Daily Fluctuations in Heart Rate Variability in Chronic Kidney Disease Patients
Overview
This study analyzed over 50 hours of wearable EKG data from 458 chronic kidney disease (CKD) patients, revealing that diabetes and higher proteinuria are significantly associated with reduced heart rate variability (HRV), measured by SDNN. These findings highlight the influence of clinical and demographic factors on HRV and suggest potential for HRV metrics as predictive biomarkers for cardiovascular risk in CKD.
Background
Heart and kidney functions are regulated by the circadian clock, which adapts physiological processes over a 24-hour cycle. Disruptions in these rhythms are linked to increased cardiovascular risk and mortality. Wearable devices enable continuous, out-of-clinic monitoring of cardiovascular function, offering new opportunities for risk stratification in CKD. Prior studies in the Chronic Renal Insufficiency Cohort (CRIC) have shown associations between HRV and mortality risk, but large-scale real-world data on HRV fluctuations in CKD patients remain limited.
Data Highlights
Parameter
Low SDNN (≤33.7 ms)
Mid SDNN (33.8–48.8 ms)
High SDNN (≥49.0 ms)
Number of participants
152
153
153
Mean age (years)
Similar across tertiles (p > 0.05)
Female (%)
Similar across tertiles (p > 0.05)
Diabetes prevalence (%)
65.8
~49
36.6
Hemoglobin A1C (%)
6.85 ± 1.69
6.18 ± 0.98
6.05 ± 1.10
Proteinuria (uPCR ≥ 0.2)
Associated with 5.73 ms lower SDNN (p = 0.027)
Diabetes effect on SDNN
7.4 ms lower SDNN compared to non-diabetics (p = 0.001)
Key Findings
Diabetes is associated with a significant reduction in HRV, with a 7.4 ms lower SDNN compared to non-diabetic CKD patients (p = 0.001).
Higher proteinuria (uPCR ≥ 0.2) correlates with a 5.73 ms decrease in SDNN (p = 0.027), indicating worse autonomic function.
Participants with low SDNN (≤33.7 ms) had a higher prevalence of diabetes (65.8%) compared to those with high SDNN (36.6%).
Hemoglobin A1C levels were significantly higher in the low SDNN group, suggesting poorer glycemic control relates to reduced HRV.
The study confirms feasibility of using wearable EKG devices for extended HRV monitoring in a large CKD cohort.
Age, sex, race, and ethnicity distributions were similar across SDNN tertiles, indicating these factors did not confound HRV differences.
Clinical Implications
Continuous HRV monitoring using wearable devices can identify CKD patients with higher cardiovascular risk, particularly those with diabetes and elevated proteinuria. Incorporating HRV metrics like SDNN into clinical assessments may improve risk stratification and guide personalized interventions. These findings support further research into time-specific HRV as a predictive biomarker for adverse cardiovascular outcomes in CKD.
Conclusion
This large-scale study demonstrates that diabetes and proteinuria significantly reduce heart rate variability in CKD patients, measurable via wearable EKG technology. These insights lay the groundwork for future predictive models using HRV to improve cardiovascular risk assessment in this population.
References
Chronic Renal Insufficiency Cohort Study -- Daily Fluctuations in Heart Rate Variability Monitored by Wearable Devices
by Carsten Skarke, Wei Yang, Daohang Sha, Nicholas F. Lahens, Tamara Isakova, Mark Unruh, Rajat Deo, Eunice Carmona-Powell, John H. Holmes, Elaine Ficarra, Jing Chen, Jiang He, Hernan Rincon-Choles, Vallabh Shah, Chi-yuan Hsu, Amanda H. Anderson, James P. Lash, Mahboob Rahman
