Investigating the Association between Resting Heart Rate and Estimated Glomerular Filtration Rate: A UK Biobank study

This study investigates the association of resting heart rate (HR) and HR variability (HRV) with estimated glomerular filtration rate (eGFR) using data from the UK Biobank. To explore this association, the RR interval (RRI) and the standard deviation of RRI (SDRRI) were calculated from 2-minute electrocardiograms (ECGs). The ECG signals were obtained from 1,555 participants who took part in the UK Biobank study. Out of the 1555 participants, 227 had (eGFR $\lt90 \mathbf{m L} / \mathbf{m i n} / 1.73 \mathbf{m}^{2}$). A multiple linear regression model was developed to predict eGFR from RRI and SDRRI after adjusting for sex, age, and body mass index (BMI). The association between RRI and eGFR was significant, $(p\lt0.05)$. These findings suggest that HRs could be used to assess kidney function.

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Published
2024-10-23
DOI
https://doi.org/10.1109/esgco63003.2024.10767052
Citations
2
Primary Topic
Heart rate and cardiovascular health
Type
article
Field-Weighted Citation Impact
0.80
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article

Investigating the Association between Resting Heart Rate and Estimated Glomerular Filtration Rate: A UK Biobank study

Samit Kumar Ghosh, Ahsan H. Khandoker, Namareq Widatalla, Patricia Buenaventura
2 citations
Heart rate and cardiovascular health
0.80
article

Investigating the Association between Resting Heart Rate and Estimated Glomerular Filtration Rate: A UK Biobank study

Samit Kumar Ghosh, Ahsan H. Khandoker, Namareq Widatalla, Patricia Buenaventura
article en
2 citations

Abstract

This study investigates the association of resting heart rate (HR) and HR variability (HRV) with estimated glomerular filtration rate (eGFR) using data from the UK Biobank. To explore this association, the RR interval (RRI) and the standard deviation of RRI (SDRRI) were calculated from 2-minute electrocardiograms (ECGs). The ECG signals were obtained from 1,555 participants who took part in the UK Biobank study. Out of the 1555 participants, 227 had (eGFR $\lt90 \mathbf{m L} / \mathbf{m i n} / 1.73 \mathbf{m}^{2}$). A multiple linear regression model was developed to predict eGFR from RRI and SDRRI after adjusting for sex, age, and body mass index (BMI). The association between RRI and eGFR was significant, $(p\lt0.05)$. These findings suggest that HRs could be used to assess kidney function.

Khalifa University of Science and Technology (AE)
Openalex Percentile: Top 22%
Heart rate and cardiovascular health
0.80
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