Orthogonal Projections To Latent Structures Research Articles

Increased serum levels of C21 steroids in female patients with multiple sclerosis.

Multiple sclerosis (MS) is one of the most common neurological diseases. This neurodegenerative autoimmune disease manifests as inflammatory and demyelinating impairment of the central nervous system (CNS). Although some studies demonstrated associations between altered steroidogenesis and pathophysiology of MS as well as the importance of steroids in the pathophysiology of MS, the knowledge concerning the steroid metabolome in female patients is limited. Hence, 51 steroids and steroid polar conjugates were measured in the serum of 12 women with MS, untreated with steroids and 6 age-corresponding female controls with the use of gas chromatography - mass spectrometry (GC-MS). The data were processed using age adjusted ANCOVA, receiver operating characteristics (ROC) analysis and orthogonal projections to latent structures (OPLS). Our data show higher levels of circulating C21 steroids including steroid modulators of ionotropic type A gamma-aminobutyric acid (GABA A) receptors and glutamate receptors. Furthermore, the levels of GABAergic androsterone and 5-androsten-3beta,7alpha,17beta-triol were also higher in the female MS patients. In conclusion, the data demonstrate higher levels of circulating C21 steroids and their polar conjugates and some bioactive C19 steroids in women with MS, which may influence neuronal activity and affect the balance between neuroprotection and excitotoxicity.

Steroid hormones related to 11beta-hydroxysteroid dehydrogenase type 1 in treated obesity.

The local concentration of glucocorticoids is intensively regulated by 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD 1). Human 11beta-HSD 1 also reversibly catalyzes the inter-conversion of 7alpha-hydroxy- and 7beta-hydroxy-dehydroepiandrosterone (DHEA) into 7-oxo-DHEA. The cohort of 282 obese adolescents, 154 girls (median age 15.31 years, range 14.17-16.68 years) and 128 boys (median age 14.95 years, range 13.87-16.16 years), BMI (Body Mass Index) >90th percentile was examined. In samples collected before and after one month of reductive diet therapy, circulating levels of steroids were analyzed by liquid chromatography-tandem mass spectrometry and radioimmunoassay methods. The model of the treatment efficacy prediction was calculated. A significant reduction in circulating levels of cortisone, E2 and increased levels of 7beta-hydroxy-DHEA after the reductive treatment was observed. Levels of cortisol, DHEA, DHT sustained without any significant change. The predictive Orthogonal Projections to Latent Structures (OPLS) model explained 20.1 % of variability of BMI, z-score change by the basal levels of 7alpha-hydroxy-DHEA, DHEA, cortisol and E2 as the strongest predictors. Reduced levels of circulating cortisone and reduced ratios of oxygenated/reduced metabolites reflect increased reductase activity of 11beta-HSD 1 with reduced BMI, z-score. We hypothesize whether these changes can be attributed to the altered activity of 11beta-HSD 1 in the liver.

Antimicrobial activity and chemometric modelling of South African propolis.

This study reports on the inhibitory and bactericidal properties of 39 South African (SA) propolis samples and three propolis samples from Brazil. Ethanolic extracts of propolis (EEP) were prepared and their antimicrobial activities tested using the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. Some samples displayed substantial antimicrobial activity with MIC and MBC values as low as 6μgml(-1) against Staphylococcus aureus. The correlation between liquid chromatography-mass spectrometry (LC-MS) chemical data and the antimicrobial activity of propolis extracts was investigated using multivariate data analysis tools. Orthogonal projections to latent structures (OPLS) models were created for the two Gram-positive bacteria (Enterococcus faecalis and S.aureus) and Candida albicans. Using the S-plot function, it was possible to identify the bioactive constituents in propolis as chrysin, pinocembrin, galangin and pinobanksin-3-O-acetate. The SA propolis samples tested displayed noteworthy antimicrobial activity, favourably comparable to that of the Brazilian comparator and 'gold standard'. The observed antimicrobial activity of SA propolis can possibly be attributed to its flavonoid content. Based on the good antimicrobial activity observed for SA propolis, this natural resource shows promise and should be considered for development which may contribute to growing the bio-economy in the region.

