Lu Y.,Wageningen University |
Lu Y.,National Institute for Public Health and the Environment |
Boekschoten M.V.,Wageningen University |
Boekschoten M.V.,The Netherlands Nutrigenomics Center |
And 6 more authors.
Physiological Genomics | Year: 2011
Comparative transcriptomic and metabolomic analysis of fenofibrate and fish oil treatments in mice. Physiol Genomics 43: 1307-1318, 2011. First published September 27, 2011; doi:10.1152/physiolgenomics.00100.2011. Elevated circulating triglycerides, which are considered a risk factor for cardiovascular disease, can be targeted by treatment with fenofibrate or fish oil. To gain insight into underlying mechanisms, we carried out a comparative transcriptomics and metabolomics analysis of the effect of 2 wk treatment with fenofibrate and fish oil in mice. Plasma triglycerides were significantly decreased by fenofibrate (-49.1%) and fish oil (- 21.8%), whereas plasma cholesterol was increased by fenofibrate (+ 29.9%) and decreased by fish oil (- 32.8%). Levels of various phospholipid species were specifically decreased by fish oil, while levels of Krebs cycle intermediates were increased specifically by fenofibrate. Plasma levels of many amino acids were altered by fenofibrate and to a lesser extent by fish oil. Both fenofibrate and fish oil upregulated genes involved in fatty acid metabolism and down-regulated genes involved in blood coagulation and fibrinolysis. Significant overlap in gene regulation by fenofibrate and fish oil was observed, reflecting their property as high or low affinity agonist for peroxisome proliferator-activated receptor-a, respectively. Fenofibrate specifically downregulated genes involved in complement cascade and inflammatory response. Fish oil specifically downregulated genes involved in cholesterol and fatty acid biosynthesis and upregulated genes involved in amino acid and arachidonic acid metabolism. Taken together, the data indicate that despite being similarly potent toward modulating plasma free fatty acids, cholesterol, and triglyceride levels, fish oil causes modest changes in gene expression likely via activation of multiple mechanistic pathways, whereas fenofibrate causes pronounced gene expression changes via a single pathway, reflecting the key difference between nutritional and pharmacological intervention. © 2011 the American Physiological Society.
van Dijk S.J.,Wageningen University |
Feskens E.J.M.,Wageningen University |
Geert Heidema A.,Wageningen University |
Bos M.B.,Wageningen University |
And 6 more authors.
PLoS ONE | Year: 2010
Background: Biomarkers that allow detection of the onset of disease are of high interest since early detection would allow intervening with lifestyle and nutritional changes before the disease is manifested and pharmacological therapy is required. Our study aimed to improve the phenotypic characterization of overweight but apparently healthy subjects and to identify new candidate profiles for early biomarkers of obesity-related diseases such as cardiovascular disease and type 2 diabetes. Methodology/Principal Findings: In a population of 56 healthy, middle-aged overweight subjects Body Mass Index (BMI), fasting concentration of 124 plasma proteins and insulin were determined. The plasma proteins are implicated in chronic diseases, inflammation, endothelial function and metabolic signaling. Random Forest was applied to select proteins associated with BMI and plasma insulin. Subsequently, the selected proteins were analyzed by clustering methods to identify protein clusters associated with BMI and plasma insulin. Similar analyses were performed for a second population of 20 healthy, overweight older subjects to verify associations found in population I. In both populations similar clusters of proteins associated with BMI or insulin were identified. Leptin and a number of pro-inflammatory proteins, previously identified as possible biomarkers for obesity-related disease, e.g. Complement 3, C Reactive Protein, Serum Amyloid P, Vascular Endothelial Growth Factor clustered together and were positively associated with BMI and insulin. IL-3 and IL-13 clustered together with Apolipoprotein A1 and were inversely associated with BMI and might be potential new biomarkers. Conclusion/ Significance: We identified clusters of plasma proteins associated with BMI and insulin in healthy populations. These clusters included previously reported biomarkers for obesity-related disease and potential new biomarkers such as IL- 3 and IL-13. These plasma protein clusters could have potential applications for improved phenotypic characterization of volunteers in nutritional intervention studies or as biomarkers in the early detection of obesity-linked disease development and progression. © 2010 van Dijk et al.