Diurnal variation of wearable device-based heart rate variability in the Chronic Renal Insufficiency Cohort study

Little is known about the prognostic value of continuous, out-of-clinic biometric monitoring of cardiovascular function in chronic kidney disease (CKD). In this study, a mean (±SD) of 50.3 ± 9.3 h of EKG recordings from wearable BioPatch devices was collected from 458 participants across seven Chronic Renal Insufficiency Cohort centers. Multivariable linear regression showed that diabetes was associated with 7.4 ms lower Standard Deviation of NN Intervals (SDNN) compared to non-diabetic participants (p = 0.001). Higher proteinuria (uPCR ≥ 0.2) was associated with 5.73 ms lower SDNN compared to lower proteinuria (p = 0.027). This study represents the largest dataset to date evaluating SDNN, a key heart rate variability metric using wearable EKG technology in CKD. Our findings highlight that specific clinical and demographic factors significantly influence HRV in this population. These results provide a critical foundation for future work to determine whether time-specific HRV metrics can serve as predictive biomarkers for cardiovascular risk and clinical outcomes in CKD.

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Journal
npj Digital Medicine
Published
2025-11-13
DOI
https://doi.org/10.1038/s41746-025-02010-5
Primary Topic
Heart Rate Variability and Autonomic Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Diurnal variation of wearable device-based heart rate variability in the Chronic Renal Insufficiency Cohort study

Wei Yang, Tamara Isakova, Chi‐yuan Hsu, Nicholas F. Lahens et al.
npj Digital Medicine
Heart Rate Variability and Autonomic Control
article

Diurnal variation of wearable device-based heart rate variability in the Chronic Renal Insufficiency Cohort study

Wei Yang, Tamara Isakova, Chi‐yuan Hsu, Nicholas F. Lahens, Mahboob Rahman, Daohang Sha, John H. Holmes, Mark L. Unruh, Debbie L. Cohen, Laura M. Dember, Rajat Deo, Vallabh O. Shah, Carsten Skarke, Eleonora Ficarra, Hernan Rincon-Choles, Eunice Carmona-Powell, Panduranga S. Rao, Jing Chen, Jiang He, Alan S. Go, James P. Lash, Amanda H. Anderson, Lawrence J. Appel
article en

Abstract

Little is known about the prognostic value of continuous, out-of-clinic biometric monitoring of cardiovascular function in chronic kidney disease (CKD). In this study, a mean (±SD) of 50.3 ± 9.3 h of EKG recordings from wearable BioPatch devices was collected from 458 participants across seven Chronic Renal Insufficiency Cohort centers. Multivariable linear regression showed that diabetes was associated with 7.4 ms lower Standard Deviation of NN Intervals (SDNN) compared to non-diabetic participants (p = 0.001). Higher proteinuria (uPCR ≥ 0.2) was associated with 5.73 ms lower SDNN compared to lower proteinuria (p = 0.027). This study represents the largest dataset to date evaluating SDNN, a key heart rate variability metric using wearable EKG technology in CKD. Our findings highlight that specific clinical and demographic factors significantly influence HRV in this population. These results provide a critical foundation for future work to determine whether time-specific HRV metrics can serve as predictive biomarkers for cardiovascular risk and clinical outcomes in CKD.

npj Digital MedicineVol. 8(1)
Northwestern University (US), Cleveland Clinic (US), Kaiser Permanente (US), Johns Hopkins University (US), University of New Mexico (US), University of California, San Francisco (US), University of Michigan (US), University Hospitals of Cleveland (US), Johns Hopkins Medicine (US), University of Alabama at Birmingham (US), University of Illinois Chicago (US), Translational Therapeutics (United States) (US), Case Western Reserve University (US), University of Pennsylvania (US), The University of Texas Southwestern Medical Center (US)
Kaiser Permanente, University of Pennsylvania, Johns Hopkins University, Deutsches Krebsforschungszentrum, Georgia Clinical and Translational Science Alliance, National Institutes of Health, University of Illinois at Urbana-Champaign, Perelman School of Medicine, University of Pennsylvania, University of California, San Francisco, Michigan Institute for Clinical and Health Research, Clinical and Translational Science Collaborative of Cleveland, School of Medicine, Case Western Reserve University, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Research Resources, National Center for Advancing Translational Sciences
Openalex Percentile: Top 49%
Heart Rate Variability and Autonomic Control
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