Sargent Lana, Nalls Mike, Amella Elaine J, Slattum Patricia W, Mueller Martina, Bandinelli Stefania, Tian Qu, Swift-Scanlan Theresa, Lageman Sarah K, Singleton Andrew
Laboratory of Neurogenetics National Institute on Aging National Institutes of Health Bethesda Maryland USA.
Virginia Commonwealth University School of Nursing Richmond Virginia USA.
Alzheimers Dement (N Y). 2020 Jul 15;6(1):e12027. doi: 10.1002/trc2.12027. eCollection 2020.
We describe findings from a large study that provide empirical support for the emerging construct of cognitive frailty and put forth a theoretical framework that may advance the future study of complex aging conditions. While cognitive impairment and physical frailty have long been studied as separate constructs, recent studies suggest they share common etiologies. We aimed to create a population predictive model to gain an understanding of the underlying biological mechanisms for the relationship between physical frailty and cognitive impairment.
Data were obtained from the longitudinal " (Aging in Chianti, InCHIANTI Study) with a representative sample (n = 1453) of older adults from two small towns in Tuscany, Italy. Our previous work informed the candidate 132 single nucleotide polymorphisms (SNPs) and 155 protein biomarkers we tested in association with clinical outcomes using a tree boosting, machine learning (ML) technique for supervised learning analysis.
We developed two highly accurate predictive models, with a Model I area under the curve (AUC) of 0.88 (95% confidence interval [CI] 0.83-0.90) and a Model II AUC of 0.86 (95% CI 0.80-0.90). These models indicate cognitive frailty is driven by dysregulation across multiple cellular processes including genetic alterations, nutrient and lipid metabolism, and elevated levels of circulating pro-inflammatory proteins.
While our results establish a foundation for understanding the underlying biological mechanisms for the relationship between cognitive decline and physical frailty, further examination of the molecular pathways associated with our predictive biomarkers is warranted. Our framework is in alignment with other proposed biological underpinnings of Alzheimer's disease such as genetic alterations, immune system dysfunction, and neuroinflammation.
我们描述了一项大型研究的结果，该研究为新兴的认知衰弱概念提供了实证支持，并提出了一个理论框架，可能会推动未来对复杂衰老状况的研究。虽然认知障碍和身体衰弱长期以来一直作为独立的概念进行研究，但最近的研究表明它们有共同的病因。我们旨在创建一个人群预测模型，以了解身体衰弱与认知障碍之间关系的潜在生物学机制。
数据来自纵向的“基安蒂地区衰老研究（InCHIANTI研究）”，该研究对意大利托斯卡纳两个小镇的老年人进行了代表性抽样（n = 1453）。我们之前的工作为我们测试的132个单核苷酸多态性（SNP）和155种蛋白质生物标志物提供了依据，我们使用树增强机器学习（ML）技术进行监督学习分析，将这些标志物与临床结果相关联。
我们开发了两个高度准确的预测模型，模型I的曲线下面积（AUC）为0.88（95%置信区间[CI] 0.83 - 0.90），模型II的AUC为0.86（95% CI 0.80 - 0.90）。这些模型表明，认知衰弱是由多种细胞过程的失调驱动的，包括基因改变、营养和脂质代谢以及循环促炎蛋白水平升高。
虽然我们的结果为理解认知衰退与身体衰弱之间关系的潜在生物学机制奠定了基础，但有必要进一步研究与我们的预测生物标志物相关的分子途径。我们的框架与阿尔茨海默病其他提出的生物学基础一致，如基因改变、免疫系统功能障碍和神经炎症。
