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. 2018 Apr 19:10:111-125.
doi: 10.2147/NSS.S155733. eCollection 2018.

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

Zhaoyang Huang  1 , Balaji Goparaju  2 , He Chen  3 , Matt T Bianchi  2
Affiliations

Heart rate phenotypes and clinical correlates in a large cohort of adults without sleep apnea

Zhaoyang Huang et al. Nat Sci Sleep. .

Abstract

Background: Normal sleep is associated with typical physiological changes in both the central and autonomic nervous systems. In particular, nocturnal blood pressure dipping has emerged as a strong marker of normal sleep physiology, whereas the absence of dipping or reverse dipping has been associated with cardiovascular risk. However, nocturnal blood pressure is not measured commonly in clinical practice. Heart rate (HR) dipping in sleep may be a similar important marker and is measured routinely in at-home and in-laboratory sleep testing.

Methods: We performed a retrospective cross-sectional analysis of diagnostic polysomnography in a clinically heterogeneous cohort of n=1047 adults without sleep apnea.

Results: We found that almost half of the cohort showed an increased HR in stable nonrapid eye movement sleep (NREM) compared to wake, while only 13.5% showed a reduced NREM HR of at least 10% relative to wake. The strongest correlates of HR dipping were younger age and male sex, whereas the periodic limb movement index (PLMI), sleep quality, and Epworth Sleepiness Scale (ESS) scores were not correlated with HR dipping. PLMI was however significantly correlated with metrics of impaired HR variability (HRV): increased low-frequency power and reduced high-frequency power. HRV metrics were unrelated to sleep quality or the ESS value. Following the work of Vgontzas et al, we also analyzed the sub-cohort with insomnia symptoms and short objective sleep duration. Interestingly, the sleep-wake stage-specific HR values depended upon insomnia symptoms more than sleep duration.

Conclusion: While our work demonstrates heterogeneity in cardiac metrics (HR and HRV), the population analysis suggests that pathological signatures of HR (nondipping and elevation) are common even in this cohort selected for the absence of sleep apnea. Future prospective work in clinical populations will further inform risk stratification and set the stage for testing interventions.

Keywords: heart rate variability; insomnia; periodic limb movements; sleep quality; sleepiness.

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Conflict of interest statement

