Staging of unipolar depression: systematic review and discussion of clinical implications

The association between adverse experiences and longitudinal allostatic load changes with the depression symptoms trajectories in middle-aged and older adults in China: A longitudinal study

BACKGROUND

This study investigated the associations between adverse childhood experiences (ACEs), adverse adulthood experiences (AAEs), allostatic load (AL) changes, and later depression symptoms trajectories in middle-aged and older Chinese longitudinally.

METHODS

1921 individuals aged ≥45 years at baseline were included from the China Health and Retirement Longitudinal Study (CHARLS). Measures included ACEs, AAEs, depression symptoms scores, health-related factors, and demographic characteristics. AL changes were assessed by the difference in AL scores between wave 3 (2015) and wave 1(2011).

RESULTS

Compared to consistently low trajectory, 2 or more ACEs (OR 1.78, 95 % CI 1.28-2.46), 2 or more AAEs (OR 1.82, 95 % CI 1.26-2.64), decreasing metabolic AL over time (OR 0.63, 95 % CI 0.46-0.86), increasing inflammatory AL over time (OR 1.60, 95 % CI 1.07-2.37), and decreasing renal AL over time (OR 1.38, 95 % CI 1.01-1.87) were associated with the low-moderate depression symptoms trajectory. Furthermore, 2 or more ACEs (OR 1.48, 95 % CI 1.10-2.00), 2 or more AAEs (OR 1.85, 95 % CI 1.32-2.60), decreasing metabolic AL over time (OR 0.75, 95 % CI 0.57-1.00), increasing inflammatory AL over time (OR 1.69, 95 % CI 1.19-2.42) were associated with the high-moderate depression symptoms trajectory.

CONCLUSION

Experiencing more ACEs and AAEs was associated with higher depression symptoms trajectories. Moreover, participants with decreasing metabolic AL over time showed a low depression symptoms trajectory, while those with increasing inflammatory AL over time and decreasing renal AL over time showed a worse depression symptoms trajectory. These findings highlighted the physiological damage caused by stress on mental health outcomes.

Efficacy of a Standalone Smartphone Application to Treat Postnatal Depression: A Randomized Controlled Trial

INTRODUCTION

Smartphone app interventions based on cognitive-behavioral therapy (CBT) are promising scalable alternatives for treating mental disorders, but the evidence of their efficacy for postpartum depression is limited. We assessed the efficacy of Motherly, a standalone CBT-based smartphone app, in reducing symptoms of postpartum depression.

METHODS

Women aged 18-40 with symptoms of postpartum depression were randomized either to intervention (Motherly app) or active control (COMVC app). The primary outcome was symptoms of depression measured by the Edinburgh Postnatal Depression Scale (EPDS) at post-treatment. Secondary outcomes were anxiety symptoms, parental stress, quality of sleep, behavioral activation, availability of response-contingent positive reinforcement, and clinical improvement at post-treatment and 1-month follow-up. Exploratory analyses were performed to investigate if app engagement was associated with treatment response.

RESULTS

From November 2021 to August 2022, 1,751 women volunteered, of which 264 were randomized, and 215 provided primary outcome data. No statistically significant differences were found between groups at post-treatment: intervention: mean (SD): 12.75 (5.52); active control: 13.28 (5.32); p = 0.604. There was a statistically significant effect of the intervention on some of the secondary outcomes. Exploratory analyses suggest a dose-response relationship between Motherly app engagement and outcomes.

CONCLUSION

Our standalone app intervention did not significantly reduce postnatal depression symptoms when compared to active control. Exploratory findings suggest that negative findings might be associated with insufficient app engagement. Consistent with current literature, our findings suggest that standalone app interventions for postpartum depression are not ready to be implemented in clinical practice.

Selective Serotonin Reuptake Inhibitor and Serotonin-Noradrenaline Reuptake Inhibitor Withdrawal Changes DSM Presentation of Mental Disorders: Results from the Diagnostic Clinical Interview for Drug Withdrawal

INTRODUCTION

Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) may cause withdrawal at dose decrease, discontinuation, or switch. Current diagnostic methods (e.g., DSM) do not take such phenomenon into account. Using a new nosographic classification of withdrawal syndromes due to SSRI/SNRI decrease or discontinuation [by Psychother Psychosom. 2015;84(2):63-71], we explored whether DSM is adequate to identify DSM disorders when withdrawal occurs.

METHODS

Seventy-five self-referred patients with a diagnosis of withdrawal syndrome due to discontinuation of SSRI/SNRI, diagnosed via the Diagnostic Clinical Interview for Drug Withdrawal 1 - New Symptoms of Selective Serotonin Reuptake Inhibitors or Serotonin-Norepinephrine Reuptake Inhibitors (DID-W1), and at least one DSM-5 diagnosis were analyzed.

RESULTS

In 58 cases (77.3%), the DSM-5 diagnosis of current mental disorder was not confirmed when the DID-W1 diagnosis of current withdrawal syndrome was established. In 13 cases (17.3%), the DSM-5 diagnosis of past mental disorder was not confirmed when criteria for DID-W1 diagnosis of lifetime withdrawal syndrome were met. In 3 patients (4%), the DSM-5 diagnoses of current and past mental disorders were not confirmed when the DID-W1 diagnoses of current and lifetime withdrawal syndromes were taken into account. The DSM-5 diagnoses most frequently mis-formulated were current panic disorder (50.7%, n = 38) and past major depressive episode (18.7%, n = 14).

