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- Kolak, Maria, et al.
(författare)
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Adipose tissue inflammation and increased ceramide content characterize subjects with high liver fat content independent of obesity
- 2007
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 56:8, s. 1960-1968
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: We sought to determine whether adipose tissue is inflamed in individuals with increased liver fat (LFAT) independently of obesity.RESEARCH DESIGN AND METHODS: A total of 20 nondiabetic, healthy, obese women were divided into normal and high LFAT groups based on their median LFAT level (2.3 +/- 0.3 vs. 14.4 +/- 2.9%). Surgical subcutaneous adipose tissue biopsies were studied using quantitative PCR, immunohistochemistry, and a lipidomics approach to search for putative mediators of insulin resistance and inflammation. The groups were matched for age and BMI. The high LFAT group had increased insulin (P = 0.0025) and lower HDL cholesterol (P = 0.02) concentrations.RESULTS: Expression levels of the macrophage marker CD68, the chemokines monocyte chemoattractant protein-1 and macrophage inflammatory protein-1alpha, and plasminogen activator inhibitor-1 were significantly increased, and those of peroxisome proliferator-activated receptor-gamma and adiponectin decreased in the high LFAT group. CD68 expression correlated with the number of macrophages and crown-like structures (multiple macrophages fused around dead adipocytes). Concentrations of 154 lipid species in adipose tissue revealed several differences between the groups, with the most striking being increased concentrations of triacylglycerols, particularly long chain, and ceramides, specifically Cer(d18:1/24:1) (P = 0.01), in the high LFAT group. Expression of sphingomyelinases SMPD1 and SMPD3 were also significantly increased in the high compared with normal LFAT group.CONCLUSIONS: Adipose tissue is infiltrated with macrophages, and its content of long-chain triacylglycerols and ceramides is increased in subjects with increased LFAT compared with equally obese subjects with normal LFAT content. Ceramides or their metabolites could contribute to adverse effects of long-chain fatty acids on insulin resistance and inflammation.
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| 2. |
- Sysi-Aho, Marko, et al.
(författare)
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Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles
- 2007
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 23:13, s. i519-i528
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.RESULTS: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.
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