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The discovery of brown adipose tissue (BAT) in adults has sparked interest in its role as a therapeutic target in metabolic disorders. Infrared thermography is a promising way to quantify BAT; however, a standardized methodology has not been established. This study aims to establish a standardized and reproducible protocol to measure thermal response to cold in the supraclavicular area using thermographic imaging. In Phase 1, we compared the thermal response to 12 °C cold after acclimation at either 32 °C or room temperature using thermographic imaging. Repeatability of the 32 °C acclimation trial was studied in a second group in Phase 2. Phase 1 included 28 men (mean age 23.9 ± 5.9 y; mean BMI 25.2 ± 3.9 kg/m2) and Phase 2 included 14 men (mean age 20.9 ± 2.4 y; mean BMI 23.6 ± 3.1 kg/m2). The thermal response was greater after 32 °C than after room temperature acclimation (0.22 ± 0.19 vs 0.13 ± 0.17 °C, p = 0.05), was not related to outdoor temperature (r = −0.35, p = 0.07), did not correlate with supraclavicular fat (r = −0.26, p = 0.21) measured with dual-energy x-ray absorptiometry and was repeatable [ICC 0.69 (0.14–0.72)]. Acclimation at 32 °C followed by cold generates a reproducible change in supraclavicular skin temperature measurable by thermal imaging that may be indicative of BAT metabolic activity.

Antipsychotic use is associated with elevated cardiometabolic risk. Guidelines for metabolic risk screening of individuals taking antipsychotics have been issued, but with little uptake into clinical practice. This review systematically assessed interventions that address this guideline-to-practice gap and described their quality, improvement strategies, and effect on screening rates.Studies of interventions that addressed metabolic risk screening of adult patients taking antipsychotics, published from inception to July 2018, were selected from MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Reviews databases. Information was extracted on study characteristics; improvement strategies at the provider, patient, and system levels; and screening rates in the intervention and comparison groups.The review included 30 complex interventions that used between one and nine unique improvement strategies. Social influence to shift provider and health organization culture to encourage metabolic risk screening was a common strategy, as were clinical prompts and monitoring tools to capture provider attention. Most studies were deemed at high risk of bias. Relative to comparison groups, the interventions were associated with an increase in median screening rates for glucose (28% to 65%), lipids (22% to 61%), weight (19% to 67%), and blood pressure (22% to 80%).This knowledge synthesis points to shortcomings of current interventions to improve antipsychotic metabolic risk screening, both in quality and in outcomes. Findings may be used to inform the design of future programs. Additional interventions are needed to address the current guideline-to-practice gap, in which approximately one-third of patients are unscreened for metabolic risk.

Background: Emerging evidence suggests an important role of the human gut microbiome in psychiatry and neurodevelopmental disorders. An increasing body of literature based on animal studies has reported that the gut microbiome influences brain development and behavior by interacting with the gut-brain axis. Furthermore, as the gut microbiome has an important role in metabolism and is known to interact with pharmaceuticals, recent evidence suggests a role for the microbiome in antipsychotic-induced metabolic side effects in animals and humans. Purpose: Here we present the protocol for a two-phase study investigating the gut microbiome in healthy controls and in patients with schizophrenia treated with antipsychotics. Methods: Phase I of our study involves humans exclusively. We recruit 25 patients who are chronically treated with clozapine and compare them with 25 healthy controls matched for age, sex, BMI, and smoking status. A second cohort consists of 25 patients newly starting on clozapine, and a third cohort includes 25 antipsychotic-naive patients. The patients in the second cohort and third cohort are prospectively assessed for up to 6 and 12 weeks, respectively. Phase II of this study will incorporate microbiota humanized mouse models to examine the influence of human fecal transplant on metabolic parameters and the gut-brain axis. Progress and Future Directions: We are underway with the first participants enrolled in all phase I treatment cohorts. This study will contribute to elucidating the role of the gut microbiome in schizophrenia and metabolic side effects. In addition, its results may help to explore potential therapeutic targets for antipsychotic-induced metabolic side effects.

Alzheimer’s disease is a genetically complex neurodegenerative disorder representing the leading cause of dementia. Advances in personal genomics are increasing the public uptake of genetic susceptibility testing for complex diseases such as late-onset Alzheimer’s disease (LOAD). For LOAD, the discovery of the major risk e4 allele of the APOE gene has prompted a debate on the ethics and utility of presymptomatic (i.e., predictive) testing. Although the mechanistic contribution of APOE to disease onset remains uncertain, presymptomatic genetic testing provides a relative risk of developing LOAD. Presymptomatic testing for complex disorders, such as LOAD is much less conclusive than early-onset Alzheimer’s disease (EOAD) which follows a Mendelian inheritance pattern. Given the lack of preventive strategies available for EOAD or LOAD, APOE genotyping offers limited clinical utility, thus, raising ethical and practical questions. We conducted a systematic search of five electronic databases or primary studies published during January 2008–January 2018 which investigated practical and ethical issues of presymptomatic APOE genotyping for LOAD risk estimation. We identified 31 articles which suggested that APOE genotyping for LOAD susceptibility provides potential benefits to at-risk patients and can guide changes in positive health-related behaviors. However, other individuals may experience test-related anxiety, depression and psychological distress. Future research should focus on developing an integrated risk assessment tool to enhance the utility of APOE genotyping. Furthermore, empirical research is required to understand actual psychological and social implications associated with testing.

