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Effects of individual metabolic brain network changes co-affected by T2DM and aging on the probabilities of T2DM: protective and risk factors.
Li, Yu-Lin; Wu, Jia-Jia; Li, Wei-Kai; Gao, Xin; Wei, Dong; Xue, Xin; Hua, Xu-Yun; Zheng, Mou-Xiong; Xu, Jian-Guang.
Affiliation
  • Li YL; Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
  • Wu JJ; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
  • Li WK; Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
  • Gao X; School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China.
  • Wei D; Shanghai Universal Medical Imaging Diagnostic Center, Shanghai 200233, China.
  • Xue X; Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
  • Hua XY; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
  • Zheng MX; Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
  • Xu JG; School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Cereb Cortex ; 34(1)2024 01 14.
Article in En | MEDLINE | ID: mdl-37991271
ABSTRACT
Neuroimaging markers for risk and protective factors related to type 2 diabetes mellitus are critical for clinical prevention and intervention. In this work, the individual metabolic brain networks were constructed with Jensen-Shannon divergence for 4 groups (elderly type 2 diabetes mellitus and healthy controls, and middle-aged type 2 diabetes mellitus and healthy controls). Regional network properties were used to identify hub regions. Rich-club, feeder, and local connections were subsequently obtained, intergroup differences in connections and correlations between them and age (or fasting plasma glucose) were analyzed. Multinomial logistic regression was performed to explore effects of network changes on the probability of type 2 diabetes mellitus. The elderly had increased rich-club and feeder connections, and decreased local connection than the middle-aged among type 2 diabetes mellitus; type 2 diabetes mellitus had decreased rich-club and feeder connections than healthy controls. Protective factors including glucose metabolism in triangle part of inferior frontal gyrus, metabolic connectivity between triangle of the inferior frontal gyrus and anterior cingulate cortex, degree centrality of putamen, and risk factors including metabolic connectivities between triangle of the inferior frontal gyrus and Heschl's gyri were identified for the probability of type 2 diabetes mellitus. Metabolic interactions among critical brain regions increased in type 2 diabetes mellitus with aging. Individual metabolic network changes co-affected by type 2 diabetes mellitus and aging were identified as protective and risk factors for the likelihood of type 2 diabetes mellitus, providing guiding evidence for clinical interventions.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Diabetes Mellitus, Type 2 Limits: Aged / Humans / Middle aged Language: En Journal: Cereb Cortex Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Diabetes Mellitus, Type 2 Limits: Aged / Humans / Middle aged Language: En Journal: Cereb Cortex Year: 2024 Document type: Article