| 1. |
|
|
| 2. |
|
|
| 3. |
- Biong, A. S., et al.
(författare)
-
Intake of dairy fat and dairy products, and risk of myocardial infarction: A case-control study
- 2008
-
Ingår i: International Journal of Food Sciences and Nutrition. - : Informa UK Limited. - 0963-7486 .- 1465-3478. ; 59:2, s. 1-11
-
Tidskriftsartikel (refereegranskat)abstract
- The role of dairy fat in the aetiology of myocardial infarction (MI) is controversial. The aim of this study was to evaluate the association between intake of dairy fat and dairy products, and risk of a first acute MI. A total of 111 MI patients with a first acute MI and 107 population controls (men and women, age 45-75 years) were studied. Diet was assessed using a 180-item food frequency questionnaire. The MI cases had higher intake of total fat, but lower intake of saturated fat and dairy fat than the control persons. No effect of dairy fat or saturated fat on the odds ratio for MI was observed, however. A significant inverse trend in odds of MI for intake of cheese was observed, but the trend was no longer significant after adjustment for smoking. The results suggest that intake of fat from dairy products may not be associated with increased risk of having a first MI. The healthy control persons had a diet that differed from the diet of the MI patients in many aspects, and dairy products were a part of this diet. This may have protected them from having a first MI.
|
|
| 4. |
- Jakobsson, P-J, et al.
(författare)
-
Proteomics and metabolomics in the classification of SLE subsets
- 2014
-
Ingår i: Scandinavian Journal of Rheumatology. - : Informa Healthcare. - 0300-9742 .- 1502-7732. ; 43:Suppl. 127 Meeting Abstract PP267, s. 95-95
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Systemic autoimmune diseases (SAIDs) affect about 0.5–1% of Europeans with a remarkable female predominance (80–90%). Present diagnostic entities are vague and rely on fairly old and unspecific criteria that do not use state-of-the-art laboratory parameters. New diagnostic tools and therapeutic/prognostic biomarkers are needed. Systemic lupus erythematosus (SLE) is regarded as a prototype for SAIDs and we hypothesized that subgroups of patients with SLE may have different pathogenesis and should consequently be subject to different treatment strategies. Our aim was to find new biomarkers to be used for the identification of more homogeneous patient populations.Method: This study involved 320 SLE patients from the Karolinska lupus cohort and 320 age- and gender-matched controls. Plasma samples were analysed using an antibody Luminex assay with 367 antibodies targeting 281 unique selected proteins. Subsets of the SLE cohort and controls were also analysed for their sphingolipid content, as well as by a metabolomic and mass spectrometry-based proteomic approach.Results: The Luminex platform revealed 66 proteins found at higher or lower levels in SLE. Mass spectrometry-based proteomics has shown very promising data for the components of the complement and coagulation cascades. Metabolomics identified patterns of plasma metabolites that separate SLE from controls. Finally, analysis of >30 sphingolipids demonstrated a specific group of these lipids at significantly higher concentrations in SLE compared to controls. Following treatment, these differences were normalized.Conclusions: Preliminary data demonstrate the involvement of several distinct biochemical pathways in SLE that can be used for biomarker discovery and a better understanding of the pathophysiological events underlying the disease.
|
|
| 5. |
- Moore, John P., et al.
(författare)
-
Analysis of Plant Cell Walls Using High-Throughput Profiling Techniques with Multivariate Methods
- 2020
-
Ingår i: The Plant Cell Wall. - New York, NY : Humana Press. - 9781071606193 - 9781071606216 ; , s. 327-337
-
Bokkapitel (refereegranskat)abstract
- Plant cell walls are composed of a number of coextensive polysaccharide-rich networks (i.e., pectin, hemicellulose, protein). Polysaccharide-rich cell walls are important in a number of biological processes including fruit ripening, plant–pathogen interactions (e.g., pathogenic fungi), fermentations (e.g., winemaking), and tissue differentiation (e.g., secondary cell walls). Applying appropriate methods is necessary to assess biological roles as for example in putative plant gene functional characterization (e.g., experimental evaluation of transgenic plants). Obtaining datasets is relatively easy, using for example gas chromatography–mass spectrometry (GC-MS) methods for monosaccharide composition, Fourier transform infrared spectroscopy (FT-IR) and comprehensive microarray polymer profiling (CoMPP); however, analyzing the data requires implementing statistical tools for large-scale datasets. We have validated and implemented a range of multivariate data analysis methods on datasets from tobacco, grapevine, and wine polysaccharide studies. Here we present the workflow from processing samples to acquiring data to performing data analysis (particularly principal component analysis (PCA) and orthogonal projection to latent structure (OPLS) methods).
|
|
| 6. |
- Nording, Malin Linder, et al.