Application of linear multivariate calibration techniques to identify the peaks responsible for the antioxidant activity of Satureja hortensis L. and Oliveria decumbens Vent. essential oils by gas chromatography-mass spectrometry.

Satureja hortensis L. and Oliveria decumbens Vent. are known for their diverse effects in drug therapy and traditional medicine. One of the most interesting properties of their essential oils is good antioxidant activity. In this paper, essential oils of aerial parts of S. hortensis L. and O. decumbens Vent. from different regions were obtained by hydrodistillation and were analyzed by gas chromatography-mass spectrometry (GC-MS). Essential oils were tested for their free radical scavenging activity using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay to identify the peaks potentially responsible for the antioxidant activity from chromatographic fingerprints by numerous linear multivariate calibration techniques. Because of its simplicity and high repeatability, orthogonal projection to latent structures (OPLS) model had the best performance in indicating the potential antioxidant compounds in S. hortensis L. and O. decumbens Vent. essential oils. In this study, P-cymene, carvacrol and β-bisabolene for S. hortensis L. and P-cymene, Ç-terpinen, thymol, carvacrol, and 1,3-benzodioxole, 4-methoxy-6-(2-propenyl) for O. decumbens Vent. are suggested as the potentially antioxidant compounds.

Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling.

Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umeå University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

Steroid hormones in prediction of normal pressure hydrocephalus

Normal pressure hydrocephalus (NPH) is a treatable neurological disorder affecting elderly people with the prevalence increasing with age. NPH is caused by abnormal cerebrospinal fluid (CSF) reabsorption and manifested as a balance impairment, urinary incontinence and dementia development. These symptoms are potentially reversible if recognized early. Diagnosis of NPH is difficult and can be easily mistaken for other neurodegenerative disorders, which makes NPH one of the major misdiagnosed diseases worldwide. The aim of the study was to find out the appropriate combination of indicators, based on CSF steroids, which would contribute to a clearer NPH diagnosis. The levels of CSF cortisol, cortisone, dehydroepiandrosterone (DHEA), 7α-OH-DHEA, 7β-OH-DHEA, 7-oxo-DHEA, 16α-OH-DHEA and aldosterone (all LC-MS/MS) were determined in our patients (n=30; NPH, 65–80 years) and controls (n=10; 65–80 years). The model of orthogonal projections to latent structures (OPLS) was constructed to predict NPH. Cortisone, 7α-OH-DHEA, 7β-OH-DHEA, 7-oxo-DHEA, aldosterone, 7α-OH-DHEA /DHEA, 7-oxo-DHEA/7α-OH-DHEA, 7β-OH-DHEA/7-oxo-DHEA and 16α-OH-DHEA/DHEA in the CSF were identified as the key predictors and the model discriminated patients from controls with 100% sensitivity and 100% specificity. The suggested model would contribute to early and accurate NPH diagnosis, enabling promptly treatment of the disease.

Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis

Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.

Monitoring the modifications of the vitreous humor metabolite profile after death: an animal model.

We applied a metabolomic approach to monitor the modifications occurring in goat vitreous humor (VH) metabolite composition at different times (0, 6, 12, 18, and 24 hours) after death. The 1H-NMR analysis of the VH samples was performed for the simultaneous determination of several metabolites (i.e., the metabolite profile) representative of the VH status at different times. Spectral data were analyzed by Principal Component Analysis (PCA) and by Orthogonal Projection to Latent Structures (OPLS) regression technique. PCA and OPLS suggested that different spectral regions were involved in time-related changes. The major time-related compositional changes, here detected, were the increase of lactate, hypoxanthine, alanine, total glutathione, choline/phosphocholine, creatine, and myo-inositol and the decrease of glucose and 3-hydroxybutyrate. We attempted a speculative interpretation of the biological mechanisms underlying these changes. These results show that multivariate statistical approach, based on 1H NMR metabolite profiling, is a powerful tool for detecting ongoing differences in VH composition and may be applied to investigate several physiological and pathological conditions.

