include maintaining a patients—defined as body mass index (BMI) ≥ consistent sleep-wake schedule (even on 30 kg/m²—regular aerobic exercise and a weekends), ensuring a dark, quiet, and cool structured

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

• Is obstructive sleep apnea a driver of cancer and chronic disease risk? A real-world analysis of over 3 million patients (2026) · doi

  Several limitations should be acknowledged. First, mis- classification and detection bias cannot be fully excluded. Diagnostic accuracy and coding practices may vary across institutions, and patients with OSA may undergo more frequent medical evaluations and diagnostic testing, increasing the likelihood that incident diseases are identi- fied during follow-up. As a result, some of the observed associations may partly reflect differences in healthcare utilization and disease ascertainment rather than true differences in disease occurrence. In addition, key sleep- specific variables and important lifestyle-related con- founders, including smoking intensity, physical activity, and diet, are not consistently captured in structured elec- tronic health record data. Propensity score matching was restricted to age and sex, while other relevant clinical and lifestyle-related confounders, such as obesity, smoking, alcohol use, baseline metabolic status, and comorbid- ity burden, were not included in the matching process. Accordingly, residual confounding cannot be excluded and may have influenced the reported effect estimates. This is particularly relevant for the overweight/obesity outcome, as obesity is closely linked to both the develop- ment and diagnosis of OSA. Second, outcomes were assessed over a 5-year follow- up period, which may be insufficient to capture long- latency processes such as carcinogenesis for certain tumor types. In addition, OSA was identified using ICD- 10 coding only, which may have introduced diagnostic misclassification, and no information on disease sever- ity, hypoxemia burden, or treatment status was available. Therefore, severity-dependent associations and poten- tial dose–response relationships could not be examined. Moreover, OSA status and treatment exposure were modeled as baseline variables, although both may change over time, potentially introducing unmeasured time- varying confounding. Finally, undiagnosed OSA in the control group repre- sents a further source of bias. Because OSA is frequently underdiagnosed, some individuals classified as controls may have had unrecognized disease. In addition, both cohorts were restricted to hospitalized patients, and the study population may therefore not be representative of the broader OSA population. This may have introduced selection bias and limits the generalizability of the find- ings, particularly to patients with milder disease man- aged exclusively in outpatient settings. These limitations should be considered when interpret- ing the magnitude and generalizability of the observed associations.

  Keywords: disease bias diagnostic patients associations addition obesity status limitations cannot excluded coding follow observed differences
• Obstructive Sleep Apnea: A Comprehensive Clinical Review of Pathophysiology, Diagnosis, Multimodal Management, and Interprofessional Care (2026) · doi

  include maintaining a patients—defined as body mass index (BMI) ≥ consistent sleep-wake schedule (even on 30 kg/m²—regular aerobic exercise and a weekends), ensuring a dark, quiet, and cool structured, hypocaloric healthy diet should be sleeping environment, avoiding electronic systematically encouraged. Robust evidence screens for at least 60 minutes prior to bedtime, from randomized controlled trials and and establishing a relaxing pre-sleep longitudinal cohort studies demonstrates that ritual.[39][40][41][42][43] Additionally, weight loss (whether achieved through lifestyle positional therapy—encouraging sleep in the intervention, pharmacotherapy, or bariatric non-supine position—may be recommended surgery) produces clinically meaningful for patients with positional OSA (defined as a improvements across multiple domains: supine AHI at least twice that of non-supine reduction in the Apnea-Hypopnea Index (AHI) AHI). Simple interventions such as the use of a and Respiratory Disturbance Index (RDI), specialized positional pillow, a wearable amelioration of objective and subjective vibrating device, or even a tennis shirt sewn daytime sleepiness, improvement in metabolic into the back of a pajama top can effectively parameters (including insulin sensitivity and reduce supine sleep time. Critically, all patients lipid profiles), and enhanced blood pressure with OSA must be advised that alcohol and control.[37][38] The mechanistic basis for sedating medications (including Uva Clinical Research Lab 2026 © Uva Clinical Anaesthesia and Intensive Care ISSN 2827-7198 Obstructive Sleep Apnea: 18 May 2026 10 Uva Clinical Research Lab 2026 © Uva Clinical Anaesthesia and Intensive Care ISSN 2827-7198 Obstructive Sleep Apnea: 18 May 2026 benzodiazepines, non-benzodiazepine airway pressure (EPAP), is useful for patients hypnotics, and opiates) exacerbate OSA who require high pressures, experience CPAP severity by reducing pharyngeal muscle tone, intolerance, or have concomitant blunting arousal responses to airway occlusion, hypoventilation or obesity hypoventilation and prolonging the duration of obstructive syndrome. Auto-titrating positive airway events, thereby worsening oxyhemoglobin pressure (APAP) automatically adjusts the desaturation. delivered pressure on a breath-by-breath basis

