Plasma Metabolites–Based Prediction in Cardiac Surgery–Associated Acute Kidney Injury DOI Creative Commons
Hao Cui, Songren Shu, Yuan Li

et al.

Journal of the American Heart Association, Journal Year: 2021, Volume and Issue: 10(22)

Published: Oct. 30, 2021

Background Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA-AKI in hospitalized patients. This study developed and evaluated diagnostic use metabolomics-based biomarkers patients with CSA-AKI. Methods Results A total 214 individuals (122 [AKI], 92 without AKI as controls) were enrolled this study. Plasma samples analyzed by liquid chromatography tandem mass spectrometry using untargeted targeted metabolomic approaches. Time-dependent effects selected metabolites investigated an swine model. Multiple machine learning algorithms used to identify plasma positively associated Metabolomic analyses from taken within 24 hours surgery useful distinguishing controls AKI. Gluconic acid, fumaric pseudouridine significantly upregulated random forest model constructed clinical parameters exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879-0.998). In model, levels 3 discriminating increased time-dependent manner (R2, 0.480-0.945). Use predictive was then confirmed validation cohort 0.972; 0.947-0.996). The remained robust when tested subset early-stage 0.943; 0.883-1.000). Conclusions High-resolution metabolomics sufficiently powerful developing novel biomarkers. identification

Language: Английский

NMR: Unique Strengths That Enhance Modern Metabolomics Research DOI
Arthur S. Edison, Maxwell B. Colonna, Gonçalo J. Gouveia

et al.

Analytical Chemistry, Journal Year: 2020, Volume and Issue: 93(1), P. 478 - 499

Published: Nov. 12, 2020

ADVERTISEMENT RETURN TO ISSUEPREVReviewNEXTNMR: Unique Strengths That Enhance Modern Metabolomics ResearchArthur S. Edison*Arthur EdisonDepartments of Biochemistry & Molecular Biology, Genetics and Institute Bioinformatics, the , Complex Carbohydrate Research Center, University Georgia, 315 Riverbend Road, Athens, Georgia 30605, USA*Email: [email protected]More by Arthur EdisonView Biographyhttp://orcid.org/0000-0002-5686-2350, Maxwell ColonnaMaxwell ColonnaDepartments USAMore ColonnaView Biography, Goncalo J. GouveiaGoncalo GouveiaDepartments GouveiaView Nicole R. HoldermanNicole HoldermanDepartments HoldermanView Michael T. JudgeMichael JudgeGenetics, JudgeView Xunan ShenXunan ShenInstitute ShenView Sicong ZhangSicong ZhangDepartments ZhangView BiographyCite this: Anal. Chem. 2021, 93, 1, 478–499Publication Date (Web):November 12, 2020Publication History Published online12 November 2020Published inissue 12 January 2021https://doi.org/10.1021/acs.analchem.0c04414Copyright © 2020 American Chemical SocietyRIGHTS PERMISSIONSArticle Views2259Altmetric-Citations37LEARN ABOUT THESE METRICSArticle Views are COUNTER-compliant sum full text article downloads since 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated to reflect usage leading up last few days.Citations number other articles citing this article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score is a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence for given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InReddit Read OnlinePDF (4 MB) Get e-AlertsSUBJECTS:Cancer,Cells,Metabolism,Metabolomics,Nuclear magnetic resonance spectroscopy e-Alerts

Language: Английский

Citations

93
Insights into predicting diabetic nephropathy using urinary biomarkers DOI
Naseer Muhammad Khan, Jing Lin, Xukun Liu

et al.

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, Journal Year: 2020, Volume and Issue: 1868(10), P. 140475 - 140475

Published: June 20, 2020

Language: Английский

Citations

73
Prediction and collection of protein–metabolite interactions DOI
Tianyi Zhao, Jinxin Liu, Xi Zeng

et al.

