The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.

The sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.

About 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15–20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.

The finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.

The COVID-19 pandemic caused significant disruption to the lives of children and their families. Pre-school children may have been particularly vulnerable to the effects of the pandemic, with the closure of childcare facilities, playgrounds, playcentres and parent and toddler groups limiting their opportunities for social interaction at a crucial stage of development. Additionally, for parents working from home, caring for pre-school aged children who require high levels of support and care, was likely challenging

We aimed to conduct an intensive longitudinal study to examine trajectories of pre-schoolers’ mental symptoms in the United Kingdom during the first year of the COVID-19 pandemic.

UK‐based parents and carers (n = 1520) of pre-school‐aged children (2 to 4 years) completed monthly online surveys about their pre-schoolers’ mental health between April 2020 and March 2021. The survey examined changes in children’s emotional symptoms, conduct problems and hyperactivity/inattention.

Pre-schoolers’ emotional problems and hyperactivity/inattention symptoms declined from April through summer 2020 and then increased again during the autumn and winter 2020/2021 as lockdowns were re-introduced. Pre-schoolers who attended childcare showed greater decline in symptom severity than those who did not. Older children, compared to younger, showed greater lability of emotion symptom severity. Attending childcare predicted lower symptom severity across all three domains of conduct problems, emotional symptoms, and hyperactivity/inattention, while the opposite pattern was observed for children whose parent had a mental health problem.

Our findings reinforce the importance of examining pre-schoolers’ mental health in the context of micro and macro-level factors. Interventions focusing on family factors such as parent mental health, as well as continued provision of childcare, may have most potential to mitigate the impact of COVID-19 on young children’s mental health.

None Declared

An interatrial communication is present in most neonates. The majority are considered the “normal” patency of the oval foramen, while a minority are abnormal atrial septal defects. Differentiation between the two with transthoracic echocardiography may be challenging, and no generally accepted method of classification is presently available. We aimed to develop and determine the reliability of a new classification of interatrial communications in newborns.

An algorithm was developed based on echocardiographic criteria from 495 newborns (median age 11[8;13] days, 51.5% females). The algorithm defines three main categories: patency of the oval foramen, atrial septal defect, and no interatrial communication as well as several subtypes. We found an interatrial communication in 414 (83.6%) newborns. Of these, 386 (93.2%) were categorised as patency of the oval foramen and 28 (6.8%) as atrial septal defects.

Echocardiograms from another 50 newborns (median age 11[8;13] days, 36.0% female), reviewed by eight experts in paediatric echocardiography, were used to assess the inter- and intraobserver variation of classification of interatrial communications into patency of the oval foramen and atrial septal defect, with and without the use of the algorithm. Review with the algorithm gave a substantial interobserver agreement (kappa = 0.66), and an almost perfect intraobserver agreement (kappa = 0.82). Without the use of the algorithm, the interobserver agreement between experienced paediatric cardiologists was low (kappa = 0.20).

A new algorithm for echocardiographic classification of interatrial communications in newborns produced almost perfect intraobserver and substantial interobserver agreement. The algorithm may prove useful in both research and clinical practice.

To determine the proportion of hospitals that implemented 6 leading practices in their antimicrobial stewardship programs (ASPs). Design: Cross-sectional observational survey.

Acute-care hospitals.

ASP leaders.

Advance letters and electronic questionnaires were initiated February 2020. Primary outcomes were percentage of hospitals that (1) implemented facility-specific treatment guidelines (FSTG); (2) performed interactive prospective audit and feedback (PAF) either face-to-face or by telephone; (3) optimized diagnostic testing; (4) measured antibiotic utilization; (5) measured C. difficile infection (CDI); and (6) measured adherence to FSTGs.

Of 948 hospitals invited, 288 (30.4%) completed the questionnaire. Among them, 82 (28.5%) had <99 beds, 162 (56.3%) had 100–399 beds, and 44 (15.2%) had ≥400+ beds. Also, 230 (79.9%) were healthcare system members. Moreover, 161 hospitals (54.8%) reported implementing FSTGs; 214 (72.4%) performed interactive PAF; 105 (34.9%) implemented procedures to optimize diagnostic testing; 235 (79.8%) measured antibiotic utilization; 258 (88.2%) measured CDI; and 110 (37.1%) measured FSTG adherence. Small hospitals performed less interactive PAF (61.0%; P = .0018). Small and nonsystem hospitals were less likely to optimize diagnostic testing: 25.2% (P = .030) and 21.0% (P = .0077), respectively. Small hospitals were less likely to measure antibiotic utilization (67.8%; P = .0010) and CDI (80.3%; P = .0038). Nonsystem hospitals were less likely to implement FSTGs (34.3%; P < .001).

Significant variation exists in the adoption of ASP leading practices. A minority of hospitals have taken action to optimize diagnostic testing and measure adherence to FSTGs. Additional efforts are needed to expand adoption of leading practices across all acute-care hospitals with the greatest need in smaller hospitals.

The aims of this study were to evaluate changes in inflammatory and oxidative stress levels following treatment with N-acetylcysteine (NAC) or mitochondrial-enhancing agents (CT), and to assess the how these changes may predict and/or moderate clinical outcomes primarily the Montgomery-Åsberg Depression Rating Scale (MADRS).

This study involved secondary analysis of a placebo-controlled randomised trial (n = 163). Serum samples were collected at baseline and week 16 of the clinical trial to determine changes in Interleukin-6 (IL-6) and total antioxidant capacity (TAC) following adjunctive CT and/or NAC treatment, and to explore the predictability of the outcome or moderator effects of these markers.

