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Palomares DE, Tran PL, Jerman C, Momayez M, Deymier P, Sheriff J, Bluestein D, Parthasarathy S, Slepian MJ. Vibro-Acoustic Platelet Activation: An Additive Mechanism of Prothrombosis with Applicability to Snoring and Obstructive Sleep Apnea. Bioengineering (Basel) 2023; 10:1414. [PMID: 38136005 PMCID: PMC10741028 DOI: 10.3390/bioengineering10121414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/28/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Introduction: Obstructive sleep apnea (OSA) and loud snoring are conditions with increased cardiovascular risk and notably an association with stroke. Central in stroke are thrombosis and thromboembolism, all related to and initiaing with platelet activation. Platelet activation in OSA has been felt to be driven by biochemical and inflammatory means, including intermittent catecholamine exposure and transient hypoxia. We hypothesized that snore-associated acoustic vibration (SAAV) is an activator of platelets that synergizes with catecholamines and hypoxia to further amplify platelet activation. Methods: Gel-filtered human platelets were exposed to snoring utilizing a designed vibro-acoustic exposure device, varying the time and intensity of exposure and frequency content. Platelet activation was assessed via thrombin generation using the Platelet Activity State assay and scanning electron microscopy. Comparative activation induced by epinephrine and hypoxia were assessed individually as well as additively with SAAV, as well as the inhibitory effect of aspirin. Results: We demonstrate that snore-associated acoustic vibration is an independent activator of platelets, which is dependent upon the dose of exposure, i.e., intensity x time. In snoring, acoustic vibrations associated with low-frequency sound content (200 Hz) are more activating than those associated with high frequencies (900 Hz) (53.05% vs. 22.08%, p = 0.001). Furthermore, SAAV is additive to both catecholamines and hypoxia-mediated activation, inducing synergistic activation. Finally, aspirin, a known inhibitor of platelet activation, has no significant effect in limiting SAAV platelet activation. Conclusion: Snore-associated acoustic vibration is a mechanical means of platelet activation, which may drive prothrombosis and thrombotic risk clinically observed in loud snoring and OSA.
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Affiliation(s)
- Daniel E. Palomares
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ 85724, USA;
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
| | - Phat L. Tran
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | - Catherine Jerman
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | - Moe Momayez
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
- Department of Mining & Geological Engineering, University of Arizona, Tucson, AZ 85724, USA
| | - Pierre Deymier
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
- Department of Materials Science & Engineering, University of Arizona, Tucson, AZ 85724, USA
| | - Jawaad Sheriff
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; (J.S.); (D.B.)
| | - Danny Bluestein
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; (J.S.); (D.B.)
| | - Sairam Parthasarathy
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA;
- Health Sciences Center for Sleep and Circadian Sciences, University of Arizona, Tucson, AZ 85724, USA
| | - Marvin J. Slepian
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ 85724, USA;
- Arizona Center for Accelerated Biomedical Innovation, University of Arizona, Tucson, AZ 85724, USA; (P.L.T.); (M.M.); (P.D.); (S.P.)
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA;
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA; (J.S.); (D.B.)
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Obstructive Sleep Apnea and Auditory Dysfunction—Does Snoring Sound Play a Role? Diagnostics (Basel) 2022; 12:diagnostics12102374. [PMID: 36292063 PMCID: PMC9600079 DOI: 10.3390/diagnostics12102374] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/10/2022] [Accepted: 09/26/2022] [Indexed: 11/16/2022] Open
Abstract
The objective of the study was to investigate the relationship between obstructive sleep apnea (OSA) and auditory dysfunction, and to clarify the role of snoring sounds in contributing to auditory dysfunction. A comprehensive assessment of OSA and the auditory system was performed, including overnight polysomnography, detection of the intra-ear canal snoring sound energy (SSE), pure tone average (PTA), tinnitus pitch matching, the tinnitus handicap inventory (THI), and the Epworth sleepiness scale (ESS). The patients were identified as having tinnitus if their THI score was higher than zero or their tinnitus pitches were matched to specific frequencies. The median age, body mass index, and apnea–hypopnea index score were 41 years, 26.4 kg/m2, and 29.9 events/h, respectively. Among the 50 participants, 46 (92%) had a normal PTA, and only 4 (8%) patients had mild hearing loss. There was no significant difference in PTA among OSA severities (p = 0.52). Among the 50 participants, 33 patients (66%) were identified as having tinnitus. In the tinnitus group (n = 33), the ESS score (p = 0.01) and intra-ear canal SSE of 851–1500 Hz (p = 0.04) were significantly higher than those in the non-tinnitus group (n = 17). OSA patients with a higher ESS score had a higher risk of tinnitus (odds ratio 1.22 [95% CI: 1.01–1.46]). OSA-related auditory dysfunction emerged in tinnitus rather than in hearing impairment. OSA patients with daytime sleepiness had a higher risk of tinnitus. High-frequency SSE can jeopardize cochlea and is a potential mechanism contributing to tinnitus. Detection of snoring sounds through an intra-ear canal device may be more precise in assessing acoustic trauma from snoring sounds to vulnerable auditory system and thus warrants further research.
