{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T20:14:28Z","timestamp":1787948068652,"version":"build-2784847793"},"reference-count":41,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR\u201015\u2010IDEX\u201002"],"award-info":[{"award-number":["ANR\u201015\u2010IDEX\u201002"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019979","name":"Fondation Universit\u00e9 Grenoble Alpes","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019979","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To 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\u2010laboratory polysomnography (PSG).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cross\u2010sectional study using the clinical data of 1128 patients. Non\u2010invasive measurements of RE were derived from the sleep mandibular jaw movements (MJM) bio\u2010signal. An explainable machine\u2010learning model was built to predict prevalent type 2 diabetes from clinical data, standard PSG indices, and MJM\u2010derived parameters (including the proportion of TST spent with increased respiratory effort [REMOV [%TST]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Original data were randomly assigned to training (n\u2009=\u2009853) and validation (n\u2009=\u2009275) subsets. The classification model based on 18 input features including REMOV showed good performance for predicting prevalent type 2 diabetes (sensitivity\u2009=\u20090.81, specificity\u2009=\u20090.89). 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\u2010hypopnea and oxygen desaturation indices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These 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.<\/jats:p>\n                  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