Evidence map›Paper›PMID 38339955›Full record

ArticleHuman brain mapping2024

Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study.

Yijun Zhou, Jing Jing, Zhe Zhang, Yuesong Pan, Xueli Cai, Wanlin Zhu, Zixiao Li, Chang Liu, Hao Liu, Xia Meng and 10 more

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 5 institutions in 2 countries.

Yijun ZhouBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Jing JingDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Zhe ZhangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yuesong PanDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Xueli CaiDepartment of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China.
Wanlin ZhuDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Zixiao LiDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Chang LiuBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Hao LiuBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Xia MengDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jian ChengSchool of Computer Science and Engineering, Beihang University, Beijing, China.
Yilong WangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Hao LiChina National Clinical Research Center for Neurological Diseases, Beijing, China.
Suying WangCerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China.
Haijun NiuBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Wei WenDivision of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA), UNSW, Sydney, New South Wales, Australia.
Perminder S SachdevDivision of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA), UNSW, Sydney, New South Wales, Australia.ORCID 0000-0002-9595-3220
Tiemin WeiDepartment of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Zhejiang, China.
Tao LiuBeijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.ORCID 0000-0002-7783-3073
Yongjun WangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Capital Medical University · CNLishui University · CNBeihang University · CNDepartment of Health and Aged Care · AUUNSW Sydney · AU

Funding

Beijing Natural Science Foundation Z200016Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences 2019-I2M-5-029Key Science & Technologies R&D Program of Lishui City 2019ZDYF18National Key R&D Program of China 2016YFC0901002National Natural Science Foundation of China 82271329Zhejiang provincial program for the Cultivation of High-level Innovative Health talents
6 · The paper itself

Abstract

The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.

Indexed as

ConnectomeHyperglycemiaPrediabetic StateAgedBrainCross-Sectional StudiesDiffusion Tensor ImagingFemaleGlucoseHumansMaleMiddle AgedNeural PathwaysGlucosediabetesdiffusion tensor imagingprediabetesrich-club organizationstructural network

Identifiers

PMID38339955
PMCPMC10839741
OpenAlexW4391613295

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.