Evidence map›Paper›PMID 40392299›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

The brain-white adipose tissue axis may play a crucial role in diabetes mellitus: a metabolic network analysis using total-body PET/CT imaging.

Lubing Sun, Yaping Wu, Junpeng Yang, Junting Liang, Panlong Li, Xuan Yu, Nan Meng, Tao Sun, Meiyun Wang, Chuanliang Chen

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Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

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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

10 authors.

Lubing SunDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Yaping WuDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Junpeng YangDepartment of Endocrinology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Junting LiangClinical Bioinformatics Experimental Center, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Panlong LiDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Xuan YuDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Nan MengDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Tao SunPaul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. tao.sun@siat.ac.cn.
Meiyun WangDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China. mywang@ha.edu.cn.
Chuanliang ChenDepartment of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China. Chencl260@zzu.edu.cn.ORCID 0009-0005-1639-9225

Funding

he National Key Research and Development Program of China 2022YFC2406906National Natural Science Foundation of China 82371934
6 · The paper itself

Abstract

purposeThis study aims to construct an individualized glucose metabolism network using total-body

methodsIn this study, we constructed covariance metabolic networks using static total-body PET images, normalized by lean body mass, from 36 patients with DM (DM group) and 36 age- and sex-matched healthy controls (HC group). Differences in network properties between the DM and HC groups were evaluated at both group and individual levels. In addition, correlation analysis was performed to explore the relationship between network properties and baseline clinical data in the DM subjects.

resultsWe observed that the same edges in the first three edges with the largest values were brain-subcutaneous adipose tissue (SAT) and brain-visceral adipose tissue (VAT) at both group and individual levels. There was a positive correlation between the brain-VAT and BMI and there was a negative correlation between the brain-SAT and age. The most perturbed organ was the brain at both group and individual levels, and there was a positive correlation between the strength of abnormality of brain and age.

conclusionThis study successfully used static total-body PET imaging to construct individualized glucose metabolism networks for patients with DM, identifying the brain-VAT and brain-SAT as the most significantly altered edge and the brain as the most affected organ. These findings provide novel insights into the role of the brain-white adipose tissue axis in glucose metabolism in DM.

Indexed as

Adipose Tissue, WhiteBrainDiabetes MellitusMetabolic Networks and PathwaysPositron Emission Tomography Computed TomographyWhole Body ImagingAdultAgedCase-Control StudiesFemaleFluorodeoxyglucose F18GlucoseHumansMaleMiddle AgedFluorodeoxyglucose F18GlucoseDiabetes mellitusMetabolic networkNetwork analysisTotal-body PET

Identifiers

PMID40392299

What OpenQuestion holds

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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.