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 22(5)

Published: Jan. 12, 2021

Abstract Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters membrane receptors can all be mediated through protein–metabolite interactions (PMIs). Compared with the rich knowledge protein–protein interactions, little is known about PMIs. To best our knowledge, no existing database has been developed for collecting recent rapid development large-scale mass spectrometry analysis biomolecules led to discovery large amounts Therefore, we PMI-DB provide a comprehensive accurate resource A total 49 785 entries were manually collected PMI-DB, corresponding 23 metabolites, 9631 4 species. Unlike other databases that only positive samples, provides non-interaction which not reduces experimental cost biological experimenters but also facilitates construction more algorithms researchers using machine learning. show convenience deep learning-based method predict PMIs compared it several methods. results area under curve precision-recall are 0.88 0.95, respectively. Overall, user-friendly interface browsing metabolites/proteins interest, techniques identifying different species, important support furthering understanding freely accessible at http://easybioai.com/PMIDB.

Language: Английский

Citations

65
Metabolomics meets systems immunology DOI Creative Commons
Jianbo Fu, Feng Zhu, Cheng‐Jian Xu

et al.

EMBO Reports, Journal Year: 2023, Volume and Issue: 24(4)

Published: March 14, 2023

Abstract Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity physiology disease. Integrating metabolomics into systems immunology allows exploration multilayered features system molecular regulatory mechanism these features. Here, we provide overview on recent technological developments metabolomic applications immunological research. To begin, two widely used approaches are compared: targeted untargeted metabolomics. Then, comprehensive workflow computational tools available, including sample preparation, raw spectra data preprocessing, processing, statistical analysis, interpretation. Third, describe how integrate with other omics studies using available tools. Finally, discuss new its prospects for This review provides guidance researchers multiomics research, thus facilitating application disease

Language: Английский

Citations

27
Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study DOI Creative Commons
Brian Kwan, Tobias Fuhrer, Jing Zhang

et al.

American Journal of Kidney Diseases, Journal Year: 2020, Volume and Issue: 76(4), P. 511 - 520

Published: May 5, 2020

Language: Английский

Citations

68
Renal Benefits of SGLT 2 Inhibitors and GLP-1 Receptor Agonists: Evidence Supporting a Paradigm Shift in the Medical Management of Type 2 Diabetes DOI Open Access

Vjera Ninčević,

Tea Omanović Kolarić, Hrvoje Roguljić

et al.

International Journal of Molecular Sciences, Journal Year: 2019, Volume and Issue: 20(23), P. 5831 - 5831

Published: Nov. 20, 2019

Diabetic nephropathy (DN) is one of the most perilous side effects diabetes mellitus type 1 and 2 (T1DM T2DM).). It known that sodium/glucose cotransporter inhibitors (SGLT 2i) glucagone like peptide-1 receptor agonists (GLP-1 RAs) have renoprotective effects, but molecular mechanisms are still unknown. In clinical trials GLP-1 analogs exerted important impact on renal composite outcomes, primarily macroalbuminuria, possibly through suppression inflammation-related pathways, however enhancement natriuresis diuresis also possible nephroprotection. Dapagliflozin, canagliflozin, empagliflozin SGLT2i drugs, useful in reducing hyperglycemia their potential mechanisms, which include blood pressure control, body weight loss, intraglomerular reduction, a decrease urinary proximal tubular injury biomarkers. this review we discussed synergistic and/or additive GLP RA SGLT2 primary onset progression kidney disease, implications current guidelines management.

Language: Английский

Citations

60
Amino Acid Metabolites Associated with Chronic Kidney Disease: An Eight-Year Follow-Up Korean Epidemiology Study DOI Creative Commons

Hansongyi Lee,

Han Byul Jang, Min‐Gyu Yoo

et al.

Biomedicines, Journal Year: 2020, Volume and Issue: 8(7), P. 222 - 222

Published: July 17, 2020

The discovery of metabolomics-based biomarkers has been a focus recent kidney dysfunction research. In the present study, we aimed to identify metabolites associated with chronic disease (CKD) in general population using cross-sectional study design. At baseline, 6.5% subjects had CKD. Pearson correlation analysis showed that 28 were significantly estimated glomerular filtration rate (eGFR) after Bonferroni correction. Among these metabolites, 4 acylcarnitines, 12 amino acids, biogenic amines, 1 phosphatidylcholine, and sphingolipid CKD (

Language: Английский

Citations

58
Application of metabolomics in urolithiasis: the discovery and usage of succinate DOI Creative Commons

Xiuzhen Zhang,

Xiongxin Lei,

Yanlin Jiang

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2023, Volume and Issue: 8(1)