Metabolic Phenotyping for Enhanced Mechanistic Stratification of Chronic Hepatitis C-Induced Liver Fibrosis

The invasive nature of biopsy alongside issues with categorical staging and sampling error has driven research into noninvasive biomarkers for the assessment of liver fibrosis in order to stratify and personalize treatment of patients with liver disease. Here, we sought to determine whether a metabonomic approach could be used to identify signatures reflective of the dynamic, pathological metabolic perturbations associated with fibrosis in chronic hepatitis C (CHC) patients. Plasma nuclear magnetic resonance (NMR) spectral profiles were generated for two independent cohorts of CHC patients and healthy controls (n=50 original and n=63 validation). Spectral data were analyzed and significant discriminant biomarkers associated with fibrosis (as graded by enhanced liver fibrosis (ELF) and METAVIR scores) identified using orthogonal projection to latent structures (O-PLS). Increased severity of fibrosis was associated with higher tyrosine, phenylalanine, methionine, citrate and, very-low-density lipoprotein (vLDL) and lower creatine, low-density lipoprotein (LDL), phosphatidylcholine, and N-Acetyl-α1-acid-glycoprotein. Although area under the receiver operator characteristic curve analysis revealed a high predictive performance for classification based on METAVIR-derived models, <40% of identified biomarkers were validated in the second cohort. In the ELF-derived models, however, over 80% of the biomarkers were validated. Our findings suggest that modeling against a continuous ELF-derived score of fibrosis provides a more robust assessment of the metabolic changes associated with fibrosis than modeling against the categorical METAVIR score. Plasma metabolic phenotypes reflective of CHC-induced fibrosis primarily define alterations in amino-acid and lipid metabolism, and hence identify mechanistically relevant pathways for further investigation as therapeutic targets.

OPLS in batch monitoring – Opens up new opportunities

In batch statistical process control (BSPC), data from a number of “good” batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA).In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data.The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as ‘batch maturity index’) from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations.The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other hand were shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox.

Multivariate Statistical Analysis of the UV-Vis Profiles of Wine Polyphenolic Extracts during Vinification

Multivariate Statistical data Analysis (MVA) was used to study the changes undergone by the UV-Vis spectral profiles of polyphenolic components during vinification. Two Sardinian wines, from the white cultivar “Vermentino” and from the red “Cannonau”, were studied. The wine samples were collected at different times from the fermentation vessels and the UV-Vis spectra of their solid phase extracts (SPE) were submitted to PCA (Principal Component Analysis) and to Orthogonal Projections to Latent Structures (OPLS) regression analysis in order to look for the main source of variability present in the spectra in terms of grape typology and winemaking times. In Vermentino, during the first weeks of vinification the prevalent modifications regarded the increase of the bands at 280 and 350 nm, attributed to HBA, stilbenes flavan-3-ols and glycosylated flavonols, respectively; at approximately 60 days a bathocromic shift of the band at 350 nm towards 380 nm took place, suggesting hydrolysis of flavonols. In Cannonau, the band at 536 nm, attributed to anthocyanins, that showed a consistent increase in the first days of vinification, after 11 days dropped significantly, exhibiting also a bathochromic shift. The band at 380 nm was linearly positively correlated with time. Our results support the potential use of this approach for qualitatively monitoring the winemaking process.

Gas chromatography/mass spectrometry based component profiling and quality prediction for Japanese sake

Sake is a Japanese traditional alcoholic beverage, which is produced by simultaneous saccharification and alcohol fermentation of polished and steamed rice by Aspergillus oryzae and Saccharomyces cerevisiae. About 300 compounds have been identified in sake, and the contribution of individual components to the sake flavor has been examined at the same time. However, only a few compounds could explain the characteristics alone and most of the attributes still remain unclear. The purpose of this study was to examine the relationship between the component profile and the attributes of sake. Gas chromatography coupled with mass spectrometry (GC/MS)-based non-targeted analysis was employed to obtain the low molecular weight component profile of Japanese sake including both nonvolatile and volatile compounds. Sake attributes and overall quality were assessed by analytical descriptive sensory test and the prediction model of the sensory score from the component profile was constructed by means of orthogonal projections to latent structures (OPLS) regression analysis. Our results showed that 12 sake attributes [ginjo-ka (aroma of premium ginjo sake), grassy/aldehydic odor, sweet aroma/caramel/burnt odor, sulfury odor, sour taste, umami, bitter taste, body, amakara (dryness), aftertaste, pungent/smoothness and appearance] and overall quality were accurately explained by component profiles. In addition, we were able to select statistically significant components according to variable importance on projection (VIP). Our methodology clarified the correlation between sake attribute and 200 low molecular components and presented the importance of each component thus, providing new insights to the flavor study of sake.

