Urine Metabonomics Reveals Early Biomarkers in Diabetic Cognitive Dysfunction

Abstract

Recently, increasing attention has been paid to diabetic encephalopathy, which is a frequent diabetic complication and affects nearly 30% of diabetics. Because cognitive dysfunction from diabetic encephalopathy might develop into irreversible dementia, early diagnosis and detection of this disease is of great significance for its prevention and treatment. This study is to investigate the early specific metabolites biomarkers in urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. An ultra-high performance liquid-chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from diabetic mice that were associated with mild cognitive impairment (MCI) and nonassociated with MCI in the stage of diabetes (prior to the onset of DCD). We then screened and validated the early biomarkers using OPLS-DA model and support vector machine (SVM) method. Following multivariate statistical and integration analysis, we found that seven metabolites could be accepted as early biomarkers of DCD, and the SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism, and sphingolipid metabolism. The present study first revealed reliable biomarkers for early diagnosis of DCD. It provides new insight and strategy for the early diagnosis and treatment of DCD.

Assessment of human plasma and urine sample preparation for reproducible and high-throughput UHPLC-MS clinical metabolic phenotyping

“…36 Deconvolution was performed by XCMS software 37 (version 1.46.0 running in the Galaxy environment 38 ) applying min peak width (urine reversed phase = 3; HILIC = 4; lipids = 6); max. peak width (30); ppm (urine reversed phase = 11; HILIC = 12; lipids = 14); mzdiff (0.001); bw (urine reversed phase = 0.5; HILIC and lipids = 0.25); mzwid (0.01); minfrac (0.2). A data matrix of peak areas for metabolite features (m/z-retention time pairs) vs. samples were constructed.…”

“…27 Preparation protocols include: (i) urine dilution with water and centrifugation prior to UHPLC-MS analysis 28 (using sodium azide 28 or the sample filtering 29 to prevent microorganism growth); and (ii) the addition of organic solvents including methanol or acetonitrile. 30,31 Organic solvent use is beneficial for clinical metabolic phenotyping as it rapidly eliminates microorganisms and removes any residual protein from samples, which is important if patients have renal damage induced proteinuriaa side effect of some diseases (e.g. multiple myeloma 32 ) and clinical treatments (e.g.…”

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

“…Classification models are frequently constructed in current transcriptomics studies for predicting samples of various disease status [65,66] or assessing the reliability of identified gene markers [67,68]. The capacity of the constructed classification model was evaluated by various metrics including accuracy ( ACC ), sensitivity ( SEN ), specificity ( SPE ), Matthews correlation coefficient ( MCC ), receiver operating characteristics ( ROC ), and area under ROC curve ( AUC value ) [69,70,71].…”

A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy

“…Classifiers are frequently constructed for predicting samples of various statuses [23,24] to assess the reliability of identified genes [25,26]. Here, support vector machine (SVM) classifiers based on four kernel functions (linear, sigmoid, polynomial, radial basis) with gene expression values as classification features was constructed to distinguish ICM and normal samples.…”