Alterations in the temporal organization and synchronous patterns among physiologic systems have been detected across the spectrum of biological systems. These dynamic relations between biomarkers provide important information about physiologic function. However, these patterns are not easily observed and difficult to characterize with traditional measures. There is a need to evolve existing approaches or develop and investigate new approaches that can provide knowledge about changes in the time-dependent regulatory behaviors of the physiologic system. The hypothalamic-pituitary axis is often considered the regulator of the endocrine system, receiving inputs from central and peripheral signals. Growth hormone (GH) is a pulsatile hormone secreted from the anterior pituitary largely regulated by somatotrophs of the hypothalamus, but it is also responsive to feedback signals from the periphery. The secretory patterns of GH not only provide information about the dynamics of the hypothalamic-pituitary axis, but the state of the entire physiologic system; information that is unobserved by single-point measures and low sampling frequencies. Similarly, there is an abundance of information imbedded within the changes in the normal RR-intervals that is not observed through heart rate (HR) alone. Measures of HR variability (HRV) assess changes in the cardiac control that are representative of acute and chronic, physical, social, and psychophysiologic stresses. The OBJECTIVE of this study was to investigate the dynamics of hypothalamic-pituitary regulation and cardiac control through GH, HRV, and additional biomarkers that share relationships with each of the associated pathways. METHODS: Eight healthy males (25.4±2.6 yrs, 174.7±7.8 cm) completed two 24-hr profiles at least 8 weeks apart (Exercise: 71.2±10.8 kg, 9.8±3.3 BF(%), VO2max 71.2±11.2 ml/kg/min and Rest: 69.8±12.1 kg, 9.0±2.7 BF(%), VO2max 67.8±9.0 ml/kg/min), where serum was collected every 10-min and RR-intervals were collected continuously. The order of the high-intensity exercise and resting- profiles were randomly assigned. The variability (standard deviation of the normal RR-interval—SDNNRR; standard deviation of the average of NN-intervals in all 5-min recordings across the 24-hr period—SDANN; root mean square of successive differences— rMSSD[RR]; low-frequency power—LF; high-frequency power—HF; and triangular index of the normal RR-intervals—TINN) and complexity (sample entropy—SampEn[RR]) of the 24- hr RR-records were assessed. In addition, the 24-hr RR-recordings were separated into 3-min epochs taken every 10 minutes—corresponding with the timing of the serum samples—and used to create additional time-series (HRV[EP]). The patterned regulation of cardiac control (SDNN[EP], rMSSD[EP], SampEn[EP]) throughout the day was assessed with recurrence analysis (RQA) and SampEn. Dynamics of paired profiles were compared using joint-entropy and cross-RQA (cRQA). Comparisons between exercise and resting conditions were made using multivariate analysis of variance. Prediction models, using long-short-term-memory (LSTM) networks, were used to predict nighttime GH output based on the changes in cardiac control throughout the day. RESULTS: The optimal parameters chosen to analyze the dynamics of each profile were different (p=0.09) between exercise and resting conditions. Determinism (DET) of the GH profile interacted with changes in fitness between conditions (p=0.04). The LSTM networks performed accurately to predict GH output; these models performed better on exercise profiles compared to rest (p=0.02). CONCLUSIONS: Our findings suggest a common attractor among the hypothalamic-pituitary axis and cardiac control; assessed by GH and HRV[EP] respectively. Assessing the relations among these profiles in parallel may provide a method of creating a scalable model that can predict GH output from changes in HRV[EP] profiles. Reliable models that can predict these relationships may provide vital information about the system that could have astounding impacts within science and medicine. Integrating this diverse data into a single analytic environment can help to provide researchers, clinicians, athletes, and patients the opportunity for earlier detection, easier assessment, more detailed monitoring, and increasingly beneficial treatment options.
|Commitee:||Goldfarb, Alan H., Henson, Robert A., Rhea, Christopher K.|
|School:||The University of North Carolina at Greensboro|
|Department:||School of Health and Human Sciences: Kinesiology|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 80/06(E), Dissertation Abstracts International|
|Keywords:||Endocrine, Growth hormone, Heart rate variability, Nonlinear dynamics, Physiology, Systems theory|
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