6533b821fe1ef96bd127c420

RESEARCH PRODUCT

Genomic and Metabolomic Profile Associated to Clustering of Cardio-Metabolic Risk Factors

José Manuel MoralesFernando MartinezJuan Carlos Martín-escuderoMercedes Pardo-tenderoRedon JosepJosep RedonFelipe J. ChavesPilar RenteroLaisa BriongosVannina G. MarrachelliInma GalanDaniel MonleonMaria L. Mansego

subject

Male0301 basic medicineHeredityPhysiologylcsh:MedicinePhysiologyBlood Pressure030204 cardiovascular system & hematologyBioinformaticsBiochemistry0302 clinical medicineGlucose MetabolismRisk FactorsPolymorphism (computer science)GenotypeMedicine and Health SciencesMetaboliteslcsh:Scienceeducation.field_of_studyMultidisciplinaryFatty AcidsGenomicsMiddle AgedLipidsGenetic MappingPhysiological ParametersCardiovascular DiseasesPhysical SciencesCarbohydrate MetabolismResearch ArticleAdultGenetic MarkersGenotypePermutationFADS2PopulationVariant GenotypesSingle-nucleotide polymorphismBiologyPolymorphism Single Nucleotide03 medical and health sciencesMetabolomicsMetabolic DiseasesGeneticsmedicineHumansMetabolomicsGenetic Predisposition to DiseaseObesityeducationGenotypingAgedDiscrete Mathematicslcsh:RBody WeightBiology and Life SciencesLipid Metabolismmedicine.diseaseObesityMetabolism030104 developmental biologyCombinatoricslcsh:QMathematics

description

Background To identify metabolomic and genomic markers associated with the presence of clustering of cardiometabolic risk factors (CMRFs) from a general population. Methods and Findings One thousand five hundred and two subjects, Caucasian, > 18 years, representative of the general population, were included. Blood pressure measurement, anthropometric parameters and metabolic markers were measured. Subjects were grouped according the number of CMRFs (Group 1: <2; Group 2: 2; Group 3: 3 or more CMRFs). Using SNPlex, 1251 SNPs potentially associated to clustering of three or more CMRFs were analyzed. Serum metabolomic profile was assessed by 1H NMR spectra using a Brucker Advance DRX 600 spectrometer. From the total population, 1217 (mean age 54±19, 50.6% men) with high genotyping call rate were analysed. A differential metabolomic profile, which included products from mitochondrial metabolism, extra mitochondrial metabolism, branched amino acids and fatty acid signals were observed among the three groups. The comparison of metabolomic patterns between subjects of Groups 1 to 3 for each of the genotypes associated to those subjects with three or more CMRFs revealed two SNPs, the rs174577_AA of FADS2 gene and the rs3803_TT of GATA2 transcription factor gene, with minimal or no statistically significant differences. Subjects with and without three or more CMRFs who shared the same genotype and metabolomic profile differed in the pattern of CMRFS cluster. Subjects of Group 3 and the AA genotype of the rs174577 had a lower prevalence of hypertension compared to the CC and CT genotype. In contrast, subjects of Group 3 and the TT genotype of the rs3803 polymorphism had a lower prevalence of T2DM, although they were predominantly males and had higher values of plasma creatinine. Conclusions The results of the present study add information to the metabolomics profile and to the potential impact of genetic factors on the variants of clustering of cardiometabolic risk factors.

https://doi.org/10.1371/journal.pone.0160656