Sleep characteristics in type 1 diabetes and associations with glycemic control: systematic review and meta-analysis

AbstractObjectivesThe association between inadequate sleep and type 2 diabetes has garnered much attention, but little is known about sleep and type 1 diabetes (T1D). Our objectives were to conduct a systematic review and meta-analysis comparing sleep in persons with and without T1D, and to explore relationships between sleep and glycemic control in T1D.MethodsStudies were identified from Medline and Scopus. Studies reporting measures of sleep in T1D patients and controls, and/or associations between sleep and glycemic control, were selected.ResultsA total of 22 studies were eligible for the meta-analysis. Children with T1D had shorter sleep duration (mean difference [MD] = −26.4 minutes; 95% confidence interval [CI] = −35.4, −17.7) than controls. Adults with T1D reported poorer sleep quality (MD in standardized sleep quality score = 0.51; 95% CI = 0.33, 0.70), with higher scores reflecting worse sleep quality) than controls, but there was no difference in self-reported sleep duration. Adults with TID who reported sleeping >6 hours had lower hemoglobin A1c (HbA1c) levels than those sleeping ≤6 hours (MD = −0.24%; 95% CI = −0.47, −0.02), and participants reporting good sleep quality had lower HbA1c than those with poor sleep quality (MD = −0.19%; 95% CI = −0.30, −0.08). The estimated prevalence of obstructive sleep apnea (OSA) in adults with TID was 51.9% (95% CI = 31.2, 72.6). Patients with moderate-to-severe OSA had a trend toward higher HbA1c (MD = 0.39%, 95% CI = −0.08, 0.87).ConclusionT1D was associated with poorer sleep and high prevalence of OSA. Poor sleep quality, shorter sleep duration, and OSA were associated with suboptimal glycemic control in T1D patients

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Abstract The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states

Inspiration Geschichte : Publikation der Arbeitsgemeinschaft des Kunsthandwerks Hamburg e.V.

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