| 1. |
- Foerster, Jana, et al.
(författare)
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Serum Lipid and Serum Metabolite Components in relation to anthropometric parameters in EPIC-Potsdam participants
- 2015
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Ingår i: Metabolism. - Maryland Heights, MO, United States : Saunders Elsevier. - 0026-0495 .- 1532-8600. ; 64:10, s. 1348-58
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND/AIM: Lipidomic and metabolomic techniques become more and more important in human health research. Recent developments in analytical techniques enable the investigation of high amounts of substances. The high numbers of metabolites and lipids that are detected with among others mass spectrometric techniques challenge in most cases the statistical processes to bring out stable and interpretable results. This study targets to use the novel non-established statistical method treelet transform (TT) to investigate high numbers of metabolites and lipids and to compare the results with the established method principal component analysis (PCA). Serum lipid and metabolite profiles are investigated regarding their association to anthropometric parameters associated to obesity.METHODS: From 226 participants of the EPIC (European Prospective Investigation into Cancer and Nutrition)-Potsdam study blood samples were investigated with an untargeted metabolomics approach regarding serum metabolites and lipids. Additionally, participants were surveyed anthropometrically to assess parameters of obesity, such as body mass index (BMI), waist-to-hip-ratio (WHR) and body fat mass. TT and PCA are used to generate treelet components (TCs) and factors summarizing serum metabolites and lipids in new, latent variables without too much loss of information. With partial correlations TCs and factors were associated to anthropometry under the control for relevant parameters, such as sex and age.RESULTS: TT with metabolite variables (p=121) resulted in 5 stable and interpretable TCs explaining 18.9% of the variance within the data. PCA on the same variables generated 4 quite complex, less easily interpretable factors explaining 37.5% of the variance. TT on lipidomic data (p=353) produced 3 TCs as well as PCA on the same data resulted in 3 factors; the proportion of explained variance was 17.8% for TT and 39.8% for PCA. In both investigations TT ended up with stable components that are easier to interpret than the factors from the PCA. In general, the generated TCs and factors were similar in their structure when the factors are considered regarding the original variables loading high on them. Both TCs and factors showed associations to anthropometric measures.CONCLUSIONS: TT is a suitable statistical method to generate summarizing, latent variables in data sets with more variables than observations. In the present investigation it resulted in similar latent variables compared to the established method of PCA. Whereby less variance is explained by the summarizing constructs of TT compared to the factors of PCA, TCs are easier to interpret. Additionally the resulting TCs are quite stable in bootstrap samples.
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| 2. |
- Kurko, Johanna, et al.
(författare)
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Imbalance of plasma amino acids, metabolites and lipids in patients with lysinuric protein intolerance (LPI)
- 2016
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Ingår i: Metabolism. - : Saunders Elsevier. - 0026-0495 .- 1532-8600. ; 65:9, s. 1361-1375
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Lysinuric protein intolerance (LPI [MIM 222700]) is an aminoaciduria with defective transport of cationic amino acids in epithelial cells in the small intestine and proximal kidney tubules due to mutations in the SLC7A7 gene. LPI is characterized by protein malnutrition, failure to thrive and hyperammonemia. Many patients also suffer from combined hyperlipidemia and chronic kidney disease (CKD) with an unknown etiology.METHODS: Here, we studied the plasma metabolomes of the Finnish LPI patients (n=26) and healthy control individuals (n=19) using a targeted platform for analysis of amino acids as well as two analytical platforms with comprehensive coverage of molecular lipids and polar metabolites.RESULTS: Our results demonstrated that LPI patients have a dichotomy of amino acid profiles, with both decreased essential and increased non-essential amino acids. Altered levels of metabolites participating in pathways such as sugar, energy, amino acid and lipid metabolism were observed. Furthermore, of these metabolites, myo-inositol, threonic acid, 2,5-furandicarboxylic acid, galactaric acid, 4-hydroxyphenylacetic acid, indole-3-acetic acid and beta-aminoisobutyric acid associated significantly (P<0.001) with the CKD status. Lipid analysis showed reduced levels of phosphatidylcholines and elevated levels of triacylglycerols, of which long-chain triacylglycerols associated (P<0.01) with CKD.CONCLUSIONS: This study revealed an amino acid imbalance affecting the basic cellular metabolism, disturbances in plasma lipid composition suggesting hepatic steatosis and fibrosis and novel metabolites correlating with CKD in LPI. In addition, the CKD-associated metabolite profile along with increased nitrite plasma levels suggests that LPI may be characterized by increased oxidative stress and apoptosis, altered microbial metabolism in the intestine and uremic toxicity.
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| 3. |
- Suvitaival, Tommi, et al.
(författare)
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Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men
- 2018
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Ingår i: Metabolism. - : Saunders Elsevier. - 0026-0495 .- 1532-8600. ; 78:January, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM.METHODS: We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n = 631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids.RESULTS: A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lower levels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BMI and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p < 0.05) for progression to T2DM. The independently-validated predictive power improved in all pairwise comparisons between the lipid model and the respective standard risk model without the lipids (integrated discrimination improvement IDI > 0; p < 0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study.CONCLUSION: This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors.
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