Long-term intermittent hypoxia in mice induces inflammatory pathways implicated in sleep apnea and steatohepatitis in humans.

Obstructive sleep apnea (OSA) induces intermittent hypoxia (IH), an independent risk factor for non-alcoholic fatty liver disease (NAFLD). While the molecular links between IH and NAFLD progression are unclear, immune cell-driven inflammation plays a crucial role in NAFLD pathogenesis. Using lean mice exposed to long-term IH and a cohort of lean OSA patients (n = 71), we conducted comprehensive hepatic transcriptomics, lipidomics, and targeted serum proteomics. Significantly, we demonstrated that long-term IH alone can induce NASH molecular signatures found in human steatohepatitis transcriptomic data. Biomarkers (PPARs, NRFs, arachidonic acid, IL16, IL20, IFNB, TNF-α) associated with early hepatic and systemic inflammation were identified. This molecular link between IH, sleep apnea, and steatohepatitis merits further exploration in clinical trials, advocating for integrating sleep apnea diagnosis in liver disease phenotyping. Our unique signatures offer potential diagnostic and treatment response markers, highlighting therapeutic targets in the comorbidity of NAFLD and OSA.

Creating an Optimal Approach for Diagnosing Sleep Apnea.

Sleep apnea is nowadays recognized as a treatable chronic disease and awareness of it has increased, leading to an upsurge in demand for diagnostic testing. Conventionally, diagnosis depends on overnight polysomnography in a sleep clinic, which is highly human-resource intensive and ignores the night-to-night variability in classical sleep apnea markers, such as the apnea-hypopnea index. In this review, the authors summarize the main improvements that could be made in the sleep apnea diagnosis strategy; how technological innovations and multi-night home testing could be used to simplify, increase access, and reduce costs of diagnostic testing while avoiding misclassification of severity.

Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.

BACKGROUND: Obstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold standard for diagnosis. Performance scores in predicting OSA are evaluated for machine learning (ML) analysis applied to 3D maxillofacial shapes. METHODS: The 3D maxillofacial shapes were scanned on 280 Caucasian men with suspected OSA. All participants underwent single night in-home or in-laboratory sleep testing with PSG (Nox A1, Resmed, Australia), with concomitant 3D scanning (Sense v2, 3D systems corporation, USA). Anthropometric data, comorbidities, medication, BERLIN, and NoSAS questionnaires were also collected at baseline. The PSG recordings were manually scored at the reference sleep center. The 3D craniofacial scans were processed by geometric morphometrics, and 13 different supervised algorithms, varying from simple to more advanced, were trained and tested. Results for OSAS recognition by ML models were then compared with scores for specificity and sensitivity obtained using BERLIN and NoSAS questionnaires. RESULTS: All valid scans (n = 267) were included in the analysis (patient mean age: 59 ± 9 years; BMI: 27 ± 4 kg/m2). For PSG-derived AHI≥15 events/h, the 56% specificity obtained for ML analysis of 3D craniofacial shapes was higher than for the questionnaires (Berlin: 50%; NoSAS: 40%). A sensitivity of 80% was obtained using ML analysis, compared to nearly 90% for NoSAS and 61% for the BERLIN questionnaire. The auROC score was further improved when 3D geometric morphometrics were combined with patient anthropometrics (auROC = 0.75). CONCLUSION: The combination of 3D geometric morphometrics with ML is proposed as a rapid, efficient, and inexpensive screening tool for OSA. TRIAL REGISTRATION NUMBER: NCT03632382; Date of registration: 15-08-2018.

Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography.

Background: The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG. Methods: 40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour). Results: 31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m2). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI -23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI < 5: MM-ORDI mean overestimation + 5.58 (95% CI + 2.03 to + 7.46) events/hour; PSG-ORDI > 5-15: MM-ORDI overestimation + 3.70 (95% CI -0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (n = 9 with PSG-ORDI 15-30 events/h and n = 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation -8.70 (95% CI -28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively. Conclusion: The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients' own home. Clinical Trial Registration: https://clinicaltrials.gov, identifier NCT04262557.

Implantable cardiac devices in sleep apnoea diagnosis: A systematic review and meta-analysis.

