Heritability of Heart Rate Response to Arousals in Twins

Treatment of Sleep Apnea and Reduction in Blood Pressure: The Role of Heart Rate Response and Hypoxic Burden

BACKGROUND

Obstructive sleep apnea is associated with increased blood pressure (BP). Obstructive sleep apnea treatment reduces BP with substantial variability, not explained by the apnea-hypopnea index, partly due to inadequate characterization of obstructive sleep apnea's physiological consequences, such as oxygen desaturation, cardiac autonomic response, and suboptimal treatment efficacy. We sought to examine whether a high baseline heart rate response (ΔHR), a marker of high cardiovascular risk in obstructive sleep apnea, predicts a larger reduction in post-treatment systolic BP (SBP). Furthermore, we aimed to assess the extent to which a reduction in SBP is explained by a treatment-related reduction in hypoxic burden (HB).

METHODS

ΔHR and HB were measured from pretreatment and posttreatment polygraphy, followed by a 24-hour BP assessment in 168 participants treated with continuous positive airway pressure or nocturnal supplemental oxygen from the HeartBEAT study (Heart Biomarker Evaluation in Apnea Treatment). Multiple linear regression models assessed whether high versus mid (reference) ΔHR predicted a larger reduction in SBP (primary outcome) and whether there was an association between treatment-related reductions in SBP and HB.

RESULTS

A high versus mid ΔHR predicted improvement in SBP (adjusted estimate, 5.8 [95% CI, 1.0-10.5] mm Hg). Independently, a greater treatment-related reduction in HB was significantly associated with larger reductions in SBP (4.2 [95% CI, 0.9-7.5] mm Hg per 2 SD treatment-related reduction in HB). Participants with substantial versus minimal treatment-related reductions in HB had a 6.5 (95% CI, 2.5-10.4) mm Hg drop in SBP.

CONCLUSIONS

A high ΔHR predicted a more favorable BP response to therapy. Furthermore, the magnitude of the reduction in BP was partly explained by a greater reduction in HB.

Unmasking Heterogeneity of Sleep Apnea

Sleep apnea is heterogeneous in multiple dimensions. There are different physiological risk factors that may have clinical relevance. However, assessing them is challenging. An approach to ascertain them using a simple model of ventilatory control has been proposed. It is based, however, on untenable assumptions. There are limited validation data and reproducibility is not stellar. There are also different symptom subtypes. They have been found in multiple population-based and clinical cohorts worldwide. Symptomatic benefit from therapy is most marked in the excessively sleepy subtype. This group may also be the group at increased CV risk from obstructive sleep apnea.

International Consensus Statement on Obstructive Sleep Apnea

BACKGROUND

Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA).

METHODS

Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus.

RESULTS

The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated.

CONCLUSION

This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.

The sleep homeostatic response to sleep deprivation in humans is heritable

STUDY OBJECTIVES

Following sleep deprivation, increases in delta power have historically been used to index increases in sleep pressure. Research in mice has demonstrated that the homeostatic delta power response to sleep deprivation is heritable. Whether this is true in humans is unknown. In the present study, we used delta power and ORP, a novel measure of sleep depth, to investigate the effects of acute sleep deprivation on sleep depth and to assess the heritability of sleep homeostasis in humans.

METHODS

ORP and delta power were examined during baseline and recovery sleep following 38 h of sleep deprivation in 57 monozygotic and 38 dizygotic same-sex twin pairs. Two complementary methods were used to estimate the trait heritability of sleep homeostasis.

RESULTS

During recovery sleep, ORP was lower and delta power was higher than at baseline, indicating deeper sleep. However, at the end of the recovery night, delta power reached baseline levels but ORP demonstrated incomplete recovery. Both ORP and delta power showed a broad sense heritability of sleep homeostasis following sleep deprivation. The classical approach demonstrated an h2 estimate of 0.43 for ORP and 0.73 for delta power. Mixed-effect multilevel models showed that the proportion of variance attributable to additive genetic transmission was 0.499 (95% CI = 0.316-0.682; p < .0001) for ORP and 0.565 (95% CI = 0.403-0.726; p < .0001 for delta power.

