Sun Lubing, Wu Yaping, Yang Junpeng, Liang Junting, Li Panlong, Yu Xuan, Meng Nan, Sun Tao, Wang Meiyun, Chen Chuanliang
Department of Medical Imaging, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Clinical Bioinformatics Experimental Center, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, Zhengzhou, China.
Eur J Nucl Med Mol Imaging. 2025 May 20. doi: 10.1007/s00259-025-07337-5.
This study aims to construct an individualized glucose metabolism network using total-body F-FDG PET imaging, which provides a comprehensive view of glucose metabolism across various organs, to explore the role of inter-organ interactions in Diabetes Mellitus (DM).
In 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.
We 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.
This 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.
本研究旨在利用全身F-FDG PET成像构建个体化的葡萄糖代谢网络,该成像可全面呈现各器官的葡萄糖代谢情况,以探讨器官间相互作用在糖尿病(DM)中的作用。
在本研究中,我们使用经瘦体重标准化的静态全身PET图像,为36例糖尿病患者(DM组)和36例年龄及性别匹配的健康对照者(HC组)构建协方差代谢网络。在组水平和个体水平评估DM组和HC组之间网络属性的差异。此外,进行相关性分析以探讨DM受试者网络属性与基线临床数据之间的关系。
我们观察到,在组水平和个体水平上,前三个值最大的边中相同的边是脑-皮下脂肪组织(SAT)和脑-内脏脂肪组织(VAT)。脑-VAT与BMI呈正相关,脑-SAT与年龄呈负相关。在组水平和个体水平上,受影响最大的器官都是脑,且脑异常强度与年龄呈正相关。
本研究成功利用静态全身PET成像为DM患者构建了个体化的葡萄糖代谢网络,确定脑-VAT和脑-SAT为变化最显著的边,脑为受影响最大的器官。这些发现为脑-白色脂肪组织轴在DM葡萄糖代谢中的作用提供了新的见解。