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SAL_11090 details
Primary information
SALIDSAL_11090
Biomarker nameSalivary alpha amylase
Biomarker TypeNA
Sampling MethodNineteen normal subjects (9 males and 10 females) participated in the experiments and measurements were performed every hr between 9:00 and 21:00 hr.
Collection MethodSaliva was allowed to accumulate passively for 2 min. Participants then spit all saliva into 10-ml polystyrene tubes.
Analysis Methodchemistry analyser and an amylase assay kit
Collection SiteSaliva
Disease CategoryHealthy
Disease/ConditionHealthy
Disease SubtypeNA
Fold Change/ ConcentrationNA
Up/DownregulatedNA
ExosomalNA
OrganismHomo sapiens
PMID30673704
Year of Publication2019
Biomarker IDP04746
Biomarker CategoryProtein
SequenceMKFFLLLFTIGFCWAQYSPNTQQGRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIYNPFRPWWERYQPVSYKLCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMCGNAVSAGTSSTCGSYFNPGSRDFPAVPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLTGLLDLALEKDYVRSKIAEYMNHLIDIGVAGFRLDASKHMWPGDIKAILDKLHNLNSNWFPAGSKPFIYQEVIDLGGEPIKSSDYFGNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFVPSDRALVFVDNHDNQRGHGAGGASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRQFQNGNDVNDWVGPPNNNGVIKEVTINPDTTCGNDWVCEHRWRQIRNMVIFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIVFNNDDWSFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFIAIHAESKL
Title of studyIntensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase
Abstract of studySalivary 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.