Analysis Method and Experimental Conditions Affect Computed Circadian Phase from Melatonin Data

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Analysis Method and Experimental Conditions Affect Computed Circadian Phase from Melatonin Data

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Title: Analysis Method and Experimental Conditions Affect Computed Circadian Phase from Melatonin Data
Author: Klerman, Hadassa; St. Hilaire, Melissa A.; Gronfier, Claude; Santhi, Nayantara; Kronauer, Richard E.; Gooley, Joshua James; Hull, Joseph Thomas; Lockley, Steven Ward; Wang, Wei; Klerman, Elizabeth Beryl

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Citation: Klerman, Hadassa, Melissa A. St. Hilaire, Richard E. Kronauer, Joshua J. Gooley, Claude Gronfier, Joseph T. Hull, Steven W. Lockley, Nayantara Santhi, Wei Wang, and Elizabeth B. Klerman. 2012. Analysis method and experimental conditions affect computed circadian phase from melatonin data. PLoS ONE 7(4): e33836.
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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.
Published Version: doi:10.1371/journal.pone.0033836
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