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

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.

Keywords: bioinformatics; cognitive frailty; cognitive impairment; frailty; machine learning.

FIGURE 1

Theoretical framework for future study of cognitive frailty

FIGURE 2

Feature importance scores for cognitive frailty in Model I. Note: Feature importance scores are generated by xgboost for cognitive frailty and ranked by their level of importance in the model. The figure demonstrates different weights for each feature's importance in predicting cognitive frailty from healthy individuals

FIGURE 3

Feature importance scores for cognitive frailty in Model II. Note: Feature importance scores are generated by xgboost for cognitive frailty and ranked by their level of importance in the model. The figure demonstrates different weights for each feature's importance in predicting cognitive frailty from healthy individuals