CONCLUSIONS

These results demonstrate that the homeostatic response to sleep deprivation is a heritable trait in humans and confirm ORP as a robust measure of sleep depth.

New Metrics from Polysomnography: Precision Medicine for OSA Interventions

Obstructive sleep apnea (OSA) is a highly preventable disease accompanied by multiple comorbid conditions. Despite the well-established cardiovascular and neurocognitive sequelae with OSA, the optimal metric for assessing the OSA severity and response to therapy remains controversial. Although overnight polysomnography (PSG) is the golden standard for OSA diagnosis, the abundant information is not fully exploited. With the development of deep learning and the era of big data, new metrics derived from PSG have been validated in some OSA consequences and personalized treatment. In this review, these metrics are introduced based on the pathophysiological mechanisms of OSA and new technologies. Emphasis is laid on the advantages and the prognostic value against apnea-hypopnea index. New classification criteria should be established based on these metrics and other clinical characters for precision medicine.

The Nadir Oxygen-Specific Heart Rate Response in Sleep Apnea Links With the Occurrence of Acute Myocardial Infarction

Background

Little is known regarding the quantification of sleep apnea- and hypoxemia-elicited heart rate (HR) response and its prognostic significance of the cardiovascular risk. We sought to explore the impact of HR response and variability specific to obstructive sleep apnea (OSA) on the occurrence of a common cardiovascular event – acute myocardial infarction (AMI).

Methods

Consecutive patients with suspected OSA were enrolled and underwent nocturnal respiratory study and electrocardiography monitoring. The minimal oxygen saturation (minSpO2) was determined from the oxygen saturation curve under a subject-specific search window. Primary HR metrics such as maximal HR in response to minSpO2 and respiratory event-specific HR variability were computed from the synchronized recordings. Multivariate regression analyses were conducted to analyze the associations between individualized HR metrics and the occurrence of AMI.

Results

Of 2,748 patients recruited, 39% (n = 1,071) had moderate-to-severe OSA (respiratory event index, REI ≥ 15), and 11.4% (n = 313) patients had AMI. Patients with AMI experienced severe OSA, severe minSpO2, and greater HR reactions. Patients with minSpO2 &lt;90% had an adjusted odds ratio (OR) of 1.48 [95% confidence interval (CI): 1.09–2.00, p = 0.012) for AMI. Notably, minSpO2-induced elevated mean HR response (HRmean &gt; 73 bpm) was significantly associated with AMI (OR 1.72, 95% CI: 1.32–2.23, p &lt; 0.001). Patients with both severe minSpO2 (&lt;90%) and elevated HRmean carried an additive OR of 2.65 (95% CI: 1.74–4.05, p &lt; 0.001) for the risk of AMI after adjustment for potential confounders. A large total power spectrum specific to respiratory events was correlated with an adjusted OR of 0.61 for AMI risk.

Conclusion

Patients with substantial HR reactions to OSA-induced oxygen nadir and restricted cardiac cycle shifting to respiratory events were likely at increased risk of developing AMI. Detection of nocturnal HR response to hypoxemia may help improve cardiovascular risk stratification.

Metrics of sleep apnea severity: beyond the apnea-hypopnea index

Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA has well established cardiovascular and neurocognitive sequelae, although the optimal metric to assess its severity and/or potential response to therapy remains unclear. The apnea-hypopnea index (AHI) is well established; thus, we review its history and predictive value in various different clinical contexts. Although the AHI is often criticized for its limitations, it remains the best studied metric of OSA severity, albeit imperfect. We further review the potential value of alternative metrics including hypoxic burden, arousal intensity, odds ratio product, and cardiopulmonary coupling. We conclude with possible future directions to capture clinically meaningful OSA endophenotypes including the use of genetics, blood biomarkers, machine/deep learning and wearable technologies. Further research in OSA should be directed towards providing diagnostic and prognostic information to make the OSA diagnosis more accessible and to improving prognostic information regarding OSA consequences, in order to guide patient care and to help in the design of future clinical trials.

