An umbrella review of candidate predictors of response, remission, recovery, and relapse across mental disorders

The association of sex and age of treatment-resistant tendency to antidepressants: A cohort study of 325,615 patients with major depressive disorder

BACKGROUND

Treatment resistance to antidepressants can impose a significant burden on patients with major depressive disorder (MDD). This study aimed to evaluate the effects of age, sex, and psychiatric and physical comorbidities on the tendency toward treatment resistance to antidepressants (TRT).

METHODS

We utilized data from the Taiwan National Health Insurance Research Database. Patients diagnosed with MDD were included in the study. Physical comorbidities were assessed using the Charlson Comorbidity Index. TRT was defined as receiving antidepressant treatment at an adequate defined daily dose, followed by a subsequent switch to another antidepressant within one year after the initial diagnosis of depression. Logistic regression was used to estimate the odds ratios (ORs) of various potential factors associated with TRT.

RESULTS

A total of 325,615 patients with MDD were included in the study. After adjusting for key confounders, patients aged 20 to 29 years had the highest OR (1.60; 95 % confidence interval [CI]: 1.50-1.70) for TRT compared to the oldest age group (≥80 years). The ORs gradually decreased with increasing age. Males had a significantly lower OR (0.89; 95 % CI: 0.88-0.91) for TRT than females. TRT was also associated with the presence of physical and psychiatric comorbidities, except for autism spectrum disorder (ASD).

LIMITATIONS

As a naturalistic observational study, our findings are subject to potential confounding factors that cannot be fully controlled for, as would be possible in a formal randomized controlled trial.

CONCLUSIONS

Our study highlights the impact of age and sex on TRT. Clinicians should consider these risk factors when managing patients with MDD.

Systematic Review and Meta-Analysis: Predictors of Relapsing, Recurrent, and Chronic Depression in Young People

OBJECTIVE

Youth depression disrupts the social and vocational transition into adulthood. Most depression burden is caused by recurring or chronic episodes. Identifying young people at risk for relapsing, recurring, or chronic depression is critical. We systematically reviewed and meta-analyzed the literature on prognostic factors for relapsing, recurrent, and chronic depression in young people.

METHOD

We searched the literature up (MEDLINE, PsycINFO, CINAHL, Embase, CENTRAL, WHO ICTRP, ClinicalTrials.gov, bioRxiv, MedRxiv) to 06 March 2024 and included cohort studies and randomized trials that assessed any prognostic factor for relapse, recurrence, or chronicity of depression in young people (aged 10-25 years at baseline) with a minimum of a three-month follow-up. We assessed individual study risk of bias of using the QUIPS tool and the certainty of evidence via the GRADE approach. We conducted random effects meta-analyses with Hartung-Knapp-Sidik-Jonkman adjustment when three or more estimates on the same prognostic factor were available. Qualitative synthesis was conducted to identify promising prognostic factors that could not be meta-analyzed.

RESULTS

Seventy-six reports of 46 studies (unique cohorts or trials) were included that tested 388 unique prognostic factors in 7,488 young people experiencing depression. Majority of the reports were at high risk of bias (87%). We conducted 22 meta-analyses on unadjusted, and seven on adjusted, prognostic factors of a poor course trajectory (i.e., combined relapse, recurrence, and chronicity). Female sex (adjusted; OR[95%CI]: 1.49 [1.15, 1.93], p=0.003), higher severity of depressive symptoms (unadjusted; SMD[95%CI]: 0.53 [0.33, 0.73], p<0.001), lower global functioning (unadjusted; SMD[95%CI]: -0.35 [-0.60, -0.10], p=0.005), more suicidal thoughts and behaviors (unadjusted; SMD[95%CI]: 0.52 [0.03, 1.01], p=0.045), and longer sleep-onset latency (unadjusted; MD[95%CI]: 6.96 [1.48, 12.44] minutes, p=0.013) at baseline predicted a poor course trajectory of depression. The certainty of the evidence was overall very low to moderate. Promising prognostic factors that could not be meta-analyzed included relational/interpersonal factors (friend relationships and family relationships/structure).