CONCLUSION

DSM needs to be complemented by clinimetric tools, such as the DID-W1, to detect withdrawal syndromes induced by SSRI/SNRI discontinuation, decrease, or switch, following long-term use.

Well-Being Therapy for Depression

Well-being therapy (WBT) is a short-term psychotherapeutic strategy, based on the technique of self-observation via the use of a structured diary and the guide of a therapist, with the goal of increasing psychological well-being, thus reaching euthymia and a balance among psychic forces. WBT showed to be suitable for application in residual symptoms of unipolar and bipolar depression, since the sequential combination with cognitive-behavioural therapy (CBT) led to a decrease in the relapse rate of recurrent depression. WBT also showed clinical utility in the treatment of cyclothymia, which represents one of the stages of bipolar disorder. Further, WBT seems to have efficacy in treatment-resistant depression and in case of withdrawal syndromes (in particular the so-called persistent post-withdrawal disorder) following antidepressant decrease, switch or discontinuation. In brief, WBT is a rather new but promising therapeutic strategy in the management of unipolar and bipolar depression. This chapter offers an overview of WBT possible applications.

Clinical staging across eating disorders: a scoping review protocol

INTRODUCTION

Clinical staging models in psychiatry assert that there are earlier, less severe or more malleable forms of illness that are distinguishable from later, more chronic forms of illness, and that these stages may have different prognostic and treatment implications. Previous reviews on clinical staging in eating disorders (EDs) suggest a staging heuristic could be useful for anorexia nervosa, but less research is available on how this applies to other EDs. An up-to-date review is required to synthesise new and heterogenous avenues of research. This scoping review aims to explore the extent and types of evidence in relation to illness staging for EDs and how these concepts are associated with treatment response and outcomes.

METHODS AND ANALYSIS

This protocol was developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol extension for Scoping Reviews checklist and the Joanna Briggs Institute Reviewer's Manual. We will consider any documents providing evidence for clinical staging such as those which describe full or partial staging models, for all EDs, across various domains of assessment and functioning. Participants will include clinical or non-clinical population samples with full-syndrome EDs or disordered eating behaviour. PubMed, PsycINFO, MEDLINE and Web of Science databases will be systematically searched for relevant literature. Two authors will export documents and screen titles, abstracts and full texts. Data will be extracted into a charting form drafted by the authors. A narrative summary of the documents will be conducted in line with the study aims. Finally, clinical and research recommendations will be outlined.

ETHICS AND DISSEMINATION

Ethical approval will not be required to synthesise published and unpublished literature. The study will be published in a peer-reviewed journal and shared at conferences, via social media, and in other communications.

Anxiety, mood, and substance use disorders in adult men and women with and without Attention-Deficit/Hyperactivity Disorder: a substantive and methodological overview

Knowledge on psychiatric comorbidity in adult ADHD is essential for prevention, detection, and treatment of these conditions. This review (1) focuses on large studies (n> 10,000; surveys, claims data, population registries) to identify (a) overall, (b) sex- and (c) age-specific patterns of comorbidity of anxiety disorders (ADs), major depressive disorder (MDD), bipolar disorder (BD) and substance use disorders (SUDs) in adults with ADHD relative to adults without ADHD; and (2) describes methodological challenges relating to establishing comorbidity in ADHD in adults as well as priorities for future research. Meta-analyses (ADHD: n=550,748; no ADHD n=14,546,814) yielded pooled odds ratios of 5.0(CI:3.29-7.46) for AD, 4.5(CI:2.44-8.34) for MDD, 8.7(CI:5.47-13.89) for BD and 4.6(CI:2.72-7.80) for SUDs, indicating strong differences in adults with compared to adults without ADHD. Moderation by sex was not found: high comorbidity held for both men and women with sex-specific patterns as in the general population: higher prevalences of ADs, MDD and BD in women and a higher prevalence of SUDs in men. Insufficient data on different phases of the adult lifespan prevented conclusions on developmental changes in comorbidity. We discuss methodological challenges, knowledge gaps, and future research priorities.

Research and Diagnostic Algorithmic Rules (RADAR) for mood disorders, recurrence of illness, suicidal behaviours, and the patient's lifetime trajectory

The top-down Diagnostic and Statistical Manual/International Statistical Classification of Diseases categories of mood disorders are inaccurate, and their dogmatic nature precludes both deductive (as indisputable) and inductive (as top-down) remodelling of case definitions. In trials, psychiatric rating scale scores employed as outcome variables are invalid and rely on folk psychology-like narratives. Using machine learning techniques, we developed a new precision nomothetic model of mood disorders with a recurrence of illness (ROI) index, a new endophenotype class, namely Major Dysmood Disorder (MDMD), characterised by increased ROI, a more severe phenome, and more disabilities. Nonetheless, our previous studies did not compute Research and Diagnostic Algorithmic Rules (RADAR) to diagnose MDMD and score ROI, lifetime (LT), and current suicidal behaviours, as well as the phenome of mood disorders. Here, we provide rules to compute bottom-up RADAR scores for MDMD, ROI, LT and current suicidal ideation and attempts, the phenome of mood disorders, and the lifetime trajectory of mood disorder patients from a family history of mood disorders and substance abuse to adverse childhood experiences, ROI, and the phenome. We also demonstrate how to plot the 12 major scores in a single RADAR graph, which displays all features in a two-dimensional plot. These graphs allow the characteristics of a patient to be displayed as an idiomatic fingerprint, allowing one to estimate the key traits and severity of the illness at a glance. Consequently, biomarker research into mood disorders should use our RADAR scores to examine pan-omics data, which should be used to enlarge our precision models and RADAR graph.