In the NAC-treated group, no difference was observed in serum IL-6 and TAC levels after 16 weeks of treatment with NAC or CT. However, results from a moderator analysis showed that in the CT group, lower IL-6 levels at baseline was a significant moderator of MADRS χ2 (df) = 4.90, p = 0.027) and Clinical Global Impression-Improvement (CGI-I, χ2 (df) = 6.28 p = 0.012). In addition, IL-6 was a non-specific but significant predictor of functioning (based on the Social and Occupational Functioning Assessment Scale (SOFAS)), indicating that individuals with higher IL-6 levels at baseline had a greater improvement on SOFAS regardless of their treatment (p = 0.023).

Participants with lower IL-6 levels at baseline had a better response to the adjunctive treatment with the mitochondrial-enhancing agents in terms of improvements in MADRS and CGI-I outcomes.

Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.

The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.

There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.

These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.

Antidepressants are amongst the most commonly prescribed classes of drugs and their use continues to grow. Adverse outcomes are part of the landscape in prescribing medications and therefore management of safety issues need to be an integral part of practice.

We have developed consensus guidelines for safety monitoring with antidepressant treatments.

To present an overview of screening and safety considerations for pharmacotherapy of clinical depressive disorders and make recommendations for safety monitoring.

Data were sourced by a literature search using Medline and a manual search of scientific journals to identify relevant articles. Draft guidelines were prepared and serially revised in an iterative manner until all co-authors gave final approval of content.

A guidelines document was produced after approval by all 19 co-authors. The final document gives guidance on; the decision to treat, baseline screening prior to commencement of treatment, and ongoing monitoring during antidepressant treatment. The guidelines state or reference screening protocols that may detect medical causes of depression as well as screening and monitoring protocols to investigate specific adverse effects associated with antidepressant treatments that may be reduced or identified earlier by baseline screening and agent-specific monitoring after commencing treatment.

The implementation of safety monitoring guidelines for treatment of clinical depression may significantly improve outcome, by improving a patient's overall physical health status.

Bipolar disorder (BD) is a psychiatric disorder with an uncertain aetiology. Recently, special attention has been given to homocysteine (Hcy), as it has been suggested that alterations in 1-carbon metabolism might be implicated in diverse psychiatric disorders. However, there is uncertainty regarding possible alterations in peripheral Hcy levels in BD.

This study comprises a meta-analysis comparing serum and plasma Hcy levels in persons with BD and healthy controls. We conducted a systematic search for all eligible English and non-English peer-reviewed articles.

Nine cross-sectional studies were included in the meta-analyses, providing data on 1547 participants. Random-effects meta-analysis showed that serum and plasma levels of Hcy were increased in subjects with BD in either mania or euthymia when compared to healthy controls, with a large effect size in the mania group (g = 0.98, 95% CI: 0.8–1.17, P < 0.001, n = 495) and a small effect in the euthymia group (g = 0.3, 95% CI: 0.11–0.48, P = 0.002, n = 1052).

Our meta-analysis provides evidence that Hcy levels are elevated in persons with BD during mania and euthymia. Peripheral Hcy could be considered as a potential biomarker in BD, both of trait (since it is increased in euthymia), and also of state (since its increase is more accentuated in mania). Longitudinal studies are needed to clarify the relationship between bipolar disorder and Hcy, as well as the usefulness of peripheral Hcy as both a trait and state biomarker in BD.

Bipolar disorder (BD) is a psychiatric disorder associated with increased rates of obesity and inflammation. Leptin is an adipokine that is mainly produced by the white adipose tissue in response to insulin. It stimulates the immune system, increasing the production of pro-inflammatory cytokines. There is currently uncertainty regarding possible alterations in peripheral leptin levels across the mood states in BD.

This study comprises a between-group meta-analysis comparing serum and plasma leptin levels in people with BD in mania, depression or euthymia and healthy controls. We conducted a systematic search for all possibly eligible-English and non-English peer-reviewed articles. We calculated the effect size (ES) utilizing Hedges’ adjusted g using random effects.

Eleven studies were included in the meta-analyses, providing data on 1118 participants. Serum and plasma leptin levels were not altered in subjects with BD when compared to healthy controls in mania (g = −0.99, 95% CI −2.43 to 0.43, P = 0.171), in depression (g = 0.17, 95% CI −0.45 to 0.79, P = 0.584), or in euthymia (g = 0.03, 95% CI −0.39 to 0.46, P = 0.882). However, we did observe a stronger association between leptin levels and both age and BMI in patients with BD in euthymia compared to healthy controls, such that the greater the age of the individuals, the greater the difference in leptin levels between BD and controls; and the higher the BMI, the greater the difference in leptin levels between BD and controls.

Our meta-analysis provides evidence that leptin levels are not altered in BD across the mood spectrum compared to healthy controls. The disproportionate increase of leptin levels with increase in BMI in BD speaks in favour of a potential inflammatory role of white adipose tissue in BD and a disproportionate increase of leptin levels with increase in age.

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through “Graphing lifestyle-environs using machine-learning methods” (GLUMM).

Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six “lifestyle-environ” variables were used from the National health and nutrition examination study (2009–2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders.

The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤ 2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤ 14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P < 0.001) and GLUMM7-1 (OR: 7.88, P < 0.001) with depression was found, with significant interactions with those married/living with partner (P = 0.001).

Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.