Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.

Publisher: Wiley Periodicals, Inc on behalf of American Neurological Association Country of Publication: United States NLM ID: 101623278 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2328-9503 (Electronic) Linking ISSN: 23289503 NLM ISO Abbreviation: Ann Clin Transl Neurol Subsets: MEDLINE

Original Publication: [Hoboken, NJ] : Wiley Periodicals, Inc on behalf of American Neurological Association, [2014]-

Continuous Positive Airway Pressure* ; Electroencephalography* ; Sleep Apnea, Obstructive*/therapy ; Sleep Apnea, Obstructive*/physiopathology ; Brain*/physiopathology ; Machine Learning*; Humans ; Male ; Female ; Middle Aged ; Adult ; Retrospective Studies ; Longitudinal Studies ; Polysomnography ; Aged ; Aging/physiology

Objective: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index (BAI) to quantify CPAP's impact on brain health and identify individually varying CPAP effects on brain aging using machine learning approaches.
Methods: We retrospectively analyzed CPAP-treated (n = 98) and untreated OSA patients (n = 88) with a minimum 12-month follow-up of polysomnography. BAI was calculated by subtracting chronological age from the predicted brain age. To investigate BAI changes before and after CPAP treatment, we compared annual ΔBAI between CPAP-treated and untreated OSA patients. To identify individually varying CPAP effectiveness and factors influencing CPAP effectiveness, machine learning approaches were employed to predict which patient displayed positive outcomes (negative annual ΔBAI) based on their baseline clinical features.
Results: CPAP-treated group showed lower annual ΔBAI than untreated (-0.6 ± 2.7 vs. 0.3 ± 2.6 years, p < 0.05). This BAI reduction with CPAP was reproduced independently in the Apnea, Bariatric surgery, and CPAP study cohort. Patients with more severe OSA at baseline displayed more positive annual ΔBAI (=accelerated brain aging) when untreated and displayed more negative annual ΔBAI (=decelerated brain aging) when CPAP-treated. Machine learning models achieved high accuracy (up to 86%) in predicting CPAP outcomes.
Interpretation: CPAP treatment can alleviate brain aging in OSA, especially in severe cases. Sleep EEG-derived BAI has potential to assess CPAP's impact on brain health. The study provides insights into CPAP's effects and underscores BAI-based predictive modeling's utility in OSA management.
(© 2024 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Inform Health Soc Care. 2022 Jul 3;47(3):274-282. (PMID: 34748437)
Neurobiol Aging. 2020 Apr;88:150-155. (PMID: 31932049)
Sleep. 2020 Mar 12;43(3):. (PMID: 31848608)
Sleep Med Rev. 2011 Dec;15(6):343-56. (PMID: 21652236)
J Clin Sleep Med. 2013 May 15;9(5):515-6. (PMID: 23674945)
PLoS Biol. 2018 Jul 12;16(7):e2005206. (PMID: 30001323)
Chest. 2015 Nov;148(5):1214-1223. (PMID: 26065720)
Lancet Respir Med. 2015 May;3(5):404-14. (PMID: 25887982)
Sleep Med. 2017 Apr;32:122-128. (PMID: 28366323)
Neurobiol Aging. 2021 Sep;105:199-204. (PMID: 34098431)
Sleep. 2010 Feb;33(2):235-41. (PMID: 20175407)
Thorax. 2012 Dec;67(12):1081-9. (PMID: 22561530)
Am J Respir Crit Care Med. 2018 Apr 15;197(8):1080-1083. (PMID: 29035093)
J Clin Sleep Med. 2019 Feb 04;15(2):335-343. (PMID: 30736887)
Front Neurol. 2012 May 29;3:87. (PMID: 22661967)
Sleep. 2008 Jun;31(6):777-94. (PMID: 18548822)
J Clin Sleep Med. 2017 May 15;13(5):665-666. (PMID: 28416048)
Chest. 2012 Jun;141(6):1601-1610. (PMID: 22670023)
Psychosom Med. 2004 Nov-Dec;66(6):925-31. (PMID: 15564359)
Thorax. 2010 Sep;65(9):829-32. (PMID: 20805182)
Brain Res Bull. 2003 Jun 30;61(1):87-92. (PMID: 12788211)
Am J Cardiol. 2000 Sep 15;86(6):688-92, A9. (PMID: 10980227)
J Am Med Inform Assoc. 2018 Oct 1;25(10):1351-1358. (PMID: 29860441)
Sleep Med Rev. 2014 Feb;18(1):35-48. (PMID: 23541562)
Sleep Med. 2019 May;57:61-69. (PMID: 30897457)
Neurobiol Aging. 2019 Feb;74:112-120. (PMID: 30448611)
Am J Respir Crit Care Med. 2005 May 15;171(10):1185-90. (PMID: 15699018)
Mayo Clin Proc. 2011 Jun;86(6):549-54; quiz 554-5. (PMID: 21628617)
JAMA Netw Open. 2020 Sep 1;3(9):e2017357. (PMID: 32986106)
Sleep. 2014 Sep 01;37(9):1465-75. (PMID: 25142557)
Neuroimage. 2022 Dec 1;264:119753. (PMID: 36400380)
Hum Brain Mapp. 2016 Jan;37(1):395-409. (PMID: 26503297)
Front Neurol. 2014 Apr 29;5:58. (PMID: 24808886)
Science. 2013 Oct 18;342(6156):373-7. (PMID: 24136970)
Chest. 2020 Aug;158(2):751-764. (PMID: 32289311)
Sci Rep. 2022 Nov 15;12(1):19563. (PMID: 36380059)
Lancet. 2014 Feb 22;383(9918):736-47. (PMID: 23910433)
Nat Commun. 2019 Nov 27;10(1):5409. (PMID: 31776335)
N Engl J Med. 2003 Mar 27;348(13):1233-41. (PMID: 12660387)
Sleep Breath. 2014 May;18(2):289-95. (PMID: 24026964)
Neuroimage. 2019 Oct 15;200:528-539. (PMID: 31201988)
Sleep. 1998 Jun 15;21(4):392-7. (PMID: 9646384)
Sleep Med. 2014 Aug;15(8):892-8. (PMID: 24916094)
Sleep. 2021 Mar 12;44(3):. (PMID: 33017007)
Eur Heart J. 2019 Apr 7;40(14):1149-1157. (PMID: 30376054)
Trends Neurosci. 2017 Dec;40(12):681-690. (PMID: 29074032)

R01HL106410 United States NH NIH HHS; 75N92019R002 United States HL NHLBI NIH HHS; R01 HL106410 United States HL NHLBI NIH HHS; K24HL127307 United States NH NIH HHS; R24 HL114473 United States HL NHLBI NIH HHS; P41EB015922 United States NH NIH HHS; P41 EB015922 United States EB NIBIB NIH HHS; K24 HL127307 United States HL NHLBI NIH HHS

Date Created: 20240224 Date Completed: 20240514 Latest Revision: 20240516

20240516

PMC11093235

10.1002/acn3.52032

38396240