Disclosure MTB has received funding from the Center for Integration of Medicine and Innovative Technology, the Milton Family Foundation, the MGH-MIT Grand Challenge, and the American Sleep Medicine Foundation, and the Department of Neurology. MTB has a patent pending on a home sleep monitoring device, has research agreements with MC10 and Insomnisolv and consulting agreements with McKesson, International Flavors and Fragrances, and Apple Inc., serves as a medical monitor for Pfizer, and has provided expert testimony in sleep medicine. This was not an industry supported study, and none of these entities had any role in the study. The other authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Examples of HR patterns from clinical PSGs. Notes: (A) A 29-year-old male with fatigue, BMI 26 kg/m2, with mildly increased HR trend in NREM sleep (all supine, absence of OSA, or PLMS). The top row shows HR values (bpm). The next row indicates scattered PLM events (limb movement). The third row indicates scattered AHI events. The bottom row shows the sleep–wake stages. In this and subsequent panels, the time scale is given as vertical dotted lines showing 1 hour intervals. In all panels, the recording time began between 10 and 10.30 pm. (B) A 51-year-old female with RLS and PLMS showing marked increase in NREM HR (all lateral position, BMI 25 kg/m2, and absence of OSA). The top row shows HR values (bpm), the next row indicates PLMS events (limb movement), and the bottom row is sleep–wake stages. (C) An 80-year-old female with severe OSA (AHI 32), sleeping supine (BMI 30 kg/m2), with reactive HR increases associated with REM desaturating events. The top row is the pulse oximetry data (SpO2), the next row is the HR, the third row is the SDB events contributing to the AHI, and the bottom row shows the sleep–wake stages. Abbreviations: AHI, apnea–hypopnea index; BMI, body mass index; HR, heart rate; NREM, nonrapid eye movement; OSA, obstructive sleep apnea; PLM, periodic limb movements; PLMS, periodic limb movements in sleep; PSG, polysomnography; REM, rapid eye movement; RLS, restless legs syndrome; SDB, sleep-disordered breathing.
Figure 2
Figure 2
HR analysis across sleep–wake stages. Notes: (A) HR values calculated from stable bouts (>5 minutes) of each sleep–wake stage. There were no differences between any of the stages. The waking HR values refer to any time spent awake while in bed for the PSG recording. (B) Histogram showing the proportion (%) of the cohort with lower HR value during N2 and N3, compared to wake of at least 0, 5, or 10% dipping. (C) Distribution of HR slope calculated for stable bouts of each sleep–wake stage across the cohort. The X-axis is the slope of a linear fit to each stable bout (in units of bpm). The zero-crossing value is the proportion of stable bouts that had a positive slope, and the inset is a zoom to show that the highest value was for wake and N1; by contrast, N3 had the greatest proportion of bouts showing a positive slope (lowest zero-crossing value on the Y-axis). Abbreviations: HR, heart rate; PSG, polysomnography; REM, rapid eye movement.
Figure 3
Figure 3
Clinical, PSG, and cardiac correlates of PLMS. Notes: (A) Box and whiskers plot showing the distribution of age (years) for three prespecified categories of PLMI values. Brackets indicate significant differences between groups (Kruskal–Wallis with Dunn’s correction, P<0.0001). (B) Box and whiskers plot showing the distribution of N1 (%) for three prespecified categories of PLMI values. Brackets indicate significant differences between groups (Kruskal–Wallis with Dunn’s correction, P<0.0001). (C) Correlation coefficients reaching the predefined minimum value of |>0.1|, with the PLMI value across the cohort. P-values were <1×10−8 for N1%, age, N3%, REM%, TST, and efficiency. The remaining significant p-values were between 0.01 and 0.0001. Abbreviations: HF, high frequency; HR, heart rate; HTN, hypertension; LF, low frequency; MP, misperception; PLM, periodic limb movements; PLMS, periodic limb movements in sleep; PLMI, periodic limb movement index; PSG, polysomnography; REM, rapid eye movement; TST, total sleep time; WHR, wake heart rate.
Figure 4
Figure 4
Clinical, PSG, and cardiac correlates of sleep quality and of misperception. Notes: (A) Box and whiskers plot showing the distribution of sleep efficiency (%) for prespecified categories of sleep quality. Bracket indicates significance (Mann–Whitney, P<0.003). (B) Box and whiskers plot showing the distribution of misperception of TST (subjective minus objective TST, in minutes) for prespecified categories of sleep quality. Bracket indicates significance (Mann–Whitney, P<0.0001). (C) Correlation coefficients reaching the predefined minimum value of |>0.1|, with the sleep quality value across the cohort. The P-values are all <0.005. (D) Correlation coefficients reaching the predefined minimum value of |>0.1|, with the TST misperception value across the cohort. The P-values are all <0.005. Abbreviations: HF, high frequency; HR, heart rate; LF, low frequency; MP, misperception; oTST, objective TST; PSG, polysomnography; Qual, quality; sTST, subjective TST; TST, total sleep time; z-drug, zolpidem, zaleplon, eszopiclone.
Figure 5
Figure 5
Insomnia symptoms and objective TST. Notes: The four possible combinations of binary total sleep time (TST; long [L] or short [S]) and insomnia symptom status (+ or −) are shown for age (A), N1 % (B), REM % (C), efficiency (D), PLMI (E), wake HR (F), N1 HR (G), N2 HR (H), N3 HR (I), and REM HR (J). In each panel, the box and whisker plots (5%–95%, with outliers shown as dots to illustrate variability) are given for four sub-cohorts based on objective TST value from the PSG (5.5 hours cutoff, for L or S values, and insomnia symptom level (high as + sign, low as - sign). These categories are given via X-axis labels, as well as the fill of the box plots: gray indicates insomnia symptom +, and speckled indicates <5.5 hours TST. Kruskal–Wallis testing results are shown with Dunn’s post hoc comparison of all possible group-wise pairs in each panel, where brackets indicate significant differences. The P-values were <0.0001 for all panels except wake and N1% (P<0.005). Abbreviations: HR, heart rate; Ins, insomnia; L, long; PLMI, periodic limb movements index; PSG, polysomnography; REM, rapid eye movement; S, short; TST, total sleep time
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