Impaired Glucose Tolerance in Sleep Disorders

BACKGROUND: Recent epidemiological and experimental data suggest a negative influence of shortened or disturbed night sleep on glucose tolerance. Due to the high prevalence of sleep disorders this might be a major health issue. However, no comparative studies of carbohydrate metabolism have been conducted in clinical sleep disorders. METHODOLOGY/PRINCIPAL FINDINGS: We performed oral glucose tolerance tests (OGTT) and assessed additional parameters of carbohydrate metabolism in patients suffering from obstructive sleep apnea syndrome (OSAS, N = 25), restless legs syndrome (RLS, N = 18) or primary insomnia (N = 21), and in healthy controls (N = 33). Compared to controls, increased rates of impaired glucose tolerance were found in OSAS (OR: 4.9) and RLS (OR: 4.7) patients, but not in primary insomnia patients (OR: 1.6). In addition, HbA1c values were significantly increased in the same two patient groups. Significant positive correlations were found between 2-h plasma glucose values measured during the OGTT and the apnea-arousal-index in OSAS (r = 0.56; p<0.05) and the periodic leg movement-arousal-index in RLS (r = 0.56, p<0.05), respectively. Sleep duration and other quantitative aspects of sleep were similar between patient groups. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that some, but not all sleep disorders considerably compromise glucose metabolism. Repeated arousals during sleep might be a pivotal causative factor deserving further experimental investigations to reveal potential novel targets for the prevention of metabolic diseases

HUNCHEST-II contributes to a shift to earlier-stage lung cancer detection: final results of a nationwide screening program

The introduction of low-dose CT (LDCT) altered the landscape of lung cancer (LC) screening and contributed to the reduction of mortality rates worldwide. Here we report the final results of HUNCHEST-II, the largest population-based LDCT screening program in Hungary, including the screening and diagnostic outcomes, and the characteristics of the LC cases.A total of 4215 high-risk individuals aged between 50 and 75 years with a smoking history of at least 25 pack-years were assigned to undergo LDCT screening. Screening outcomes were determined based on the volume, growth, and volume doubling time of pulmonary nodules or masses. The clinical stage distribution of screen-detected cancers was compared with two independent practice-based databases consisting of unscreened LC patients.The percentage of negative and indeterminate tests at baseline were 74.2% and 21.7%, respectively, whereas the prevalence of positive LDCT results was 4.1%. Overall, 76 LC patients were diagnosed throughout the screening rounds (1.8% of total participants), out of which 62 (1.5%) patients were already identified in the first screening round. The overall positive predictive value of a positive test was 58%. Most screen-detected malignancies were stage I LCs (60.7%), and only 16.4% of all cases could be classified as stage IV disease. The percentage of early-stage malignancies was significantly higher among HUNCHEST-II screen-detected individuals than among the LC patients in the National Koranyi Institute of Pulmonology's archive or the Hungarian Cancer Registry (p < 0.001).HUNCHEST-II demonstrates that LDCT screening for LC facilitates early diagnosis, thus arguing in favor of introducing systematic LC screening in Hungary.HUNCHEST-II is the so-far largest population-based low-dose CT screening program in Hungary. A positive test's overall positive predictive value was 58%, and most screen-detected malignancies were early-stage lesions. These results pave the way for expansive systematic screening in the region.• Conducted in 18 medical facilities, HUNCHEST-II is the so far largest population-based low-dose CT screening program in Hungary. • The vast majority of screen-detected malignancies were early-stage lung cancers, and the overall positive predictive value of a positive test was 58%. • HUNCHEST-II facilitates early diagnosis, thus arguing in favor of introducing systematic lung cancer screening in Hungary

Immunologic and metabolic changes in sleep disorders

Department of Neurology First Faculty of Medicine and General University Hospital in PragueNeurologická klinika 1. LF UK a VFN v PrazeFirst Faculty of Medicine1. lékařská fakult

Central Disorders of Hypersomnolence: Association with Fatigue, Depression and Sleep Inertia Prevailing in Women

Fatigue, depression, and sleep inertia are frequently underdiagnosed manifestations in narcolepsy and idiopathic hypersomnia. Our cross-sectional study design included diagnostic interview accompanied by assessment instruments and aimed to explore how these factors influence disease severity as well as to elucidate any sex predisposition. One hundred and forty-eight subjects (female 63%) were divided into narcolepsy type 1 (NT1; n = 87, female = 61%), narcolepsy type 2 (NT2; n = 22, female = 59%), and idiopathic hypersomnia (IH; n = 39, female = 69%). All subjects completed a set of questionnaires: Epworth Sleepiness Scale (ESS), Hospital Anxiety and Depression Scales (HADS), Fatigue Severity Scale (FSS), and Sleep Inertia Questionnaire (SIQ). In narcoleptic subjects, questionnaire data were correlated with the Narcolepsy Severity Scale (NSS), and in subjects with idiopathic hypersomnia, with the Idiopathic Hypersomnia Severity Scale (IHSS). The highest correlation in narcoleptic subjects was found between NSS and ESS (r = 0.658; p p p p p = 0.0005), and HADS anxiety scale (r = 0.528; p p p p p p = 0.057). Our study illustrates that more attention should be focused on pathophysiological mechanisms and associations of fatigue, depression, as well as sleep inertia in these diseases; they influence the course of both illnesses, particularly in women