CONCLUSION

Our findings demonstrate the prognostic value of demographic and clinical factors for poor course trajectories of depression in young people. More research is needed to confirm the potential value of relational/interpersonal factors in predicting poor depression course. Limitations of the literature include the high risk of bias of included studies, which indicates that future studies should include large sample sizes and wider diversity of prognostic markers (e.g., genetic and neurobiological) in multivariable models. The critical next step is to combine the identified prognostic factors and evaluate their clinical value in identifying those at risk for a poor course trajectory of depression during youth, a life stage where most of the disability and burden attributable to depression can be averted.

Longitudinal bidirectional association between psychosocial function and depression in Chinese patients with clinically remitted depression: a cross-lagged panel model analysis

BACKGROUND

Major depressive disorder (MDD) imposes serious effect on patient's psychosocial function, which hinders the full recovery from the disease and increases the risk of recurrence, although the participants had achieved clinical remission. To understand the relationship between psychosocial function and depressive symptoms could facilitate recurrence prevention. Therefore, the purpose of this study was to analyze the relation among psychosocial function and BDI score in Chinese patients with clinically cured depression within 1 year follow-up.

METHODS

One hundred nineteen valid participants were assessed at baseline(t1), months 6(t2) and months 12(t3). Beck Depression Inventory-II (BDI-II) was used to assess depressive symptoms and indicate the possibility of depression level. Generic Quality of Life Inventory (GQOLI) was used to assessed the participants' psychosocial function(F), including body function (BF), psychological function (PF) and social function (SF). The application of a cross-lagged panel model (CLPM) approach revealed an association between BDI and psychosocial function.

RESULT

The CLPM results showed total average score of psychosocial function have reciprocal influence on BDI score. The model analyzed by structural equation modeling satisfied all indices of goodness-of-fit (chi-square = 10.306, TLI = 0.959, CFI = 0.988 RMSEA = 0.115). And body function, psychological function, social function and BDI score also affect each other. Depressive symptoms and psychosocial function could predict scores of each other 6 months later. By comparing standardized cross-lagged path, only social function has a more pronounced impact on depressive symptoms, since the absolute effect of SFt1 → BDIt2 is larger than that of BDIt1 → SFt2 (a1 vs. b1 = -.267, SE = .108, P < 0.05, 95%CI[-.485,-.063]) and the absolute effect of SFt2 → BDIt3 is larger than that of BDIt2 → SFt3 (a2 vs. b2 = -.317, SE = .096, P < 0.01,95%CI[-.508,-.129]).

CONCLUSION

The current study showed a significant bidirectional association between depressive level and psychosocial function, and the social function exerted more effect on the depression.

Quality of life in persons at risk for bipolar disorder: a two year prospective-longitudinal observational cohort study (BipoLife)

BACKGROUND

Improving quality of life (QoL) is important for the treatment of people with bipolar disorder (BD). Early-BipoLife is a German multicentre naturalistic, prospective-longitudinal observational cohort study investigating early recognition and intervention in people at increased risk of developing a BD. This analysis aims to investigate influencing factors and changes in QoL as a basis for the development of early intervention strategies in patients with at risk syndrome for BD.

METHOD

A cohort of 1086 participants (15-35 years) with at least one risk factor (EPIbipolar criteria) for BD was assessed over the course of 2 years. Changes in QoL (WHOQOL-BREF) were evaluated in a mixed model for repeated measures.

RESULTS

Compared to an age-matched comparison group, people at risk for BD showed significant lower QoL in all domains at baseline. The overall QoL of the psychological well-being domain of the WHOQOL-BREF increased over the 2 year study course (p < 0.001). The bipolar risk group (EPIbipolar) change from baseline divided into (a) decreasing, (b) increasing and (c) constant risk group in the course of 2 years. Baseline risk group assignment was not a significant predictor of change in QoL over 2 years for any of the QoL domains, but participants with an increase in risk over the 2-year course had a significantly smaller gain in QoL than the group with constant risk (p = 0.014) or decreasing risk (p < 0.001). Higher levels of QoL were associated with a higher self-rated ability to use coping strategies. Moreover, a higher level of functioning (GAF) at baseline was positively correlated with improvement of different QoL domains after 2 years.

CONCLUSION

Patients with a risk syndrome for BD reported significantly reduced QoL compared to their age-matched comparison group. Risk status monitoring might be beneficial to identify individuals who could profit from an intervention to increase their QoL. Further studies promoting the development of coping strategies for successful self-management could be helpful to improve overall mental health and positively influence QoL.

