Urine Metabonomics Reveals Early Biomarkers in Diabetic Cognitive Dysfunction
journal contributionposted on 19.07.2017, 00:00 by Lili Song, Pengwei Zhuang, Mengya Lin, Mingqin Kang, Hongyue Liu, Yuping Zhang, Zhen Yang, Yunlong Chen, Yanjun Zhang
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.
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Diabetic Cognitive Dysfunctionmass spectrometryOPLS-DA modelglutathione metabolismDCDUrine Metabonomicsnicotinamide metabolismmetabolomics technologySVM resultsprediction accuracyUPLC-Qurine samplestryptophan metabolismsphingolipid metabolismsupport vector machineMCIintegration analysisdiagnosismetabolites biomarkers