  Keywords: sleep patients supine pressure clinical index positional apnea obstructive airway defined even least including basis
• Comparative effects of upper-body plyometric training and circuit interval training in obese adults with moderate obstructive sleep apnea: a pilot study (2026) · doi

  This pilot study has several limitations. First, the sample size was small, which limits statistical power and generalizability. Second, no dietary intake monitoring or nutritional control was implemented, which may have influenced changes in body composition. Third, neither participants nor assessors the risk of performance and were blinded, measurement bias. Fourth, no follow­up assessment was conducted to determine whether the observed changes were maintained over time. Fifth, no correction for multiple comparisons was applied, increasing the risk of Type I error. Finally, the Apnea–Hypopnea Index (AHI) and other objective sleep­related outcomes were not the intervention, limiting conclusions regarding the impact of these interventions on OSA severity. Some calculated t­values were unexpectedly high relative to the sample size and magnitude of mean change. Although the analyses were performed using SPSS, these values should be interpreted cautiously, as they may reflect statistical instability associated with the small pilot sample. reassessed after R. Jayabharathi E­mail: [email protected] 1. Messineo, L., Bakker, J. P., Cronin, J., Yee, J., & White, D. P. (2024). Obstructive sleep apnea and obesity: A review of epidemiology, pathophysiology and the effect of weight­loss treatments. Sleep medicine reviews, 78, 101996. https://doi.org/10.1016/ j.smrv.2024.101996 2. Chang, J. L., Goldberg, A. N., Alt, J. A., Mohammed, A., Ashbrook, L., Auckley, D., Ayappa, I., Bakhtiar, H., Barrera, J. E., Bartley, B. L., Billings, M. E., Boon, M. S., Bosschieter, P., Braverman, I., Brodie, K., Cabrera­Muffly, C., Caesar, R., Cahali, M. B., Cai, Y., Cao, M. Rosen, I. M. (2023). International Consensus Statement on Obstructive Sleep Apnea. International forum of allergy & rhinology, 13(7), 1061–1482. https://doi.org/10.1002/alr.23079 3. Lee, W., Nagubadi, S., Kryger, M. H., & Mokhlesi, B. (2008). Epidemiology of Obstructive Sleep Apnea: a Population­based Perspective. Expert review of respiratory medicine, 2(3), 349–364. https://doi.org/10.1586/17476348.2.3.349 4. Romero­Corral, A., Caples, S. M., Lopez­Jimenez, F., & Somers, V. K. (2010). Interactions between obesity and obstructive sleep apnea: implications for treatment. Chest, 137(3), 711–719. https://doi.org/10.1378/chest.09­0360 5. Dempsey, J. A., Veasey, S. C., Morgan, B. J., & O'Donnell, C. P. (2010). Pathophysiology of sleep apnea. Physiological reviews, 90(1), 47–112. https://doi.org/10.1152/physrev.00043.2008 6. Isono, Shiroh. (2012). Obesity and obstructive sleep apnoea: Mechanisms for increased collapsibility of the passive pharyngeal airway. Respirology (Carlton, Vic.). 17. 32­42. 10.1111/j.1440­1843.2011.02093.x. 7. Eckert, D. J., & Malhotra, A. (2008). Pathophysiology of adult obstructive sleep apnea. Proceedings of the American Thora

  Keywords: sleep apnea obstructive https sample obesity pathophysiology pilot size small statistical changes risk values review