Multidimensional outcome of first-episode psychosis: a network analysis

BACKGROUND

Few studies have examined the long-term outcomes of first-episode psychosis (FEP) among patients beyond symptomatic and functional remission. This study aimed to broaden the scope of outcome indicators by examining the relationships between 12 outcomes of FEP patients at 20.9 years after their initial diagnosis.

METHODS

At follow-up, 220 out of 550 original patients underwent a new assessment. Twelve outcomes were assessed via semistructured interviews and complementary scales: symptom severity, functional impairment, personal recovery, social disadvantage, physical health, number of suicide attempts, number of episodes, current drug use, dose-years of antipsychotics (DYAps), cognitive impairment, motor abnormalities, and DSM-5 final diagnosis. The relationships between these outcome measures were investigated using Spearman's correlation analysis and exploratory factor analysis, while the specific connections between outcomes were ascertained using network analysis.

RESULTS

The outcomes were significantly correlated; specifically, symptom severity, functioning, and personal recovery showed the strongest correlations. Exploratory factor analysis of the 12 outcomes revealed two factors, with 11 of the 12 outcomes loading on the first factor. Network analysis revealed that symptom severity, functioning, social disadvantage, diagnosis, cognitive impairment, DYAps, and number of episodes were the most interconnected outcomes.

CONCLUSION

Network analysis provided new insights into the heterogeneity between outcomes among patients with FEP. By considering outcomes beyond symptom severity, the rich net of interconnections elucidated herein can facilitate the development of interventions that target potentially modifiable outcomes and generalize their impact on the most interconnected outcomes.

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review

BACKGROUND

Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.

METHODS

This systematic review adheres to the PRISMA statement (Prospero CRD42023401194). Databases searched included MEDLINE, EMBASE, PsycInfo, IEEE Xplore, Engineering Village, Web of Science, and Scopus from database inception to 28/08/24. Included studies used ML methods with feature importance to predict CMDs in adults. Risk of bias (ROB) was assessed using PROBAST. Model performance metrics were compared. The ten most important variables reported by each study were assigned to broader categories to evaluate their frequency across studies.

RESULTS

117 studies were included (111 model development-only, 16 development and validation). Deep learning methods showed best accuracy for predicting CMD cases. Studies commonly incorporated features from multiple categories (n = 56), and frequently identified demographic-environmental predictors in their top ten most important variables (63/69 models). These tended to be in combination with psycho-social and biological variables (n = 15). Lifestyle data were infrequently examined as sole predictors of CMDs across included studies (4.27 %). Studies commonly had high heterogeneity and ROB ratings.

CONCLUSION

This review is the first to evaluate the utility of diagnostic ML for CMDs, assess their ROB, and evaluate predictor types. CMDs were able to be predicted, however studies had high ROB and lifestyle data were underutilised, precluding full identification of a robust predictor set.

Dual-roles and beyond: values, ethics, and practices in forensic mental health decision-making

Forensic mental health services (FMHS) involve restricting certain individual rights to uphold or promote other ethical values - the restriction of liberty in various forms is justified with reference to health and safety of the individual and the community. The tension that arises from this has been construed as a hallmark of the practice and an ever-present quandary for practitioners. Stating this ethical dilemma upfront is a common point of departure for many texts discussing FMHS. But do we run the risk of missing something important if setting the ethical scene rather than exploring it? This paper draws on interviews with three types of interested parties in mental health law proceedings - patients, psychiatrists and public defenders, and seeks to tease out what values are enacted when they describe and discuss experiences of FMHS and court proceedings. In doing so, we find emphasized values such as acceptance, telling it like it is, atonement, normality, and ensuring the future. We find that well-delineated and separate values are not necessarily the basis for decisions. We also find potential for explanation and guidance in bringing ethical discourse closer to everyday practice.

Modelling the impact of environmental and social determinants on mental health using generative agents

Mental health is shaped by socio-environmental determinants, yet traditional research approaches struggle to capture their complex interactions. This review explores the potential of generative agents, powered by large language models, to simulate human-like behaviour in virtual environments for mental health research. We outline potential applications including the modelling of adverse life events, urbanicity, climate change, discuss potential challenges and describe how generative agents could transform mental health research.

