Insight into the 24-hour ambulatory central blood pressure in adolescents and young adults.
Angeliki NtineriAnastasios KolliasMaria Elena ZeniodiAndriani Gerasimidi-VazeouAlexandra SoldatouGeorge S StergiouPublished in: Journal of clinical hypertension (Greenwich, Conn.) (2020)
This study attempted to investigate the behavior of 24-hour central ambulatory blood pressure (ABP) in adolescents and young adults. Adolescents and young adults (age 10-25 years) referred for elevated blood pressure (BP) and healthy volunteers had simultaneous 24-hour peripheral (brachial) and central (aortic) ABP monitoring using the same automated upper-arm cuff device (Mobil-O-Graph 24h PWA). Central BP was calculated by the device using two different calibration methods (C1SBP using peripheral systolic (pSBP)/diastolic BP and C2SBP using mean arterial/diastolic BP). A total of 136 participants (age 17.9 ± 4.7 years, 54% adolescents, 77% males, 25% volunteers, 34% with elevated peripheral ABP) were analyzed. Twenty-four-hour pSBP was higher than C1SBP, with this difference being more pronounced during daytime than nighttime (16.3 ± 4.5 and 10.5 ± 3.2 mm Hg, respectively, P < .001). Younger age, higher body height, and male gender were associated with greater systolic ABP amplification (pSBP-C1SBP difference). C1SBP followed the variation pattern of pSBP, yet with smaller nighttime dip (8.4 ± 6.0% vs 11.9 ± 4.6%, P < .001), whereas C2SBP increased (2.4 ± 7.2%) during nighttime sleep (P < .001 for comparison with pSBP change). Older age remained independent determinant of larger nighttime BP fall for pSBP and C1SBP, whereas male gender predicted a larger nighttime C2SBP rise. These data suggest that the calibration method of the BP monitor considerably influences the diurnal variation in central BP, showing a lesser nocturnal dip than pSBP or even nocturnal BP rise, which are determined by the individual's age and gender.
Keyphrases
- blood pressure
- hypertensive patients
- heart rate
- physical activity
- mental health
- body mass index
- machine learning
- sleep quality
- left ventricular
- obstructive sleep apnea
- heart failure
- deep learning
- adipose tissue
- pulmonary artery
- coronary artery
- big data
- depressive symptoms
- electronic health record
- convolutional neural network
- insulin resistance
- single cell
- nucleic acid
- neural network
- label free