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Study Objectives: There has been long-standing interest in potential links between obstructive sleep apnea (OSA) and eye disease. This study used retinal photography to identify undiagnosed retinal abnormalities in a cohort of sleep clinic patients referred for polysomnography (PSG) and then determined associations with PSG-quantified sleep-disordered breathing (SDB) severity., Methods: Retinal photographs (n = 396 patients) were taken of each eye prior to polysomnography and graded according to validated, standardized, grading scales. SDB was quantified via in-laboratory polysomnography (PSG; n = 385) using standard metrics. A questionnaire (n = 259) documented patient-identified pre-existing eye disease. Within-group prevalence rates were calculated on a per patient basis. Data were analyzed using multivariate logistic regression models to determine independent predictors for retinal abnormalities. P < 0.05 was considered significant., Results: Main findings were (1) 76% of patients reported no pre-existing "eye problems"; (2) however, 93% of patients had at least one undiagnosed retinal photograph-identified abnormality; (3) most common abnormalities were drusen (72%) and peripapillary atrophy (PPA; 47%); (4) age was the most common risk factor; (5) diabetes history was an expected risk factor for retinopathy; (6) patients with very severe levels of SDB (apnea hypopnea index ≥ 50 events/h) were nearly three times more likely to have PPA., Conclusion: Retinal photography in sleep clinic settings will likely detect a range of undiagnosed retinal abnormalities, most related to patient demographics and comorbidities and, except for PPA, not associated with SDB. PPA may be indicative of glaucoma, and any association with severe SDB should be confirmed in larger prospective studies., (© 2022. The Author(s).)

Introduction: Obstructive sleep apnea (OSA) may increase stroke risk; retinal arteriolar (central retinal arteriolar equivalent, CRAE) diameter narrowing and/or retinal venular (central retinal venule equivalent, CRVE) widening may predict stroke. We examined relationships between sleep disordered breathing (SDB) and CRAE and CRVE and in a diabetes-free sleep clinic cohort., Methods: Patients for SDB assessment were recruited (Main Group, n = 264, age: 58.5 ± 8.9 yrs [mean ± SD]; males: 141) for in-laboratory polysomnography (standard metrics, eg apnea hypopnea index, AHI) and retinal photographs (evening and morning). A more severe SDB sub-group (n = 85) entered a 12-month cardiovascular risk factor minimisation (hypertension/hypercholesterolemia control; RFM) and continuous positive airway pressure (CPAP) intervention (RFM/CPAP Sub-Group); successfully completed by n = 66 (AHI = 32.4 [22.1-45.3] events/hour, median[IQR]). Univariate (Spearman's correlation, t-test) and multiple linear regression models examined non-SDB and SDB associations with CRAE and CRVE measures., Results: Main Group: Evening CRAE predictors were: systolic blood pressure (0.18μm decrease per mmHg, p = 0.001), age (2.47μm decrease per decade, p = 0.012), Caucasian ethnicity (4.45 μm versus non-Caucasian, p = 0.011), height (0.24 μm decrease per cm increase, p = 0.005) and smoking history (3.08 μm increase, p = 0.052). Evening CRVE predictors were: Caucasian ethnicity (11.52 μm decrease versus non-Caucasian, p>0.001), diastolic blood pressure (0.34 μm increase in CRVE per mmHg, p = 0.001), hypertension history (6.5 μm decrease, p = 0.005), and smoking history (4.6 μm increase, p = 0.034). No SDB metric (all p>0.08) predicted CRAE or CRVE measures. RFM/CPAP Sub-Group: A one-unit increase in ln(AHI+1) was associated with a 0.046μm increase in CRAE (n = 85; p = 0.029). Mean evening CRAE and CRVE values did not change across the intervention (n = 66), but evening CRVE decreased ~6.0 μm for individuals with AHI >30 events/hr., Conclusion: No major SDB associations with CRAE or CRVE were identified, although the RFM/CPAP intervention reduced evening CRVE for severe OSA patients. Implications for cerebro-vascular disease risk remain uncertain., Trial Registration: The protocol was registered with the Australian New Zealand Clinical Trials Registry (Trial Id: ACTRN12620000694910)., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kairaitis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: REM-related obstructive sleep apnea (REM-OSA), as defined using revised apnea-hypopnea index (AHI) criteria, might represent a specific OSA phenotype. However, there is a lack of data on outcomes of treatment in this population. This study evaluated the effects of CPAP treatment over 12 months on clinical outcomes for patients with the polysomnography phenotype of REM-OSA., Methods: We conducted a prospective observational study with the following inclusion criteria: subjective sleepiness and diagnostic polysomnography demonstrating AHI REM ≥15 events/h, AHI NREM <5 events/h, and ≥ 30 min of REM sleep. Clinical outcomes assessed included Epworth Sleepiness Scale (ESS), psychomotor vigilanc test reaction time (PVT-RT), and CPAP adherence at baseline, 1, 3, 6, and 12 months; Functional Outcomes of Sleep Questionnaire (FOSQ) and Depression Anxiety Stress Scales (DASS-21) at baseline, 1, 3 and 12 months. The reason is the first 3 outcomes (ESS, PVT, adherence) were assessed at baseline, 1, 3, 6, and 12 months, while the next 2 outcomes (FOSQ, DASS) were assessed at baseline, 1, 3, and 12 months. The edited version is not as clear in separating these outcomes into 2 groups; Functional Outcomes of Sleep Questionnaire (FOSQ); and Depression Anxiety Stress Scales (DASS-21) at baseline, 1, 3, and 12 months. Linear mixed effects models were used to investigate the joint effects of time and average CPAP adherence on our outcomes of interest., Results: Twenty participants completed a minimum of 1 month of CPAP treatment and were included for analysis. During the trial, 8 participants discontinued CPAP (4 before 3 months, 1 before 6 months, 3 before 12 months), and 19 participants completed 12 months of treatment. Baseline ESS was elevated at 12.6 units. Average CPAP usage for all 27 participants over 12 months was 2.9 ± 2.4 h. There was a significant decrease in ESS and increase in FOSQ at all time points, and the decrease in ESS was only seen in the CPAP-adherent subgroup. Decreases in DASS-21 and PVT-RT were not sustained., Conclusions: CPAP treatment in sleepy patients with moderate to severe REM-OSA is associated with reduced sleepiness and improved quality of life., Trial Registration: The trial was registered in the Australian New Zealand Clinical Trials Registry: ACTRN12620000576921, 18/05/2020 (retrospectively registered)., (© 2021. Crown.)

