Effects of long-term blood pressure variability on renal function in community population

Abstract

High blood pressure is a significant contributor to premature mortality, resulting in nearly 10 million deaths and over 200 million disabilities worldwide.¹ In recent years, hypertension treatment has shifted focus not only to average blood pressure but also to blood pressure variability (BPV), categorized into very short-term, short-term, and long-term BPV based on the time period of occurrence.^{2, 3} Long-term BPV has emerged as clinically significant, with studies demonstrating its superiority in predicting long-term cardiovascular events, stroke, and mortality compared to short-term variability. Given its association with pre-renal function decline, reducing blood pressure fluctuations is imperative.

Chronic kidney disease (CKD) poses a global public health challenge, with its incidence rising alongside aging populations and increasing rates of conditions like diabetes and hypertension. Hypertension and kidney disease are closely intertwined, with hypertension exacerbating renal damage. At present, the management of hypertension mainly focuses on average blood pressure, but the average blood level does not accurately reflect the long-term control status of blood pressure. Notably, some patients with ostensibly controlled average blood pressure still experience renal function deterioration within 5–10 years, potentially due to blood pressure fluctuations. Emerging evidence suggests a link between cardiovascular events, renal injury, and BPV, independent of average blood pressure.^{4, 5} However, the precise relationship between BPV and renal function remains elusive. This study aimed to explore the association between fluctuating blood pressure and rapid renal function decline in a prospective community health checkup-based cohort.

A total of 7153 patients aged ≥18 years who received at least twice regular physical examinations at the Community Health Service Centre in Beijing, between 2015 and 2021, were recruited consecutively in this study. Exclusion criteria included CKD stage 4–5, acute stroke, myocardial infarction, and heart failure (<3 months). Finally, 7130 patients were enrolled in the analysis (Figure S1).

Sociodemographic information, comorbidities, and lifestyle habits were obtained through questionnaires. Blood pressure was measured twice during each visit, and BPV indices were calculated based on measurements across all visits. Additionally, blood samples were collected after an 8-h fast for biochemical analyses. We calculated several indicators as measures of BPV based on data from all visits, including standard deviation (SD), coefficient of variation (CV), variation independent of the mean (VIM), and average successive variability (ASV). The measures have been used in previous studies.^{6, 7}

Baseline data for this study were derived from the results of the initial annual health checkup, while endpoint data were obtained from the final annual health checkup. The primary endpoint of the study was the decline in the estimated glomerular filtration rate (eGFR), defined as an eGFR slope ≥3 mL/min/1.73 m². The secondary study endpoint was the new onset of CKD, defined as an eGFR mL/min/1.73 m². The eGFR slope was calculated as the regression coefficient between eGFR and time, expressed in mL/min/1.73 m²/year.

The continuous variable of clinical features was expressed as mean (SD), and the categorical variable was expressed as numbers (%). Logistic regression analyses were conducted to explore the correlation between GFR decline and BPV. Cox proportional hazards model analysis was used to investigate the impact of baseline BPV on the new onset of CKD. Multivariate analysis was performed with adjustment for confounding factors including age, gender, mean blood pressure, body mass index (BMI), waist circumference, smoking, and alcohol consumption. Hazard ratios (HRs) and 95% confidence intervals (CI) were calculated. In all cases, a two-sided p < 0.05 was considered significant. All statistical analyses were performed using SPSS 25.0.

Of the 7130 study participants (mean age, 60.7 ± 6.8 years), 5108 (71.6%) were women and 2022 (28.4%) were men. Among the participants, 35.5% had hypertension, and 21% had diabetes. The average BMI was 26.1 ± 3.7 kg/m² and the average eGFR was 96.0 ± 19.3. The demographic and clinical characteristics of the study participants are detailed in Table S1. During the 5-year follow-up period, 1394 participants received three times physical examinations, 1476 participants received four times physical examinations, 1729 participants received five times physical examinations, and 2531 participants received three times physical examinations.

A total of 1802 participants reached the primary composite endpoint during the 5-year follow-up period. After adjusting for confounders, including age, gender, mean blood pressure, BMI, waist circumference, smoking, and alcohol consumption, the multivariate logistic regression showed significant associations between SBP-ASV and eGFR decline (odds ratio [OR]: 1.01, 95% confidence interval [CI]: 1.00–1.02, p = 0.005). Similarly, DBP-ASV was also associated with eGFR decline (OR: 1.02, 95% CI: 1.01–1.03, p = 0.001) (Table 1).