The Sleep Apnea-Specific Pulse-Rate Response Predicts Cardiovascular Morbidity and Mortality

Rationale: Randomized controlled trials have been unable to detect a cardiovascular benefit of continuous positive airway pressure in unselected patients with obstructive sleep apnea (OSA). We hypothesize that deleterious cardiovascular outcomes are concentrated in a subgroup of patients with a heightened pulse-rate response to apneas and hypopneas (ΔHR). Methods: We measured the ΔHR in the MESA (Multi-Ethnic Study of Atherosclerosis) (N = 1,395) and the SHHS (Sleep Heart Health Study) (N = 4,575). MESA data were used to determine the functional form of the association between the ΔHR and subclinical cardiovascular biomarkers, whereas primary analyses tested the association of the ΔHR with nonfatal or fatal cardiovascular disease (CVD) and all-cause mortality in longitudinal data from the SHHS. Measurements and Main Results: In the MESA, U-shaped relationships were observed between subclinical CVD biomarkers (coronary artery calcium, NT-proBNP [N-terminal prohormone BNP], and Framingham risk score) and the ΔHR; notably, a high ΔHR (upper quartile) was associated with elevated biomarker scores compared with a midrange ΔHR (25th-75th centiles). In the SHHS, individuals with a high ΔHR compared with a midrange ΔHR were at increased risk of nonfatal or fatal CVD and all-cause mortality (nonfatal adjusted hazard ratio [95% confidence interval (CI)], 1.60 [1.28-2.00]; fatal adjusted hazard ratio [95% CI], 1.68 [1.22-2.30]; all-cause adjusted hazard ratio [95% CI], 1.29 [1.07-1.55]). The risk associated with a high ΔHR was particularly high in those with a substantial hypoxic burden (nonfatal, 1.93 [1.36-2.73]; fatal, 3.50 [2.15-5.71]; all-cause, 1.84 [1.40-2.40]) and was exclusively observed in nonsleepy individuals. Conclusions: Individuals with OSA who demonstrate an elevated ΔHR are at increased risk of cardiovascular morbidity and mortality. This study identifies a prognostic biomarker for OSA that appears useful for risk stratification and patient selection for future clinical trials.

Characteristics and Reproducibility of Novel Sleep EEG Biomarkers and their Variation with Sleep Apnea and Insomnia in a Large Community-Based Cohort

STUDY OBJECTIVES

New EEG features became available for use in polysomnography and have shown promise in early studies. They include a continuous index of sleep depth (Odds-Ratio-Product; ORP), agreement between right and left sleep depth (R/L coefficient), dynamics of sleep recovery following arousals (ORP-9), general EEG amplification (EEG Power), alpha intrusion and arousal intensity. This study was undertaken to establish ranges and reproducibility of these features in subjects with different demographics and clinical status.

METHODS

We utilized data from the two phases of the Sleep-Heart-Health-Study (SHHS1 and SHHS2). Polysomnograms of 5804 subjects from SHHS1 were scored to determine the above features. Feature values were segregated according to clinical status of Obstructive Sleep Apnea (OSA), insomnia, insomnia plus OSA, no clinical sleep disorder, and demographics (age, gender and race). Results from SHHS visit2 were compared with SHHS1 results.

RESULTS

All features varied widely among clinical groups and demographics. Relative to participants with no sleep disorder, wake ORP was higher in participants reporting insomnia symptoms and lower in those with OSA (p<0.0001 for both), reflecting opposite changes in sleep pressure, while NREM ORP was higher in both insomnia and OSA (p<0.0001), reflecting lighter sleep in both groups. There were significant associations with age, gender, and race. EEG Power, and REM ORP were highly reproducible across the two studies (ICC>0.75).

