Gene methylation parallelisms between peripheral blood cells and oral mucosa samples in relation to overweight
R. San-Cristobal; S. Navas-Carretero; F. I. Milagro; J. I. Riezu-Boj; E. Guruceaga; C. Celis-Morales; K. M. Livingstone; L. Brennan; J. A. Lovegrove; H. Daniel; W. H. Saris; I. Traczyk; Y. Manios; E. R. Gibney; M. J. Gibney; J. C. Mathers; J. A. Martinez
Year of publication
J Physiol Biochem
Epigenetics has an important role in the regulation of metabolic adaptation to environmental modifications. In this sense, the determination of epigenetic changes in non-invasive samples during the development of metabolic diseases could play an important role in the procedures in primary healthcare practice. To help translate the knowledge of epigenetics to public health practice, the present study aims to explore the parallelism of methylation levels between white blood cells and buccal samples in relation to obesity and associated disorders. Blood and buccal swap samples were collected from a subsample of the Spanish cohort of the Food4Me study. Infinium HumanMethylation450 DNA Analysis was carried out for the determination of methylation levels. Standard deviation for β values method and concordance correlation analysis were used to select those CpG which showed best parallelism between samples. A total of 277 CpGs met the criteria and were selected for an enrichment analysis and a correlation analysis with anthropometrical and clinical parameters. From those selected CpGs, four presented high associations with BMI (cg01055691 in GAP43; r = -0.92 and rho = -0.84 for blood; r = -0.89 and rho = -0.83 for buccal sample), HOMA-IR (cg00095677 in ATP2A3; r = 0.82 and rho = -0.84 for blood; r = -0.8 and rho = -0.83 for buccal sample) and leptin (cg14464133 in ADARB2; r = -0.9182 and rho = -0.94 for blood; r = -0.893 and rho = -0.79 for buccal sample). These findings demonstrate the potential application of non-invasive buccal samples in the identification of surrogate epigenetic biomarkers and identify methylation sites in GAP43, ATP2A3 and ADARB2 genes as potential targets in relation to overweight management and insulin sensibility.