Midlife Blood Pressure Variability and Risk of All-Cause Mortality and Cardiovascular Events During Extended Follow-up

Age-Specific Association Between Visit-to-Visit Blood Pressure Variability and Hearing Loss: A Population-Based Cohort Study

Background and Objectives

Hearing loss is common and undertreated, and the impact of blood pressure variability (BPV) on the development of hearing loss remains unclear. We aimed to examine the age-specific association between visit-to-visit BPV and hearing loss.

Research Design and Methods

This nationally representative cohort study included 3,939 adults over 50 years from the Health and Retirement Study in the United States. Variabilities of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were assessed by standard deviation (SD), coefficient of variation, and variability independent of the mean (VIM), using SBP and DBP from 3 visits. Hearing loss was assessed by self-rated questions. Cox proportional risk models were used to evaluate age-specific associations (50-64, 65-79, and ≥80 years) between BPV and hearing loss. The generalized additive Cox models were further used to visualize the combined effect of age and BPV.

Results

During the follow-up up to 7.0 years, 700 participants developed hearing loss. Among people aged under 65 years, we observed a 36% increased risk of hearing loss with per-SD increment in VIM of SBP (hazard ratio [HR] per SD 1.36, 95% confidence interval [CI] 1.13-1.63) and a slightly significant association between VIM of DBP (HR per SD 1.21, 95% CI 1.01-1.45) and hearing loss. We did not observe significant associations among groups aged over 65 years (p > .05). The generalized additive Cox models also showed younger participants had stronger associations between BPV and hearing loss.

Discussion and Implications

Higher visit-to-visit variabilities of SBP were associated with an increased risk of hearing loss in middle-aged adults (50-65 years). Intervention in early BPV may help decrease hearing loss in adults aged over 50 years.

The effects of obstructive sleep apnea on blood pressure variability and load in patients with hypertension

BACKGROUND

Hypertension frequently coexists with obstructive sleep apnea (OSA), and their interplay substantially impacts the prognosis of affected individuals. Investigating the influence of OSA on blood pressure variability (BPV) and blood pressure load (BPL) in hypertensive patients has become a focal point of clinical research.

METHODS

This cross-sectional study recruited hypertensive patients (n = 265) without discrimination and classified them into four groups based on their apnea-hypopnea index (AHI): control group (n = 40), AHI < 5; mild group (n = 74), 5 ≤ AHI ≤ 15; moderate group (n = 68), 15 < AHI ≤ 30; severe group (n = 83), AHI > 30. All participants underwent comprehensive assessments, including polysomnography (PSG) monitoring, 24-h ambulatory blood pressure (ABP) monitoring, cardiac Doppler ultrasound, and additional examinations when indicated.

RESULTS

BPV and BPL exhibited significant elevations in the moderate and severe OSA groups compared to the control and mild OSA groups (P < 0.05). Moreover, interventricular septum thickness and left ventricular end-diastolic volume (LVEDV) demonstrated higher values in the moderate and severe OSA groups (P < 0.05). Multiple stepwise regression analysis identified noteworthy risk factors for elevated BPV in hypertensive patients with OSA, including AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea.

CONCLUSION

Hypertensive patients with moderate to severe OSA exhibited substantially increased BPV and BPL. Moreover, BPV was correlated with AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea in hypertensive patients with OSA.

Brachial-ankle pulse wave velocity is a stronger predictor than blood pressure for atherosclerotic cardiovascular diseases and all-cause mortality: a cohort study

Whether brachial-ankle pulse wave velocity (baPWV) is a better predictive indicator than blood pressure (BP) for atherosclerotic cardiovascular diseases (ASCVD) events and all-cause mortality in the general population has not yet been established. The current study included 47,659 participants from the Kailuan cohort in China, who underwent the baPWV test and were free of ASCVD, atrial fibrillation, and cancer at baseline. The hazard ratios (HRs) of ASCVD and all-cause mortality were evaluated using the Cox proportional hazards model. The predictive ability of baPWV, systolic BP (SBP), and diastolic BP (DBP) for ASCVD and all-cause mortality was evaluated using the area under the curve (AUC) and concordance index (C-index). Within the median follow-up period of 3.27 and 3.32 person-years, 885 ASCVD events and 259 deaths occurred, respectively. The HRs of ASCVD and all-cause mortality increased with the increase of baPWV, SBP, and DBP. When baPWV, SBP, and DBP were analyzed as continuous variables, the adjusted HRs were 1.29 (95% CI, 1.22-1.37), 1.28 (95% CI, 1.20-1.37), and 1.26 (95% CI, 1.17-1.34) for each standard deviation increase, respectively. The AUC and C-index for baPWV in predicting ASCVD and all-cause mortality were 0.744 and 0.750, respectively, while those for SBP were 0.697 and 0.620, those for DBP were 0.666 and 0.585. The AUC and C-index of baPWV were higher than those of SBP and DBP (P < 0.001). Therefore, baPWV is an independent predictor of ASCVD and all-cause mortality in the general Chinese population, and its predictive ability is superior to that of BP. baPWV is a more ideal screening method for ASCVD in large-scale population.

Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study

Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04-0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples.

Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes

Despite strenuous efforts to reduce cardiovascular disease (CVD) risk by improving cardiometabolic risk factors, such as glucose and cholesterol levels, and blood pressure, there is still residual risk even in patients reaching treatment targets. Recently, researchers have begun to focus on the variability of metabolic variables to remove residual risks. Several clinical trials and cohort studies have reported a relationship between the variability of metabolic parameters and CVDs. Herein, we review the literature regarding the effect of metabolic factor variability and CVD risk, and describe possible mechanisms and potential treatment perspectives for reducing cardiometabolic risk factor variability.