BACKGROUND: A particularly high burden of sleep apnoea is reported in patients treated with cardiac implants such as pacemakers and defibrillators. Sleep apnoea diagnosis remains a complex procedure mainly based on sleep and respiratory indices captured by polysomnography (PSG) or respiratory polygraphy (PG). AIM: We aimed to evaluate the performance of implantable cardiac devices for sleep apnoea diagnosis compared to reference methods. METHOD: Systematic structured literature searches were performed in PubMed, Embase and. Cochrane Library was performed to identify relevant studies. Quantitative characteristics of the studies were summarized and a qualitative synthesis was performed by a randomized bivariate meta-analysis and completed by pre-specified sensitivity analyses for different implant types and brands. RESULTS: 16 studies involving 999 patients met inclusion criteria and were included in the meta-analysis. The majority of patients were men, of mean age of 64 ± 4.6 years. Sensitivity of cardiac implants for sleep apnoea diagnosis ranged from 60 to 100%, specificity from 50 to 100% with a prevalence of sleep apnoea varying from 22 to 91%. For an apnoea-hypopnoea index threshold ≥30 events/h during polysomnography (corresponding to severe sleep apnoea), the overall performance of the implants was relevant with a sensitivity of 78% and a specificity of 79%. Subgroup analyses on implant type and brand provided no additional information owing to the small number of studies. CONCLUSION: The respiratory disturbance index provided by cardiac implants is clinically relevant and might improve access to sleep apnoea diagnosis in at-risk cardiovascular populations. PROSPERO Registration number: CRD42020181656.

Greatest changes in objective sleep architecture during COVID-19 lockdown in night owls with increased REM sleep.

STUDY OBJECTIVES: The COVID-19 pandemic has had dramatic effects on society and people's daily habits. In this observational study, we recorded objective data on sleep macro- and microarchitecture repeatedly over several nights before and during the COVID-19 government-imposed lockdown. The main objective was to evaluate changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control. METHODS: Participants were regular users of a sleep-monitoring headband that records, stores, and automatically analyzes physiological data in real time, equivalent to polysomnography. We measured sleep onset duration, total sleep time, duration of sleep stages (N2, N3, and rapid eye movement [REM]), and sleep continuity. Via the user's smartphone application, participants filled in questionnaires on how lockdown changed working hours, eating behavior, and daily life at home. They also filled in the Insomnia Severity Index, reduced Morningness-Eveningness Questionnaire, and Hospital Anxiety and Depression Scale questionnaires, allowing us to create selected subgroups. RESULTS: The 599 participants were mainly men (71%) of median age 47 (interquartile range: 36-59). Compared to before lockdown, during lockdown individuals slept more overall (mean +3·83 min; SD: ±1.3), had less deep sleep (N3), more light sleep (N2), and longer REM sleep (mean +3·74 min; SD: ±0.8). They exhibited less weekend-specific changes, suggesting less sleep restriction during the week. Changes were most pronounced in individuals reporting eveningness preferences, suggesting relative sleep deprivation in this population and exacerbated sensitivity to societal changes. CONCLUSION: This unique dataset should help us understand the effects of lockdown on sleep architecture and on our health.

Coupling Microfluidic Platforms, Microfabrication, and Tissue Engineered Scaffolds to Investigate Tumor Cells Mechanobiology.

The tumor microenvironment (TME) is composed of dynamic and complex networks composed of matrix substrates, extracellular matrix (ECM), non-malignant cells, and tumor cells. The TME is in constant evolution during the disease progression, most notably through gradual stiffening of the stroma. Within the tumor, increased ECM stiffness drives tumor growth and metastatic events. However, classic in vitro strategies to study the TME in cancer lack the complexity to fully replicate the TME. The quest to understand how the mechanical, geometrical, and biochemical environment of cells impacts their behavior and fate has been a major force driving the recent development of new technologies in cell biology research. Despite rapid advances in this field, many challenges remain in order to bridge the gap between the classical culture dish and the biological reality of actual tissue. Microfabrication coupled with microfluidic approaches aim to engineer the actual complexity of the TME. Moreover, TME bioengineering allows artificial modulations with single or multiple cues to study different phenomena occurring in vivo. Some innovative cutting-edge tools and new microfluidic approaches could have an important impact on the fields of biology and medicine by bringing deeper understanding of the TME, cell behavior, and drug effects.

Mitochondrial Morphology and Function of the Pancreatic ß-Cells INS-1 Model upon Chronic Exposure to Sub-Lethal Cadmium Doses.

The impact of chronic cadmium exposure and slow accumulation on the occurrence and development of diabetes is controversial for human populations. Islets of Langerhans play a prominent role in the etiology of the disease, including by their ability to secrete insulin. Conversion of glucose increase into insulin secretion involves mitochondria. A rat model of pancreatic ß-cells was exposed to largely sub-lethal levels of cadmium cations applied for the longest possible time. Cadmium entered cells at concentrations far below those inducing cell death and accumulated by factors reaching several hundred folds the basal level. The mitochondria reorganized in response to the challenge by favoring fission as measured by increased circularity at cadmium levels already ten-fold below the median lethal dose. However, the energy charge and respiratory flux devoted to adenosine triphosphate synthesis were only affected at the onset of cellular death. The present data indicate that mitochondria participate in the adaptation of ß-cells to even a moderate cadmium burden without losing functionality, but their impairment in the long run may contribute to cellular dysfunction, when viability and ß-cells mass are affected as observed in diabetes.