| 1. |
- Dunstan, R. H., et al.
(author)
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Diverse characteristics of the urinary excretion of amino acids in humans and the use of amino acid supplementation to reduce fatigue and sub-health in adults
- 2017
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In: Nutrition Journal. - : Springer Science and Business Media LLC. - 1475-2891. ; 16:19
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Journal article (peer-reviewed)abstract
- Background: The excretion of amino acids in urine represents an important avenue for the loss of key nutrients. Some amino acids such as glycine and histidine are lost in higher abundance than others. These two amino acids perform important physiological functions and are required for the synthesis of key proteins such as haemoglobin and collagen. Methods: Stage 1 of this study involved healthy subjects(n = 151) who provided first of the morning urine samples and completed symptom questionnaires. Urine was analysed for amino acid composition by gas chromatography. Stage 2 involved a subset of the initial cohort (n = 37) who completed a 30 day trial of an amino acid supplement and subsequent symptom profile evaluation. Results: Analyses of urinary amino acid profiles revealed that three groups could be objectively defined from the 151 participants using k-means clustering. The amino acid profiles were significantly different between each of the clusters (Wilks' Lambda = 0.13, p < 0.0001). Cluster 1 had the highest loss of amino acids with histidine being the most abundant component. Cluster 2 had glycine present as the most abundant urinary amino acid and cluster 3 had equivalent abundances of glycine and histidine. Strong associations were observed between urinary proline concentrations and fatigue/pain scores (r =.56 to.83) for females in cluster 1, with several other differential sets of associations observed for the other clusters. Conclusions: Different phenotypic subsets exist in the population based on amino acid excretion characteristics found in urine. Provision of the supplement resulted in significant improvements in reported fatigue and sleep for 81% of the trial cohort with all females reporting improvements in fatigue.
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| 2. |
- Dunstan, R. H., et al.
(author)
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Sex differences in amino acids lost via sweating could lead to differential susceptibilities to disturbances in nitrogen balance and collagen turnover
- 2017
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In: Amino Acids. - : Springer Science and Business Media LLC. - 0939-4451 .- 1438-2199. ; 49:8, s. 1337-1345
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Journal article (peer-reviewed)abstract
- Fluid collected during sweating is enriched with amino acids derived from the skin's natural moisturising factors and has been termed "faux" sweat. Little is known about sex differences in sweat amino acid composition or whether faux sweat amino acid losses affect nitrogen balance. Faux sweat collected by healthy adults (n = 47) after exercise, and at rest by chronic fatigue patients, was analysed for amino acid composition. Healthy females had higher total amino acid concentrations in sweat (10.5 +/- 1.2 mM) compared with healthy males (6.9 +/- 0.9 mM). Females had higher levels of 13 amino acids in sweat including serine, alanine and glycine. Higher hydroxyproline and proline levels suggested greater collagen turnover in females. Modelling indicated that with conservative levels of exercise, amino acid losses in females via faux sweat were triple than those predicted for urine, whereas in males they were double. It was concluded that females were more susceptible to key amino acid loss during exercise and/or hot conditions. Females reporting chronic fatigue had higher levels of methionine in faux sweat than healthy females. Males reporting chronic fatigue had higher levels of numerous amino acids in faux sweat compared to healthy males. Higher amino acid loss in faux sweat associated with chronic fatigue could contribute to a hypometabolic state. Depending on activity levels, climatic conditions and gender, amino acid losses in sweat and skin leachate could influence daily protein turnover where periods of continuously high turnover could lead to a negative net nitrogen balance.
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| 3. |
- Eriksson, Lennart, et al.
(author)
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Editorial
- 2007
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In: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 397-
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Journal article (pop. science, debate, etc.)abstract
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| 4. |
- Eriksson, Lennart, et al.
(author)
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Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)
- 2004
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 380:3, s. 419-29
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Journal article (peer-reviewed)abstract
- This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA.
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| 5. |
- Gottfries, Johan, et al.
