Ball Brendan K, Hyun Park Jee, 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.
bioRxiv. 2024 Dec 12:2024.12.11.627991. doi: 10.1101/2024.12.11.627991.
Type 2 diabetes (T2D) is a significant risk factor for Alzheimer's disease (AD). Despite multiple studies reporting this connection, the mechanism by which T2D exacerbates AD is poorly understood. It is challenging to design studies that address co-occurring and comorbid diseases, limiting the number of existing evidence bases. To address this challenge, we expanded the applications of a computational framework called Translatable Components Regression (TransComp-R), initially designed for cross-species translation modeling, to perform cross-disease modeling to identify biological programs of T2D that may exacerbate AD pathology. Using TransComp-R, we combined peripheral blood-derived T2D and AD human transcriptomic data to identify T2D principal components predictive of AD status. Our model revealed genes enriched for biological pathways associated with inflammation, metabolism, and signaling pathways from T2D principal components predictive of AD. The same T2D PC predictive of AD outcomes unveiled sex-based differences across the AD datasets. We performed a gene expression correlational analysis to identify therapeutic hypotheses tailored to the T2D-AD axis. We identified six T2D and two dementia medications that induced gene expression profiles associated with a non-T2D or non-AD state. Finally, we assessed our blood-based T2DxAD biomarker signature in post-mortem human AD and control brain gene expression data from the hippocampus, entorhinal cortex, superior frontal gyrus, and postcentral gyrus. Using partial least squares discriminant analysis, we identified a subset of genes from our cross-disease blood-based biomarker panel that significantly separated AD and control brain samples. Our methodological advance in cross-disease modeling identified biological programs in T2D that may predict the future onset of AD in this population. This, paired with our therapeutic gene expression correlational analysis, also revealed alogliptin, a T2D medication that may help prevent the onset of AD in T2D patients.
2型糖尿病（T2D）是阿尔茨海默病（AD）的一个重要风险因素。尽管多项研究报告了这种关联，但T2D加剧AD的机制仍知之甚少。设计针对同时存在和共病疾病的研究具有挑战性，限制了现有证据库的数量。为应对这一挑战，我们扩展了一种名为可翻译成分回归（TransComp-R）的计算框架的应用，该框架最初是为跨物种翻译建模而设计的，用于进行跨疾病建模，以识别可能加剧AD病理的T2D生物学程序。使用TransComp-R，我们结合了外周血来源的T2D和AD人类转录组数据，以识别预测AD状态的T2D主成分。我们的模型揭示了在预测AD的T2D主成分中，与炎症、代谢和信号通路相关的生物学途径中富集的基因。预测AD结果的同一T2D主成分揭示了AD数据集中基于性别的差异。我们进行了基因表达相关性分析，以确定针对T2D-AD轴的治疗假设。我们确定了六种T2D药物和两种痴呆症药物，它们诱导了与非T2D或非AD状态相关的基因表达谱。最后，我们在死后人类AD以及来自海马体、内嗅皮质、额上回和中央后回的对照脑基因表达数据中评估了我们基于血液的T2DxAD生物标志物特征。使用偏最小二乘判别分析，我们从跨疾病血液生物标志物面板中确定了一组基因，这些基因显著区分了AD和对照脑样本。我们在跨疾病建模方面的方法进步确定了T2D中可能预测该人群未来AD发病的生物学程序。这与我们的治疗性基因表达相关性分析相结合，还揭示了阿格列汀，一种可能有助于预防T2D患者AD发病的T2D药物。