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Liu L, Su X, Zhao Z, Han J, Li J, Xu W, He Z, Gao Y, Chen K, Zhao L, Gao Y, Wang H, Guo J, Lin J, Li T, Fang X. Association of Metabolic Syndrome With Long-Term Cardiovascular Risks and All-Cause Mortality in Elderly Patients With Obstructive Sleep Apnea. Front Cardiovasc Med 2022; 8:813280. [PMID: 35198606 PMCID: PMC8859338 DOI: 10.3389/fcvm.2021.813280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/30/2021] [Indexed: 12/11/2022] Open
Abstract
Background Evidence suggests that an increased risk of major adverse cardiac events (MACE) and all-cause mortality is associated with obstructive sleep apnea (OSA), particularly in the elderly. Metabolic syndrome (MetS) increases cardiovascular risk in the general population; however, less is known about its influence in patients with OSA. We aimed to assess whether MetS affected the risk of MACE and all-cause mortality in elderly patients with OSA. Methods From January 2015 to October 2017, 1,157 patients with OSA, aged ≥60 years, no myocardial infarction (MI), and hospitalization for unstable angina or heart failure were enrolled at baseline and were followed up prospectively. OSA is defined as an apnea-hypopnea index of ≥5 events per hour, as recorded by polysomnography. Patients were classified on the basis of the presence of MetS, according to the definition of the National Cholesterol Education Program (NCEP). Incidence rates were expressed as cumulative incidence. Cox proportional hazards analysis was used to estimate the risk of all events. The primary outcomes were MACE, which included cardiovascular death, MI, and hospitalization for unstable angina or heart failure. Secondary outcomes were all-cause mortality, components of MACE, and a composite of all events. Results MetS was present in 703 out of 1,157 (60.8%) elderly patients with OSA. During the median follow-up of 42 months, 119 (10.3%) patients experienced MACE. MetS conferred a cumulative incidence of MACE in elderly patients with OSA (log-rank, P < 0.001). In addition, there was a trend for MACE incidence risk to gradually increase in individuals with ≥3 MetS components (P = 0.045). Multivariate analysis showed that MetS was associated with an incidence risk for MACE [adjusted hazard ratio (aHR), 1.86; 95% confidence interval (CI), 1.17–2.96; P = 0.009], a composite of all events (aHR, 1.54; 95% CI, 1.03–2.32; P = 0.036), and hospitalization for unstable angina (aHR, 2.01; 95% CI, 1.04–3.90; P = 0.039). No significant differences in the risk of all-cause mortality and other components of MACE between patients with and without MetS (P > 0.05). Subgroup analysis demonstrated that males (aHR, 2.23; 95% CI, 1.28–3.91, P = 0.05), individuals aged <70 years (aHR, 2.36; 95% CI, 1.27–4.39, P = 0.006), overweight and obese individuals (aHR, 2.32; 95% CI, 1.34–4.01, P = 0.003), and those with moderate-severe OSA (aHR, 1.81;95% CI: 1.05–3.12, P = 0.032) and concomitant MetS were at a higher risk for MACE. Conclusion MetS is common in elderly patients with OSA in the absence of MI, hospitalization for unstable angina or heart failure. Further, it confers an independent, increased risk of MACE, a composite of all events, and hospitalization for unstable angina. Overweight and obese males, aged <70 years with moderate-severe OSA combined with MetS presented a significantly higher MACE risk.
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Affiliation(s)
- Lin Liu
- Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xiaofeng Su
- Medical College, Yan'an University, Yan'an, China
| | - Zhe Zhao
- Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jiming Han
- Medical College, Yan'an University, Yan'an, China
| | - Jianhua Li
- Cardiology Department of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Weihao Xu
- Cardiology Department of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Zijun He
- Medical College, Yan'an University, Yan'an, China
| | - Yinghui Gao
- PKU-UPenn Sleep Center, Peking University International Hospital, Beijing, China
| | - Kaibing Chen
- Sleep Center, The Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou City, China
| | - Libo Zhao
- Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yan Gao
- Department of General Practice, 960th Hospital of PLA, Jinan, China
| | | | - JingJing Guo
- Department of Pulmonary and Critical Care Medicine, Sleep Medicine Center, Peking University People's Hospital, Beijing, China
| | - Junling Lin
- Department of Pulmonary and Critical Care Medicine, Beijing Chaoyang Hospital Affiliated to Capital Medical University, Beijing, China
- *Correspondence: Xiangqun Fang
| | - Tianzhi Li
- The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- Tianzhi Li
| | - Xiangqun Fang
- Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
- Junling Lin
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Wang B, Tang X, Ai H, Li Y, Xu W, Wang X, Han D. Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning. Nat Sci Sleep 2022; 14:2033-2045. [PMID: 36394068 PMCID: PMC9653035 DOI: 10.2147/nss.s373367] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder. METHODS We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard. RESULTS A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%. CONCLUSION Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.
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Affiliation(s)
- Bochun Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China
| | - Xianwen Tang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Hao Ai
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Yanru Li
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Wen Xu
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
| | - Xingjun Wang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Demin Han
- Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, 100730, People's Republic of China.,Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, 100730, People's Republic of China
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