Among the 6991 patients with normal renal function baseline, a total of 199 participants reached the secondary endpoint during the 5-year follow-up period, and the median follow-up time was 48 months. After adjusting for confounders, the Cox proportional hazards model analysis showed significant associations between SBP variability indices (SBP-SD, SBP-VIM, and SBP-ASV) and the new-onset of CKD. Similarly, DBP variability indices (DBP-SD, DBP-CV, DBP-VIM, and DBP-ASV) were also associated with the new onset of CKD (Table 2).

Our study showed the predictive value of BPV in forecasting future eGFR decline and the onset of CKD, both for SBP and DBP. Therefore, it is imperative to prioritize the monitoring of elevated BPV.

In 2010, Rothwell et al. demonstrated that follow-up systolic blood pressure variability (SBPV) independently predicted stroke risk, highlighting the importance of BPV alongside mean blood pressure for hypertensive patient prognosis.⁸ Subsequent studies have further solidified the link between increased BPV and cardiovascular and cerebrovascular diseases, regardless of mean blood pressure levels. Notably, long-term BPV has emerged as a particularly valuable predictor of target organ damage and cardiovascular events.^{9, 10} Furthermore, a large cohort study conducted in the United States revealed a notable rise in the incidence of all-cause mortality, coronary heart disease, stroke, and end-stage renal disease with escalating SBPV levels within the population.⁵ The mechanism by which BPV affects renal function may involve micro-vascular resistance, leading to pathological changes such as thickening of the glomerular basement membrane, hyaline degeneration of renal arterioles, and exudation of monocytes, ultimately resulting in renal injury.^{8, 11}

The kidneys are among the primary target organs vulnerable to damage from hypertension. Our study uncovered a strong link between increased BPV and eGFR decline, consistent with findings from a Japanese study.⁹ However, previous studies did not account for eGFR trends over time. In our study, we introduced a new definition of renal function damage based on eGFR decline (eGFR slope ≥3 mL/min/1.73 m²) to address this issue, which is crucial for individuals with eGFR levels nearing the CKD threshold.

The impact of BPV on renal function was inconsistent across studies.^{9, 12, 13} Post hoc analyses of the ASPREE trial, ONTARGET, and TRANSCEND trials yielded negative results partly due to the composition of the study populations. The ASPREE trial included individuals with an average age over 70 years, suggesting that advanced age may mitigate the effect of BPV. Moreover, the ONTARGET and TRANSCEND trials excluded patients with blood pressure levels ≥160/100 mmHg, resulting in relatively low BPV. Our study, based on a real-world population representing both middle-aged and elderly participants, offers valuable insights into the impact of variability in visit-to-visit SBP on renal function management, even among patients with normal blood pressure.¹⁴

There were several limitations to this study that warrant consideration. First, due to limitations in the available check-up information, the analysis of antihypertensive medication usage was not included. Additionally, the composition of the population attending the health service center skewed toward females, potentially influencing the overall BPV observed in the study. Finally, the follow-up period lasted for 31.8 months, and further longitudinal follow-up could yield additional information regarding the long-term impact of BPV on renal damage.

In conclusion, long-term BPV is associated with future renal damage, and further attention should be paid to the variability in visit-to-visit SBP.

Rongchong Huang, Feng Zhao, and Zhiquan Jing contributed to the conception of the study. Rongchong Huang, Feng Zhao, Zhiquan Jing, Zeya Li, Gang Wang, Shanshan Wu, Dan Li, Jing Hao, Chunlei Yang, Jiashu Song, and Xianzhong Gu contributed to the data acquisition. Rongchong Huang, Feng Zhao, Zhiquan Jing, Zeya Li, and Shanshan Wu contributed to the data analysis. Rongchong Huang, Feng Zhao, Zhiquan Jing, and Zeya Li contributed to the manuscript preparation, editing, and review.

The authors declare no conflict of interest.

The study protocol was approved by the Ethics Committee of Beijing Friendship Hospital associated with Capital Medical University (Beijing, China) (approval no. 2021-P2-163-02) and conducted in accordance with the ethical principles for medical research involving human subjects described in the Declaration of Helsinki.