With the emergence of knowledge implicating the human gut microbiome in the development and regulation of several physiological systems, evidence has accumulated to suggest a role for the gut microbiome in psychiatric conditions and drug response. A complex relationship between the enteric nervous system, the gut microbiota and the central nervous system has been described which allows for the microbiota to influence and respond to a variety of behaviors and psychiatric conditions. Additionally, the use of pharmaceuticals may interact with and alter the microbiota to potentially contribute to adverse effects of the drug. The gut microbiota has been described in several psychiatric disorders including depression and anxiety, but only a few reports have discussed the role of the microbiome in schizophrenia. The following review examines the evidence surrounding the gut microbiota in behavior and psychiatric illness, the role of the microbiota in schizophrenia and the potential for antipsychotics to alter the gut microbiota and promote adverse metabolic events.

Background Antipsychotic (AP) medications are the cornerstone of treatment for schizophrenia (SCZ), with off-label prescription rapidly increasing in youth and adolescent populations. However, APs have been associated with metabolic side effects including diabetes and obesity. Although several mechanisms have been proposed, the gut microbiome (GMB) has been suggested as a potential mediator of AP-induced metabolic side effects due to its role in weight and metabolic regulation; as well as emerging evidence demonstrating a shift in the microbiome of AP-treated animals and humans. The purpose of the current study is to 1) Investigate the GMB in SCZ patients compared to healthy individuals and 2) To examine the role of GMB in SCZ and AP-induced metabolic side effects. Methods Three groups of 25 participants are being recruited. Group A: Long-term AP-treated patients (for at least 6 months) taking clozapine (CLZ). Group B: Healthy controls matched with Group A for BMI, age, sex, and smoking status. Group C: Treatment-naive SCZ patients starting an AP or patients newly switching to CLZ. Groups A and B will be assessed at a single time point (week 0) whereas Group C will be assessed prospectively at weeks 0, 3, and 12 with the same measures collected. The following clinical measures and metabolic indices are collected at baseline, and if applicable, at follow up visits: the Questionnaire of Eating and Weight Patterns (QEWP), the Dutch Eating Behavior Questionnaire (DEBQ), the Power of Food Scale (POFS), the Positive and Negative Syndrome Scale (PANSS), the Gastrointestinal Symptom Rating Scale (GSRS), Exercise/Physical Activity Evaluation, body mass index, oral glucose tolerance test, lipid profile, and serum clozapine levels. We also collect fecal samples for DNA extraction and microbiome sequencing. We are currently performing a baseline comparison across groups A, B, and C in order to evaluate intrinsic differences between schizophrenia and healthy controls, as well as, effects of new AP exposure versus chronic exposure. The difference between analytical groups (i.e. groups A, B, and C) at different time points (i.e. baseline, week 3 and 6) for variables such as BMI and metabolic parameters marker change are analyzed using two-way repeated measures ANCOVA, including significant covariates. The relationship between different measures, such as body weight and metabolic parameters, GMB composition (abundance of each species), and AIWG will be tested using Spearman’s correlation. Analysis of the Operational Taxonomic Unit network generated using QIIME will be further analyzed using linear discriminant analysis (LDA) effect size (LEfSe) method. This method uses LDA scores to estimate the effect size of differentially abundant taxa (at phyla, class or other levels) and ranks the relative difference of microbial taxa that discriminate groups with biological consistency and statistical significance. The GMB composition of within group weight gainers versus non-weight gainer will also be assessed in a subgroup of subjects using LEfSe. Results To date, 17 patients enrolled (13 patients in group A (9 men, mean±SD age: 31.6 ± 5.1 years; BMI, 31.4 ± 5.5), two control in group B (1 men, mean±SD age: 29.0 ± 1.4 years; BMI, 30.3 ± 0.8), two in group C (1 men, mean±SD age: 30.0 ± 9.9 years; BMI, 25.8 ± 5.6)). Further analyses including GMB composition in each group are now being conducted. We are currently analyzing these individuals for their GMB composition and results will be presented at the SIRS meeting. Discussion To our knowledge, this is the first study to assess GMB sample in SCZ and investigate the association the GMB and AP-induced metabolic side effects. Our study has the potential to help to unravel the role of GMB in SCZ and AP response.