Ball Brendan K, Proctor Elizabeth A, Brubaker Douglas K
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA.
Pac Symp Biocomput. 2025;30:426-440. doi: 10.1142/9789819807024_0031.
Alzheimer's disease (AD), the predominant form of dementia, is influenced by several risk factors, including type 2 diabetes (T2D), a metabolic disorder characterized by the dysregulation of blood sugar levels. Despite mouse and human studies reporting this connection between T2D and AD, the mechanism by which T2D contributes to AD pathobiology is not well understood. A challenge in understanding mechanistic links between these conditions is that evidence between mouse and human experimental models must be synthesized, but translating between these systems is difficult due to evolutionary distance, physiological differences, and human heterogeneity. To address this, we employed a computational framework called translatable components regression (TransComp-R) to overcome discrepancies between pre-clinical and clinical studies using omics data. Here, we developed a novel extension of TransComp-R for multi-disease modeling to analyze transcriptomic data from brain samples of mouse models of AD, T2D, and simultaneous occurrence of both disease (ADxT2D) and postmortem human brain data to identify enriched pathways predictive of human AD status. Our TransComp-R model identified inflammatory and estrogen signaling pathways encoded by mouse principal components derived from models of T2D and ADxT2D, but not AD alone, predicted with human AD outcomes. The same mouse PCs predictive of human AD outcomes were able to capture sex-dependent differences in human AD biology, including significant effects unique to female patients, despite the TransComp-R being derived from data from only male mice. We demonstrated that our approach identifies biological pathways of interest at the intersection of the complex etiologies of AD and T2D which may guide future studies into pathogenesis and therapeutic development for patients with T2D-associated AD.
阿尔茨海默病（AD）是痴呆症的主要形式，受多种风险因素影响，包括2型糖尿病（T2D），这是一种以血糖水平失调为特征的代谢紊乱疾病。尽管小鼠和人类研究报告了T2D与AD之间的这种联系，但T2D导致AD病理生物学的机制尚不完全清楚。理解这些疾病之间机制联系的一个挑战在于，必须综合小鼠和人类实验模型之间的证据，但由于进化距离、生理差异和人类异质性，在这些系统之间进行转化很困难。为了解决这个问题，我们采用了一种称为可翻译成分回归（TransComp-R）的计算框架，利用组学数据克服临床前和临床研究之间的差异。在此，我们开发了一种用于多疾病建模的TransComp-R新扩展，以分析来自AD、T2D以及两种疾病同时发生（ADxT2D）小鼠模型的脑样本转录组数据和死后人类脑数据，以识别预测人类AD状态的富集通路。我们的TransComp-R模型确定了由源自T2D和ADxT2D模型而非单独AD模型的小鼠主成分编码的炎症和雌激素信号通路，这些通路可预测人类AD结果。尽管TransComp-R仅源自雄性小鼠的数据，但预测人类AD结果的相同小鼠主成分能够捕捉人类AD生物学中的性别依赖性差异，包括女性患者特有的显著影响。我们证明，我们的方法确定了AD和T2D复杂病因交叉点上感兴趣的生物学通路，这可能为未来T2D相关AD患者的发病机制研究和治疗开发提供指导。
