Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA.
Alzheimers Dement. 2024 Mar;20(3):1538-1549. doi: 10.1002/alz.13538. Epub 2023 Nov 30.
Alzheimer's disease (AD), the leading cause of dementia worldwide, represents a human and financial impact for which few effective drugs exist to treat the disease. Advances in molecular imaging have enabled assessment of cerebral glycolytic metabolism, and network modeling of brain region have linked to alterations in metabolic activity to AD stage.
We performed F-FDG positron emission tomography (PET) imaging in 4-, 6-, and 12-month-old 5XFAD and littermate controls (WT) of both sexes and analyzed region data via brain metabolic covariance analysis.
The 5XFAD model mice showed age-related changes in glucose uptake relative to WT mice. Analysis of community structure of covariance networks was different across age and sex, with a disruption of metabolic coupling in the 5XFAD model.
The current study replicates clinical AD findings and indicates that metabolic network covariance modeling provides a translational tool to assess disease progression in AD models.
阿尔茨海默病（AD）是全球痴呆症的主要病因，对人类和经济造成了重大影响，目前针对该疾病的有效治疗药物寥寥无几。分子成像技术的进步使得评估大脑糖酵解代谢成为可能，对大脑区域网络模型的研究将代谢活性的改变与 AD 阶段联系起来。
我们对 4、6 和 12 个月大的 5XFAD 转基因小鼠及其同窝对照（WT）雌雄小鼠进行 F-FDG 正电子发射断层扫描（PET）成像，并通过脑代谢协方差分析对区域数据进行分析。
5XFAD 模型小鼠的葡萄糖摄取与 WT 小鼠相比存在年龄相关性变化。协方差网络的社区结构分析在不同年龄和性别之间存在差异，5XFAD 模型中代谢偶联受到破坏。
本研究复制了临床 AD 发现，并表明代谢网络协方差建模为评估 AD 模型中的疾病进展提供了一种转化工具。
