Serum amino acid profiles in patients with myasthenia gravis.

Myasthenia gravis (MG) is an autoimmune disease characterized by weakness and rapid fatigue. Diagnostic methods used for myasthenia gravis are not conclusive and satisfactory, therefore it is necessary to develop reliable tools to help diagnose myasthenia gravis as early as possible. The aim of the study was to use HPLC-MS in conjunction with multivariate statistical analyses to investigate changes in the amino acid metabolic profiles between myasthenia gravis patients compared and controls. In addition, the effect of treatment regimens and myasthenia gravis type, on the observed changes in amino acid metabolic profiles were assessed. Serum levels of 29 amino acids were determined in 2 groups of individuals-28 patients with myasthenia gravis and 53 control subjects (CS). The results of our study indicate that serum levels of several amino acids in patients with myasthenia gravis changed significantly compared to the control group. Statistical analysis revealed differences between amino acids concentration in patients with different therapeutic scheme. In conclusion, amino acids may be involved in mechanisms underlying myasthenia gravis pathogenesis as well as may be potential biomarkers in MG patients diagnosis. However, considering the multifactorial, heterogenous and complex nature of this disease, validation on a larger study sample in further research is required before application into diagnostic practice.

Serum amino acid profiles in patients with mild cognitive impairment and in patients with mild dementia or moderate dementia.

Neurodegenerative disorders are one of the greatest global challenges for social and health care in the twenty-first century. Nowadays, determination of cerebrospinal fluid biomarkers for early diagnosis is served by a complex sample preparation procedure with limited diagnostic accuracy. Furthermore, neuroimaging methods are expensive, time-consuming and are not readily available for use as a complimentary and common screening method. Recently, researchers have shown an increased interest in the identification and characterization of new blood biomarkers of dementia to minimize the limitations associated with the current methods of biomarker determination. Amino acids play many important roles in the central nervous system, acting as neuromodulators, neurotransmitters and regulators of energy metabolism. The aim of this study was to evaluate if serum amino acid levels change along the continuum from no cognitive impairment to moderate dementia, and to identify putative biomarkers for early diagnosis of neurodegenerative diseases. Serum levels of 16 amino acids were determined in 3 groups of patients-22 with mild cognitive impairment, 45 with mild dementia and 28 with moderate dementia-by high-performance liquid chromatography (HPLC) with fluorescence detection using AccQ Tag column (Waters). The most exciting result is the significantly elevated concentration of arginine in patients with both stages of dementia as compared to mild cognitive impairment individuals. Recent accumulating evidence suggests the implication of changed arginine metabolism in the pathogenesis of neurodegenerative diseases. We conclude that amino acids profiling might be helpful in searching for biomarkers of neurogenerative diseases. In the present study, we discovered that arginine in plasma may have a putative diagnostic value for dementia.

Amino Acid Levels as Potential Biomarker of Elderly Patients with Dementia.

Dementia is a clinical syndrome characterized by cognitive impairment, in which there is disturbance of multiple higher cortical functions. The primary risk factor of dementia is old age, and due to significant changes in the worldwide demographic structure, the prevalence of cognitive impairment is increasing dramatically with aging populations in most countries. Alzheimer's disease is the predominant and leading cause of dementia. The aim of this study was to evaluate the modifications of amino acids that characterize the initial stages of dementia to help our understanding of the complex and multifactorial pathogenesis of neurodegenerative disorders. A total of 123 participants were divided into two groups: healthy elderly subjects and patients with mild or moderate dementia. The results of this study indicate that the serum levels of three amino acids were changed significantly in patients with dementia, in relation to the subjects without dementia. In particular, we observed differences in concentrations for serine, arginine and isoleucine (all of them were significantly increased in patients with dementia, compared with the control group). Our results suggest that the metabolisms of some amino acids seem be changed in patients with dementia. We conclude that amino acid profiling might be helpful for the better understanding of biochemical and metabolic changes related to the pathogenesis and progression of dementia. However, considering the multifactorial, heterogenous and complex nature of this disease, validation with a greater study sample in further research is required.

Metabolomic Signature of Early Vascular Aging (EVA) in Hypertension.