(author)
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On the impact of uncorrelated variation in regression mathematics
- 2008
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 565-70
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Journal article (peer-reviewed)abstract
- The objective of the present study is to investigate if, and if so, how uncorrelated variation relates to regression mathematics as exemplified by partial least squares (PLS) methodology. In contrast to previous methods, orthogonal partial least squares (OPLS) method requires a multi-focus, in the sense that in parallel to calculation of correlation it requires an analysis of orthogonal variation, i.e. the uncorrelated structure in a comprehensive way. Subsequent to the estimation of the correlation is the remaining orthogonal variation, i.e. uncorrelated data, divided into uncorrelated structure and stochastic noise by the OPLS component. Thus, it appears obvious that it is of interest to understand how the uncorrelated variation can influence the interpretation of the regression model. We have scrutinized three examples that pinpoint additional value from OPLS regarding the modelling of the orthogonal, i.e. uncorrelated, variation in regression mathematics. In agreement with the results, we conclude that uncorrelated variations do impact interpretations of regression analyses output and provides not only opportunities by OPLS but also an obligation for the user to maximize benefit from OPLS.
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| 6. |
- Lundstedt, Torbjörn, et al.
(author)
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Editorial
- 2006
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In: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 323-324
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Journal article (pop. science, debate, etc.)
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| 7. |
- Pinto, Rui Climaco, et al.
(author)
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Advantages of orthogonal inspection in chemometrics
- 2012
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 26:6, s. 231-235
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Journal article (peer-reviewed)abstract
- The demand for chemometrics tools and concepts to study complex problems in modern biology and medicine has prompted chemometricians to shift their focus away from a traditional emphasis on model predictive capacity toward optimizing information exchange via model interpretation for biological validation. The interpretation of projection-based latent variable models is not straightforward because of its confounding of different systematic variations in the model components. Over the last 15?years, this has spurred the development of orthogonal-based methods that are capable of separating the correlated variation (to Y) from the noncorrelated (orthogonal to Y) variations in a single model. Here, we aim to provide a conceptual explanation of the advantages of orthogonal variation inspection in the context of Partial Least Squares (PLS) in multivariate classification and calibration. We propose that by inspecting the orthogonal variation, both model interpretation and information quality are improved by enhancement of the resulting level of knowledge. Although the predictive capacity of PLS using orthogonal methods may be identical to that of PLS alone, the combined result can be superior when it comes to the model interpretation. By discussing theory and examples, several new advantages revealed by inspection of orthogonal variation are highlighted. Copyright (c) 2012 John Wiley & Sons, Ltd.
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| 8. |
- Stenlund, Hans, et al.
(author)
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Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares
- 2009
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In: Analytical Chemistry. ; 81:1, s. 203-9
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Journal article (peer-reviewed)abstract
- Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood’s anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands “process understanding” in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.
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| 9. |
- Svelander, Cecilia, 1980, et al.
(author)
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Postprandial lipid and insulin responses among healthy, overweight men to mixed meals served with baked herring, pickled herring or baked, minced beef
- 2015
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In: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6207 .- 1436-6215. ; 54:6, s. 945-958
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Journal article (peer-reviewed)abstract
- PURPOSE: The aim was to compare postprandial lipid, insulin and vitamin D responses after consumption of three otherwise identical meals served either with baked herring, pickled herring or with baked, minced beef. METHODS: Seventeen healthy, overweight men (mean age 58 years, BMI 26.4-29.5 kg/m2) consumed standardized lunches together with baked herring, pickled herring or baked, minced beef on three occasions in a crossover design. Blood samples were taken just before and up to 7 h after the meal. The postprandial response was measured as serum concentrations of triglycerides (TG), total cholesterol and lipoproteins (LDL, HDL and VLDL), insulin, 25-OH vitamin D and plasma fatty acid composition. RESULTS: There was no difference in postprandial lipid responses between the two herring meals, whereas a slower TG clearance was observed after the baked, minced beef meal. The 150 g servings of baked and pickled herring provided 3.3 and 2.8 g of long-chain n-3 polyunsaturated fatty acids (LC n-3 PUFA), respectively, which was reflected in a substantial postprandial increase in plasma LC n-3 PUFA levels. The pickled herring contained 22 % sugar and consequently gave a higher insulin response compared with the other two meals. CONCLUSIONS: Both pickled and baked herring are good sources of LC n-3 PUFA in the diet, but the presence of sugar in pickled herring should be taken into consideration, especially if large amounts are consumed. The faster postprandial TG clearance after a meal with baked herring compared with baked beef supports previous studies on the beneficial effects of herring on cardiovascular health.
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| 10. |
- Wiklund, Susanne, et al.
(author)
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Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
- 2008
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In: Analytical Chemistry. - Columbus, OH : American Chemical Society. - 0003-2700 .- 1520-6882. ; 80:1, s. 115-22
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Journal article (peer-reviewed)abstract
- Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.
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