BACKGROUND: In clinical cardiology, heart rate variability is a putative index of autonomic cardiovascular function. Signs of reduced vagal activity are not only associated with an enhanced risk of sudden cardiac death, but such impaired heart rate variability became a new predictor of sudden cardiac death and other mortality in patients with a variety of diseased states. HYPOTHESIS: It is postulated (1) that the time structure (chronome) of heart rate variability in clinical health includes a circadian rhythm and deterministic chaos, the latter gauged by the correlation dimensions of RR intervals; and (2) that this chronome is altered in patients with coronary artery disease (CAD). METHODS: From 24-h Holter records of 11 healthy controls and 10 patients with CAD, 500-s sections around 02:00, 06:00, 10:00, 14:00, 18:00 and 22:00 hours were analyzed for smoothed RR intervals sampled at 4 Hz. Correlation integrals were estimated for embedding dimensions from 1 to 20 with a 1.0-s time lag, using an algorithm modified from Grassberger and Procaccia. The Wilcoxon signed-rank test compares circadian end points assessed by cosinor between the CAD patients and age-matched controls. RESULTS: A circadian rhythm characterizes the correlation dimension of healthy subjects peaking during the night (p < 0.005). Patients with CAD have a lowered correlation dimension (p < 0.05) and an altered circadian variation which requires the consideration of an approximately 12-h (circasemidian) component. CONCLUSION: The results demonstrate the sensitivity of circadian rhythms for the detection of disease. A partial 24- to 12-h (circadian-to-circasemidian) frequency multiplication (or partial variance transposition) in CAD of the correlation dimension, apart from being a potential clue to the etiology of the disease, adds a new feature to a chronocardiology combining, with the fractal scaling, an assessment of circadian and circasemidian components as measures of predictable variability to be tested for use in diagnosis, prognosis, and as putative guides to treatment timing.
BACKGROUND: In clinical cardiology, heart rate variability is a putative index of autonomic cardiovascular function. Signs of reduced vagal activity are not only associated with an enhanced risk of sudden cardiac death, but such impaired heart rate variability became a new predictor of sudden cardiac death and other mortality in patients with a variety of diseased states. HYPOTHESIS: It is postulated (1) that the time structure (chronome) of heart rate variability in clinical health includes a circadian rhythm and deterministic chaos, the latter gauged by the correlation dimensions of RR intervals; and (2) that this chronome is altered in patients with coronary artery disease (CAD). METHODS: From 24-h Holter records of 11 healthy controls and 10 patients with CAD, 500-s sections around 02:00, 06:00, 10:00, 14:00, 18:00 and 22:00 hours were analyzed for smoothed RR intervals sampled at 4 Hz. Correlation integrals were estimated for embedding dimensions from 1 to 20 with a 1.0-s time lag, using an algorithm modified from Grassberger and Procaccia. The Wilcoxon signed-rank test compares circadian end points assessed by cosinor between the CAD patients and age-matched controls. RESULTS: A circadian rhythm characterizes the correlation dimension of healthy subjects peaking during the night (p < 0.005). Patients with CAD have a lowered correlation dimension (p < 0.05) and an altered circadian variation which requires the consideration of an approximately 12-h (circasemidian) component. CONCLUSION: The results demonstrate the sensitivity of circadian rhythms for the detection of disease. A partial 24- to 12-h (circadian-to-circasemidian) frequency multiplication (or partial variance transposition) in CAD of the correlation dimension, apart from being a potential clue to the etiology of the disease, adds a new feature to a chronocardiology combining, with the fractal scaling, an assessment of circadian and circasemidian components as measures of predictable variability to be tested for use in diagnosis, prognosis, and as putative guides to treatment timing.
Authors: Herbert F Jelinek; C Karmakar; A M Kiviniemi; A J Hautala; M P Tulppo; T H Mäkikallio; H V Huikuri; A H Khandoker; M Palaniswami Journal: Eur J Appl Physiol Date: 2015-05-21 Impact factor: 3.078
Authors: F Halberg; G Cornélissen; P Faraone; B Poeggeler; R Hardeland; G Katinas; O Schwartzkopff; K Otsuka; E E Bakken Journal: Biomed Pharmacother Date: 2005-10 Impact factor: 6.529
Authors: Carlos E D'Negri; Liliana Marelich; Daniel Vigo; Rafael S Acunzo; Luis A Girotti; Daniel P Cardinali; Leonardo Nicola Siri Journal: Clin Auton Res Date: 2005-06 Impact factor: 4.435