(författare)
-
Individual Variation in Lipidomic Profiles of Healthy Subjects in Response to Omega-3 Fatty Acids
- 2013
-
Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 8:10, s. e76575-
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction: Conflicting findings in both interventional and observational studies have resulted in a lack of consensus on the benefits of omega 3 fatty acids in reducing disease risk. This may be due to individual variability in response. We used a multi-platform lipidomic approach to investigate both the consistent and inconsistent responses of individuals comprehensively to a defined omega 3 intervention. Methods: The lipidomic profile including fatty acids, lipid classes, lipoprotein distribution, and oxylipins was examined multi- and uni-variately in 12 healthy subjects pre vs. post six weeks of omega 3 fatty acids (1.9 g/d eicosapentaenoic acid [EPA] and 1.5 g/d docosahexaenoic acid [DHA]). Results: Total lipidomic and oxylipin profiles were significantly different pre vs. post treatment across all subjects (p=0.00007 and p=0.00002 respectively). There was a strong correlation between oxylipin profiles and EPA and DHA incorporated into different lipid classes (r(2)=0.93). However, strikingly divergent responses among individuals were also observed. Both omega 3 and omega 6 fatty acid metabolites displayed a large degree of variation among the subjects. For example, in half of the subjects, two arachidonic acid cyclooxygenase products, prostaglandin E-2 (PGE(2)) and thromboxane B2 (TXB2), and a lipoxygenase product, 12-hydroxyeicosatetraenoic acid (12-HETE) significantly decreased post intervention, whereas in the other half they either did not change or increased. The EPA lipoxygenase metabolite 12-hydroxyeicosapentaenoic acid (12-HEPE) varied among subjects from an 82% decrease to a 5,000% increase. Conclusions: Our results show that certain defined responses to omega 3 fatty acid intervention were consistent across all subjects. However, there was also a high degree of inter-individual variability in certain aspects of lipid metabolism. This lipidomic based phenotyping approach demonstrated that individual responsiveness to omega 3 fatty acids is highly variable and measurable, and could be used as a means to assess the effectiveness of omega 3 interventions in modifying disease risk and determining metabolic phenotype.
|
|
| 7. |
- Thuvander, A., et al.
(författare)
-
Levels of ochratoxin A in blood from Norwegian and Swedish blood donors and their possible correlation with food consumption
- 2001
-
Ingår i: Food and Chemical Toxicology. - 0278-6915 .- 1873-6351. ; 39:12, s. 1145-1151
-
Tidskriftsartikel (refereegranskat)abstract
- Blood levels of ochratoxin A were determined in 406 Scandinavian blood donors (206 from Oslo, Norway, and 200 from Visby on the island of Gotland, Sweden), using an HPLC method. In connection with the blood collection, the subjects were asked to fill in a food questionnaire to obtain individual dietary information relevant to ochratoxin A exposure. The mean plasma level of ochratoxin A was 0.18 ng/ml in Oslo and slightly higher, 0.21 ng/ml (P = 0.046) in Visby. There was no correlation between plasma levels of ochratoxin A and the estimated total dietary intake of ochratoxin A based on consumption data and levels in food (retrieved from the literature), neither was the plasma level of ochratoxin A correlated with the total amount of food consumed. However, consumption of several foods, including cereal products, wine, beer and pork, were to some minor degree related to high plasma levels of ochratoxin A. The strongest correlations (correlation coefficient r >0.4; P <0.001) were observed for women in relation to the consumption of beer or medium brown bread. Correlation analysis of combinations of two or more food categories did not result in any statistically significant correlation.
|
|
| 8. |
- Zietsman, Anscha J. J., et al.
(författare)
-
Following the compositional changes of fresh grape skin cell walls during the fermentation process in the presence and absence of maceration enzymes
- 2015
-
Ingår i: Journal of Agricultural and Food Chemistry. - : American Chemical Society (ACS). - 0021-8561 .- 1520-5118. ; 63:10, s. 2798-2810
-
Tidskriftsartikel (refereegranskat)abstract
- Cell wall profiling technologies were used to follow compositional changes that occurred in the skins of grape berries (from two different ripeness levels) during fermentation and enzyme maceration. Multivariate data analysis showed that the fermentation process yielded cell walls enriched in hemicellulose components because pectin was solubilized (and removed) with a reduction as well as exposure of cell wall proteins usually embedded within the cell wall structure. The addition of enzymes caused even more depectination, and the enzymes unravelled the cell walls enabling better access to, and extraction of, all cell wall polymers. Overripe grapes had cell walls that were extensively hydrolyzed and depolymerized, probably by natural grape-tissue-ripening enzymes, and this enhanced the impact that the maceration enzymes had on the cell wall monosaccharide profile. The combination of the techniques that were used is an effective direct measurement of the hydrolysis actions of maceration enzymes on the cell walls of grape berry skin.
|
|
| 9. |
|
|
| 10. |
- Blaise, Benjamin J., et al.
(författare)
-
Statistical analysis in metabolic phenotyping
- 2021
-
Ingår i: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
-
Forskningsöversikt (refereegranskat)abstract
- Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
|
|
