The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

A plethora of research has implicated hundreds of putative biomarkers for depression, but has not yet fully elucidated their roles in depressive illness or established what is abnormal in which patients and how biologic information can be used to enhance diagnosis, treatment and prognosis. This lack of progress is partially due to the nature and heterogeneity of depression, in conjunction with methodological heterogeneity within the research literature and the large array of biomarkers with potential, the expression of which often varies according to many factors. We review the available literature, which indicates that markers involved in inflammatory, neurotrophic and metabolic processes, as well as neurotransmitter and neuroendocrine system components, represent highly promising candidates. These may be measured through genetic and epigenetic, transcriptomic and proteomic, metabolomic and neuroimaging assessments. The use of novel approaches and systematic research programs is now required to determine whether, and which, biomarkers can be used to predict response to treatment, stratify patients to specific treatments and develop targets for new interventions. We conclude that there is much promise for reducing the burden of depression through further developing and expanding these research avenues.

Background: Depression is considered to have the highest disability burden of all conditions. Although treatment-resistant depression (TRD) is a key contributor to that burden, there is little understanding of the best treatment approaches for it and specifically the effectiveness of available augmentation approaches.Aims: We conducted a systematic review and meta-analysis to search and quantify the evidence of psychological and pharmacological augmentation interventions for TRD.Method: Participants with TRD (defined as insufficient response to at least two antidepressants) were randomised to at least one augmentation treatment in the trial. Pre-post analysis assessed treatment effectiveness, providing an effect size (ES) independent of comparator interventions.Results: Of 28 trials, 3 investigated psychological treatments and 25 examined pharmacological interventions. Pre-post analyses demonstratedN-methyl-d-aspartate-targeting drugs to have the highest ES (ES = 1.48, 95% CI 1.25–1.71). Other than aripiprazole (four studies, ES = 1.33, 95% CI 1.23–1.44) and lithium (three studies, ES = 1.00, 95% CI 0.81–1.20), treatments were each investigated in less than three studies. Overall, pharmacological (ES = 1.19, 95% CI 1.08–1.30) and psychological (ES = 1.43, 95% CI 0.50–2.36) therapies yielded higher ESs than pill placebo (ES = 0.78, 95% CI 0.66–0.91) and psychological control (ES = 0.94, 95% CI 0.36–1.52).Conclusions: Despite being used widely in clinical practice, the evidence for augmentation treatments in TRD is sparse. Although pre-post meta-analyses are limited by the absence of direct comparison, this work finds promising evidence across treatment modalities.Declaration of interest: In the past 3 years, A.H.Y. received honoraria for speaking from AstraZeneca, Lundbeck, Eli Lilly and Sunovion; honoraria for consulting from Allergan, Livanova and Lundbeck, Sunovion and Janssen; and research grant support from Janssen. In the past 3 years, A.J.C. received honoraria for speaking from AstraZeneca and Lundbeck; honoraria for consulting with Allergan, Janssen, Livanova, Lundbeck and Sandoz; support for conference attendance from Janssen; and research grant support from Lundbeck. B.B. has recently been (soon to be) on the speakers/advisory board for Hexal, Lilly, Lundbeck, Mundipharma, Pfizer, and Servier. No other conflicts of interest.

Recent studies suggest that anti-inflammatory medication may play a role in the treatment of mood disorders. The purpose of this study was to determine the efficacy of anti-inflammatory drugs in patients with major depressive disorder and bipolar disorder. The Cochrane Central Register of Controlled Trials, PubMed, EMBASE, PsychINFO and Clinicaltrials.gov were searched from inception until 15 April 2017 for completed and on-going randomized controlled trials of anti-inflammatory agents for major depressive disorder and bipolar disorder. Data from randomized controlled trials assessing the antidepressant and anti-manic effect of adjunctive mechanistically diverse anti-inflammatory agents were pooled to determine standard mean differences (SMDs) compared with placebo and/or treatment as usual. Patients receiving anti-inflammatory agents showed lower post-treatment depressive symptom scores compared with those receiving placebo with a standard mean difference of −0.71 (six randomized controlled trials, n=214, 95% CI −1.24 to −0.17, p=0.009). Anti-inflammatory treatment was found to reduce post-treatment manic symptom scores with a standard mean difference of −0.72 (three randomized controlled trials, n=96, 95% CI −1.31 to −0.13, p=0.02). Anti-inflammatories did not show a statistically significant improvement in the secondary outcome measure (change in symptom scores from baseline to outcome). Further high quality trials are needed before making recommendations for the routine clinical use of anti-inflammatories in the treatment of mood disorders.