Diane C. Lim

Obstructive sleep apnea and cognitive impairment: Addressing the blood–brain barrier

Increasing data support a connection between obstructive sleep apnea (OSA) and cognitive impairment but a causal link has yet to be established. Although neuronal loss has been linked to cognitive impairment, emerging theories propose that changes in synaptic plasticity can cause cognitive impairment. Studies demonstrate that disruption to the blood-brain barrier (BBB), which is uniquely structured to tightly maintain homeostasis inside the brain, leads to changes in the brains microenvironment and affects synaptic plasticity. Cyclical intermittent hypoxia is a stressor that could disrupt the BBB via molecular responses already known to occur in either OSA patients or animal models of intermittent hypoxia. However, we do not yet know if or how intermittent hypoxia can cause cognitive impairment by mechanisms operating at the BBB. Therefore, we propose that initially, adaptive homeostatic responses at the BBB occur in response to increased oxygen and nutrient demand, specifically through regulation of influx and efflux BBB transporters that alter microvessel permeability. We further hypothesize that although these responses are initially adaptive, these changes in BBB transporters can have long-term consequences that disrupt the brains microenvironment and alter synaptic plasticity leading to cognitive impairment.

Developing Biomarker Arrays Predicting Sleep and Circadian-Coupled Risks to Health

Janet M. Mullington, PhD1; Sabra M. Abbott, MD, PhD2; Judith E. Carroll, PhD3; Christopher J. Davis, MS, PhD4; Derk-Jan Dijk, PhD5; David F. Dinges, PhD6; Philip R. Gehrman, PhD7; Geoffrey S. Ginsburg, MD, PhD8; David Gozal, MD, MBA9; Monika Haack, PhD1; Diane C. Lim, MD10; Madalina Macrea, MD, MPH, PhD11,12; Allan I. Pack, MBChB, PhD, FRCP13; David T. Plante, MD14; Jennifer A. Teske, PhD15; Phyllis C. Zee, MD, PhD2

Effects of obesity on the association between long-term sleep apnea treatment and changes in interleukin-6 levels: the Icelandic Sleep Apnea Cohort.

The aim of this study was to evaluate changes in interleukin (IL)‐6 and soluble IL‐6 receptor levels in obstructive sleep apnea patients and assess the role of positive airway pressure treatment and obesity on these changes. A total of 309 newly diagnosed subjects with sleep apnea from the Icelandic Sleep Apnea Cohort were referred for treatment and reassessed at a 2‐year follow‐up. Full treatment was defined objectively as use ≥4 h day−1 and ≥20 days month−1. At the 2‐year follow‐up, there were 177 full users, 44 partial users and 88 non‐users. The mean change in biomarker levels from baseline to the 2‐year follow‐up was assessed in a primary model that included adjustment for baseline biomarker levels, baseline body mass index and change in body mass index, as well as after adjustment for numerous relevant covariates. No significant overall difference in IL‐6 level change was found among full, partial and non‐users. However, in severely obese patients (body mass index ≥35), a significant increase in IL‐6 levels during the 2‐year period was found in partial and non‐users, compared to no change in full users. Results were attenuated in a smaller propensity score matched subsample, although similar trends were observed. No differences were found in soluble IL‐6 receptor levels between full users and non‐users, after adjustment for confounders. In conclusion, among untreated obese sleep apnea patients, IL‐6 levels increase substantially during 2 years, while adherence to positive airway pressure treatment may prevent further increases in this inflammatory biomarker.

Obstructive Sleep Apnea: Update and Future

Obstructive sleep apnea (OSA) is a worldwide disease whose prevalence is increasing as obesity rates increase. The link between obesity and OSA is likely to be the deposition of fat in the tongue, compromising upper airway size. The role of obesity varies in different ethnic groups, with Chinese being particularly sensitive to increases in weight. OSA lends itself to a personalized approach to diagnosis and therapy. For example, different clinical OSA subtypes likely benefit from therapy in different ways. Hypoglossal nerve stimulation is a useful second-line therapy in patients who cannot tolerate continous positive airway pressure (CPAP) machines or intraoral devices. Technological advances allow patients to participate in their own care, and doing so improves CPAP compliance. We are entering a future where we can focus efforts to predict and prevent OSA on an individual level.

P4 medicine approach to obstructive sleep apnoea

P4 medicine is an evolving approach to personalized medicine. The four Ps offer a means to: Predict who will develop disease and co‐morbidities; Prevent rather than react to disease; Personalize diagnosis and treatment; have patients Participate in their own care. P4 medicine is very applicable to obstructive sleep apnoea (OSA) because each OSA patient has a different pathway to disease and its consequences. OSA has both structural and physiological mechanisms with different clinical subgroups, different molecular profiles and different consequences. This may explain why there are different responses to alternative therapies, such as intraoral devices and hypoglossal nerve stimulation therapy. Currently, technology facilitates patients to participate in their own care from screening for OSA (snoring and apnoea apps) to monitoring response to therapy (sleep monitoring, blood pressure, oxygen saturation and heart rate) as well as monitoring their own continuous positive airway pressure (CPAP) compliance. We present a conceptual framework that provides the basis for a new, P4 medicine approach to OSA and should be considered more in depth: predict and prevent those at high risk for OSA and consequences, personalize the diagnosis and treatment of OSA and build in patient participation to manage OSA.

