Polysomnographic findings in the ultra-rare McLeod syndrome: further documentation of sleep apnea as a possible feature.

STUDY OBJECTIVES: McLeod syndrome is a very rare multisystemic neurodegenerative disease linked to mutations in the XK gene. It has cardiac, neurologic, and neuromuscular manifestations and shares similarities with Huntington's disease. The aim of this study was to evaluate sleep patterns of patients affected by McLeod syndrome. METHODS: This retrospective case series of four males who underwent diagnostic polysomnography (mean age 53.8 ± 2.5 years) includes self-reported and objective evaluation of sleep using the Epworth Sleepiness Scale, genetic tests, documentation of clinical course and features, and laboratory-based full-night attended video-polysomnography. RESULTS: In three out of four patients, an Epworth Sleepiness Scale score ≥ 7 was evident. The average apnea-hypopnea index was 45.0 ± 19.0, with predominantly obstructive phenotype in three patients and predominant central events (central sleep apnea syndrome) in one patient. A significantly increased periodic limb movement index during sleep was observed in all patients. All patients tolerated continuous positive airway pressure or pressure controlled therapy. CONCLUSIONS: Polysomnography of all patients confirmed sleep apnea syndrome as a feature of McLeod syndrome. Three patients were diagnosed with obstructive sleep apnea and one with central sleep apnea syndrome. In addition, periodic limb movement index was increased in all patients. CITATION: Dieter M, Kevin P, Tobias V, et al. Polysomnographic findings in the ultra-rare McLeod syndrome: further documentation of sleep apnea as a possible feature. J Clin Sleep Med. 2024;20(3):339-344.

Proteome reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Many studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. To this end, we biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of this specific disease phase assignment, proteome analysis revealed a severity dependent general type-2 centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response together with the regulation of proteins related to SARS-CoV-2-specific symptoms by unbiased proteome screening both confirms results from targeted approaches and provides novel information for biomarker and therapy development. Graphical AbstractSars-CoV-2 remains a challenging threat to our health care system with many pathophysiological mechanisms not fully understood, especially in high-risk patients. Therefore, we characterized a cohort of hospitalized COVID-19 patients with multiple comorbidities by quantitative plasma proteomics and deep clinical phenotyping. The individual patients disease progression was determined and the subsequently assigned proteome profiles compared with a healthy and a chronically inflamed control cohort. The identified disease phase and severity specific protein profiles revealed an antiviral immune response together with coagulation activation indicating the formation of NETosis side-by-side with tissue remodeling related to the inflammatory signature. O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/22271106v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1e791faorg.highwire.dtl.DTLVardef@20d3d6org.highwire.dtl.DTLVardef@1339e42org.highwire.dtl.DTLVardef@1db3710_HPS_FORMAT_FIGEXP M_FIG C_FIG