a scoping review of human neuroimaging in psychopharmacology research

Although sex differences in psychiatric disorders abound, few neuropsychopharmacology (NPP) studies consider sex as a biological variable (SABV). We conducted a scoping review of this literature in humans by systematically searching PubMed to identify peer-reviewed journal articles published before March 2020 that (1) studied FDA-approved medications used to treat psychiatric disorders (or related symptoms) and (2) adequately evaluated sex differences using in vivo neuroimaging methodologies. Of the 251 NPP studies that included both sexes and considered SABV in analyses, 80% used methodologies that eliminated the effect of sex (e.g., by including sex as a covariate to control for its effect). Only 20% (50 studies) adequately evaluated sex differences either by testing for an interaction involving sex or by stratifying analyses by sex. Of these 50 studies, 72% found statistically significant sex differences in at least one outcome. Sex differences in neural and behavioral outcomes were studied more often in drugs indicated for conditions with known sex differences. Likewise, the majority of studies conducted in those drug classes noted sex differences: antidepressants (13 of 16), antipsychotics (10 of 12), sedative-hypnotics (6 of 10), and stimulants (6 of 10). In contrast, only two studies of mood stabilizers evaluated SABV, with one noting a sex difference. By mapping this literature, we bring into sharp relief how few studies adequately evaluate sex differences in NPP studies. Currently, all NIH-funded studies are required to consider SABV. We urge scientific journals, peer reviewers, and regulatory agencies to require researchers to consider SABV in their research. Continuing to ignore SABV in NPP research has ramifications both in terms of rigor and reproducibility of research, potentially leading to costly consequences and unrealized benefits.

Impaired response to sleep deprivation in heterozygous Disc1 mutant mice

OBJECTIVES

Sleep/circadian rhythm disturbances are environmental stress factors that might interact with genetic risk factors and contribute to the pathogenesis of psychiatric disorders.

METHODS

In this study, the multiple-platform method was used to induce sleep deprivation (SD). We evaluated the impact of 72-hour SD in behavioural, anatomical, and biochemical aspects in heterozygous Disc1 mutant (Disc1 Het) mice, an animal model of schizophrenia.

RESULTS

The sleep pattern and circadian activity were not altered in Disc1 Het mice. Yet, we observed differential responses to SD stress between genotypes. Increased microglial density and reduced neuronal proliferative activity were found in the dentate gyrus, a neurogenic niche, in Het-SD mice. Notably, SD-induced Bdnf mRNA elevations were evident in both WT and Het mice, while only in WT-SD mice did we observe increased BDNF protein expression. Our results suggested an SD-induced physical response featured by the elevation of BDNF protein expression to counteract the harmful influences of SD and sufficient DISC1 is required in this process.

CONCLUSIONS

The present study proposes that sleep disturbance could be pathogenic especially in genetically predisposed subjects who fail to cope with the stress. Potential therapeutic strategies for psychiatric disorders targeting the mRNA translation machinery could be considered.

CBT for Childhood Anxiety: Reviewing the State of Personalised Intervention Research

This article presents a mini-review of the state of personalised intervention research in the field of child and adolescent anxiety. We evaluated narrative, systematic and meta-analytic reviews of key research methodologies and how they relate to current approaches for personalising CBT, specifically. Preliminary evidence of predictors (severity of primary disorder, social anxiety disorder (SoAD), comorbid depression, parental psychopathology, parental involvement and duration of treatment), moderators (type of primary disorder) and mediators (self-talk, coping, problem-solving and comorbid symptoms) of CBT outcomes provides content for several personalised approaches to treatment. Finally, we present a novel conceptual model depicting the state of personalised intervention research in childhood anxiety and propose a research agenda for continued progress.

Multivariate alterations in insula - Medial prefrontal cortex linked to genetics in 12q24 in schizophrenia

The direct effect of genetic variations on clinical phenotypes within schizophrenia (SZ) remains elusive. We examined the previously identified association of reduced gray matter concentration in the insula - medial prefrontal cortex and a quantitative trait locus located in 12q24 in a SZ dataset. The main analysis was performed on 1461 SNPs and 830 participants. The highest contributing SNPs were localized in five genes including TMEM119, which encodes a microglial marker, that is associated with neuroinflammation and Alzheimer's disease. The gene set in 12q4 may partially explain brain alterations in SZ, but they may also relate to other psychiatric and developmental disorders.

