Resting Heart Rate, Short-Term Heart Rate Variability and Incident Atrial Fibrillation (from the Multi-Ethnic Study of Atherosclerosis (MESA))

Habibi, Mehdi; Chahal, Harjit; Greenland, Philip; Güallar, Eliseo; Lima, João A.C.; Soliman, Elsayed Z.; Alonso, Álvaro; Heckbert, Susan R.; Nazarian, Saman

doi:10.1016/j.amjcard.2019.08.025

Cited by 34 publications

(32 citation statements)

References 27 publications

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“…This study finds an association between sinus node function and AF risk ,though the mechanism is unclear. Large longitudinal studies have noted that those with a higher resting heart have increased risk of incident AF 14,15 , consistent with our findings here. Changes in resting heart rate as well as heart rate variability suggest the mechanism may lie within the autonomic nervous system.…”

Section: Cardiac Electrophysiologysupporting

confidence: 92%

Non-atrial fibrillation cardiac phenotypes associated with common atrial fibrillation genotypic risk

Kapur

Gillon

MacRae

2020

Preprint

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Background: Atrial fibrillation (AF) genetics studies have focused on a linear genotype- phenotype relationship, i.e. genetic predisposition to the arrhythmia. Genome wide association studies have implicated numerous upstream mechanisms responsible for AF. Objective: We hypothesized that the genetic predisposing factors for AF might be associated with non-AF clinical phenotypes and sought to characterize electrophysiology parameters as a function of AF genetic risk. Methods:. Biosamples were obtained from 405 subjects for classification of carrier status at 12 single nucleotide polymorphisms with a known association to AF allowing calculation of a validated AF genetic risk score. We then analyzed subgroups within the total population; in order to understand the effect on (a) sinus node function and cardiac conduction (b) primary atrial flutter (c) left atrial appendage morphology. Results: We evaluated 405 patients consisting of a range of genetic risk scores from −1.016 to +2.178. Within this, we identified 86 patients without prescribed chronotropic pharmacotherapy with a 24-hour Holter recording to investigate sinus node function; 181 patients with invasive H-V measurement at the time of electrophysiologic study to investigate cardiac conduction; 78 undergoing cavotricuspid isthmus ablation for typical atrial flutter without prior diagnosis of AF; and 284 patients with cardiac imaging of the left atrial appendage. Conclusions: A common AF genetic risk score is associated with a number of non-AF electrophysiologic relevant phenotypes. Sinus node function, AV node physiology, post flutter ablation AF risk, atrial appendage morphology all appear to be associated with the common genetic AF risk.

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Section: Cardiac Electrophysiologysupporting

confidence: 92%

Non-atrial fibrillation cardiac phenotypes associated with common atrial fibrillation genotypic risk

Kapur

Gillon

MacRae

2020

Preprint

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“…In addition, studies on the association of short-term heart rate variability (HRV) with AF also presented a "U" shaped curve. 9,[23][24][25][26] The Multi-Ethnic Study of Atherosclerosis (MESA) showed that both low and high beat-to-beat HRV related to incident AF among individuals without cardiovascular diseases. 23 RHR changes from beat-to-beat to visit-to-visit, a long-term variation may have additional prognostic value.…”

Section: Discussionmentioning

confidence: 99%

Frequency of Visit-to-Visit Variability of Resting Heart Rate and the Risk of New-Onset Atrial Fibrillation in the General Population