Czech Sleep Norms

StudyThe CzechSleepNorms.exe is a graphical user interface for the Czech Sleep Norms described in the publication:J. Nepožitek, S. Dostálová, J. Hlavnička, J. Košťálová, E. M. Horvat, T. Vorlová, V. Ibarburu, L. Račanská, P. Peřinová, K. Šonka (2024). Evolution of sleep in aging: a polysomnographic study stratified over five decades.RequirementsOperating system: Windows 11, Windows 10 (version 1909 or higher), Windows Server 2019Processor: Any Intel or AMD x86-64 processor (minimum), any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support (recommended)RAM: 4 GB minimumMATLAB Runtime is free and will be downloaded and installed during installationStorage: 3 MB max (after installation), ~750 MB including MATLAB RuntimeDatabaseSeventy-six healthy individuals with no history of sleep disorders or neurological diseases were included in the study. Five equal-sized age brackets balanced in gender were created: 20-30, 30-40, 40-50, 50-60, and 60-70 years. The study protocol consisted of polysomnography and questionnaires assessing insomnia, sleepiness, depression, and state/trait anxiety.AbbreviationsBMI = body mass indexBDI-II = Beck Depression Inventory, second editionSTAI X1 = State-Trait Anxiety Inventory, state anxietySTAI X2 = State-Trait Anxiety Inventory, trait anxietyESS = Epworth Sleepiness ScaleISI = Insomnia Severity IndexTST = total sleep timeSL = sleep latencySE = sleep efficiencyREM = rapid eye movement sleepN1, N2, N3 = non-rapid eye movement sleep stage 1, 2, 3AHI = apnoea-hypopnoea indexODI = oxygen desaturation indexT90 = time under 90% oxygen saturationPLMI = periodic limb movements indexEFM = excessive fragmentary myoclonusOSA = obstructive sleep apnoeaPLMS = periodic limb movements in sleepHow to UninstallPlease find the CzechSleepNorms in the Start menu and select Uninstall after right click. A new window "Programs and Features" pops up. Please select the CzechSleepNorms and click Uninstall. You can also remove the Matlab Runtime if you do not need it for other apps.Another way is to go to Windows Settings and click on Apps. You can find the CzechSleepNorms in the list and click on Uninstall.</p

Association between the 2 hour-plasma glucose and the PLMS-arousal-index in restless legs patients.

<p>Pearson's correlation coefficient; r = 0.56, <i>p</i><0.05.</p

Metabolic Parameters.

<p>Data are mean (SD). Statistical comparison was done using ANCOVA.</p><p>OSAS, Obstructive sleep apnea syndrome; RLS, Restless legs syndrome; INS, Insomnia; CON, controls; FPG = Fasting plasma glucose; FPI = Fasting plasma insulin; 2h-PG = 2h-Postload glucose; 2h-PI = 2h-Postload insulin; AUCg = Area under the curve for glucose; HOMA1-IR = Homeostasis model assessment-1 of insulin resistance; ISIcomposite = Insulin sensitivity index composite.</p><p>* p<0.05, † p<0.01, ‡ p<0.001.</p

Baseline Characteristics of Study Participants.

<p>Data are mean (SD). Statistical comparison was done using Gabriel- or Games-Howell-corrected oneway ANOVA.</p><p>OSAS, Obstructive sleep apnea syndrome; RLS, Restless legs syndrome; INS, Insomnia; CON, controls; BMI, Body mass index; PSQI, Pittsburgh Sleep Quality Index; ESS, Epworth Sleepiness Scale.</p><p>* p<0.05, † p<0.01, ‡ p<0.001, vs controls.</p><p>⁺ p<0.05, □ p<0.01, ∇ p<0.001, between groups.</p><p>OSAS: χ² (1) = 18.21, p<0.001; RLS: χ² (1) = 0.433, p>0.05; INS: χ² (1) = 0.163, p>0.05, vs controls.</p

Frequency of Patients with Normal or Elevated HbA1c (≥5.5%) & FPG (≥100mg/dl).

<p>OSAS, Obstructive sleep apnea syndrome; RLS, Restless legs syndrome; INS, Insomnia; CON, controls; FPG = Fasting plasma glucose.</p><p>χ² (3) = 25·31, p<0·001.</p