Altered individual-based morphological brain network in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our st...

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Main Authors: Wang Yan, Ge Limin, Sun Zhizhong, Cao Zidong, Qiu Shijun
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Brain Research Bulletin
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Online Access:http://www.sciencedirect.com/science/article/pii/S0361923025000401
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author Wang Yan
Ge Limin
Sun Zhizhong
Cao Zidong
Qiu Shijun
author_facet Wang Yan
Ge Limin
Sun Zhizhong
Cao Zidong
Qiu Shijun
author_sort Wang Yan
collection DOAJ
description Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our study, we enrolled 123 patients with T2DM and 91 healthy controls (HC). We constructed the networks using symmetric Kullback–Leibler (KL) divergence-based similarity (KLS) and calculated various global and nodal metrics based on graph theory to describe the topological properties of the networks. Firstly, T2DM exhibited increased nodal degree in the left para-hippocampus, left amygdala, left precuneus, bilateral putamen, and right inferior temporal gyrus, and the concentrations of glycosylated hemoglobin (HbA1c) were positively correlated with the nodal degree of the left precuneus. Secondly, we identified hypo-connected and hyper-connected subnetworks, primarily involved with reward circuits and attention network, respectively. Lastly, altered morphological connectivity (MC) was linked to cognitive performance, and the aforementioned subnetworks may serve as predictors of cognitive performance. In conclusion, this study provided neuroimaging evidence for understanding cognitive changes by analyzing the properties and connections of individual-based morphological brain networks (MBNs) in T2DM patients.
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spelling doaj-art-e4cd7662960a4731bfd3361cd94aac6a2025-02-06T05:11:01ZengElsevierBrain Research Bulletin1873-27472025-03-01222111228Altered individual-based morphological brain network in type 2 diabetes mellitusWang Yan0Ge Limin1Sun Zhizhong2Cao Zidong3Qiu Shijun4Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou, China; Corresponding author at: Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our study, we enrolled 123 patients with T2DM and 91 healthy controls (HC). We constructed the networks using symmetric Kullback–Leibler (KL) divergence-based similarity (KLS) and calculated various global and nodal metrics based on graph theory to describe the topological properties of the networks. Firstly, T2DM exhibited increased nodal degree in the left para-hippocampus, left amygdala, left precuneus, bilateral putamen, and right inferior temporal gyrus, and the concentrations of glycosylated hemoglobin (HbA1c) were positively correlated with the nodal degree of the left precuneus. Secondly, we identified hypo-connected and hyper-connected subnetworks, primarily involved with reward circuits and attention network, respectively. Lastly, altered morphological connectivity (MC) was linked to cognitive performance, and the aforementioned subnetworks may serve as predictors of cognitive performance. In conclusion, this study provided neuroimaging evidence for understanding cognitive changes by analyzing the properties and connections of individual-based morphological brain networks (MBNs) in T2DM patients.http://www.sciencedirect.com/science/article/pii/S0361923025000401Type 2 diabetes mellitusIndividual-based morphological brain networkGrapy theoryCognitive impairmentMorphological connectivity
spellingShingle Wang Yan
Ge Limin
Sun Zhizhong
Cao Zidong
Qiu Shijun
Altered individual-based morphological brain network in type 2 diabetes mellitus
Brain Research Bulletin
Type 2 diabetes mellitus
Individual-based morphological brain network
Grapy theory
Cognitive impairment
Morphological connectivity
title Altered individual-based morphological brain network in type 2 diabetes mellitus
title_full Altered individual-based morphological brain network in type 2 diabetes mellitus
title_fullStr Altered individual-based morphological brain network in type 2 diabetes mellitus
title_full_unstemmed Altered individual-based morphological brain network in type 2 diabetes mellitus
title_short Altered individual-based morphological brain network in type 2 diabetes mellitus
title_sort altered individual based morphological brain network in type 2 diabetes mellitus
topic Type 2 diabetes mellitus
Individual-based morphological brain network
Grapy theory
Cognitive impairment
Morphological connectivity
url http://www.sciencedirect.com/science/article/pii/S0361923025000401
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AT gelimin alteredindividualbasedmorphologicalbrainnetworkintype2diabetesmellitus
AT sunzhizhong alteredindividualbasedmorphologicalbrainnetworkintype2diabetesmellitus
AT caozidong alteredindividualbasedmorphologicalbrainnetworkintype2diabetesmellitus
AT qiushijun alteredindividualbasedmorphologicalbrainnetworkintype2diabetesmellitus