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The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males

  • Sleep Breathing Physiology and Disorders • Original Article
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Abstract

Objectives

Heart rate variability (HRV) is becoming more prevalent as a measurable parameter in wearable sleep-monitoring devices, which are simple and effective instruments for illness evaluation. Currently, most studies on investigating OSA severity and HRV have measured heart rates during wakefulness or sleep. Therefore, the objective of this study was to investigate the circadian rhythm of HRV in male patients with OSA and its value for the estimation of OSA severity using group-based trajectory modeling.

Methods

Patients with complaints of snoring were enrolled from the Sleep Center of Shandong Qianfoshan Hospital. Patients were divided into 3 groups according to apnea hypopnea index (AHI in events/h), as follows: (<15, 15≤AHI<30, and ≥30). HRV parameters were calculated using 24 h Holter monitoring, which included time-domain and frequency-domain indices. Circadian differences in the standard deviation of normal to normal (SDNN) were evaluated for OSA severity using analysis of variance, trajectory analysis, and multinomial logistic regression.

Results

A total of 228 patients were enrolled, 47 with mild OSA, 48 moderate, and 133 severe. Patients with severe OSA exhibited reduced triangular index and higher very low frequency than those in the other groups. Circadian HRV showed that nocturnal SDNN was considerably higher than daytime SDNN in patients with severe OSA. The difference among the OSA groups was significant at 23, 24, 2, and 3 o’clock sharp between the severe and moderate OSA groups (all P<0.05). The heterogeneity of circadian HRV trajectories in OSA was strongly associated with OSA severity, including sleep structure and hypoxia-related parameters. Among the low-to-low, low-to-high, high-to-low, and high-to-high groups, OSA severity in the low-to-high group was the most severe, especially compared with the low-to-low and high-to-low SDNN groups, respectively.

Conclusions

Circadian HRV in patients with OSA emerged as low daytime and high nocturnal in SDNN, particularly in men with severe OSA. The heterogeneity of circadian HRV revealed that trajectories with low daytime and significantly high nighttime were more strongly associated with severe OSA. Thus, circadian HRV trajectories may be useful to identify the severity of OSA.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Dos Santos RR et al (2022) Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea. Front Netw Physiol 2:958550

    Article  PubMed  PubMed Central  Google Scholar 

  2. Shieu MM et al (2022) Positive airway pressure and cognitive disorders in adults with obstructive sleep apnea: a systematic review of the literature. Neurology 99(4):e334–e346

    Article  PubMed  PubMed Central  Google Scholar 

  3. Redline S, Azarbarzin A, Peker Y (2023) Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat Rev Cardiol 20(8):560–573

    Article  CAS  PubMed  Google Scholar 

  4. Paidi G et al (2022) The management of obstructive sleep apnea in primary care. Cureus 14(7):e26805

    PubMed  PubMed Central  Google Scholar 

  5. Khurana S et al (2021) Current and future strategies for diagnostic and management of obstructive sleep apnea. Expert Rev Mol Diagn 21(12):1287–1301

    Article  CAS  PubMed  Google Scholar 

  6. Sleep disorder group of chinese thoracic society, group of sleep disordered breathing, committee of respiratory diseases of china association of medical equipment [Expert consensus for the use of home sleep apnea test in the diagnosis of obstructive sleep apnea in adults] (2022). Zhonghua Jie He He Hu Xi Za Zhi 45(2):133–142

  7. Ucak S et al (2021) Heart rate variability and obstructive sleep apnea: current perspectives and novel technologies. J Sleep Res 30(4):e13274

    Article  PubMed  Google Scholar 

  8. Pumprla J et al (2002) Functional assessment of heart rate variability: physiological basis and practical applications. Int J Cardiol 84(1):1–14

    Article  PubMed  Google Scholar 

  9. Fang SC, Wu YL, Tsai PS (2020) Heart rate variability and risk of all-cause death and cardiovascular events in patients with cardiovascular disease: a meta-analysis of cohort studies. Biol Res Nurs 22(1):45–56