A multivariate approach linking reported side effects of clinical antidepressant and antipsychotic trials to in vitro binding affinities

The vast majority of approved antidepressants and antipsychotics exhibit a complex pharmacology. The mechanistic understanding of how these psychotropic medications are related to adverse drug reactions (ADRs) is crucial for the development of novel drug candidates and patient adherence. This study aims to associate in vitro assessed binding affinity profiles (39 compounds, 24 molecular drug targets) and ADRs (n=22) reported in clinical trials of antidepressants and antipsychotics (n>59.000 patients) by the use of robust multivariate statistics. Orthogonal projection to latent structures (O-PLS) regression models with reasonable predictability were found for several frequent ADRs such as nausea, diarrhea, hypotension, dizziness, headache, insomnia, sedation, sleepiness, increased sweating, and weight gain. Results of the present study support many well-known pharmacological principles such as the association of hypotension and dizziness with α1-receptor or sedation with H1-receptor antagonism. Moreover, the analyses revealed novel or hardly investigated mechanisms for common ADRs including the potential involvement of 5-HT6-antagonism in weight gain, muscarinic receptor antagonism in dizziness, or 5-HT7-antagonism in sedation. To summarize, the presented study underlines the feasibility and value of a multivariate data mining approach in psychopharmacological development of antidepressants and antipsychotics.

Comparison of simple projection methods (OPLS, PLSO, and TP) for separation of predicting and non‐predicting information in PLSR and PCR, with focus on DA

Orthogonal projections to latent structures (OPLS) and target projection (TP) are two alternative methods for separation of predicting and non‐predicting parts of the predictor matrix in partial least squares regression (PLSR), which can also be applied on principal component regression (PCR). An additional new method called PLSO is developed in the paper. In all three methods, the predicting score vector is a scaled version of the fitted response vector. Otherwise, the resulting score and loading vectors are different, although OPLS and TP are identical within similarity transformations. All these relations are here found by simple projections of the fitted predictor matrix from PLSR or PCR, and the similarities and differences are illustrated in discriminant analysis examples. Copyright © 2014 John Wiley &amp; Sons, Ltd.

SAT0122 Prediction of Coronary Artery Calcification and Association with Inflammation in Rheumatoid Arthritis: A Follow-Up Study

BackgroundWe previously reported increased prevalence of atherosclerosis in patients with RA (1).ObjectivesTo evaluate (i) the relation between inflammatory markers, measures of atherosclerosis and potential biomarkers of atherosclerosis at baseline and...

Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)

A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real‐world cases. Copyright © 2014 John Wiley &amp; Sons, Ltd.

OPLS methods for the analysis of hyperspectral images—comparison with MCR‐ALS

Two new, orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization of multiple chemical compounds in one matrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR‐ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS‐based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm (pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR‐ALS method enabled easier visual interpretation compared to the OPLS‐based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Distinct Inflammatory Mediator Patterns Characterize Infectious and Sterile Systemic Inflammation in Febrile Neutropenic Hematology Patients

