Patterns of Circadian Variation in 24-Hour Ambulatory Blood Pressure, Heart Rate, and Sympathetic Tone Correlate with Cardiovascular Disease Risk: A Cluster Analysis.

Myung Han Hyun,Jun Hyuk Kang,Chang Gyu Park,Eunmi Lee,Jin Oh Na,Seung-Woon Rha,Jin Won Kim,Hong Seog Seo,Cheol Ung Choi,Eung Ju Kim,Sunghwan Kim

doi:10.1155/2020/4354759

Abstract

To investigate whether specific time series patterns for blood pressure (BP), heart rate (HR), and sympathetic tone are associated with metabolic factors and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). A total of 989 patients who underwent simultaneous 24-hour ambulatory BP and Holter electrocardiogram monitoring were enrolled. The patients were categorized into sixteen groups according to their circadian patterns using the consensus clustering analysis method. Metabolic factors, including cholesterol profiles and apolipoprotein, were compared. The 10-year ASCVD risk was estimated based on the Framingham risk model. Overall, 16 significant associations were found between the clinical variables and cluster groups. Age was commonly associated with all clusters in systolic BP (SBP), diastolic BP (DBP), HR, and sympathetic tone. Metabolic indicators, including diabetes, body mass index, total cholesterol, high-density lipoprotein, and apolipoprotein, were associated with the four sympathetic tone clusters. In the crude analysis, the ASCVD risk increased incrementally from clusters 1 to 4 across SBP, DBP, HR, and sympathetic tone. After adjustment for multiple variables, however, only sympathetic tone clusters 3 and 4 showed a significantly high proportion of patients at high risk (≥7.5%) of 10-year ASCVD (odds ratio (OR) = 5.90, 95% confidential interval (CI) = 1.27–27.46, and P value = 0.024 and OR = 15.28, 95% CI = 3.59–65.11, and P value < 0.001, respectively). Time series patterns of BP, HR, and sympathetic tone can serve as an indicator of aging. Circadian variations in sympathetic tone can provide prognostic information about patient metabolic profiles and indicate future ASCVD risk.

Highlights

Circadian variations in blood pressure (BP) and heart rate (HR) are associated with aging and metabolic abnormalities such as obesity, insulin resistance, and dyslipidemia [1,2,3]
In our time series clustering analyses, we found three notable associations between the time series patterns and clinical variables: (1) age was associated with all patterns (SBP, diastolic BP (DBP), HR, and sympathetic tone); (2) metabolic indicators (DM, BMI, serum total cholesterol (T-C) level, serum HDL-C level, and plasma ApoB level) were associated with the sympathetic tone pattern; and (3) the sympathetic tone clusters were significantly associated with 10-year atherosclerotic cardiovascular disease (ASCVD) risk
These results indicate that the time series patterns of BP, HR, and sympathetic tone can provide decipherable information about patient clinical status and future ASCVD risk

Summary

Introduction

Circadian variations in blood pressure (BP) and heart rate (HR) are associated with aging and metabolic abnormalities such as obesity, insulin resistance, and dyslipidemia [1,2,3]. Diurnal variations in BP and HR are known to be associated with autonomic dysfunction that might contribute to cardiovascular disease [4,5,6]. Cardiovascular Therapeutics and vagal activity) variability could be significantly associated with clinical cardiovascular disease (CVD) risk factors, such as age, sex, body mass index, serum glucose level, serum insulin level, and cholesterol level that reflect metabolic and autonomic abnormalities [10,11,12]. Previous trials could not examine associations between an individual’s circadian patterns of BP, HR, and sympathetic tone and clinical cardiovascular risk factors or estimate CVD risks based on how those patterns changed throughout the day and night. Given the uncertainty left by previous analyses, we used the consensus clustering method, which provides pattern recognition tools with consistent reliability by assessing an entire dataset based on each time point; this method has successfully identified gene expression patterns in microarray data [16,17,18]

Methods

Results

Conclusion