Abstract

Diabetes related cognitive dysfunction (DACD), one of the chronic complications of diabetes, seriously affect the quality of life in patients and increase family burden. Although the initial stage of DACD can lead to metabolic alterations or potential pathological changes, DACD is difficult to diagnose accurately. Moreover, the details of the molecular mechanism of DACD remain somewhat elusive. To understand the pathophysiological changes that underpin the development and progression of DACD, we carried out a global analysis of metabolic alterations in response to DACD. The metabolic alterations associated with DACD were first investigated in humans, using plasma metabonomics based on high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. The related pathway of each metabolite of interest was searched in database online. The network diagrams were established KEGGSOAP software package. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic accuracy of metabolites. This is the first report of reliable biomarkers of DACD, which were identified using an integrated strategy. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of DACD. The disorders of sphingolipids metabolism, bile acids metabolism, and uric acid metabolism pathway were found in T2DM and DACD. On the other hand, differentially expressed plasma metabolites offer unique metabolic signatures for T2DM and DACD patients. These are potential biomarkers for disease monitoring and personalized medication complementary to the existing clinical modalities.

Highlights

  • Type 2 diabetes (T2DM) is a chronic metabolic disorder characterized by hyperglycaemia resulting from insulin resistance and insufficiency [1]

  • The % RSD of these 97 metabolites from plasma quality control (QC) samples varied from 2.4 to 19.3% with a median of 7.2%, which indicated the robustness of our metabolic profiling platform, and this robustness could be suggested by the principal component analysis (PCA) scores plot comprising T2DM, diabetes— related cognitive dysfunction (DACD), healthy control, and the QCs as well

  • DACD is one of chronic complications in diabetic patients, which was diagnosed by using TCD, rheoencephalogram, brain CT scan, nuclear magnetic resonance spectrum, MoCA and MMSE for cognitive dysfunction, and clinical manifestation and serum indexes for diabetes mellitus

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Summary

Introduction

Type 2 diabetes (T2DM) is a chronic metabolic disorder characterized by hyperglycaemia resulting from insulin resistance and insufficiency [1]. Metabolomics, the global assessment of endogenous small molecule metabolites within a biological system [6], provides a powerful platform for identifying biomarkers and understanding biochemical pathways to improve diagnosis, prognosis, and treatment of disease [7, 8]. It has been successfully utilized in diabetes for metabolomic profiling using either human or animal model of diabetes mellitus (DM) biofluids (obese Zucker rat, db/db mouse, ddY-H mouse and streptozotocin (STZ) rat) [9,10,11,12,13,14,15]. Data from metabolomic analyses of DM indicate that alterations in sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2DM [16,17,18]

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