Nocturnal heart rate variation in diabetic and non-diabetic patients with sleep apnea syndrome

Clinical, polysomnographic, and heart rate variability in highland obstructive sleep apnea patients responding to one-night nocturnal oxygen supplementation: A post-hoc analysis from a randomized, crossover trial

OBJECTIVE

/Background: This study aimed to explore the clinical, polysomnographic, and heart rate variability (HRV) characteristics of highland obstructive sleep apnea (OSA) patients receiving one-night nocturnal oxygen supplementation (NOS) and to identify factors predicting response.

PATIENTS/METHODS

Thirty-four highland OSA patients living in Shangri-La were randomly assigned to receive NOS and sham oxygen in a randomized, placebo-controlled, crossover trial. Clinical assessments, polysomnography, and HRV were measured. A responder was defined as a ≥50% reduction in apnea-hypopnea index (AHI) with NOS compared with sham oxygen.

RESULTS

Eighteen participants responded and 16 did not respond, with a median (interquartile range [IQR]) age of 46.5 (36.5-53.0) and 48.0 (44.3-53.3) years, respectively. The median treatment effect (95% CI) on total AHI was -23.2/h (-30.0 to -17.5) and -12.0/h (-16.6 to -7.6) in responders and non-responders (p = 0.004), with similar effects on oxygen desaturation index. The mean OAH duration was prolonged by 7 s in responders together with improved sleep quality and daytime blood pressure. The mean OAH duration at baseline predicted responses to NOS with a sensitivity and specificity of 88.9% and 68.7% (AUC 0.809) at a cut-off point of 24.9 s. Changes in HRV parameters were negatively correlated with changes in mean oxygen saturation and daytime systolic blood pressure only in responders.

CONCLUSIONS

NOS significantly improved OSA severity and clinical outcomes in responders, which was related to improvements in parasympathetic activity. Highlanders with shorter mean OAH may be suitable candidates for NOS. These findings provide new information about tailored treatment strategies for highland OSA patients.

The Impact of Glycolipid Metabolic Disorders on Severity Stage-Specific Variation of Cardiac Autonomic Function in Obstructive Sleep Apnea: A Data-Driven Clinical Study

BACKGROUND

Cardiac autonomic dysfunction (CAD) is a common pathology in cardiovascular diseases; however, the role of glycolipid metabolic disorders in CAD development in obstructive sleep apnea (OSA) remains poorly understood.

METHODS

In total, 4152 patients with suspected OSA were recruited in our sleep center. Metabolic characteristics including anthropometric and glycolipid data were collected. Heart rate variability (HRV) was measured to assess the risk of CAD; its dose-response relationship with OSA severity was evaluated via restricted cubic spline (RCS) analysis. A segmented multivariate linear regression (SMLR) model was used to evaluate the roles of metabolic variables in different stages of OSA.

RESULTS

The RCS showed that CAD risk increased in a nonlinear relationship pattern with OSA severity, from slow fluctuation at earlier stages to rapid change in later stages. After integrating the clinical definition and RCS selected knots, we obtained the new four OSA severity stages. SMLR model showed that the overall value of glycolipid variables for prediction of HRV abnormalities was greater than the value of OSA variables at earlier stages, while OSA variables were more effective predictors in more severe stages. The discordance in respective relationship of HRV with metabolic and OSA variables sheds the light how metabolic disorders promoted the development of CAD in OSA, the later further in turn deteriorates cardiac function.

CONCLUSION

These results are indicative of stage-specific involvement of glycolipid metabolic factors underlying CAD nonlinear changes in patients with OSA. Early control glycolipid disorders may help the control of CAD development in patients with OSA.

Heart rate variability in type 2 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND

Cardiac autonomic neuropathy in type 2 dibetes mellitus (T2DM) patients is frequent and associated with high cardiovascular mortality. Heart rate variability (HRV) is the gold standard to measure cardiac autonomic neuropathy. We aimed to conduct a systematic review and meta-analysis to evaluate the impact of T2DM on HRV parameters.

METHODS

The PubMed, Cochrane Library, Embase and Science Direct databases were searched on 1st October 2017 using the keywords "diabetes" AND ("heart rate variability" OR "HRV"). Included articles had to report HRV parameters in T2DM patients and healthy controls measured during 24 hours with a Holter-electrocardiogram. Measurements of HRV retieved were: RR-intervals (or Normal to Normal intervals-NN), standard deviation of RR intervals (SDNN), percetange of adjacent NN intervals differing by more than 50 milliseconds (pNN50), square root of the mean squared difference of successive RR intervals (RMSSD), total power, Low Frequency (LF), High Frequency (HF) and LF/HF ratio, as per Task Force recommendations.

RESULTS

We included twenty-five case-control studies with 2,932 patients: 1,356 with T2DM and 1,576 healthy controls. T2DM patients had significantly (P<0.01) lower RR-intervals (effect size = -0.61; 95%CI -1.21 to -0.01), lower SDNN (-0.65; -0.83 to -0.47), lower RMSSD (-0.92; -1.37 to -0.47), lower pNN50 (-0.46; -0.84 to -0.09), lower total power (-1.52; -2.13 to -0.91), lower LF (-1.08; -1.46 to -0.69]), and lower HF (-0.79; -1.09 to -0.50). LF/HF did not differ between groups. Levels of blood glucose and HbA1c were associated with several HRV parameters, as well as Time from diagnosis of T2DM.