Zhang

Zhao

Sun

et al. 2021

The American Journal of Cardiology

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Resting heart rate (RHR) has been an established predictor for atrial fibrillation (AF). However, the association of visit-to-visit heart rate variability (VVHRV) with new-onset AF risk over long term remains unclear. Our study investigates the relation of VVHRV to new-onset AF in general population in the prospective study of the Kailuan cohort. A total of 46,126 individuals without arrhythmia were included. They underwent 3 health examinations from 2006 to 2010 and performed follow up. VVHRV was measured by coefficient of variation (CV), variability independent of the mean (VIM), and standard deviation (SD). Participants were separately divided into 5 categories by quintiles of visit-to-visit RHR-CV, RHR-VIM and RHR-SD. Multivariate Cox regression and restricted cubic spline models were performed to establish the association between VVHRV and new-onset AF. 241 new-onset AF occurred during a median follow-up of 7.54 years. The incidence of new-onset AF in the group of the lowest (Q1) and highest quintiles (Q5) of RHR-CV were higher than that in other groups. The HRs for the newonset AF were 2.07 (95% CI, 1.34−3.21, p < 0.01), in the highest quintile group(Q5) compared with group Q2, and 1.89(95% CI, 1.20−2.97, p < 0.01) in the lowest quintile group (Q1) compared with group Q2. The risk for new-onset AF showed a similar trend using RHR-VIM (p < 0.01) and RHR-SD (p < 0.05) parameters. Further sensitivity analyses indicated the consistent results in subjects without prior cardiovascular disease and without taking beta blockers or CCB. To match the covariates, analyses were also performed by propensity score matching, and prominent trends were also found in RHR-SD and RHR-VIM. In conclusion, the study indicated that higher and lower VVHRV were associated with the increasing risk of new-onset AF, which supporting a U-shaped curve existence.

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“…Recently, ultra-short-term recordings for HRV assessment have received notable attention in cardiovascular medicine [13][14][15], metabolic disease [16], cognitive function [8,9], exercise testing [17][18][19], and sports training [11,20] studies due to the time efficiency it offers to both patients and practitioners. Ultra-short-term recording only requires R-R intervals of less than 60 seconds.…”

Section: Ultra-short-term Hrv Studiesmentioning

confidence: 99%

A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study (Preprint)

Chen¹,

Lu²,

Pagaduan³

et al. 2020

Preprint

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BACKGROUND Smartphone application for heart rate variability (HRV) measurement has been extensively developed in this decade. However, this implementation to ultra-short-term HRV recordings with wearable devices has not been examined. OBJECTIVE The purposes of this study were 1) to compare the validity and reliability of ultra-short-term and short-term HRV of time-domain and frequency-domain variables in a novel smartphone application, Pulse Express Pro (PEP); and 2) to determine the agreement of HRV assessments between electrocardiographic (ECG) and PEP. METHODS Sixty healthy adults were recruited to participate in this study (age: 22.3 ± 3.0 yrs, height: 168.4 ± 8.0 cm, body weight: 64.2 ± 11.5 kg). A 5-min resting HRV was assessed via the ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval, (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalised very low-frequency power (nVLF), normalised low-frequency power (nLF), and normalised high-frequency power (nHF) were analysed within nine time segments of HRV records: 0-1 min, 1-2 min, 2-3 min, 3-4 min, 4-5 min, 0-2 min, 0-3 min, 0-4 min, and 0-5 min (standard). Standardized differences (ES), interclass correlation coefficients (ICC), and the Spearman’s product-moment correlation were used to compare the validity and reliability of each time segment to 0-5 min standard measures. Limits of agreement were assessed by using the Bland-Altman plot analysis. RESULTS Compared to standard measures in both ECG and PEP, SDNN and RMSSD variables showed trivial ES (< 0.2) and very large to nearly perfect ICC and Spearman correlation coefficient in all time segments (> 0.8). The nVLF, nLF and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-min records for SDNN, RMSSD and pNN50. Acceptable limits of agreement were found after consecutive 3-min records for nLF, and nHF. CONCLUSIONS Using the PEP app to facilitate 1-min ultra-short-term recording is suggested to time-domain HRV indices (SDNN, RMSSD and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP, at least 3-min recordings is needed for accurate measurement.

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Resting Heart Rate, Short-Term Heart Rate Variability and Incident Atrial Fibrillation (from the Multi-Ethnic Study of Atherosclerosis (MESA))

Cited by 34 publications

References 27 publications

Non-atrial fibrillation cardiac phenotypes associated with common atrial fibrillation genotypic risk

Non-atrial fibrillation cardiac phenotypes associated with common atrial fibrillation genotypic risk

Frequency of Visit-to-Visit Variability of Resting Heart Rate and the Risk of New-Onset Atrial Fibrillation in the General Population

A Novel Smartphone App for the Measurement of Ultra–Short-Term and Short-Term Heart Rate Variability: Validity and Reliability Study (Preprint)

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