Michal Bechny (Switzerland)

Sleep-Stage Dynamics Predict Current Sleep-Disordered Breathing and Future Cardiovascular Risk

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YES!

Simple physiological / statistical chain implies that

sleep (macrostructure + dynamics) and cardiovascular events

are correlated

➡ Sleep can be a marker of future cardiovascular events

Primary data: SHHS1(E = 0, M = 0)

• Subset of SHHS1; N = 2,579
• E = 0: prior events-free
• M = 0: medication-free (𝛼,β- blockers, psychopharmaca, diuretics, aspirin,...)

SDB can be reliably detected from sleep-stage patterns (macrostructure+dynamics) and common risk factors

Performance (AUROC):

• SHHS1(E = 0, M = 0): 76.1%
• {REM, NREM, Mixed}-dominant phenotypes: 74.1-79.4%
• Remaining SHHS1/2 strata (E = 1 or M = 1): 69.5-80.6%
• Clinical cohort (BSWR) with sleep disorders: 76.0 %

Capability to identify SDB ≈ ability to predict cardiovascular risk (?)

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SDB can be reliably detected from sleep-stage patterns (macrostructure+dynamics) and common risk factors

Mostly monotonic risk-profiles (= effects) of predictors for SDB

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Performance (AUROC):

• SHHS1(E = 0, M = 0): 76.1%
• {REM, NREM, Mixed}-dominant phenotypes: 74.1-79.4%
• Remaining SHHS1/2 strata (E = 1 or M = 1): 69.5-80.6%
• Clinical cohort (BSWR) with sleep disorders: 76.0 %

Capability to identify SDB ≈ ability to predict cardiovascular risk (?)

​

Cardiovascular risk can be reliably predicted from sleep-stage patterns (macrostructure+dynamics) and common risk factors

Performance (C-index):

• SHHS1(E = 0, M = 0): 73.3%
• SHHS1/2 strata with medications: 64.9-79.3%
• For subjects with prior events: not so good

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Same performance (±1%) as with including AHI predictor!

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lower = better

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higher = better

Cardiovascular risk can be reliably predicted from sleep-stage patterns (macrostructure+dynamics) and common risk factors

Performance (C-index):

• SHHS1(E = 0, M = 0): 73.3%
• SHHS1/2 strata with medications: 64.9-79.3%
• For subjects with prior events: not so good

​

​

​

Same performance (±1%) as with including AHI predictor!

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Non-linear, often U-shaped, risk-profiles (extending [16-22]):

• = DIGITAL BIOMARKERS for early risk-stratification or screening

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10 yr event-free probability

Rare transitions are highly sensitive markers, associated +10% risk (vs. 2-3% for smoking)

Conclusions

• Sleep-stage characteristics predict current SDB and future cardiovascular risk.
• Monotonic risk-profiles for SDB vs. U-Shaped for cardiovascular events suggest distinct root-causes and physiological optima.
• Certain transitions are sensitive digital markers of SDB/cardiac risk.
• Sleep assessment can extend cardiovascular risk scales (e.g., FRS, SCORE2).
• Despite lower precision in comparison to PSG, wearables offer huge potential.

Thanks for your attention!

References:

SDB and cardiovascular risk:

1. Peppard, P. E. et al. Prospective study of the association between sleep-disordered breathing and hypertension. New Engl. J. Medicine 342, 1378–1384 (2000).
2. Leung, R. S. & Douglas Bradley, T. Sleep apnea and cardiovascular disease. Am. journal respiratory critical care medicine 164, 2147–2165 (2001).
3. Gami, A. S. et al. Day–night pattern of sudden death in obstructive sleep apnea. New Engl. J. Medicine 352, 1206–1214 (2005).
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10. Javaheri, S. et al. Sleep apnea: types, mechanisms, and clinical cardiovascular consequences. J. Am. Coll. Cardiol. 69, 841–858 (2017).

SDB & sleep-macrostructure:

11. Vgontzas, A. N. et al. Sleep apnea and sleep disruption in obese patients. Arch. internal medicine 154, 1705–1711 (1994).
12. Kimoff, R. J. Sleep fragmentation in obstructive sleep apnea. Sleep 19, S61–S66 (1996).
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15. Shahveisi, K. et al. Sleep architecture in patients with primary snoring and obstructive sleep apnea. Basic Clin. Neurosci. 9, 147 (2018).

