Literature DB >> 18266560

Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs.

Harmen H M Draisma1, Theo H Reijmers, Ivana Bobeldijk-Pastorova, Jacqueline J Meulman, G Frederiek Estourgie-Van Burk, Meike Bartels, Raymond Ramaker, Jan van der Greef, Dorret I Boomsma, Thomas Hankemeier.   

Abstract

Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatography-mass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine.

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Year:  2008        PMID: 18266560     DOI: 10.1089/omi.2007.0048

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  6 in total

1.  Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families.

Authors:  Harmen H M Draisma; Theo H Reijmers; Jacqueline J Meulman; Jan van der Greef; Thomas Hankemeier; Dorret I Boomsma
Journal:  Eur J Hum Genet       Date:  2012-06-20       Impact factor: 4.246

2.  Multivariate genetic analyses in heterogeneous populations.

Authors:  Gitta Lubke; Daniel McArtor
Journal:  Behav Genet       Date:  2013-12-06       Impact factor: 2.805

3.  Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry.

Authors:  Tomas Cajka; Oliver Fiehn
Journal:  Trends Analyt Chem       Date:  2014-10-01       Impact factor: 12.296

4.  Dietary fat and not calcium supplementation or dairy product consumption is associated with changes in anthropometrics during a randomized, placebo-controlled energy-restriction trial.

Authors:  Jennifer T Smilowitz; Michelle M Wiest; Dorothy Teegarden; Michael B Zemel; J Bruce German; Marta D Van Loan
Journal:  Nutr Metab (Lond)       Date:  2011-10-05       Impact factor: 4.169

5.  Crossing the Boundaries of Our Current Healthcare System by Integrating Ultra-Weak Photon Emissions with Metabolomics.

Authors:  Rosilene C Rossetto Burgos; Eduard P A van Wijk; Roeland van Wijk; Min He; Jan van der Greef
Journal:  Front Physiol       Date:  2016-12-15       Impact factor: 4.566

Review 6.  Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders.

Authors:  Sumit Sethi; Elisa Brietzke
Journal:  Int J Neuropsychopharmacol       Date:  2015-10-09       Impact factor: 5.176

  6 in total

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