ArticleDiabetes, obesity & metabolism2023
Respiratory effort during sleep and the rate of prevalent type 2 diabetes in obstructive sleep apnoea.
Article in Diabetes, obesity & metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Home-testing devices for diagnosing obstructive sleep apnoea hypopnoea syndrome - a systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2026Pooled it
- From classic to cutting-edge: technological approaches to respiratory physiological signals in assessing sleep-disordered breathing.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Review
- Respiratory effort burden measured by mandibular jaw movements as a digital marker with clinical insights in obstructive sleep apnea.Communications medicine · 2026Article
- Exploring the Dose-Response Relationship between Mandibular Protrusion and Respiratory Effort Burden in Oral Appliance Therapy for Obstructive Sleep Apnea.Annals of the American Thoracic Society · 2025Article
- Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
Abstract
aimTo determine the association between total sleep time (TST) spent in increased respiratory effort (RE) and the prevalence of type 2 diabetes in a large cohort of individuals with suspected obstructive sleep apnoea (OSA) referred for in-laboratory polysomnography (PSG). MATERIALS AND
methodsWe conducted a retrospective cross-sectional study using the clinical data of 1128 patients. Non-invasive measurements of RE were derived from the sleep mandibular jaw movements (MJM) bio-signal. An explainable machine-learning model was built to predict prevalent type 2 diabetes from clinical data, standard PSG indices, and MJM-derived parameters (including the proportion of TST spent with increased respiratory effort [REMOV [%TST]).
resultsOriginal data were randomly assigned to training (n = 853) and validation (n = 275) subsets. The classification model based on 18 input features including REMOV showed good performance for predicting prevalent type 2 diabetes (sensitivity = 0.81, specificity = 0.89). Post hoc interpretation using the Shapley additive explanation method found that a high value of REMOV was the most important risk factor associated with type 2 diabetes after traditional clinical variables (age, sex, body mass index), and ahead of standard PSG metrics including the apnoea-hypopnea and oxygen desaturation indices.
conclusionsThese findings show for the first time that the proportion of sleep time spent in increased RE (assessed through MJM measurements) is an important predictor of the association with type 2 diabetes in individuals with OSA.
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