|VAN ERK, MARJAN|
|VAN VLIET, TRINETTE|
|VAN DER GREEF, JAN|
|VAN OMMEN, BEN|
Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/23/2010
Publication Date: 2/23/2010
Citation: Van Erk, M.J., Wopereis, S., Rubingh, C., Van Vliet, T., Verheij, E., Cnubben, N., Pedersen, T.L., Newman, J.W., Age, S., Van Der Greef, J., Hendriks, H., Van Ommen, B. 2010. Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study. Biomed Central (BMC) Genomics. 3:5.
Interpretive Summary: Chronic, low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Therefore, physicians do not have the tools needed to systematically identify subjects with low grade inflammation and are currently unable to quantitatively assess the response of such subjects to anti-inflammatory therapy. To address this limitation, we have extensively characterized the low-grade inflammatory status in mildly obese men during an intervention with a low dose of the non-steroidal anti-inflammatory NSAID diclofenac, using a broad based and integrative “omics” approach. Parameters measured included the identity of genes within peripheral blood mononuclear cells (PBMC) with altered levels of transcription, as well as concentrations of 80 plasma proteins and >300 metabolites, which included lipids, free fatty acids, oxygenated lipids, and a range of more polar compounds. These diverse data sets were analyzed using integrated multivariate and correlation analyses and biological response networks were constructed. A panel of genes, proteins and metabolites were identified that describe a diclofenac response network (43 genes in PBMC, 12 plasma proteins and 2 plasma metabolites). Novel candidate markers of inflammatory modulation included the PBMC expression of annexin A1 and cathepsin S, plasma levels of interleukin 15, macrophage-derived chemokine (MDC) and the arachidonic acid metabolite 5,6-DHET. This study demonstrates that an integrated analysis of a wide range of parameters can lead to a network of markers responding to inflammatory modulation that advances our ability to understand and quantify the status and modulation of inflammation in humans. The full utility and implication of the identified response network remains to be evaluated in other models of low-grade inflammation and anti-inflammatory therapies.
Technical Abstract: Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity.