Metabolomics in Nephrotoxicity

Zhao, Ying-Yong; Lint, Rui-Chao

doi:10.1016/b978-0-12-800141-7.00003-6

Cited by 84 publications

(49 citation statements)

References 91 publications

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“…Systems biology allows on-time analysis of regulatory and biologic networks in cell metabolism [21][22][23]. Comprehensive characterization of renal diseases could provide important and integrative information to better characterize molecular relationships underlying this pathophysiology in order to develop more reliable and specific markers for diagnosis, prognosis, prevention, and therapeutic response [2,24].…”

Section: Kidney Diseasementioning

confidence: 99%

Lipidomics

Zhao

Vaziri

Lin

2015

Advances in Clinical Chemistry

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Section: Kidney Diseasementioning

confidence: 99%

Lipidomics

Zhao

Vaziri

Lin

2015

Advances in Clinical Chemistry

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“…Due to the complexity and various factorial interactions of diseases, the situation seems difficult in the field of clinical chemistry for which multiplexed targeted approaches provide the clinician with information on a limited number of metabolites by using MS/MS analysis performed on triple quadruple MS. Furthermore, recent improvements in UPLC-QTOF/MS/MS E have improved the efficacy of global approaches by facilitating the identification of metabolites of interest thanks to high-resolution and accurate mass measurements, which Q-TOF/MS/MS E can simultaneously acquire MS and MS/MS (MS E ) data of all the metabolites through alternating between high and low collision energies during a single chromatographic run [112][113][114][115][116][117][118][119][120].…”

Section: Concluding Remarks and Perspectivesmentioning

confidence: 99%

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Zhao

Cheng

Vaziri

et al. 2014

Clinical Biochemistry

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120

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a b s t r a c t a r t i c l e i n f oObjectives: Metabonomics is a powerful and promising analytic tool that allows assessment of global lowmolecular-weight metabolites in biological systems. It has a great potential for identifying useful biomarkers for early diagnosis, prognosis and assessment of therapeutic interventions in clinical practice. The aim of this review is to provide a brief summary of the recent advances in UPLC-based metabonomic approach for biomarker discovery in a variety of diseases, and to discuss their significance in clinical chemistry.Design and methods: All the available information on UPLC-based metabonomic applications for discovering biomarkers of diseases were collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Springer, Google Scholar, etc.).Results: Metabonomics has been used in clinical chemistry to identify and evaluate potential biomarkers and therapeutic targets in various diseases affecting the liver (hepatocarcinoma and liver cirrhosis), lung (lung cancer and pneumonia), gastrointestinal tract (colorectal cancer) and urogenital tract (prostate cancer, ovarian cancer and chronic kidney disease), as well as metabolic diseases (diabetes) and neuropsychiatric disorders (Alzheimer's disease and schizophrenia), etc.Conclusions: The information provided highlights the potential value of determination of endogenous lowmolecular-weight metabolites and the advantages and potential drawbacks of the application of UPLC-based metabonomics in clinical setting.

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“…“Metabolomics is a global biochemical approach for biomarker discovery and provides insight into metabolic profiling across a wide range of biochemical pathways in response to a perturbation (disease, drugs, and toxins)” [2]. As, the endogenous metabolites are the final product of interactions between gene expression, protein expression, and the cellular environment; it provides information that is both complementary to these other molecular domains.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, GC-MS or LC-MS have become more widely used due to its greater sensitivity based on chromatographic separation but failed to identify some metabolites due to uncertainty in the estimation of metabolites’ mass-to-charge ratios. “Continued advances in NMR spectroscopic hardware and increased parallel application of analytical platforms such as Ultra-performance liquid chromatography (UPLC) [4, 5] coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) will enable even wider coverage of the metabolome” [2]. …”

Section: Introductionmentioning

confidence: 99%

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Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

Luck

Bertho

Bateson³

et al. 2016

PLoS ONE

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1H Nuclear Magnetic Resonance (NMR)-based metabolic profiling is very promising for the diagnostic of the stages of chronic kidney disease (CKD). Because of the high dimension of NMR spectra datasets and the complex mixture of metabolites in biological samples, the identification of discriminant biomarkers of a disease is challenging. None of the widely used chemometric methods in NMR metabolomics performs a local exhaustive exploration of the data. We developed a descriptive and easily understandable approach that searches for discriminant local phenomena using an original exhaustive rule-mining algorithm in order to predict two groups of patients: 1) patients having low to mild CKD stages with no renal failure and 2) patients having moderate to established CKD stages with renal failure. Our predictive algorithm explores the m-dimensional variable space to capture the local overdensities of the two groups of patients under the form of easily interpretable rules. Afterwards, a L2-penalized logistic regression on the discriminant rules was used to build predictive models of the CKD stages. We explored a complex multi-source dataset that included the clinical, demographic, clinical chemistry, renal pathology and urine metabolomic data of a cohort of 110 patients. Given this multi-source dataset and the complex nature of metabolomic data, we analyzed 1- and 2-dimensional rules in order to integrate the information carried by the interactions between the variables. The results indicated that our local algorithm is a valuable analytical method for the precise characterization of multivariate CKD stage profiles and as efficient as the classical global model using chi2 variable section with an approximately 70% of good classification level. The resulting predictive models predominantly identify urinary metabolites (such as 3-hydroxyisovalerate, carnitine, citrate, dimethylsulfone, creatinine and N-methylnicotinamide) as relevant variables indicating that CKD significantly affects the urinary metabolome. In addition, the simple knowledge of the concentration of urinary metabolites classifies the CKD stage of the patients correctly.

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Metabolomics in Nephrotoxicity

Cited by 84 publications

References 91 publications

Lipidomics

Lipidomics

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Rule-Mining for the Early Prediction of Chronic Kidney Disease Based on Metabolomics and Multi-Source Data

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