Moving towards the detection of frailty with biomarkers: A population health study.

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets.

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

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.

Anticholinergic Drug Induced Cognitive and Physical Impairment: Results from the InCHIANTI Study.

Background: The aims of this study were to evaluate the relationship between anticholinergic drug burden (ACB) cognitive impairment, physical frailty, and cognitive frailty, and to determine if ACB is predictive of these phenotypes when modeled with biological and genomic biomarkers.

Methods: In a retrospective cohort study, a total of 1,453 adults aged 20-102 years were used to examine ACB as a predictor for cognitive impairment, physical frailty, and cognitive frailty. Anticholinergic burden is examined as a predictor for all phenotypes in a cross-sectional analysis using logistic, ordinal regression models, and Extreme Gradient Boosting for population predictive modeling.

Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts.

Background And Objective: The fall risk assessment tool (FRAT-up) is a tool for predicting falls in community-dwelling older people based on a meta-analysis of fall risk factors. Based on the fall risk factor profile, this tool calculates the individual risk of falling over the next year. The objective of this study is to evaluate the performance of FRAT-up in predicting future falls in multiple cohorts.