CONCLUSIONS

T2DM was associated with an overall decrease in the HRV of T2DM patients. Both sympathetic and parasympathetic activity were decreased, which can be explained by the deleterious effects of altered glucose metabolism on HRV, leading to cardiac autonomic neuropathy.

Association between Age and Changes in Heart Rate Variability after Hemodialysis in Patients with Diabetes

Background: Heart rate variability (HRV) represents changes in the time between successive heart beats, and it has been used to assess the autonomic nervous system. Previous studies have reported autonomic dysfunction in diabetic patients undergoing hemodialysis (HD), however, no studies have evaluated the effects of age on changes in HRV in these patients. The aim of this study was to examine the effects of age on changes in HRV in diabetic HD patients.

Methods: We enrolled 84 diabetic patients receiving maintenance HD. HRV was measured before and after HD to assess changes in HRV (ΔHRV). The patients were divided into two groups based on their age (65 years< or ≥65 years).

Results: Compared to the patients aged <65 years, those aged ≥65 years had a higher high frequency (HF) % (p = 0.032) before HD. The patients aged <65 years had a significant increase in very low frequency, low frequency (LF), and HF after HD. The patients aged ≥65 years had a significant increase in LF, but a significant decrease in HF% after HD. There was a significant interaction between age and change of HF% (p = 0.023) after HD. After multivariate adjustments for clinical, biochemical data and medications, systolic blood pressure, total cholesterol, hemoglobin, and hemoglobin were associated with ΔLF, whereas cerebrovascular disease, systolic blood pressure, and fasting glucose were associated with ΔHF% in patients aged ≥65 years.

Conclusion: Our study demonstrated significant changes in HRV after HD in diabetic patients. In the patients aged ≥65 years, LF was increased, whereas HF% was decreased significantly after HD. Among the HRV parameters, age had an interaction with the change of HF%.

Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent

Objective: Measurements of heart rate variability (HRV) during sleep have become increasingly popular as sleep could provide an optimal state for HRV assessments. While sleep stages have been reported to affect HRV, the effect of sleep stages on the variance of HRV parameters were hardly investigated. We aimed to assess the variance of HRV parameters during the different sleep stages. Further, we tested the accuracy of an algorithm using HRV to identify a 5-min segment within an episode of slow wave sleep (SWS, deep sleep).

Methods: Polysomnographic (PSG) sleep recordings of 3 nights of 15 healthy young males were analyzed. Sleep was scored according to conventional criteria. HRV parameters of consecutive 5-min segments were analyzed within the different sleep stages. The total variance of HRV parameters was partitioned into between-subjects variance, between-nights variance, and between-segments variance and compared between the different sleep stages. Intra-class correlation coefficients of all HRV parameters were calculated for all sleep stages. To identify an SWS segment based on HRV, Pearson correlation coefficients of consecutive R-R intervals (rRR) of moving 5-min windows (20-s steps). The linear trend was removed from the rRR time series and the first segment with rRR values 0.1 units below the mean rRR for at least 10 min was identified. A 5-min segment was placed in the middle of such an identified segment and the corresponding sleep stage was used to assess the accuracy of the algorithm.

Results: Good reproducibility within and across nights was found for heart rate in all sleep stages and for high frequency (HF) power in SWS. Reproducibility of low frequency (LF) power and of LF/HF was poor in all sleep stages. Of all the 5-min segments selected based on HRV data, 87% were accurately located within SWS.

Conclusions: SWS, a stable state that, in contrast to waking, is unaffected by internal and external factors, is a reproducible state that allows reliable determination of heart rate, and HF power, and can satisfactorily be detected based on R-R intervals, without the need of full PSG. Sleep may not be an optimal condition to assess LF power and LF/HF power ratio.

New Insight into the Role of Obstructive Sleep Apnea in Cardiometabolic Diseases

Humans spend almost one third of their life sleeping, thus sleep deprivation or poor sleep quality will have consequences upon the quality of life. Obstructive sleep apnea (OSA) is the most common sleep disorder that represents a respiratory cessation for at least ten seconds, which appears repeatable during sleep and it is accompanied by decreased oxygen saturation. The diagnosis of OSA is possible by filling in the STOP, STOP BANG, BERLIN questionnaires and performing the polysomnography, an accessible and more accurate method but yet very expensive. The prevalence of OSA is continuously increasing, but because of the nonspecific symptoms, the percentage of un-diagnosed cases is further increased. Data from 11 epidemiological studies published between 1993 and 2014 indicated an OSA prevalence of 22% in men and 17% in women. It has been suggested that there is a bidirectional causal relationship between OSA and obesity, and numerous studies have shown association of OSA with insulin resistance, diabetes mellitus, diabetic micro- and macrovascular complications and atrial fibrillation.