Analysis of Diurnal Variations in Heart Rate: Potential Applications for Chronobiology and Cardiovascular Medicine

Administration of blue light in the morning and no blue-ray light in the evening improves the circadian functions of non-24-hour shift workers

In modern 24-hour society, various round-the-clock services have entailed shift work, resulting in non-24-hour schedules. However, the extent of behavioral and physiological alterations by non-24-hour schedules remains unclear, and particularly, effective interventions to restore the circadian functions of non-24-hour shift workers are rarely explored. In this study, we investigate the effects of a simulated non-24-hour military shift work schedule on daily rhythms and sleep, and establish an intervention measure to restore the circadian functions of non-24-hour shift workers. The three stages of experiments were conducted. The stage-one experiment was to establish a comprehensive evaluation index of the circadian rhythms and sleep for all 60 participants by analyzing wristwatch-recorded physiological parameters and sleep. The stage-two experiment evaluated the effects of an intervention strategy on physiological rhythms and sleep. The stage-three experiment was to examine the participants' physiological and behavioral disturbances under the simulated non-24-hour military shift work schedule and their improvements by the optimal lighting apparatus. We found that wristwatch-recorded physiological parameters display robust rhythmicity, and the phases of systolic blood pressures and heart rates can be used as reliable estimators for the human body time. The simulated non-24-hour military shift work schedule significantly disrupts the daily rhythms of oxygen saturation levels, blood pressures, heart rates, and reduces sleep quality. Administration of blue light in the morning and no blue-ray light in the evening improves the amplitude and synchronization of daily rhythms of the non-24-hour participants. These findings demonstrate the harmful consequences of the non-24-hour shift work schedule and provide a non-invasive strategy to improve the well-being and work efficiency of the non-24-hour shift population.

A critical signal for phenotype transition driven by negative feedback loops

The biological rhythms governed by negative feedback loops have undergone extensive investigation. However, developing reliable and versatile warning signals to predict periodic fluctuations in physiological processes and behaviors associated with these rhythms remains a challenge. Here, we monitored the heart rate and tracked ovulation dates of 91 fertile women. The finding strongly links the velocity (derivative) of heart rate with ovulation in menstrual cycles, providing a predictive warning signal. Similarly, an analysis of calcium signaling in the suprachiasmatic nucleus (SCN) of mice reveals that the maximum velocity of rising calcium signal aligns with locomotor activity offsets. To demonstrate the generality of derivative-transitions link, numerical simulations using a negative feedback loop model were conducted. Statistical analysis indicated that over 90% of the oscillations exhibited a correlation between maximum velocity and transition points. Consequently, the maximum velocity derived from oscillatory curves holds significant potential as an early warning signal for critical transitions.

Nomogram Model Based on Clinical Risk Factors and Heart Rate Variability for Predicting All-Cause Mortality in Stage 5 CKD Patients

Background: Heart rate variability (HRV), reflecting circadian rhythm of heart rate, is reported to be associated with clinical outcomes in stage 5 chronic kidney disease (CKD5) patients. Whether CKD related factors combined with HRV can improve the predictive ability for their death remains uncertain. Here we evaluated the prognosis value of nomogram model based on HRV and clinical risk factors for all-cause mortality in CKD5 patients.

Methods: CKD5 patients were enrolled from multicenter between 2011 and 2019 in China. HRV parameters based on 24-h Holter and clinical risk factors associated with all-cause mortality were analyzed by multivariate Cox regression. The relationships between HRV and all-cause mortality were displayed by restricted cubic spline graphs. The predictive ability of nomogram model based on clinical risk factors and HRV were evaluated for survival rate.

Results: CKD5 patients included survival subgroup (n = 155) and all-cause mortality subgroup (n = 45), with the median follow-up time of 48 months. Logarithm of standard deviation of all sinus R-R intervals (lnSDNN) (4.40 ± 0.39 vs. 4.32 ± 0.42; p = 0.007) and logarithm of standard deviation of average NN intervals for each 5 min (lnSDANN) (4.27 ± 0.41 vs. 4.17 ± 0.41; p = 0.008) were significantly higher in survival subgroup than all-cause mortality subgroup. On the basis of multivariate Cox regression analysis, the lnSDNN (HR = 0.35, 95%CI: 0.17–0.73, p = 0.01) and lnSDANN (HR = 0.36, 95% CI: 0.17–0.77, p = 0.01) were associated with all-cause mortality, their relationships were negative linear. Spearman’s correlation analysis showed that lnSDNN and lnSDANN were highly correlated, so we chose lnSDNN, sex, age, BMI, diabetic mellitus (DM), β-receptor blocker, blood glucose, phosphorus and ln intact parathyroid hormone (iPTH) levels to build the nomogram model. The area under the curve (AUC) values based on lnSDNN nomogram model for predicting 3-year and 5-year survival rates were 79.44% and 81.27%, respectively.

Conclusion: In CKD5 patients decreased SDNN and SDANN measured by HRV were related with their all-cause mortality, meanwhile, SDNN and SDANN were highly correlated. Nomogram model integrated SDNN and clinical risk factors are promising for evaluating their prognosis.