The Regulation of Feed Intake as Part of Energy Homeostasis in Periparturient High Yielding Dairy Cows
Teil: Untersuchung der Herzfrequenzvariabilität

Projektleitung: Prof. Dr. Elmar Mohr, Frau PD Dr. Monika Schweigel

Förderung: DFG

Kooperation: FBN

Zeitraum: 2009 - 2012

Einordung:
Institut: Professur Tiergesundheit und Tierschutz

Projektbeschreibung:
Sub-aim 7: Accessing the symphato-vagal balance by heart rate variability (HRV). HRV is a non-invasive tool to gather information about the sympatho-vagal balance of an organism which mainly determines energy and substrate metabolism. Moreover, it can be used as a sensitive indicator of stress. Therefore, we will perform measurements of heartbeat activity (R-R interval) by using a heart rate monitor (Polar, Finland) as described under preliminary work. Data will be taken
• once weekly during weeks -7, -6, -5, -3, +4 and +5,
• every day during the respiration trials, and
• twice weekly during weeks -2, -1, 1 and 2.
Data processing and analysis will be done in cooperation with Prof. E. Mohr (University of Rostock). Recurrence plots will be created to visualise qualitative changes in the regulating system and to derive non-linear indexes of HRV by Recurrence Quantification Analysis (RQA).
The following linear (A) and non-linear (B) parameters will be calculated:
(A)
• standard deviation of IBIs (SDNN),
• root mean square of successive differences between IBIs (RMSSD),
• power in the high frequency (HF) band (0.15 – 0.4 Hz, represents vagal activity) and low frequency (LF) band (0.04 – 0.15 Hz, associated with sympathetic activity).
(B)
• percentage of recurrence (%REC),
• percentage of determinism (%DET),
• Entropy (ENT),
• maxline (LMAX),
• trend.

A main point of the data evaluation will be to get more information on the physiological meaning of the non-linear indices of HRV in farm animals. From studies with humans it is concluded that these indices of HRV are more reliable markers of sympathetic and parasympathetic activation compared to parameters generated from time- and frequency-domain analysis. By correlating the outcomes from other parts of this project (hormones, metabolic parameters, and energy expenditure) we will select those parameters giving the best information about this topic. Moreover, we want to know if recurrence plots and the quantitative parameters derived from them are useful as indicators/predictors of adaptation success and/or danger of disease conditions.

Bearbeiter:

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