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PLoS One. 2017 Mar 9;12(3):e0173615. doi: 10.1371/journal.pone.0173615. eCollection 2017.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

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Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital de Bicêtre, Service de Génétique moléculaire, Pharmacogénétique et Hormonologie, Le Kremlin Bicêtre, France.
Inserm U1185, Fac Med Paris Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France.
Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Paris-Sud, Hôpital de Bicêtre, Service d'Endocrinologie et des Maladies de la Reproduction, Le Kremlin Bicêtre, France.
Institut de Recherches Internationales Servier, Suresnes, France.
Technologie Servier, Orléans, France.
Univ Paris Sud, INSERM UMR 1178, Service de Psychiatrie, équipe "Dépression et Antidépresseurs", Hôpital Bicêtre, Assistance Publique Hôpitaux de Paris, Le Kremlin Bicêtre, France.
UPMC Univ Paris 06, INSERM UMR S938, Centre de Recherche Saint-Antoine, Hôpital Saint-Antoine, Assistance Publique Hôpitaux de Paris, Paris, France.
Département de Pharmacologie, Faculté de médecine Paris-Sud, Université Paris-Sud, UMR 1184, CEA, DSV/iMETI, Division d'Immuno-Virologie, IDMIT, INSERM Centre d'Immunologie des Infections virales et des Maladies Autoimmunes, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, Le Kremlin Bicêtre, France.


Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.

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