    Article  PubMed  Google Scholar 

  10. Nordholm D et al (2023) A longitudinal study on physiological stress in individuals at ultra high-risk of psychosis. Schizophr Res 254:218–226

    Article  Google Scholar 

  11. Statello R et al (2023) Nocturnal heart rate variability might help in predicting severe obstructive sleep-disordered breathing. Biology (Basel) 12(4):533

    PubMed  Google Scholar 

  12. Qin H et al (2021) Heart rate variability during wakefulness as a marker of obstructive sleep apnea severity. Sleep 44(5):zsab018

  13. Lao M et al (2021) The predictive value of Holter monitoring in the risk of obstructive sleep apnea. J Thorac Dis 13(3):1872–1881

    Article  PubMed Central  Google Scholar 

  14. Martín-González S et al (2023) Combining heart rate variability and oximetry to improve apneic event screening in non-desaturating patients. Sensors (Basel, Switzerland) 23(9):4267

    Article  PubMed  Google Scholar 

  15. Correia FJ et al (2019) Repercussion of medium and long treatment period with continuous positive airways pressure therapy in heart rate variability of obstructive sleep apnea. Sleep Sci 12(2):110–115

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kwon S, Kim H, Yeo WH (2021) Recent advances in wearable sensors and portable electronics for sleep monitoring. iScience 24(5):102461

    Article  PubMed  PubMed Central  Google Scholar 

  17. Li Y et al (2022) Sleep quality evaluation based on single-lead wearable cardiac cycle acquisition device. Sensors (Basel, Switzerland) 23(1):328

    Article  PubMed  Google Scholar 

  18. Pastore CA et al (2019) Applicability of the electro-vectorcardiogram in current clinical practice. Arq Bras Cardiol 113(1):87–99

    PubMed  PubMed Central  Google Scholar 

  19. Saffitz JE, Corradi D (2016) The electrical heart: 25 years of discovery in cardiac electrophysiology, arrhythmias and sudden death. Cardiovasc Pathol 25(2):149–157

    Article  PubMed  Google Scholar 

  20. Ooi EL, Rajendran S (2023) Obstructive sleep apnea in coronary artery disease. Curr Probl Cardiol 48(8):101178

    Article  PubMed  Google Scholar 

  21. Dong R et al (2018) Prevalence, risk factors, outcomes, and treatment of obstructive sleep apnea in patients with cerebrovascular disease: a systematic review. J Stroke Cerebrovasc Dis 27(6):1471–1480

    Article  PubMed  Google Scholar 

  22. Bradicich M et al (2020) Nocturnal heart rate variability in obstructive sleep apnoea: a cross-sectional analysis of the Sleep Heart Health Study. J Thorac Dis 12(Suppl 2):S129–s138

    Article  PubMed  PubMed Central  Google Scholar 

  23. Fitzpatrick M et al (2020) SIESTA - Home sleep study with BresoDx for obstructive sleep apnea: a randomized controlled trial. Sleep Med 65:45–53

    Article  PubMed  Google Scholar 

  24. Wang MP et al (2021) Association of fluid balance trajectories with clinical outcomes in patients with septic shock: a prospective multicenter cohort study. Mil Med Res 8(1):40

    PubMed  PubMed Central  Google Scholar 

  25. Nagin DS, Odgers CL (2010) Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol 6:109–138

    Article  PubMed  Google Scholar 

  26. Shi Q et al (2013) Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy. Qual Life Res 22(9):2331–2339

    Article  PubMed  Google Scholar 

  27. Ram N, Grimm KJ (2009) Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev 33(6):565–576

    Article  PubMed  PubMed Central  Google Scholar 

  28. Berry RB et al (2012) Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine. J Clin Sleep Med 8(5):597–619

    Article  PubMed  PubMed Central  Google Scholar 

  29. Tanaka K et al (2022) Temporal trajectory of systolic blood pressure and outcomes in acute intracerebral hemorrhage: ATACH-2 trial cohort. Stroke 53(6):1854–1862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhang MQ et al (2023) Effect of objective daytime sleepiness on heart rate variability in patients with obstructive sleep apnea. Sichuan Da Xue Xue Bao Yi Xue Ban 54(2):298–303

