Integrative genetic and metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma

• Published in: Allergy (Copenhagen) Vol. 68; no. 5; pp. 629 - 636
• Main Authors: Ried, J. S., Baurecht, H., Stückler, F., Krumsiek, J., Gieger, C., Heinrich, J., Kabesch, M., Prehn, C., Peters, A., Rodriguez, E., Schulz, H., Strauch, K., Suhre, K., Wang‐Sattler, R., Wichmann, H.‐E., Theis, F. J., Illig, T., Adamski, J., Weidinger, S., Simon, Hans‐Uwe
• Format: Journal Article
• Language: English
• Published: Denmark Blackwell Publishing Ltd 01-05-2013
• Subjects: Adult; Aged; Aged, 80 and over; Alleles; Asthma; Asthma - genetics; Asthma - metabolism; Biomarkers; Cross-Sectional Studies; Female; Gene Expression Profiling; Genetic Loci; Genetics; Genotype; Humans; lipids; Male; Metabolites; Metabolome; metabolomics; Middle Aged; Odds Ratio; Phosphatidylcholines - metabolism; Polymorphism, Single Nucleotide; Risk factors
• ISSN: 0105-4538; 1398-9995
• DOI: 10.1111/all.12110

Background
Genome‐wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites.
Methods
We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population‐based KORA F4 study (N = 2925). In addition, we analysed effects of single‐nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites.
Results
Increased levels of various phosphatidylcholines and decreased levels of various lyso‐phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions.
Conclusions
Our study demonstrated the potential of metabolomics to infer asthma‐related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.