Combining Asian and American pedagogy to improve clinical psychopharmacology practice

As the world becomes increasingly interconnected, psychiatrists across geographical regions and from various international organizations need to collaborate to promote global health and wellness. A necessary step is for nations of the world to develop combined teaching initiatives and curricula to ensure best practices are shared globally. In no field of medicine is this more pressing than in psychiatry - especially psychopharmacology given the recent advances in the field. This paper highlights the need to work collaboratively in developing teaching curricula in psychopharmacology in order to incorporate pedagogy and content from international partners-here from Asia and America.

Why do sleep disorders belong to mental disorder classifications? A network analysis of the "Sleep-Wake Disorders" section of the DSM-5

This article proposes to investigate how Sleep disorders have been conceptualized within the DSM-5 through symptom network analysis of the diagnostic criteria of the "Sleep-Wake Disorders" section in the DSM-5. We hypothesize that the analysis of the most central symptoms will allow us to better analyze the position of Sleep disorders in Mental disorders. We thus i) extracted the symptoms of the DSM-5 diagnostic criteria of Sleep-Wake disorders, ii) built the Sleep-Wake disorder DSM-5 network representation, and iii) quantified its structure at local and global levels using classical symptom network analysis. Thirty-four different symptoms were identified among the 53 DSM-5 diagnostic criteria of the 9 main disorders of the "Sleep-Wake Disorders" section. The symptom network structure of this section showed that the most central sleep symptoms are "Daytime Sleepiness", the Insomnia symptoms group ("Insomnia initiating", "Insomnia maintaining" and "Non-restorative sleep"), and Behavioral sleep symptoms (such as "Altered oniric activity", "Ambulation", "Abnormal responsiveness"). This network analysis shown that the belonging of Sleep-Wake disorders in the DSM-5 have been associated with central sleep symptoms considered as "Mental", given their phenomenality (qualitative nature of the experience) and subjectivity (in personal mental lives). Such a symptom network analysis can serve as an organizing framework to better understand the complexity of Sleep-Wake disorders by promoting research to connect the architecture of the symptom network to relevant biological, psychological and sociocultural factors.

Genetic and Environmental Contributions to Mental Illness With Implications for Evaluation and Treatment

From the outside looking in, it may appear that nurse practitioner practice in mental health care is relatively easy compared to other nurse practitioner population care. The current article presents a brief overview of recent theories on the etiology of mental disorders, specifically major depressive disorder, bipolar disorder, and schizophrenia, with implications for practice. Pharmacological treatments targeting important stress response and immune and inflammatory targets lag behind the science. A practical framework for psychiatric evaluation, formulation, and treatment planning that combines four distinctive ways of viewing patients' concerns is presented as a useful method for providing person-centered mental health care. [Journal of Psychosocial Nursing and Mental Health Services, 59(1), 9-13.].

Economic Impact of the Application of a Precision Medicine Model (5SPM) on Psychotic Patients

BACKGROUND

Schizophrenia is a severe mental disorder that often manifests within the first three decades of life. Its prognosis is uncertain and may result in a prolonged treatment that could extend throughout the entire lifespan of the patient. Antipsychotic drugs are characterized by a high interindividual variability when considering therapeutic effect and emergence of adverse effects. Such interindividual variability is thought to be associated primarily with pharmacokinetic matters.

OBJECTIVE

The objective of this study was to evaluate the economic impact of the application of the 5-Step Precision Medicine model (5SPM), an approach based on the pharmacogenetic analysis of the primary genes involved in the metabolism of the therapy for each patient, restructuring treatment as necessary.

PATIENTS AND METHODS

One hundred eighty-eight psychiatry patients were analysed for single nucleotide polymorphisms on genes CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5 and ABCB1. Information on patients' diagnosis, pharmacotherapy, and hospitalizations was collected.

RESULTS

We achieved a cost-benefit ratio of 3.31-3.59 with a reduction of direct cost (hospitalizations plus pharmacotherapy) with a reduction of total cost in 67% of the patients who underwent the clinical intervention.

CONCLUSION

A rational Precision Medicine-based approach to psychiatric patients could result in a reduction on number of drugs required to control exacerbations, and the underlying pathologies, reducing the risk of adverse effects and improving adherence to treatment, leading to a potential decrease in direct costs. This methodology has been shown to be cost-dominant and, being based on a pharmacogenetic analysis, it has a lifelong nature, as the data obtained can be applied to other medical disciplines.

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.

Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.

Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).

Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

A Sequential Multiple Assignment Randomized Trial (SMART) study of medication and CBT sequencing in the treatment of pediatric anxiety disorders

BACKGROUND

Treatment of a child who has an anxiety disorder usually begins with the question of which treatment to start first, medication or psychotherapy. Both have strong empirical support, but few studies have compared their effectiveness head-to-head, and none has investigated what to do if the treatment tried first isn't working well-whether to optimize the treatment already begun or to add the other treatment.

