{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T10:04:42Z","timestamp":1779789882979,"version":"3.53.1"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Respiratory J"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Sleep\u2010disordered breathing (SDB) is linked to memory decline, but the exact relationship between sleep fragmentation, nocturnal hypoxemia, and cognitive impairment remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aimed to investigate the associations between micro\u2010arousal burden, nocturnal oxygen desaturation, and memory decline in patients with moderate\u2010to\u2010severe OSA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data were retrieved from the clinical and overnight polysomnographic (PSG) records of adult patients evaluated for suspected SDB. The primary clinical endpoint was the presence and severity of memory decline, ascertained via a standardized Subjective Cognitive Decline (SCD) instrument. A multidimensional array of variables was systematically extracted, encompassing baseline demographic characteristics, cardiometabolic comorbidities, and high\u2010resolution sleep architecture metrics, with a distinct emphasis on stage\u2010specific micro\u2010arousal burdens and the morphological profiles of nocturnal oxygen desaturation. Then, independent\n                      <jats:italic>t<\/jats:italic>\n                      tests and  tests were initially utilized to characterize PSG disparities between the memory\u2010normal and memory\u2010decline groups. And interpretable machine learning algorithms, utilizing rigorously partitioned training and validation sets, were deployed to predict cognitive trajectories and elucidate the relative prognostic importance of specific sleep\u2010related parameters.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final analytical sample comprised 884 participants with complete primary outcome data (memory\u2010normal:\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009408; memory\u2010decline:\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009476). Initial comparative analyses revealed the memory\u2010decline group was older (50.24 vs. 45.95\u2009years,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) with a significantly higher prevalence of cardiometabolic comorbidities, including hypertension (47.3% vs. 40.2%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.035) and diabetes (24.4% vs. 8.8%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Polysomnographically, this group exhibited a distinct hypopnea\u2010predominant phenotype: despite a comparable overall AHI (45.82 vs. 48.64 events\/h,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.099) and global arousal index (26.98 vs. 28.85 events\/h,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.172), they demonstrated a significantly higher hypopnea count (122.25 vs. 110.40,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.047) and prolonged awake time with SpO\n                      <jats:sub>2<\/jats:sub>\n                      \u2009&lt;\u200995% (33.71 vs. 27.71\u2009min,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.015). Paradoxically, their nadir SpO\n                      <jats:sub>2<\/jats:sub>\n                      was elevated (76.68% vs. 74.39%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.009), maximal obstructive events were shorter (51.42\u2009s vs. 57.49\u2009s,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), and obstructive desaturation events were fewer (180.33 vs. 219.70,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006), indicating a shift toward shallower, persistent desaturation morphologies. Furthermore, interpretable machine learning models, rigorously evaluated on the independent validation set, identified spontaneous NREM micro\u2010arousals, total REM micro\u2010arousals, and obstructive desaturation metrics as the highest\u2010ranking predictive determinants of memory decline.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Memory decline in SDB is more robustly associated with the morphological profile of oxygen exposure rather than absolute event frequencies. A hypopnea\u2010dominant profile with mild, persistent low oxygen levels offers an associative framework for understanding cognitive decline. Future research and clinical interventions should prioritize hypoxic burden as a key factor in phenotype identification and memory decline treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/crj.70188","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T14:08:03Z","timestamp":1778249283000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the Role of Nocturnal Hypoxemia and Sleep Fragmentation in Memory Decline: Insights From Explainable Machine Learning Models"],"prefix":"10.1111","volume":"20","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7559-3158","authenticated-orcid":false,"given":"Yingying","family":"Zhu","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fansu","family":"Meng","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangan","family":"Lai","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuling","family":"Liang","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Ting","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengnan","family":"Mai","sequence":"additional","affiliation":[{"name":"Doctor of Physiotherapy, University of Sydney  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[{"name":"The Tenth Clinical Medical College of Guangzhou University of Traditional Chinese Medicine Zhongshan Hospital of Traditional Chinese Medicine  Zhongshan China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/07853890.2025.2548386"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-64615-y"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38842-y"},{"key":"e_1_2_11_5_1","article-title":"Prevalence of Obstructive Sleep Apnea Among Patients With Venous Thromboembolism: A Systematic Review and Meta\u2010Analysis","author":"Cueva C. 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