Sleep-macrostructure and cardiovascular risk:

16. Fung, M. M. et al. Decreased slow wave sleep increases risk of developing hypertension in elderly men. Hypertension 58, 596–603 (2011).
17. Javaheri, S. et al. Slow-wave sleep is associated with incident hypertension: the sleep heart health study. Sleep 41, zsx179 (2018).
18. Heslop, P., et al. Sleep duration and mortality: the effect of short or long sleep duration on cardiovascular and all-cause mortality in working men and women. Sleep medicine 3, 305–314 (2002). Note: SELF-REPORTED ASSESSMENT.
19. Khan, H., et al. Sleep duration and risk of fatal coronary heart disease, sudden cardiac death, cancer death, and all-cause mortality. The Am. journal medicine 131, 1499–1505 (2018). Note: SELF-REPORTED ASSESSMENT.
20. Kwok, C. S. et al. Self-reported sleep duration and quality and cardiovascular disease and mortality: a dose-response meta-analysis. J. Am. Hear. Assoc. 7, e008552 (2018). Note: SELF-REPORTED ASSESSMENT.
21. Leary, E. B. et al. Association of rapid eye movement sleep with mortality in middle-aged and older adults. JAMA neurology 77, 1241–1251 (2020).
22. Ai, S. et al. Association of disrupted delta wave activity during sleep with long-term cardiovascular disease and mortality. J. Am. Coll. Cardiol. 83, 1671–1684 (2024).

SDB & sleep-dynamics:

23. Penzel, T., Kantelhardt, J. W., Lo, C.-C., Voigt, K. & Vogelmeier, C. Dynamics of heart rate and sleep stages in normals and patients with sleep apnea. Neuropsychopharmacology 28, S48–S53 (2003).
24. Chervin, R. D., Fetterolf, J. L., Ruzicka, D. L., Thelen, B. J. & Burns, J. W. Sleep stage dynamics differ between children with and without obstructive sleep apnea. Sleep 32, 1325–1332 (2009).
25. Bianchi, M. T. et al. Probabilistic sleep architecture models in patients with and without sleep apnea. J. sleep research 21, 330–341 (2012).
26. Wächter, M. et al. Unique sleep-stage transitions determined by obstructive sleep apnea severity, age and gender. J. sleep research 29, e12895 (2020).
27. Bechny, M. et al. Novel digital markers of sleep dynamics: causal inference approach revealing age and gender phenotypes in obstructive sleep apnea. Sci. Reports 15, 12016 (2025).

Sleep dynamics as a physiological marker beyond SDB:

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30. Lo, C.-C. et al. Dynamics of sleep-wake transitions during sleep. Europhys. Lett. 57, 625 (2002).
31. Burns, J. W., Crofford, L. J. & Chervin, R. D. Sleep stage dynamics in fibromyalgia patients and controls. Sleep Medicine 9, 689–696 (2008).
32. Kishi, A.,et al. Dynamics of sleep stage transitions in healthy humans and patients with chronic fatigue syndrome. Am. J. Physiol. Regul. Integr. Comp. Physiol. 294, R1980–R1987 (2008).
33. Kim, J. et al. Markov analysis of sleep dynamics. Phys. Rev. Lett. 102, 178104 (2009).
34. Laffan, A. et al. Utility of sleep stage transitions in assessing sleep continuity. Sleep 33, 1681–1686 (2010).
35. Kishi, A., Natelson, B.H., Togo, F., Struzik, Z.R., Rapoport, D.M., Yamamoto, Y.: Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia. Sleep 34(11), 1551–1560 (2011)
36. Wei, Y. et al. Sleep stage transition dynamics reveal specific stage 2 vulnerability in insomnia. Sleep 40, zsx117 (2017).
37. Yetton, B. D. et al. Quantifying sleep architecture dynamics and individual differences using big data and bayesian networks. PloS one 13, e0194604 (2018).
38. Kishi, A. et al. Sleep stage dynamics in young patients with sleep bruxism. Sleep 43, zsz202 (2020).
39. Hermans, L. W. et al. Representations of temporal sleep dynamics: Review and synthesis of the literature. Sleep Med. Rev. 63, 101611 (2022).
40. Bechny, M. et al. Unveiling sleep dysregulation in chronic fatigue syndrome with and without fibromyalgia through bayesian networks. In International Conference on Artificial Intelligence in Medicine, 33–43 (Springer, 2025)

SHHS study:

• Quan, S. F. et al. The sleep heart health study: design, rationale, and methods. Sleep 20, 1077–1085 (1997).

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