2 research outputs found
Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva
Most
studies investigating human metabolomics measurements are
limited to a single biofluid, most often blood or urine. An organism’s
biochemical pool, however, comprises complex transboundary relationships,
which can only be understood by investigating metabolic interactions
and physiological processes spanning multiple parts of the human body.
Therefore, we here propose a data-driven network-based approach to
generate an integrated picture of metabolomics associations over multiple
fluids. We performed an analysis of 2251 metabolites measured in plasma,
urine, and saliva, from 374 participants of the Qatar Metabolomics
Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were
used to estimate metabolite-metabolite interactions on different subsets
of the data set. First, we compared similarities and differences of
the metabolome and the association networks between the three fluids.
Second, we investigated the cross-talk between the fluids by analyzing
correlations occurring between them. Third, we propose a framework
for the analysis of medically relevant phenotypes by integrating type
2 diabetes, sex, age, and body mass index into our networks. In conclusion,
we present a generic, data-driven network-based approach for structuring
and visualizing metabolite correlations within and between multiple
body fluids, enabling unbiased interpretation of metabolomics multifluid
data
Metabolic GWAS of elite athletes reveals novel genetically-influenced metabolites associated with athletic performance
Genetic research of elite athletic performance has been hindered by the complex phenotype and the relatively small effect size of the identified genetic variants. The aims of this study were to identify genetic predisposition to elite athletic performance by investigating genetically-influenced metabolites that discriminate elite athletes from non-elite athletes and to identify those associated with endurance sports. By conducting a genome wide association study with high-resolution metabolomics profiling in 490 elite athletes, common variant metabolic quantitative trait loci (mQTLs) were identified and compared with previously identified mQTLs in non-elite athletes. Among the identified mQTLs, those associated with endurance metabolites were determined. Two novel genetic loci in FOLH1 and VNN1 are reported in association with N-acetyl-aspartyl-glutamate and Linoleoyl ethanolamide, respectively. When focusing on endurance metabolites, one novel mQTL linking androstenediol (3alpha, 17alpha) monosulfate and SULT2A1 was identified. Potential interactions between the novel identified mQTLs and exercise are highlighted. This is the first report of common variant mQTLs linked to elite athletic performance and endurance sports with potential applications in biomarker discovery in elite athletic candidates, non-conventional anti-doping analytical approaches and therapeutic strategies.Other Information Published in: Scientific Reports License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41598-019-56496-7</p