BackgroundInvasive infections and sterile tissue damage can both give rise to systemic inflammation with fever and production of inflammatory mediators. This makes it difficult to diagnose infections in patients who are already inflamed, e.g. due to cell and tissue damage. For example, fever in patients with hematological malignancies may depend on infection, lysis of malignant cells, and/or chemotherapy-induced mucosal damage. We hypothesized that it would be possible to distinguish patterns of inflammatory mediators characterizing infectious and non-infectious causes of inflammation, respectively. Analysis of a broad range of parameters using a multivariate method of pattern recognition was done for this purpose.MethodsIn this prospective study, febrile (>38°C) neutropenic patients (n = 42) with hematologic malignancies were classified as having or not having a microbiologically defined infection by an infectious disease specialist. In parallel, blood was analyzed for 116 biomarkers, and 23 clinical variables were recorded for each patient. Using O-PLS (orthogonal projection to latent structures), a model was constructed based on these 139 variables that could separate the infected from the non-infected patients. Non-discriminatory variables were discarded until a final model was reached. Finally, the capacity of this model to accurately classify a validation set of febrile neutropenic patients (n = 10) as infected or non-infected was tested.ResultsA model that could segregate infected from non-infected patients was achieved based on discrete differences in the levels of 40 variables. These variables included acute phase proteins, cytokines, measures of coagulation, metabolism, organ stress and iron turn-over. The model correctly identified the infectious status of nine out of ten subsequently recruited febrile neutropenic hematology patients.ConclusionsIt is possible to separate patients with infectious inflammation from those with sterile inflammation based on inflammatory mediator patterns. This strategy could be developed into a decision-making tool for diverse clinical applications.

Towards a complete identification of orthogonal variation in multiple regression from a PLS1 modeling point of view: including OPLS by a change of orthogonal basis

It is well known that the predictions of the single response orthogonal projections to latent structures (OPLS) and the single response partial least squares regression (PLS1) regression are identical in the single‐response case. The present paper presents an approach to identification of the complete y‐orthogonal structure by starting from the viewpoint of standard PLS1 regression. Three alternative non‐deflating OPLS algorithms and a modified principal component analysis (PCA)‐driven method (including MATLAB code) is presented. The first algorithm implements a postprocessing routine of the standard PLS1 solution where QR factorization applied to a shifted version of the non‐orthogonal scores is the key to express the OPLS solution. The second algorithm finds the OPLS model directly by an iterative procedure. By a rigorous mathematical argument, we explain that orthogonal filtering is a ‘built‐in’ property of the traditional PLS1 regression coefficients. Consequently, the capabilities of OPLS with respect to improving the predictions (also for new samples) compared with PLS1 are non‐existing. The PCA‐driven method is based on the fact that truncating off one dimension from the row subspace of X results in a matrix Xorth with y‐orthogonal columns and a rank of one less than the rank of X. The desired truncation corresponds exactly to the first X deflation step of Martens non‐orthogonal PLS algorithm. The significant y‐orthogonal structure of X found by PCA of Xorth is split into two fundamental parts: one part that is significantly contributing to correct the first PLS score toward y and one part that is not. The third and final OPLS algorithm presented is a modification of Martens non‐orthogonal algorithm into an efficient dual PLS1–OPLS algorithm. Copyright © 2014 John Wiley &amp; Sons, Ltd.

A chemometrics toolbox based on projections and latent variables

A personal view is given about the gradual development of projection methods—also called bilinear, latent variable, and more—and their use in chemometrics. We start with the principal components analysis (PCA) being the basis for more elaborate methods for more complex problems such as soft independent modeling of class analogy, partial least squares (PLS), hierarchical PCA and PLS, PLS‐discriminant analysis, Orthogonal projection to latent structures (OPLS), OPLS‐discriminant analysis and more.From its start around 1970, this development was strongly influenced by Bruce Kowalski and his group in Seattle, and his realization that the multidimensional data profiles emerging from spectrometers, chromatographs, and other electronic instruments, contained interesting information that was not recognized by the current one variable at a time approaches to chemical data analysis.This led to the adoption of what in statistics is called the data analytical approach, often called also the data driven approach, soft modeling, and more. This approach combined with PCA and later PLS, turned out to work very well in the analysis of chemical data. This because of the close correspondence between, on the one hand, the matrix decomposition at the heart of PCA and PLS and, on the other hand, the analogy concept on which so much of chemical theory and experimentation are based. This extends to numerical and conceptual stability and good approximation properties of these models.The development is informally summarized and described and illustrated by a few examples and anecdotes. Copyright © 2014 John Wiley &amp; Sons, Ltd.