title: [Correlation between long time systolic blood pressure variability and short time systolic blood pressure in aged population].
abstract: OBJECTIVE
  To investigate the correlation between long time systolic blood pressure variability(SBPV)and short time SBPV in aged population.
  
  
  METHODS
  A total of 752 subjects aged ≥60 years of Kailuan Group who took part in 2006-2007, 2008-2009, 2010-2011 and 2012-2013 health examination were included by cluster sampling method.Long time SBPV was calculated by standard deviation of mean systolic blood pressure measured in 2006-2007, 2008-2009, 2010-2011 and 2012-2013, standard deviation represents short time systolic blood pressure which is derived from 24 hour ambulatory blood pressure monitoring. The observation population was divided into three groups according to the third tertiles of the time systolic blood pressure variability: the first point(<9.09 mmHg (1 mmHg=0.133 kPa)), second point (≥9.09 mmHg, and <14.29 mmHg), and third point (≥14.29 mmHg). Multivariate logistic regression analysis was used to analyze the correlation between long time systolic blood pressure variability and short time systolic blood pressure.
  
  
  RESULTS
  (1) The participants' age were (67.0±5.7) years old (284 women). (2) The 24 hours and daytime SSD were (14.7±4.0) mmHg, (14.7±3.5) mmHg, (15.7±4.4) mmHg (P=0.010) and (14.1±4.4) mmHg, (14.2±3.5) mmHg and (15.4±4.6) mmHg (P<0.001) according to the tertiles of long time systolic blood pressure variability, respectively, nighttime SSD were (12.0±4.4) mmHg, (11.8±4.8) mmHg and (11.9±4.9) mmHg (P=0.900). (3) Multiple logistic regression analysis showed that the tertiles of long time SSD was the risk factor for increasing daytime SSD>14.00 mmHg (OR=1.51, 95%CI: 1.03-2.23, P=0.037), but not a risk factor for increasing 24 hours SSD>14.41 mmHg (OR=1.10, 95%CI: 0.75-1.61, P=0.639) and nighttime SSD>11.11 mmHg (OR=0.98, 95%CI: 0.67-1.42, P=0.899).
  
  
  CONCLUSIONS
  Increased long time SBPV is a risk factor for increasing daytime SBPV.
authors.affiliation: Graduate School, Hebei United University, Tangshan 063000, China.
authors.forename: Shasha
authors.initials: S
authors.lastname: An
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Zheng
authors.forename: Zhifang
authors.initials: Z
authors.lastname: Li
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
authors.forename: Yuntao
authors.initials: Y
authors.lastname: Wu
authors.forename: Wenyan
authors.initials: W
authors.lastname: Zhang
authors.forename: Haiyan
authors.initials: H
authors.lastname: Zhao
authors.forename: Aiping
authors.initials: A
authors.lastname: Wu
authors.forename: Ruixia
authors.initials: R
authors.lastname: Wang
authors.forename: Jie
authors.initials: J
authors.lastname: Tao
authors.forename: Xinying
authors.initials: X
authors.lastname: Gao
authors.affiliation: Email: drwusl@163.com.
authors.forename: Shouling
authors.initials: S
authors.lastname: Wu
mesh_terms: D000368:Aged; D001794:Blood Pressure; D018660:Blood Pressure Monitoring, Ambulatory; D006801:Humans; D006973:Hypertension; D008875:Middle Aged; D012307:Risk Factors; D013599:Systole