Shared mechanisms for cognitive impairment and physical frailty: A model for complex systems

Alzheimer’s & Dementia: Translational Research & Clinical Interventions

Sarah K. Lageman¹  Patricia W. Slattum²  Stefania Bandinelli³  Lana Sargent⁴  Mike Nalls⁴  Andrew Singleton⁴  Qu Tian⁵  Martina Mueller⁶  Elaine J. Amella⁶  Theresa Swift‐Scanlan⁷ 

[1] Department of Neurology Virginia Commonwealth School of Medicine Richmond Virginia USA;Department of Pharmacotherapy & Outcomes Science Geriatric Pharmacotherapy Program, School of Pharmacy Virginia Commonwealth University Richmond VA USA;Laboratory of Clinical Epidemiology InCHIANTI Study Group Local Health Unit Tuscany Center Florence Italy;Laboratory of Neurogenetics National Institute on Aging National Institutes of Health Bethesda Maryland USA;Longitudinal Studies Section Translational Gerontology Branch National Institute on Aging Baltimore Maryland USA;Medical University of South Carolina School of Nursing Charleston North Carolina USA;Virginia Commonwealth University School of Nursing Richmond Virginia USA;

关键词: bioinformatics;    cognitive frailty;    cognitive impairment;    frailty;    machine learning;   

DOI  :  10.1002/trc2.12027

来源: DOAJ

【 摘 要 】

Abstract Introduction 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. Methods Data were obtained from the longitudinal “Invecchaiare in Chianti” (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. Results 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. Discussion 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.

【 授权许可】

Unknown