Calcium-dependent transcriptional changes in human pancreatic islet cells reveal functional diversity in islet cell subtypes.

AIMS/HYPOTHESIS: Pancreatic islets depend on cytosolic calcium (Ca2+) to trigger the secretion of glucoregulatory hormones and trigger transcriptional regulation of genes important for islet response to stimuli. To date, there has not been an attempt to profile Ca2+-regulated gene expression in all islet cell types. Our aim was to construct a large single-cell transcriptomic dataset from human islets exposed to conditions that would acutely induce or inhibit intracellular Ca2+ signalling, while preserving biological heterogeneity. METHODS: We exposed intact human islets from three donors to the following conditions: (1) 2.8 mmol/l glucose; (2) 16 mmol/l glucose and 40 mmol/l KCl to maximally stimulate Ca2+ signalling; and (3) 16 mmol/l glucose, 40 mmol/l KCl and 5 mmol/l EGTA (Ca2+ chelator) to inhibit Ca2+ signalling, for 1 h. We sequenced 68,650 cells from all islet cell types, and further subsetted the cells to form an endocrine cell-specific dataset of 59,373 cells expressing INS, GCG, SST or PPY. We compared transcriptomes across conditions to determine the differentially expressed Ca2+-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells. RESULTS: Based on the number of Ca2+-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of Ca2+ response. We also showed that polyhormonal clusters expressing both INS and GCG, or both INS and SST, are defined by Ca2+-regulated genes specific to each cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of Ca2+-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function. CONCLUSIONS/INTERPRETATION: Here we use our large-scale, multi-condition, single-cell dataset to show that human islets have cell-type-specific Ca2+-regulated gene expression profiles, some of them specific to subpopulations. In our dataset, we identify PCDH7 as a novel marker of beta cells having an increased number of Ca2+-regulated genes and enhanced insulin secretory function. DATA AVAILABILITY: A searchable and user-friendly format of the data in this study, specifically designed for rapid mining of single-cell RNA sequencing data, is available at https://lynnlab.shinyapps.io/Human_Islet_Atlas/ . The raw data files are available at NCBI Gene Expression Omnibus (GSE196715).

Masters Athlete Screening Study (MASS): Insights Into the Psychological Impact of Cardiovascular Preparticipation Screening.

OBJECTIVE: To determine the psychological impact of a cardiovascular disease (CVD) diagnosis identified during preparticipation screening (PPS) of masters athletes. DESIGN: Cross-sectional study. SETTING: Masters athletes diagnosed with CVD through the Masters Athletes Screening Study. PARTICIPANTS: Sixty-seven athletes (89.6% male, mean age at diagnosis 60.1 ± 7.1 years, range 40-76) with diagnoses of coronary artery disease (CAD) (73.1%), high premature ventricular contraction burden (9.0%), mitral valve prolapse (7.5%), atrial fibrillation (AF) (3.0%), bicuspid aortic valve (3.0%), aortic dilatation (1.5%), coronary anomaly (1.5%), and rheumatic heart disease (1.5%). Three participants had multiple diagnoses. INTERVENTION: Online survey distributed to masters athletes identified with CVD. MAIN OUTCOME MEASURES: Assessment of psychological distress [Impact of Event Scale-Revised (IES-R)], perceptions of screening, and preferred support by CVD type. RESULTS: The median total IES-R and subscale scores were within the normal range {median [interquartile range (IQR)] total 2.0 [0-6.0]; intrusion 1.0 [0-3.0]; avoidance 0 [0-3.0]; hyperarousal 0 [0-1.0]}. Athletes with bicuspid aortic valve [20.5 (IQR, 4.0-37.0)], AF [7.0 (IQR, 0-14.0)], and severe CAD [5.5 (IQR, 1.0-12.0)] had the highest total IES-R scores. One individual with bicuspid aortic valve reported a significant stress reaction. Ten athletes (14.9%) had scores >12. Ninety-three percent of athletes were satisfied having undergone PPS. Preferred type of support varied by cardiovascular diagnosis. CONCLUSIONS: The majority of masters athletes diagnosed with CVD through PPS do not experience significant levels of psychological distress. Athletes diagnosed with more severe types of CVD should be monitored for psychological distress. Support should be provided through a multidisciplinary and individualized approach.

Automated External Defibrillator and Emergency Action Plan Preparedness Amongst Canadian University Athletics.

Sudden cardiac death is the leading medical cause of death in athletes. The use of an automated external defibrillator (AED) and an emergency action plan (EAP) are effective strategies for improving outcomes of sudden cardiac arrest. The availability of an AED and the presence of an EAP amongst Canadian universities (U-SPORTS) are unknown. Surveys were sent to the athletic directors from U-SPORTS representing the universities within Canada. Questions were directed towards AED and EAP preparedness. All schools reported an on-site AED for sanctioned events. However, less than half of schools reported bringing the AED on-site for field sports. A total of 89% of U-SPORTS universities estimated that their EAP is capable of delivering defibrillation within 5 minutes of collapse. The majority of U-SPORTS universities have accessible AEDs and satisfactory EAP strategies. However, AED availability and EAPs during sport require continuous improvement.

Defining Athletes and Exercisers.

Current definitions of an athlete range from loosely defined to overly restrictive, rely on qualitative subjective descriptors, and fail to capture the majority of physically active patients. To improve the understanding between exercise and health metrics a more standardized and granular classification of exercising patients is required. We propose a simplified algorithm to categorize and define exercising patients based on the: (1) intent of exercise, (2) volume of exercise (hours/week), and (3) level of competition. Further classification of physically active patients can be derived based on the intensity and volume of activity using metabolic equivalent-hours per week. In conclusion, a formal framework to classify athletes and quantify both the volume and intensity of exercise will enable more precise and meaningful associations between exercise and health outcomes.