METHODS

This is a single-blind Sequential Multiple Assignment Randomized Trial (SMART) of 24 weeks duration with two levels of randomization, one in each of two 12-week stages. In Stage 1, children will be randomized to fluoxetine or Coping Cat Cognitive Behavioral Therapy (CBT). In Stage 2, remitters will continue maintenance-level therapy with the single-modality treatment received in Stage 1. Non-remitters during the first 12 weeks of treatment will be randomized to either [1] optimization of their Stage 1 treatment, or [2] optimization of Stage 1 treatment and addition of the other intervention. After the 24-week trial, we will follow participants during open, naturalistic treatment to assess the durability of study treatment effects. Patients, 8-17 years of age who are diagnosed with an anxiety disorder, will be recruited and treated within 9 large clinical sites throughout greater Los Angeles. They will be predominantly underserved, ethnic minorities. The primary outcome measure will be the self-report score on the 41-item youth SCARED (Screen for Child Anxiety Related Disorders). An intent-to-treat analysis will compare youth randomized to fluoxetine first versus those randomized to CBT first ("Main Effect 1"). Then, among Stage 1 non-remitters, we will compare non-remitters randomized to optimization of their Stage 1 monotherapy versus non-remitters randomized to combination treatment ("Main Effect 2"). The interaction of these main effects will assess whether one of the 4 treatment sequences (CBT➔CBT; CBT➔med; med➔med; med➔CBT) in non-remitters is significantly better or worse than predicted from main effects alone.

DISCUSSION

Findings from this SMART study will identify treatment sequences that optimize outcomes in ethnically diverse pediatric patients from underserved low- and middle-income households who have anxiety disorders.

TRIAL REGISTRATION

This protocol, version 1.0, was registered in ClinicalTrials.gov on February 17, 2021 with Identifier: NCT04760275 .

Prediction of Treatment Outcome of Adolescents With Borderline Personality Disorder: A 2-Year Follow-Up Study

This study examined prediction of various clinical outcomes in adolescents with borderline personality disorder (BPD) features. Of the 112 adolescents who participated at baseline, 97 were seen at 2-year follow-up, of which 49 (50.5%) had clinically improved, defined as a decrease in BPD pathology of minimum 12 points on the Borderline Personality Features Scale for Children (BPFS-C). Eighty-one adolescents fulfilled the diagnostic criteria for BPD and scored above clinical cutoff on the BPFS-C at baseline, of which 26 (32%) had remitted at follow-up by self-report on the BPFS-C. Results showed that adolescents with comorbid oppositional defiant disorder at baseline were less likely to have clinically improved or remitted at follow-up. Participants with increased self-reported depression and less exposure to physical abuse at baseline had increased odds of remission. Our findings suggest that more internalizing and less externalizing symptoms increase the odds of positive treatment outcome in adolescents with BPD.

The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study

Data generated within social media platforms may present a new way to identify individuals who are experiencing mental illness. This study aimed to investigate the associations between linguistic features in individuals' blog data and their symptoms of depression, generalised anxiety, and suicidal ideation. Individuals who blogged were invited to participate in a longitudinal study in which they completed fortnightly symptom scales for depression and anxiety (PHQ-9, GAD-7) for a period of 36 weeks. Blog data published in the same period was also collected, and linguistic features were analysed using the LIWC tool. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and symptoms between subjects. Multivariate regression models were used to predict longitudinal changes in symptoms within subjects. A total of 153 participants consented to the study. The final sample consisted of the 38 participants who completed the required number of symptom scales and generated blog data during the study period. Between-subject analysis revealed that the linguistic features "tentativeness" and "non-fluencies" were significantly correlated with symptoms of depression and anxiety, but not suicidal thoughts. Within-subject analysis showed no robust correlations between linguistic features and changes in symptoms. The findings may provide evidence of a relationship between some linguistic features in social media data and mental health; however, the study was limited by missing data and other important considerations. The findings also suggest that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time.

Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials

Importance

Antidepressants are commonly used to treat major depressive disorder (MDD). Antidepressant outcomes can vary based on individual differences; however, it is unclear whether specific factors determine this variability or whether it is at random.

Objective

To investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether variability is associated with MDD severity, antidepressant class, or study publication year.

Data Sources

Data used were updated from a network meta-analysis of treatment with licensed antidepressants in adults with MDD. The Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycInfo were searched from inception to March 21, 2019. Additional sources were international trial registries and sponsors, drug companies and regulatory agencies' websites, and reference lists of published articles. Data were analyzed between June 8, 2020, and June 13, 2020.