Early change in specific depression symptoms and later outcome in internet-delivered psychotherapy for depression: A cohort study and cross-lagged network analysis

BACKGROUND

Symptom reduction occurring early in depression treatment is associated with favourable post-treatment outcome, but it is not known how early reduction in specific depression symptoms affect treatment outcome. We aimed to determine the impact of symptom-specific change from pre-treatment to week four during internet-delivered CBT (ICBT) on overall and symptom-specific depression severity at post-treatment. We hypothesized that change in mood and emotional involvement would be most strongly associated with later overall depression severity.

METHODS

1300 participants with Major Depressive Disorder were followed over 12 weeks of ICBT using the self-report Montgomery-Åsberg Depression Rating Scale gauging nine symptoms. Linear models, informed by causal inference and cross-lagged network analysis methods, were used to estimate associations between early symptom-specific change and post-treatment depression severity, controlling for register-based and self-reported pre-treatment confounders.

RESULTS

Early reduction in all symptoms was associated with lower overall and symptom-specific depression severity post-ICBT. Seven symptoms showed similar associations between early change and overall depression severity post-treatment: mood (standardized beta [β] = 0.44), feelings of unease (β = 0.39), ability to concentrate (β = 0.46), initiative (β = 0.43), emotional involvement (β = 0.42), pessimism (β = 0.44), and zest for life (β = 0.42). Change in sleep (β = 0.27) and appetite (β = 0.27) had weaker associations with overall depression severity at post-treatment and were the only symptoms showing the hypothesized difference compared with mood and emotional involvement.

CONCLUSIONS

The impact of early symptom-specific reduction on post-treatment depression severity in ICBT for MDD may be similar across most symptoms, but less for the sleep and appetite symptoms, although causal interpretations rests on several assumptions.

Neurodevelopmental predictors of treatment response in schizophrenia and bipolar disorder

BACKGROUND

Treatment resistance is a major challenge in psychiatric disorders. Early detection of potential future resistance would improve prognosis by reducing the delay to appropriate treatment adjustment and recovery. Here, we sought to determine whether neurodevelopmental markers can predict therapeutic response.

METHODS

Healthy controls (N = 236), patients with schizophrenia (N = 280) or bipolar disorder (N = 78) with a known therapeutic outcome, were retrospectively included. Age, sex, education, early developmental abnormalities (obstetric complications, height, weight, and head circumference at birth, hyperactivity, dyslexia, epilepsy, enuresis, encopresis), neurological soft signs (NSS), and ages at first subjective impairment, clinical symptoms, treatment, and hospitalization, were recorded. A supervised algorithm leveraged NSS and age at first clinical signs to classify between resistance and response in schizophrenia.

RESULTS

Developmental abnormalities were more frequent in schizophrenia and bipolar disorder than in controls. NSS significantly differed between controls, responsive, and resistant participants with schizophrenia (5.5 ± 3.0, 7.0 ± 4.0, 15.0 ± 6.0 respectively, p = 3 × 10-10) and bipolar disorder (5.5 ± 3.0, 8.3 ± 3.0, 12.5 ± 6.0 respectively, p < 1 × 10-10). In schizophrenia, but not in bipolar disorder, age at first subjective impairment was three years lower, and age at first clinical signs two years lower, in resistant than responsive subjects (p = 2 × 10-4 and p = 9 × 10-3, respectively). Age at first clinical signs and NSS accurately predicted treatment response in schizophrenia (area-under-curve: 77 ± 8%, p = 1 × 10-14).

CONCLUSIONS

Neurodevelopmental features such as NSS and age of clinical onset provide a means to identify patients who may require rapid treatment adaptation.

Anxiety Symptom Trajectories Predict Depression Symptom Trajectories up to Four Years After CBT for Youth Anxiety Disorders

Long-term data on depression symptoms after cognitive behavioral therapy (CBT) for youth anxiety disorders are scant. We examined depression symptoms up to four years post CBT for anxiety addressing youth age and gender, family social class, and parent mental health as predictors. The sample comprised 179 youth (M age at pre-treatment = 11.5 years; SD = 2.1) in a randomized controlled trial. Clinically assessed anxiety diagnoses and youth and parent-reported anxiety and depression symptoms were measured before, after, and one and four years after CBT. Parent self-reported mental health was measured before CBT. We used regression analyses to determine whether full diagnostic recovery at post-CBT predicted depression trajectories across the four-year assessment period. We used growth curve models to determine whether anxiety trajectories predicted depression trajectories across the four-year assessment period. Youth who lost their anxiety diagnoses after CBT had significantly lower parent-reported depression levels over time, but not lower youth self-reported depression levels. The anxiety symptom trajectory predicted the depression symptom trajectory up to four years post-treatment. There was more explained variance for within-informant (youth-youth; parent-parent) than cross-informants. Being older, female, having lower socio-economic status and parents with poorer mental health were associated with more youth-rated depression over time. However, these demographic predictors were not significant when anxiety symptoms trajectories were added to the models. Successful CBT for anxiety in children is associated with less depression symptoms for as long as four years. Anxiety symptom improvement appears to be a stronger predictor that demographic variables and parent mental health.