Arterial stiffening is a hallmark of early vascular aging (EVA) syndrome and an independent predictor of cardiovascular morbidity and mortality. In this case-control study we sought to identify plasma metabolites associated with EVA syndrome in the setting of hypertension. An untargeted metabolomic approach was used to identify plasma metabolites in an age-, BMI-, and sex-matched groups of EVA (n = 79) and non-EVA (n = 73) individuals with hypertension. After raw data processing and filtration, 497 putative compounds were characterized, out of which 4 were identified as lysophosphaditylcholines (LPCs) [LPC (18:2), LPC (16:0), LPC (18:0), and LPC (18:1)]. A main finding of this study shows that identified LPCs were independently associated with EVA status. Although LPCs have been shown previously to be positively associated with inflammation and atherosclerosis, we observed that hypertensive individuals characterized by 4 down-regulated LPCs had 3.8 times higher risk of EVA compared to those with higher LPC levels (OR = 3.8, 95% CI 1.7-8.5, P < 0.001). Our results provide new insights into a metabolomic phenotype of vascular aging and warrants further investigation of negative association of LPCs with EVA status. This study suggests that LPCs are potential candidates to be considered for further evaluation and validation as predictors of EVA in patients with hypertension.

Bayesian multilevel model of micro RNA levels in ovarian-cancer and healthy subjects.

In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals.

How to model temporal changes in nontargeted metabolomics study? A Bayesian multilevel perspective.

Analysis of time series data addresses the question on mechanisms underlying normal physiology and its alteration under pathological conditions. However, adding time variable to high-dimension, collinear, noisy data is a challenge in terms of mining and analysis. Here, we used Bayesian multilevel modeling for time series metabolomics in vivo study to model different levels of random effects occurring as a consequence of hierarchical data structure. A multilevel linear model assuming different treatment effects with double exponential prior, considering major sources of variability and robustness to outliers was proposed and tested in terms of performance. The treatment effect for each metabolite was close to zero suggesting small if any effect of cancer on metabolomics profile change. The average difference in 964 signals for all metabolites varied by a factor ranging from 0.8 to 1.25. The inter-rat variability (expressed as a coefficient of variation) ranged from 3-30% across all metabolites with median around 10%, whereas the inter-occasion variability ranged from 0-30% with a median around 5%. Approximately 36% of metabolites contained outlying data points. The complex correlation structure between metabolite signals was revealed. We conclude that kinetics of metabolites can be modeled using tools accepted in pharmacokinetics type of studies.

Multilevel pharmacokinetics-driven modeling of metabolomics data.

INTRODUCTION: Multilevel modeling is a quantitative statistical method to investigate variability and relationships between variables of interest, taking into account population structure and dependencies. It can be used for prediction, data reduction and causal inference from experiments and observational studies allowing for more efficient elucidation of knowledge. OBJECTIVES: In this study we introduced the concept of multilevel pharmacokinetics (PK)-driven modelling for large-sample, unbalanced and unadjusted metabolomics data comprising nucleoside and creatinine concentration measurements in urine of healthy and cancer patients. METHODS: A Bayesian multilevel model was proposed to describe the nucleoside and creatinine concentration ratio considering age, sex and health status as covariates. The predictive performance of the proposed model was summarized via area under the ROC, sensitivity and specificity using external validation. RESULTS: Cancer was associated with an increase in methylthioadenosine/creatinine excretion rate by a factor of 1.42 (1.09-2.03) which constituted the highest increase among all nucleosides. Age influenced nucleosides/creatinine excretion rates for all nucleosides in the same direction which was likely caused by a decrease in creatinine clearance with age. There was a small evidence of sex-related differences for methylthioadenosine. The individual a posteriori prediction of patient classification as area under the ROC with 5th and 95th percentile was 0.57(0.5-0.67) with sensitivity and specificity of 0.59(0.42-0.76) and 0.57(0.45-0.7), respectively suggesting limited usefulness of 13 nucleosides/creatinine urine concentration measurements in predicting disease in this population. CONCLUSION: Bayesian multilevel pharmacokinetics-driven modeling in metabolomics may be useful in understanding the data and may constitute a new tool for searching towards potential candidates of disease indicators.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data.

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

The metabolic profiles of pterin compounds as potential biomarkers of bladder cancer-Integration of analytical-based approach with biostatistical methodology.

Cancer disease is the second leading cause of death across the world. The analysis of potential biomarkers of cancer can be useful in cancer screening or cancer diagnosis, and may provide valuable information on the disease risk and progression. Pterin compounds have been studied as candidates of potential biomarkers as their elevated levels have been reported in various cancer diseases. The objective of the study was to compare the profiles of six pterin compounds in urine of 35 healthy subjects and 46 patients diagnosed of bladder cancer with the use of HPLC coupled with fluorimetric detection. The results of the chromatographic analysis together with biostatistical-based approach showed, that the concentrations of pterin compounds in bladder cancer patients were higher as compared to healthy individuals, and statistically significant differences between patients and controls were reported for xanthopterin and isoxanthopterin. Moreover, gender-specific analysis revealed, that the concentrations of pterins in the group of women reached higher values in comparison to men. For metabolites juxtaposed in pairs, namely xanthopterin and isoxanthopterin as well as for neopterin and biopterin, we found significant positive correlations in the group of both, patients and healthy individuals. We therefore conclude, that chromatographic analysis with simultaneous extensive biostatistical-based interpretation of the metabolite profiles may provide deeper understanding of the relationships between pterin metabolites. The results do not prejudge the possibility of using pterin compounds in the diagnosis of bladder tumors. However the results may have an impact on the study of bladder cancer biomarkers.

Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer.

AIM: We aimed at evaluation the potential diagnostic role of urinary nucleosides in urogenital tract cancer. MATERIALS & METHODS: Concentrations of 12 nucleosides determined by LC-MS/MS were subjected to correlation, association and interaction analyses. RESULTS: We identified six pairs of nucleosides differently correlated in the group of patients and controls (p < 0.05). N-2-methylguanosine (odds ratio: 4.82; 95% CI: 1.78-12.93; p = 0.002) and N,N-dimethylguanosine (odds ratio: 5.45; 95% CI: 1.78-16.44; p = 0.003), were significantly associated with the disease risk (p-corrected = 0.004). Interaction between N-2-methylguanosine and adenosine (p-interaction = 0.019) suggested their multiplicative effect on the outcome. CONCLUSION: Urinary nucleosides, namely N,N-dimethylguanosine and N-2-methylguanosine may have the potential to serve as prognostic biomarkers. Gender-specific differences in urogenital tract cancer are likely to occur.

Least absolute shrinkage and selection operator and dimensionality reduction techniques in quantitative structure retention relationship modeling of retention in hydrophilic interaction liquid chromatography.

The objective of this study was to model the retention of nucleosides and pterins in hydrophilic interaction liquid chromatography (HILIC) via QSRR-based approach. Two home-made (Amino-P-C18, Amino-P-C10) and one commercial (IAM.PC.DD2) HILIC stationary phases were considered. Logarithm of retention factor at 5% of acetonitrile (logkACN) along with descriptors obtained for 16 nucleosides and 11 pterins were used to develop QSRR models. We used and compared the predictive performance of three regression techniques: partial least square (PLS), the least absolute shrinkage and selection operator (LASSO), and the LASSO followed by stepwise multiple linear regression. The highest predictive squared correlation coefficient (QLOOCV(2)) in PLS analysis was found for Amino-P-C10 (QLOOCV(2)=0.687) and IAM.PC.DD2 (QLOOCV(2)=0.506) and the lowest for IAM.PC.DD2 (QLOOCV(2)=-0.01). Much higher values were obtained for the LASSO model. The QLOOCV(2) equaled 0.9 for Amino-P-C10, 0.66 for IAM.PC.DD2 and 0.59 for Amino-P-C18. The combination of LASSO with stepwise regression provided models with comparable predictive performance as the LASSO, however with possibility of calculating the standard error of estimates. The use of LASSO itself and in combination with classical stepwise regression may offer greater stability of the developed models thanks to more smooth change of coefficients and reduced susceptibility towards chance correlation. Application of QSRR-based approach, along with the computational methods proposed in this work, may offer a useful approach in the modeling of retention of nucleoside and pterin compounds in HILIC.

The application of connected QSRR and QSAR strategies to predict the physicochemical interaction of acridinone derivatives with DNA.

Acridinone derivatives as imidazoacridinones and triazoloacridinones are the new potent antitumor agents characterized by different mechanisms of action related to their ability to interact with DNA. The analysis undertaken in this study involves searching of QSAR (Quantitative Structure-Activity Relationship) and QSRR (Quantitative Structure- Retention Relationship) models, which would allow to predict the biological activity of acridinones expressed as the ability to stabilize the secondary structure of DNA (ΔT), based on their structural parameters and chromatographic retention data. For this purpose, 20 acridinone derivatives were subjected to chromatographic analyses and molecular modeling, followed by statistical analyses using multiple linear regression method (MLR). As a novelty aspect, except for RP-HPLC approach, hydrophilic interaction chromatography (HILIC) columns were tested. As a result of performed analysis, appropriate QSAR and QSRR models were obtained, and each model was analyzed in terms of prediction of acridinones' ability to interact with DNA. Derived QSAR and QSRR models were characterized as one, with good prediction performance. Conclusively, the proposed connected QSAR and QSRR strategies allow to predict in silico the ability of acridinones to interact with DNA without the necessity of performing any biological experiments under in vitro and in vivo conditions.