Study Selection

Analysis was restricted to double-blind, randomized placebo-controlled trials with depression scores available at the study's end point.

Data Extraction and Synthesis

Baseline means, number of participants, end point means and SDs of total depression scores, antidepressant type, and publication year were extracted.

Main Outcomes and Measures

Log SDs (bln σ̂) were derived for treatment groups (ie, antidepressant and placebo). A random-slope mixed-effects model was conducted to estimate the difference in bln σ̂ between treatment groups while controlling for end point mean. Secondary models determined whether differences in variability between groups were associated with baseline MDD severity; antidepressant class (selective serotonin reuptake inhibitors and other related drugs; serotonin and norepinephrine reuptake inhibitors; norepinephrine-dopamine reuptake inhibitors; noradrenergic agents; or other antidepressants); and publication year.

Results

In the 91 eligible trials (18 965 participants), variability in response did not differ significantly between antidepressants and placebo (bln σ̂, 1.02; 95% CI, 0.99-1.05; P = .19). This finding is consistent with a range of treatment effect SDs (up to 16.10), depending on the association between the antidepressant and placebo effects. Variability was not associated with baseline MDD severity or publication year. Responses to noradrenergic agents were 11% more variable than responses to selective serotonin reuptake inhibitors (bln σ̂, 1.11; 95% CI, 1.01-1.21; P = .02).

Conclusions and Relevance

Although this study cannot rule out the possibility of treatment effect heterogeneity, it does not provide empirical support for personalizing antidepressant treatment based solely on total depression scores. Future studies should explore whether individual symptom scores or biomarkers are associated with variability in response to antidepressants.

Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis

No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.

Pattern classification as decision support tool in antipsychotic treatment algorithms

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance

Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.

Case Report: Pharmacogenetics Applied to Precision Psychiatry Could Explain the Outcome of a Patient With a New CYP2D6 Genotype

Precision medicine applied to psychiatry provides new insight into the promising field of precision psychiatry. Psychotic disorders are heterogeneous, complex, chronic, and severe mental disorders. Not only does the prognosis and the course of the disease vary among patients suffering from psychotic disorders, but the treatment response varies as well. Although antipsychotic drugs are the cornerstone of the treatment of schizophrenia, many patients only partially respond to these drugs. Furthermore, patients often experience adverse events which can lead to poor treatment adherence. Interindividual variability in drug response could be related to age, gender, ethnicity, lifestyle factors, pharmacological interactions, obesity, and genetics, all of which influence the process of drug metabolism. Commonly prescribed antipsychotics are metabolized by cytochrome P450 (CYP450) enzymes, and CYP450 genes are highly polymorphic. Pharmacogenetic testing is increasingly being used to predict a patient's drug response and could help to find the most appropriate therapy for an individual patient. In this report, we describe a psychotic patient who did not receive adequate clinical follow-up and subsequently presented adverse events, which could be explained by his pharmacogenetic profile and the drug interactions resulting from the polypharmacy prescribed.

Temporal patterns of anxiety disorders and cortisol activity in older adults

INTRODUCTION

Studies focusing on anxiety temporal patterns and cortisol activity in older adults are scarce. The objectives of this study were to examine in older adults the relationship between anxiety temporal patterns and cortisol activity and ascertain the presence of sex differences.

METHODS

Data were retrieved from the Étude sur la santé des ainés - Services study in Quebec and included N = 762 community living adults aged ≥ 65 years having participated in interviews at baseline (T₁) and at 4 years follow-up (T₂). A standardized questionnaire, based on DSM-5 criteria, was used to ascertain in the past 6 months the presence of anxiety (absence, remission, incidence, persistence). Cortisol activity during the interview and cortisol concentration on a regular day (at T₂) were the dependent variables. Adjusted multivariable linear regression models, stratified by sex, were used.

RESULTS

Results showed higher cortisol activity during the interview in participants with anxiety in remission (Beta: 2.59; 95% CI: 0.62 , 4.57), specifically in males, and lower activity in participants with persistent anxiety (Beta: -3.97; 95% CI: -7.05, -0.88). Cortisol concentration on a regular day was higher in males reporting incident anxiety (Beta: 8.07; 95% CI: 2.39 , 13.76).

LIMITATIONS

The convenience sample with losses to follow-up may have led to a potential selection bias.

CONCLUSION

Anxiety temporal patterns were associated with cortisol activity profiles in older adults with sex being a significant moderator. Future studies are recommended to ascertain the longitudinal changes in cortisol activity and anxiety temporal patterns, which may further inform personalized treatment of anxiety.