People with serious mental illness exhibit higher morbidity and mortality rates of chronic diseases than the general population.

The aim of this study was to establish a dedicated clinic for patients with chronic mental illness to monitor physical health in accordance with best practice guidelines.

Patients were invited to attend the metabolic clinic. The following areas were examined: Personal and family history of cardiovascular disease, diet, exercise, smoking. Mental state examination, waist circumference, BP, pulse, ECG and BMI. Laboratory tests including U+E, LFTs, HbA1c, Lipid profile and other tests as appropriate such as serum lithium. AIMS scale, HoNOS and WHOQOL-BREF scales as additional indicators of global health.

A total of 80 patients attended during 3.5 years of clinic. Mean age was 54.9 years (SD:13.81) at first contact and 45% were females. Mean years in the service was 19.66 (SD:11.54) and mean number of previous hospital admissions was 4.4 (SD:5.63). Metabolic syndrome was present in 42% at first assessment and 20% had at least one new physical abnormality identified during the clinic. A statistically significant difference was found for the psychological domain of the WHOQOL-BREF and the HoNOs particularly at third assessment. (β=4.64, Wald x2=7.38, df:1, p=0.007, CI:1.3-8.1, β =-.889, Wald x2=4.08, df:1, p=0.043, CI: -1.752 to-.026) respectively.

The results show a high prevalence of physical health conditions in this cohort, some of which represent a new diagnosis. This implicates better allocation of existing resources for screening and early detection, and potential to run joint clinics with primary care.

No significant relationships.

The development of obesity and metabolic abnormalities that seed future ill-health occur early with antipsychotic treatment. In 2013, the 12-week Keeping the Body in Mind (KBIM) pilot lifestyle intervention was delivered to a small sample of youth experiencing first-episode psychosis (FEP) with <4 weeks of antipsychotic exposure in a cluster-controlled design. The control group experienced significant increases in weight (mean 7.8kg) and waist circumference (mean 7.1cm) compared to non-significant increases (mean 1.8kg) in the KBIM group.

To evaluate the effect of KBIM as routine care on anthropometry and metabolic biochemistry in a larger sample of youth with FEP across three mental health services.

This retrospective chart audit was conducted on youth with FEP, prescribed a therapeutic dose of antipsychotic medication, and who engaged with KBIM between 2015 and 2019. Primary outcomes were weight and waist circumference. Secondary outcomes were blood pressure, blood glucose and blood lipids. Outcomes were collected in a pre-post design. Implementation elements were also obtained from the participant’s medical file.

One-hundred and eighty-two people met inclusion criteria. Follow-up data were available on up to 134 people for individual outcomes. Mean number of sessions attended was 11.1 (SD=7.3). Weight and waist changes were limited to 1.5kg (SD=5.3, t(133)=3.2, p=0.002) and 0.7cm (SD=5.8, t(109)=1.2, p=0.23). Nineteen percent experienced clinically significant weight gain. There were no changes to blood pressure or metabolic biochemistry.

The positive outcomes for weight and waist circumference found in the initial pilot study were maintained with implementation as routine care.

No significant relationships.

First-episode psychosis (FEP) is associated with metabolic alterations. However, it is not known if there is heterogeneity in these alterations beyond what might be expected due to normal individual differences, indicative of subgroups of patients at greater vulnerability to metabolic dysregulation.

We employed meta-analysis of variance, indexed using the coefficient of variation ratio (CVR), to compare variability of the following metabolic parameters in antipsychotic naïve FEP and controls: fasting glucose, glucose post-oral glucose tolerance test (OGTT), fasting insulin, insulin resistance, haemoglobin A1c (HbA1c), total-cholesterol, low-density lipoprotein (LDL)-cholesterol, high-density lipoprotein (HDL)-cholesterol, and triglycerides. Standardised mean difference in metabolic parameters between groups was also calculated; meta-regression analyses examined physiological/demographic/psychopathological moderators of metabolic change.

Twenty-eight studies were analysed (1716 patients, 1893 controls). Variability of fasting glucose [CVR = 1.32; 95% confidence interval (CI) 1.12–1.55; p = 0.001], glucose post-OGTT (CVR = 1.43; 95% CI 1.10–1.87; p = 0.008), fasting insulin (CVR = 1.31; 95% CI 1.09–1.58; p = 0.01), insulin resistance (CVR = 1.34; 95% CI 1.12–1.60; p = 0.001), HbA1c (CVR = 1.18; 95% CI 1.06–1.27; p < 0.0001), total-cholesterol (CVR = 1.15; 95% CI 1.01–1.31; p = 0.03), LDL-cholesterol (CVR = 1.28; 95% CI 1.09–1.50; p = 0.002), and HDL-cholesterol (CVR = 1.15; 95% CI 1.00–1.31; p < 0.05), but not triglycerides, was greater in patients than controls. Mean glucose, glucose post-OGTT, fasting insulin, insulin resistance, and triglycerides were greater in patients; mean total-cholesterol and HDL-cholesterol were reduced in patients. Increased symptom severity and female sex were associated with worse metabolic outcomes.

Patients with FEP present with greater variability in metabolic parameters relative to controls, consistent with a subgroup of patients with more severe metabolic changes compared to others. Understanding determinants of metabolic variability could help identify patients at-risk of developing metabolic syndrome. Female sex and severe psychopathology are associated with poorer metabolic outcomes, with implications for metabolic monitoring in clinical practice.

Paediatric obesity is a worldwide health burden, with growing evidence linking obesity to myocardial function impairments. The study aims to evaluate left ventricular functions among prepubertal obese children to obesity-related clinical and metabolic parameters.

Between June 2019 and March 2020, 40 prepubertal children with obesity were recruited and compared to 40 healthy controls. Patients were assessed for body mass index z scores, waist circumference, body adiposity by bioimpedance analysis, and obesity-related laboratory tests, for example, serum chemerin. Left ventricular functions were assessed using variable echocardiographic modalities, such as M-mode, tissue Doppler, and two-dimensional speckle tracking.

Mean patients’ age was 9.25 ± 1.05 years. Left ventricular mass index, E/E’, and myocardial performance index were significantly increased in obese children than controls. Although M-mode-derived ejection fraction was comparable in both groups, two-dimensional speckle tracking-derived ejection fraction, global longitudinal strain, and global circumferential strain were significantly lower in cases than controls. Left ventricular mass index displayed a positive correlation with body mass index z score (p = 0.003), fat mass index (p = 0.037), and trunk fat mass (p = 0.021). Global longitudinal strain was negatively correlated with body mass index z score (p = 0.015) and fat mass index (p = 0.016). Serum chemerin was positively correlated with myocardial performance index (p = 0.01).

Alterations of left ventricular myocardial functions in prepubertal obese children could be detected using different echocardiographic modalities. Chemerin, body mass index z score, fat mass index, and trunk fat mass were correlated with subclinical left ventricular myocardial dysfunction parameters before puberty. Our results reinforce early and strict management of childhood obesity upon detection of changes in anthropometric and body adiposity indices.