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

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Ethical approval will not be required to synthesise published and unpublished literature.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.