    PubMed  Google Scholar 

  31. Scherbakov N et al (2020) Early rehabilitation after stroke: relationship between the heart rate variability and functional outcome. ESC Heart Fail 7(5):2983–2991

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hämmerle P et al (2020) Heart rate variability triangular index as a predictor of cardiovascular mortality in patients with atrial fibrillation. J Am Heart Assoc 9(15):e016075

    Article  PubMed  PubMed Central  Google Scholar 

  33. Mamode N et al (2001) The role of myocardial perfusion scanning, heart rate variability and D-dimers in predicting the risk of perioperative cardiac complications after peripheral vascular surgery. Eur J Vasc Endovasc Surg 22(6):499–508

    Article  CAS  PubMed  Google Scholar 

  34. Marin JM et al (2005) Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet 365(9464):1046–1053

    Article  PubMed  Google Scholar 

  35. Frenneaux MP (2004) Autonomic changes in patients with heart failure and in post-myocardial infarction patients. Heart 90(11):1248–1255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Platisa MM, Gal V (2006) Reflection of heart rate regulation on linear and nonlinear heart rate variability measures. Physiol Meas 27(2):145–154

    Article  PubMed  Google Scholar 

  37. Meng G et al (2022) Successful continuous positive airway pressure treatment reduces skin sympathetic nerve activity in patients with obstructive sleep apnea. Heart Rhythm 19(1):127–136

    Article  PubMed  Google Scholar 

  38. Noda A et al (2019) Very low frequency component of heart rate variability as a marker for therapeutic efficacy in patients with obstructive sleep apnea: Preliminary study. J Res Med Sci 24:84

    Article  PubMed  PubMed Central  Google Scholar 

  39. Shaffer F, McCraty R, Zerr CL (2014) A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Front Psychol 5:1040

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hietakoste S et al (2020) Longer apneas and hypopneas are associated with greater ultra-short-term HRV in obstructive sleep apnea. Sci Rep 10(1):21556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ryan ML et al (2011) Clinical applications of heart rate variability in the triage and assessment of traumatically injured patients. Anesthesiol Res Pract 2011:416590

    PubMed  PubMed Central  Google Scholar 

  42. Tiwari R et al (2021) Analysis of heart rate variability and implication of different factors on heart rate variability. Curr Cardiol Rev 17(5):e160721189770

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kim JB, Seo BS, Kim JH (2019) Effect of arousal on sympathetic overactivity in patients with obstructive sleep apnea. Sleep Med 62:86–91

    Article  PubMed  Google Scholar 

  44. May AM, Van Wagoner DR, Mehra R (2017) OSA and cardiac arrhythmogenesis: mechanistic insights. Chest 151(1):225–241

    Article  PubMed  Google Scholar 

  45. Becker H et al (1995) Reversal of sinus arrest and atrioventricular conduction block in patients with sleep apnea during nasal continuous positive airway pressure. Am J Respir Crit Care Med 151(1):215–218

    Article  CAS  PubMed  Google Scholar 

  46. Teo YH et al (2022) Prevalence, types and treatment of bradycardia in obstructive sleep apnea - a systematic review and meta-analysis. Sleep Med 89:104–113

    Article  PubMed  Google Scholar 

  47. Sakamoto JT et al (2018) Heart rate variability analysis in patients who have bradycardia presenting to the emergency department with chest pain. J Emerg Med 54(3):273–280

    Article  PubMed  Google Scholar 

  48. McLachlan CS et al (2010) Increased total heart rate variability and enhanced cardiac vagal autonomic activity in healthy humans with sinus bradycardia. Proc (Bayl Univ Med Cent) 23(4):368–370