Automated Protein Localization of Blood Brain Barrier Vasculature in Brightfield IHC Images

In this paper, we present an objective method for localization of proteins in blood brain barrier (BBB) vasculature using standard immunohistochemistry (IHC) techniques and bright-field microscopy. Images from the hippocampal region at the BBB are acquired using bright-field microscopy and subjected to our segmentation pipeline which is designed to automatically identify and segment microvessels containing the protein glucose transporter 1 (GLUT1). Gabor filtering and k-means clustering are employed to isolate potential vascular structures within cryosectioned slabs of the hippocampus, which are subsequently subjected to feature extraction followed by classification via decision forest. The false positive rate (FPR) of microvessel classification is characterized using synthetic and non-synthetic IHC image data for image entropies ranging between 3 and 8 bits. The average FPR for synthetic and non-synthetic IHC image data was found to be 5.48% and 5.04%, respectively.

Different cyclical intermittent hypoxia severities have different effects on hippocampal microvasculature.

Recent studies have shown an association between obstructive sleep apnea (OSA) and cognitive impairment. This study was done to investigate whether varied levels of cyclical intermittent hypoxia (CIH) differentially affect the microvasculature in the hippocampus, operating as a mechanistic link between OSA and cognitive impairment. We exposed C57BL/6 mice to sham [continuous air, arterial O2 saturation (SaO2 ) 97%], severe CIH to inspired O2 fraction (FiO2 ) = 0.10 (CIH10; SaO2 nadir of 61%), or very severe CIH to FiO2 = 0.05 (CIH5; SaO2 nadir of 37%) for 12 h/day for 2 wk. We quantified capillary length using neurostereology techniques in the dorsal hippocampus and utilized quantitative PCR methods to measure changes in sets of genes related to angiogenesis and to metabolism. Next, we employed immunohistochemistry semiquantification algorithms to quantitate GLUT1 protein on endothelial cells within hippocampal capillaries. Capillary length differed among CIH severity groups (P = 0.013) and demonstrated a linear relationship with CIH severity (P = 0.002). There was a strong association between CIH severity and changes in mRNA for VEGFA (P < 0.0001). Less strong, but nominally significant associations with CIH severity were also observed for ANGPT2 (PANOVA = 0.065, PTREND = 0.040), VEGFR2 (PANOVA = 0.032, PTREND = 0.429), and TIE-2 (PANOVA = 0.006, PTREND = 0.010). We found that the CIH5 group had increased GLUT1 protein relative to sham (P = 0.006) and CIH10 (P = 0.001). There was variation in GLUT1 protein along the microvasculature in different hippocampal subregions. An effect of CIH5 on GLUT1 mRNA was seen (PANOVA = 0.042, PTREND = 0.012). Thus CIH affects the microvasculature in the hippocampus, but consequences depend on CIH severity.

Home sleep apnea testing: comparison of manual and automated scoring across international sleep centers

PurposeTo determine the agreement between the manual scoring of home sleep apnea tests (HSATs) by international sleep technologists and automated scoring systems.MethodsFifteen HSATs, previously recorded using a type 3 monitor, were saved in European Data Format. The studies were scored by nine experienced technologists from the sleep centers of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) using the locally available software. Each study was scored separately by human scorers using the nasal pressure (NP), flow derived from the NP signal (transformed NP), or respiratory inductive plethysmography (RIP) flow. The same procedure was followed using two automated scoring systems: Remlogic (RLG) and Noxturnal (NOX).ResultsThe intra-class correlation coefficients (ICCs) of the apnea-hypopnea index (AHI) scoring using the NP, transformed NP, and RIP flow were 0.96 [95% CI 0.93–0.99], 0.98 [0.96–0.99], and 0.97 [0.95–0.99], respectively. Using the NP signal, the mean differences in AHI between the average of the manual scoring and the automated systems were − 0.9 ± 3.1/h (AHIRLG vs AHIMANUAL) and − 1.3 ± 2.6/h (AHINOX vs AHIMANUAL). Using the transformed NP, the mean differences in AHI were − 1.9 ± 3.3/h (AHIRLG vs AHIMANUAL) and 1.6 ± 3.0/h (AHINOX vs AHIMANUAL). Using the RIP flow, the mean differences in AHI were − 2.7 ± 4.5/h (AHIRLG vs AHIMANUAL) and 2.3 ± 3.4/h (AHINOX vs AHIMANUAL).ConclusionsThere is very strong agreement in the scoring of the AHI for HSATs between the automated systems and experienced international technologists. Automated scoring of HSATs using commercially available software may be useful to standardize scoring in future endeavors involving international sleep centers.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals. OBJECTIVE In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individuals manifestation of OSA. SIGNIFICANCE Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

Symptom-Based subgroups of koreans with obstructive sleep apnea

STUDY OBJECTIVES Recently, the Icelandic Sleep Apnea Cohort (ISAC) identified three subgroups in patients with obstructive sleep apnea (OSA) recruited from the sleep clinic based on clinical symptoms and comorbidities: excessively sleepy, minimally symptomatic, and disturbed sleep. This study sought to determine whether the three OSA subgroups are applicable to a population-based cohort in Korea. METHODS Study subjects are participants of an ongoing cohort study in Korea. Of the 2,918 participants, 422 new moderate to severe OSA cases (apneahypopnea index [AHI] ≥ 15 events/h) were diagnosed by home sleep studies. All participants completed a detailed questionnaire on sleep-related symptoms, comorbidities, medication, and other information. A latent class analysis was performed. RESULTS When examining solutions for up to 10 clusters, the a priori three-cluster solution was the optimal clustering solution. The three-cluster solution demonstrated a subgroup presentation similar to the clusters identified in the ISAC. The minimally symptomatic subgroup was most prevalent (55.7%) in the Korean cohort. Among the three subgroups, there were no differences in mean AHI and body mass index; however, the disturbed sleep subgroup was older and had more females. CONCLUSIONS Combined with the ISAC data, we suggest that the three-symptom cluster solution for patients with OSA may be more widely applicable, irrespective of ethnicity and study population.