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.

A state-of-the-art overview of candidate diagnostic biomarkers for Attention-deficit/hyperactivity disorder (ADHD)

INTRODUCTION

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental conditions and is highly heterogeneous in terms of symptom profile, associated cognitive deficits, comorbidities, and outcomes. Heterogeneity may also affect the ability to recognize and diagnose this condition. The diagnosis of ADHD is primarily clinical but there are increasing research efforts aiming at identifying biomarkers that can aid the diagnosis.

AREAS COVERED

We first discuss the definition of biomarkers and the necessary research steps from discovery to implementation. We then provide a broad overview of research studies on candidate diagnostic biomarkers in ADHD encompassing genetic/epigenetic, biochemical, neuroimaging, neurophysiological and neuropsychological techniques. Finally, we critically appraise current limitations in the field and suggest possible ways forward.

EXPERT OPINION

Despite the large number of studies and variety of techniques used, no promising biomarkers have been identified so far. Clinical and biological heterogeneity as well as methodological limitations, including small sample size, lack of standardization, confounding factors, and poor replicability, have hampered progress in the field. Going forward, increased international collaborative efforts are warranted to support larger and more robustly designed studies, develop multimodal datasets to combine biomarkers and improve diagnostic accuracy, and ensure reproducibility and meaningful clinical translation.

PTSD and lower respiratory symptoms: A systematic review of longitudinal associations in early 9/11 World Trade Center responders

OBJECTIVE

The objective of this systematic review is to examine longitudinal associations between post-traumatic stress disorder (PTSD) and lower respiratory symptoms (LRS) specifically among responders present at the World Trade Center (WTC) site on September 11, 2001 (9/11). This group, which we refer to as "9/11 early responders," appears to have particularly high rates of both mental and physical illness relative to other 9/11-exposed populations.

METHODS

We performed a systematic literature review to examine associations between PTSD and LRS among 9/11 early responders in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. After a structured search of Pubmed and the Fire Department of New York (FDNY) WTC bibliography for relevant articles, we identified 4 articles commenting on associations between PTSD and LRS in this population; all 4 passed quality review and were included in our primary analysis. 10 other articles we found in our research discussed rates of PTSD and LRS, but not associations between them, in the population in question; we commented on these in a secondary analysis.

RESULTS

The data demonstrate that there are significant associations between PTSD and LRS among 9/11 early responders. The data also suggest that both of these phenomena are more prevalent among 9/11 early responders relative to other 9/11-exposed populations.

CONCLUSIONS

These findings are relevant for optimizing care for the population in question, as well as for other survivors of past and future disasters with both psychiatric and medical sequelae.

Cognitive Behavioral Therapy and Mindfulness-Based Cognitive Therapy for Depressive Disorders: Enhancing Access and Tailoring Interventions in Diverse Settings

Depressive disorders are an enormous societal burden given their high prevalence and impact on all facets of being human (e.g., relationships, emotions, motivation). There is a variety of evidence-based psychological treatments, with cognitive behavioral therapy (CBT) being the gold standard for major depression. Research has shown that mindfulness-based interventions (MBIs) such as mindfulness-based cognitive therapy (MBCT) are an effective relapse prevention and treatment for depression and that MBIs can be integrated in individual therapy. Furthermore, various delivery modes (e.g., digital-delivered therapy) and settings are offered to best meet different needs and improve accessibility: Evidence suggests that therapist-guided digital CBT, blended therapy, and, to some degree, digitalized MBIs may be an efficacious supplement to traditional face-to-face therapy. This chapter provides an overview of the principles and evidence base for CBT and MBCT as well as different delivery modes for depressive disorders in adults. Finally, chances and challenges of integration are discussed as implications for practice, as well as recommendations and ideas for future research.