Published: Jan. 21, 2023

Abstract Urinary stone is conceptualized as a chronic metabolic disorder punctuated by symptomatic events. It has been shown that the occurrence of calcium oxalate monohydrate (COM) during formation regulated crystal growth modifiers. Although crystallization inhibitors have recognized therapeutic modality for decades, limited progress made in discovery effective modifiers to intervene with disease. In this study, we used metabolomics technologies, powerful approach identify biomarkers screening urine components dynamic progression bladder model. By in-depth mining and analysis data, screened five differential metabolites. Through density functional theory studies bulk crystallization, found three them (salicyluric, gentisic acid succinate) could effectively inhibit nucleation vitro. We thereby assessed impact an EG-induced rat model kidney stones. Notably, succinate, key player tricarboxylic cycle, decrease deposition injury Transcriptomic further showed protective effect succinate was mainly through anti-inflammation, inhibition cell adhesion osteogenic differentiation. These findings indicated may provide new option urinary

Language: Английский

Citations

22
Classification of Chronic Kidney Disease in Sonography Using the GLCM and Artificial Neural Network DOI Creative Commons
Dong Hyun Kim,

Soo-Young Ye

Diagnostics, Journal Year: 2021, Volume and Issue: 11(5), P. 864 - 864

Published: May 11, 2021

Chronic kidney disease (CKD) can be treated if it is detected early, but as the progresses, recovery becomes impossible. Eventually, renal replacement therapy such transplantation or dialysis necessary. Ultrasound a test method with which to diagnose cancer, inflammatory disease, nodular chronic etc. It used determine degree of inflammation using information size and internal echo characteristics. The progression in current clinical trial based on value glomerular filtration rate. However, changes even observed ultrasound. In this study, from total 741 images, 251 normal 328 mild moderate CKD 162 severe images were tested. order practice, three ROIs set: cortex kidney, boundary between medulla, are areas examined obtain ultrasound images. Parameters extracted each ROI GLCM algorithm, widely image analysis. When parameter was areas, 57 parameters extracted. Finally, 58 by adding important for diagnosis disease. artificial neural network (ANN) composed input parameters, 10 hidden layers, 3 output layers (normal, CKD, CKD). Using ANN model, final classification rate 95.4%, epoch needed training 38 times, misclassification 4.6%.

Language: Английский

Citations

38
Cinnamaldehyde Improves Metabolic Functions in Streptozotocin-Induced Diabetic Mice by Regulating Gut Microbiota DOI Creative Commons
Hong-Lei Zhao, Hongyan Wu,

Meitao Duan

et al.

Drug Design Development and Therapy, Journal Year: 2021, Volume and Issue: Volume 15, P. 2339 - 2355

Published: May 31, 2021

The aim of the present study was to examine protective effects cinnamaldehyde (CA) on type 1 diabetes mellitus (T1DM) and explore underlying molecular mechanisms by using multiple omics technology.T1DM induced streptozotocin in mice. Immunostaining performed evaluate glycogen synthesis liver morphological changes heart. Gut microbiota analyzed 16S rRNA gene amplification sequencing. serum metabolomics were determined liquid chromatography-mass spectrometry. relevant expression levels quantitative real-time PCR.CA treatment significantly improved glucose metabolism insulin sensitivity T1DM CA increased protected myocardial injury affected gut particularly increasing relative abundance Lactobacillus johnsonii decreasing murinus level positively correlated with 88 functional pathways negatively 2 microbiota. Insulin resistance 11 pathways. analysis showed that taurochenodeoxycholic acid, tauroursodeoxycholic tauro-α-muricholic acid tauro-β-muricholic taurodeoxycholic taurocholic taurohyodeoxycholic Taurohyodeoxycholic highly blood levels. Furthermore, Faecalibacterium prausnitzii AKT2, like growth factor receptor, E2F1 receptor substrate mRNA levels, while IRS1 level.Our results indicated may interfere affect host metabolomics, especially bile acids, so as directly or indirectly modulate metabolism-related genes, thus subsequently reducing

Language: Английский

Citations

37