Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

• Published in: PloS one Vol. 14; no. 1; p. e0209475
• Main Authors: Rosel, Jesús F., Jara, Pilar, Machancoses, Francisco H., Pallarés, Jacinto, Torrente, Pedro, Puchol, Sara, Canales, Juan J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.01.2019; Public Library of Science (PLoS)
• Subjects: Adult; Amylases; Autoregressive models; Autoregressive processes; Behavior; Bioindicators; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - metabolism; Blood pressure; Circadian Rhythm - physiology; Circadian rhythms; Clinical psychology; Data analysis; Data processing; Diurnal; Engineering and Technology; Enzymatic activity; Female; Females; Heart rate; Humans; Laboratories; Longitudinal Studies; Male; Males; Medical research; Medicine and Health Sciences; Methods; Models, Biological; Nervous system; Pediatrics; Physical Sciences; Physiology; Regression Analysis; Research and Analysis Methods; Research methodology; Salivary alpha-Amylases - metabolism; Social psychology; Social Sciences; Statistical analysis; Stress; Stress, Physiological - physiology; Sympathetic nervous system; Sympathetic Nervous System - physiology; Time series; Variables; Young Adult; α-Amylase; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0209475

Salivary alpha-amylase (sAA) activity has been widely used in psychological and medical research as a surrogate marker of sympathetic nervous system activation, though its utility remains controversial. The aim of this work was to compare alternative intensive longitudinal models of sAA data: (a) a traditional model, where sAA is a function of hour (hr) and hr squared (sAAj,t = f(hr, hr2), and (b) an autoregressive model, where values of sAA are a function of previous values (sAAj,t = f(sAA j,t-1, sAA j,t-2, …, sAA j,t-p). Nineteen normal subjects (9 males and 10 females) participated in the experiments and measurements were performed every hr between 9:00 and 21:00 hr. Thus, a total of 13 measurements were obtained per participant. The Napierian logarithm of the enzymatic activity of sAA was analysed. Data showed that a second-order autoregressive (AR(2)) model was more parsimonious and fitted better than the traditional multilevel quadratic model. Therefore, sAA follows a process whereby, to forecast its value at any given time, sAA values one and two hr prior to that time (sAA j,t = f(SAAj,t-1, SAAj,t-2) are most predictive, thus indicating that sAA has its own inertia, with a "memory" of the two previous hr. These novel findings highlight the relevance of intensive longitudinal models in physiological data analysis and have considerable implications for physiological and biobehavioural research involving sAA measurements and other stress-related biomarkers.