Obstructive sleep apnea (OSA) is more common in men than women. Body size is greater in males (sexual dimorphism), but large body habitus is associated with OSA for both genders. We speculated that male-female phenotypical convergence (reduced sexual dimorphism via identical phenotype acquisition) occurs with OSA and tested hypotheses: (1) phenotypical features pathogenic for OSA differ between OSA and healthy subjects irrespective of gender; and (2) such characteristics exhibit phenotypical convergence. Utilizing an existing database, we calculated male-female (group average) ratios for eight anthropometric and 33 surface cephalometric variables from 104 Caucasian OSA patients [72 males; apnea-hypopnea index (events h(-1) ): males: 42.3 ± 24.7 versus females: 42.6 ± 26.1 (P > 0.9)] and 85 Caucasian, healthy, non-OSA, community volunteers (36 males). Log-transformed data were analysed using a general linear model with post-hoc unpaired t-tests and significance at P < 0.0012 (Bonferroni multiple-comparison correction). OSA patients were older (56.9 ± 14.4 versus 38.0 ± 13.8 years), but there were no within-group gender-based age differences. All anthropometric variables (except height), plus cranial base width, mandibular breadth and retromandibular width diagonal were larger in gender-matched OSA versus healthy comparisons; thus satisfying hypothesis (1). Male-female ratios were mostly >1.0 across groups, but with no significant group × gender interactions no variable satisfied hypothesis (2). Thus, in this exploratory study, OSA patients had gender-common phenotypical differences to healthy subjects, but sexual dimorphism was preserved. Lack of complete phenotypical convergence may indicate gender-based critical phenotype-level attainment for OSA and/or gender-based OSA prevalence arises from factors other than those in this study., (© 2014 European Sleep Research Society.)

Study Objective: We used statistical modelling to probe the contributions of anthropometric and surface cephalometric variables to the OSA phenotype., Design: The design is prospective cohort study., Setting: The setting is community-based and sleep disorder laboratory., Patients or Participants: Study #1-Model development study: 147 healthy asymptomatic volunteers (62.6 % Caucasian; age, 18-76 years; 81 females; median multivariable apnea prediction index=0.15) and 140 diagnosed OSA patients (84.3 % Caucasian; age, 18-83 years; 41 females; polysomnography [PSG] determined apnea-hypopnea index >10 events/h). Study #2-Model test study: 345 clinic patients (age, 18-86 years; 129 females) undergoing PSG for diagnosis of OSA., Intervention: We measured 10 anthropometric and 34 surface cephalometric dimensions (calipers) and calculated mandibular enclosure volumes for study #1 and recorded age and neck circumference for study #2. Statistical modelling included principal component (PC), logistic regression, and receiver-operator curve analyses., Measurements and Results: Model development study: A regression model incorporating three identified PC predicted OSA with 88 % sensitivity and specificity. However, a simplified model based on age and NC alone was equally effective (87 % sensitivity and specificity). Model test study: The simplified model predicted OSA with high sensitivity (93 %) but poor specificity (21 %)., Conclusion: We conclude that in our clinic-based cohort, craniofacial bony and soft tissue structures (excluding neck anatomy) do not play a substantial role in distinguishing patients with OSA from those without. This may be because craniofacial anatomy does not contribute greatly to the pathogenesis of OSA in this group or because referral bias has created a relatively homogeneous phenotypic population.