title: Identification of human plasma metabolites exhibiting time-of-day variation using an untargeted liquid chromatography-mass spectrometry metabolomic approach.
abstract: Although daily rhythms regulate multiple aspects of human physiology, rhythmic control of the metabolome remains poorly understood. The primary objective of this proof-of-concept study was identification of metabolites in human plasma that exhibit significant 24-h variation. This was assessed via an untargeted metabolomic approach using liquid chromatography-mass spectrometry (LC-MS). Eight lean, healthy, and unmedicated men, mean age 53.6 (SD ± 6.0) yrs, maintained a fixed sleep/wake schedule and dietary regime for 1 wk at home prior to an adaptation night and followed by a 25-h experimental session in the laboratory where the light/dark cycle, sleep/wake, posture, and calorific intake were strictly controlled. Plasma samples from each individual at selected time points were prepared using liquid-phase extraction followed by reverse-phase LC coupled to quadrupole time-of-flight MS analysis in positive ionization mode. Time-of-day variation in the metabolites was screened for using orthogonal partial least square discrimination between selected time points of 10:00 vs. 22:00 h, 16:00 vs. 04:00 h, and 07:00 (d 1) vs. 16:00 h, as well as repeated-measures analysis of variance with time as an independent variable. Subsequently, cosinor analysis was performed on all the sampled time points across the 24-h day to assess for significant daily variation. In this study, analytical variability, assessed using known internal standards, was low with coefficients of variation <10%. A total of 1069 metabolite features were detected and 203 (19%) showed significant time-of-day variation. Of these, 34 metabolites were identified using a combination of accurate mass, tandem MS, and online database searches. These metabolites include corticosteroids, bilirubin, amino acids, acylcarnitines, and phospholipids; of note, the magnitude of the 24-h variation of these identified metabolites was large, with the mean ratio of oscillation range over MESOR (24-h time series mean) of 65% (95% confidence interval [CI]: 49-81%). Importantly, several of these human plasma metabolites, including specific acylcarnitines and phospholipids, were hitherto not known to be 24-h variant. These findings represent an important baseline and will be useful in guiding the design and interpretation of future metabolite-based studies.
authors: Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, The Institute of Cancer Research, Sutton, Surrey, UK. Jooern.Ang@icr.ac.uk
authors: Joo Ern
authors: JE
authors: Ang
authors: Victoria
authors: V
authors: Revell
authors: Anuska
authors: A
authors: Mann
authors: Simone
authors: S
authors: Mäntele
authors: Daniella T
authors: DT
authors: Otway
authors: Jonathan D
authors: JD
authors: Johnston
authors: Alfred E
authors: AE
authors: Thumser
authors: Debra J
authors: DJ
authors: Skene
authors: Florence
authors: F
authors: Raynaud
mesh_terms: D000108:Acetylcarnitine / Q000097:blood; D001774:Blood Chemical Analysis; D002853:Chromatography, Liquid; D002940:Circadian Rhythm / Q000502:physiology*; D006801:Humans; D006854:Hydrocortisone / Q000097:blood; D008246:Lysophospholipids / Q000097:blood; D008297:Male; D013058:Mass Spectrometry; D055442:Metabolome; D055432:Metabolomics / Q000379:methods* / Q000706:statistics & numerical data; D008875:Middle Aged; D011392:Proline / Q000097:blood
chemical_list: D008246:Lysophospholipids; D000108:Acetylcarnitine; D011392:Proline; D006854:Hydrocortisone