Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0033836
Title: Analysis method and experimental conditions affect computed circadian phase from melatonin data.
Authors: Klerman H.
St Hilaire M.A.
Kronauer R.E.
Gooley J.J. 
Gronfier C.
Hull J.T.
Lockley S.W.
Santhi N.
Wang W.
Klerman E.B.
Keywords: biological marker
circadian rhythm signaling protein
melatonin
adolescent
adult
age
aged
article
biological model
blood
circadian rhythm
human
metabolism
middle aged
Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Biological Markers
Circadian Rhythm
Circadian Rhythm Signaling Peptides and Proteins
Humans
Melatonin
Middle Aged
Models, Biological
Issue Date: 2012
Citation: Klerman H., St Hilaire M.A., Kronauer R.E., Gooley J.J., Gronfier C., Hull J.T., Lockley S.W., Santhi N., Wang W., Klerman E.B. (2012). Analysis method and experimental conditions affect computed circadian phase from melatonin data.. PloS one 7 (4). ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0033836
Abstract: Accurate determination of circadian phase is necessary for research and clinical purposes because of the influence of the master circadian pacemaker on multiple physiologic functions. Melatonin is presently the most accurate marker of the activity of the human circadian pacemaker. Current methods of analyzing the plasma melatonin rhythm can be grouped into three categories: curve-fitting, threshold-based and physiologically-based linear differential equations. To determine which method provides the most accurate assessment of circadian phase, we compared the ability to fit the data and the variability of phase estimates for seventeen different markers of melatonin phase derived from these methodological categories. We used data from three experimental conditions under which circadian rhythms - and therefore calculated melatonin phase - were expected to remain constant or progress uniformly. Melatonin profiles from older subjects and subjects with lower melatonin amplitude were less likely to be fit by all analysis methods. When circadian drift over multiple study days was algebraically removed, there were no significant differences between analysis methods of melatonin onsets (P = 0.57), but there were significant differences between those of melatonin offsets (P<0.0001). For a subset of phase assessment methods, we also examined the effects of data loss on variability of phase estimates by systematically removing data in 2-hour segments. Data loss near onset of melatonin secretion differentially affected phase estimates from the methods, with some methods incorrectly assigning phases too early while other methods assigning phases too late; missing data at other times did not affect analyses of the melatonin profile. We conclude that melatonin data set characteristics, including amplitude and completeness of data collection, differentially affect the results depending on the melatonin analysis method used.
Source Title: PloS one
URI: https://scholarbank.nus.edu.sg/handle/10635/162015
ISSN: 19326203
DOI: 10.1371/journal.pone.0033836
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