CONCLUSIONS

The reported results serve as bases for interpreting studies that utilize novel sleep EEG biomarkers and identify characteristic EEG changes that vary with age, gender and may help distinguish insomnia from OSA.

Spindles are highly heritable as identified by different spindle detectors

STUDY OBJECTIVES

Sleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11-16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics.

METHODS

We utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability.

RESULTS

We found robust evidence to support strong heritability of spindles regardless of detector method (h2 > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results.

CONCLUSIONS

The sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.

Night-to-night variability of sleep electroencephalography-based brain age measurements

OBJECTIVE

Brain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient's BAI.

METHODS

86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured.

RESULTS

The within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively.

CONCLUSIONS

Averaging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI.

SIGNIFICANCE

With increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients' homes to identify patients who should undergo further investigation or monitoring.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

Further Development of P4 Approach to Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a heterogeneous disorder. Cluster analysis has identified different physiologic subtypes with respect to symptoms. A difference exists in cardiovascular risk from OSA between the 7 subtypes identified. There are 3 basic subtypes replicated in multiple studies: (a) a group where insomnia is the main symptom; (b) an asymptomatic group; (c) a group with marked excessive sleepiness. The symptomatic benefit from treatment with nasal CPAP varies between these 3 subtypes. Data from the Sleep Heart Health Study reveal that the increased risk of cardiovascular disease from OSA occurs only in the excessively sleepy group.

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

From CPAP to tailored therapy for obstructive sleep Apnoea

Obstructive Sleep Apnoea (OSA) is a common sleep disorder that is associated with daytime symptoms and a range of comorbidity and mortality. Continuous Positive Airway Pressure (CPAP) therapy is highly efficacious at preventing OSA when in use and has long been the standard treatment for newly diagnosed patients. However, CPAP therapy has well recognised limitations in real world effectiveness due to issues with patient acceptance and suboptimal usage. There is a clear need to enhance OSA treatment strategies and options. Although there are a range of alternative treatments (e.g. weight loss, oral appliances, positional devices, surgery, and emerging therapies such as sedatives and oxygen), generally there are individual differences in efficacy and often OSA will not be completely eliminated. There is increasing recognition that OSA is a heterogeneous disorder in terms of risk factors, clinical presentation, pathophysiology and comorbidity. Better characterisation of OSA heterogeneity will enable tailored approaches to therapy to ensure treatment effectiveness. Tools to elucidate individual anatomical and pathophysiological phenotypes in clinical practice are receiving attention. Additionally, recognising patient preferences, treatment enhancement strategies and broader assessment of treatment effectiveness are part of tailoring therapy at the individual level. This review provides a narrative of current treatment approaches and limitations and the future potential for individual tailoring to enhance treatment effectiveness.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

P4 medicine approach to obstructive sleep apnoea

P4 medicine is an evolving approach to personalized medicine. The four Ps offer a means to: Predict who will develop disease and co-morbidities; Prevent rather than react to disease; Personalize diagnosis and treatment; have patients Participate in their own care. P4 medicine is very applicable to obstructive sleep apnoea (OSA) because each OSA patient has a different pathway to disease and its consequences. OSA has both structural and physiological mechanisms with different clinical subgroups, different molecular profiles and different consequences. This may explain why there are different responses to alternative therapies, such as intraoral devices and hypoglossal nerve stimulation therapy. Currently, technology facilitates patients to participate in their own care from screening for OSA (snoring and apnoea apps) to monitoring response to therapy (sleep monitoring, blood pressure, oxygen saturation and heart rate) as well as monitoring their own continuous positive airway pressure (CPAP) compliance. We present a conceptual framework that provides the basis for a new, P4 medicine approach to OSA and should be considered more in depth: predict and prevent those at high risk for OSA and consequences, personalize the diagnosis and treatment of OSA and build in patient participation to manage OSA.