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Alteration of Individual Metabolic Network of Brain Based on Jensen-Shannon Divergence Similarity Estimation in the Elderly with Type 2 Diabetes Mellitus

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posted on 28.02.2022, 22:27 by Yu-Lin Li, Jia-Jia Wu, Jie Ma, Si-Si Li, Xin Xue, Dong Wei, Chun-Lei Shan, Xu-Yun Hua, Mou-Xiong Zheng, Jian-Guang Xu
The present study aimed to investigate the interactive effect between aging and type 2 diabetes mellitus (T2DM) on brain glucose metabolism, individual metabolic connectivity and network properties. A 2 × 2 factorial-designed study was conducted, 83 T2DM patients (40 elderly and 43 middle-aged) and 69 gender-matched HCs (34 elderly and 35 middle-aged) underwent an 18F fluorodeoxyglucose-positron emission tomography/magnetic resonance (18F-FDG PET/MR) scanning. The Jensen-Shannon Divergence was applied to construct individual metabolic connectivity and network. The topological properties of network were quantified with graph theoretical analysis. The general linear model (GLM) was used to mainly estimate the interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity and network. There was an interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity and regional metabolic network properties (all P < 0.05). The post-hoc analyses showed that compared with the elderly HCs and middle-aged with T2DM groups, elderly T2DM group had decreased glucose metabolism, increased metabolic connectivity and regional metabolic network properties in cognition-related brain regions (all P < 0.05). The age and fasting plasma glucose had negative correlations with glucose metabolism, and positive correlations with metabolic connectivity. The elderly with T2DM had glucose hypometabolism, strengthened functional integration and increased efficiency of information communication mainly located in cognition-related brain regions, above metabolic connectivity patterns changes might be compensatory changes for glucose hypometabolism.

Funding

This work was supported by the National Key R&D Program of China [Grant Nos.: 2018YFC2001600, and 2018YFC2001604]; National Natural Science Foundation of China (Grant Nos.: 81802249, 81871836, 81874035, and 81902301); Shanghai Science and Technology Committee (Grant Nos.: 18511108300, 18441903900, and 18441903800); Shanghai Rising-Star Program (Grant No.: 19QA1409000); Shanghai Municipal Commission of Health and Family Planning (Grant No.: 2018YQ02, and 201840224); Shanghai Youth Top Talent Development Plan and Shanghai "Rising Stars of Medical Talent" Youth Development Program (Grant No.: RY411.19.01.10); Program of Shanghai Academic Research Leader [Grant No.: 19XD1403600].

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