Ye Fengdan, Funk Quentin, Rockers Elijah, Shulman Joshua M, Masdeu Joseph C, Pascual Belen
Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA.
Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA.
Brain Commun. 2022 Aug 25;4(5):fcac216. doi: 10.1093/braincomms/fcac216. eCollection 2022.
Neuroimaging in the preclinical phase of Alzheimer's disease provides information crucial to early intervention, particularly in people with a high genetic risk. Metabolic network modularity, recently applied to the study of dementia, is increased in Alzheimer's disease patients compared with controls, but network modularity in cognitively unimpaired elderly with various risks of developing Alzheimer's disease needs to be determined. Based on their 5-year cognitive progression, we stratified 117 cognitively normal participants (78.3 ± 4.0 years of age, 52 women) into three age-matched groups, each with a different level of risk for Alzheimer's disease. From their fluorodeoxyglucose PET we constructed metabolic networks, evaluated their modular structures using the Louvain algorithm, and compared them between risk groups. As the risk for Alzheimer's disease increased, the metabolic connections among brain regions weakened and became more modular, indicating network fragmentation and functional impairment of the brain. We then set out to determine the correlation between regional brain metabolism, particularly in the modules derived from the previous analysis, and the regional expression of Alzheimer-risk genes in the brain, obtained from the Allen Human Brain Atlas. In all risk groups of this elderly population, the regional brain expression of most Alzheimer-risk genes showed a strong correlation with brain metabolism, particularly in the module that corresponded to regions of the brain that are affected earliest and most severely in Alzheimer's disease. Among the genes, and showed the strongest negative correlation and showed the strongest positive correlation with brain metabolism. The Pearson correlation coefficients remained significant when contrasted against a null-hypothesis distribution of correlation coefficients across the whole transcriptome of 20 736 genes (: = 0.0130; , = 0.0136; : = 0.0093). The strong regional correlation between Alzheimer-related gene expression in the brain and brain metabolism in older adults highlights the role of brain metabolism in the genesis of dementia.
阿尔茨海默病临床前期的神经影像学可为早期干预提供至关重要的信息，尤其是对于具有高遗传风险的人群。代谢网络模块化最近被应用于痴呆症研究，与对照组相比，阿尔茨海默病患者的代谢网络模块化有所增加，但需要确定有各种患阿尔茨海默病风险的认知未受损老年人的网络模块化情况。基于他们5年的认知进展，我们将117名认知正常的参与者（年龄78.3±4.0岁，52名女性）分为三个年龄匹配组，每组患阿尔茨海默病的风险水平不同。通过他们的氟脱氧葡萄糖PET构建代谢网络，使用Louvain算法评估其模块化结构，并在风险组之间进行比较。随着患阿尔茨海默病风险的增加，脑区之间的代谢连接减弱并变得更加模块化，表明大脑网络碎片化和功能受损。然后我们着手确定区域脑代谢之间的相关性，特别是在前述分析得出的模块中，以及从艾伦人类大脑图谱获得的大脑中阿尔茨海默病风险基因的区域表达之间的相关性。在该老年人群的所有风险组中，大多数阿尔茨海默病风险基因的区域脑表达与脑代谢显示出强烈的相关性，特别是在与阿尔茨海默病中最早和最严重受影响的脑区相对应的模块中。在这些基因中，[具体基因1]和[具体基因2]与脑代谢显示出最强的负相关，[具体基因3]与脑代谢显示出最强的正相关。当与20736个基因的整个转录组的相关系数零假设分布进行对比时，皮尔逊相关系数仍然显著（[具体基因1]：P = 0.0130；[具体基因2]，P = 0.0136；[具体基因3]：P = 0.0093）。老年人脑内阿尔茨海默病相关基因表达与脑代谢之间的强烈区域相关性突出了脑代谢在痴呆症发生中的作用。
