Urine metabonomics reveals early biomarkers in diabetic cognitive dysfunction

Lili Song, Pengwei Zhuang, Mengya Lin, Mingqin Kang, Hongyue Liu, Yuping Zhang, Zhen Yang, Yunlong Chen, Yanjun Zhang

Index: 10.1021/acs.jproteome.7b00168

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Abstract

Recently, increasing attention has been paid to diabetic encephalopathy which is one of frequent diabetic complications and affects nearly 30% diabetics. Since cognitive dysfunction from diabetic encephalopathy might develop 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 the urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. Ultra high performance liquid-flight time-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from the diabetic mice which were associated with mild cognitive impairment (MCI) and non-associated with MCI at the stage of diabetes (prior to the onset of DCD), and 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 7 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 firstly revealed reliable biomarkers for early diagnosis of DCD. It would provide a new insight and strategy for the early diagnosis and treatment of DCD.