Zhang Zhenhong, Wang Shunyin, Yan Junru, Xu Zhiwen, Liang Dongliang, Liu Baohua, Liang Junjie, Chen Mingjie
Department of Cardiology, The Second People's Hospital of Foshan (The Affiliated Hospital at Foshan, Southern Medical University), Foshan, China.
J Int Med Res. 2021 Jun;49(6):3000605211016144. doi: 10.1177/03000605211016144.
We assessed differences and correlations between 24-hour ambulatory blood pressure (ABP) and office blood pressure (OBP) monitoring.
We conducted an observational study among 85 untreated patients with essential hypertension and measured 24-hour ABP, OBP, target organ damage (TOD) markers, and metabolism indexes. Variance analysis and the Pearson method were used to compare differences and correlation between the two methods. The Spearman or Pearson method was applied to compare the correlation between TOD markers, blood pressure index, and metabolism index. Linear regression analysis was applied to estimate the quantitative relationship between the blood pressure index and TOD markers.
There were significant differences in the mean and variance of systolic blood pressure (SBP) and diastolic blood pressure and a positive correlation between ABP and OBP. Correlations between the left ventricular mass index (LVMI) and average ambulatory SBP, daytime ambulatory SBP, nighttime ambulatory SBP, and fasting blood glucose were significant. Correlations between left intima-media thickness (IMT) and average ambulatory SBP, nighttime ambulatory SBP, right IMT, and nighttime ambulatory SBP were significant. In linear regression analysis of the LVMI (y) and ambulatory SBP (x), the equation was expressed as y = 0.637*x.
Nighttime ambulatory SBP may be an optimal predictor of TOD.
我们评估了24小时动态血压(ABP)与诊室血压(OBP)监测之间的差异及相关性。
我们对85例未经治疗的原发性高血压患者进行了一项观察性研究,测量了24小时ABP、OBP、靶器官损害(TOD)标志物和代谢指标。采用方差分析和Pearson法比较两种方法之间的差异和相关性。应用Spearman或Pearson法比较TOD标志物、血压指数和代谢指标之间的相关性。采用线性回归分析估计血压指数与TOD标志物之间的定量关系。
收缩压(SBP)和舒张压的均值及方差存在显著差异,ABP与OBP之间呈正相关。左心室质量指数(LVMI)与动态平均SBP、日间动态SBP、夜间动态SBP和空腹血糖之间的相关性显著。左内膜中层厚度(IMT)与动态平均SBP、夜间动态SBP、右IMT和夜间动态SBP之间的相关性显著。在LVMI(y)与动态SBP(x)的线性回归分析中,方程表示为y = 0.637 * x。
夜间动态SBP可能是TOD的最佳预测指标。