    PubMed  Google Scholar 

  49. Koo BB et al (2016) Observational study of obstructive sleep apnea in wake-up stroke: the SLEEP TIGHT study. Cerebrovasc Dis 41(5-6):233–241

    Article  PubMed  Google Scholar 

  50. Park J et al (2020) Sleep-disordered breathing and wake-up stroke: a differential association depending on etiologic subtypes. Sleep Med 76:43–47

    Article  PubMed  Google Scholar 

  51. Gülbay B et al (2022) Is it important to know the predominant respiratory event in AHI for the management of patients with OSA? Tuberk Toraks 70(2):187–196

    Article  PubMed  Google Scholar 

  52. Yoo CS et al (2011) Association of heart rate variability with the framingham risk score in healthy adults. Korean J Fam Med 32(6):334–340

    Article  PubMed Central  Google Scholar 

  53. Blackwell JN et al (2019) Sleep Apnea and sudden cardiac death. Circ Rep 1(12):568–574

    Article  PubMed Central  Google Scholar 

  54. Ottaviani G, Buja LM (2020) Pathology of unexpected sudden cardiac death: obstructive sleep apnea is part of the challenge. Cardiovasc Pathol 47:107221

    Article  PubMed  Google Scholar 

  55. Nokes B, Cooper J, Cao M (2022) Obstructive sleep apnea: personalizing CPAP alternative therapies to individual physiology. Expert Rev Respir Med 16(8):917–929

    Article  CAS  PubMed  Google Scholar 

  56. Acharya R et al (2020) Obstructive sleep apnea: risk factor for arrhythmias, conduction disorders, and cardiac arrest. Cureus 12(8):e9992

    PubMed Central  Google Scholar 

  57. Qiu K et al (2022) Gender-specific association between obstructive sleep apnea and cognitive impairment among adults. Sleep Med 98:158–166

    Article  PubMed  Google Scholar 

  58. Umetani K et al (1998) Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. J Am Coll Cardiol 31(3):593–601

    Article  CAS  PubMed  Google Scholar 

  59. Sprenger N et al (2022) Feasibility and reliability of smartwatch to obtain precordial lead electrocardiogram recordings. Sensors (Basel, Switzerland) 22(3):1217

    Article  PubMed  Google Scholar 

  60. Agarwal P et al (2021) Assessing the quality of mobile applications in chronic disease management: a scoping review. NPJ Digit Med 4(1):46

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the Natural Science Foundation of Shandong Province (No.ZR2020MH160), the National Natural Science Foundation of China (No. 81471345) and the Natural Science Foundational of Shandong Province (No. ZR2020QH127).

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Authors and Affiliations

  1. Department of Neurology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, NO. 16766, Jingshi Road, Jinan, Shandong, 250014, People’s Republic of China

    Baokun Zhang & Jiyou Tang

  2. Stem Cell Clinical Research Center, National Joint Engineering Laboratory, Regenerative Medicine Center, The First Affiliated Hospital of Dalian Medical University, Dalian Innovation Institute of Stem Cell and Precision Medicine, Dalian, Liaoning Province, China

    Mengke Zhao

  3. Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China

    Xiao Zhang, Xiaoyu Zhang, Xiaomin Liu, Weiwei Huang, Shanshan Lu, Juanjuan Xu, Ying Liu, Wei Xu, Xiuhua Li & Jiyou Tang

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  1. Baokun Zhang
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Contributions

Baokun Zhang: data analysis and preparation of the manuscript; Jiyou Tang and Xiuhua Li: study design and preparation of the manuscript; Mengke Zhao, Wei Xu, Weiwei Huang, and Xiao Zhang collected the data; Shanshan Lu, Juanjuan Xu, Xiaoyu Zhang, Xiaomin Liu, and Ying Liu contributed in experimental design. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiuhua Li or Jiyou Tang.

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This is a retrospective study, and was approved by the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital.

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Zhang, B., Zhao, M., Zhang, X. et al. The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males. Sleep Breath (2024). https://doi.org/10.1007/s11325-023-02983-1

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