Currently, an estimated 25-30% of people ages 85 or older have dementia, with a projected 115 million people worldwide living with dementia by 2050. With this worldwide phenomenon fast approaching, early detection of at-risk older adults and development of interventions focused on preventing loss in quality of life are increasingly important. A new construct defined by the International Consensus Group (I.A.N.A/I.A.G.G) as «cognitive frailty» combines domains of physical frailty with cognitive impairment and provides a framework for research that may provide a means to identify individuals with cognitive impairment caused by nonneurodegenerative conditions. Using the integrative review method of Whittemore and Knafl., 2005 this study examines and appraises the optimal measures for detecting cognitive frailty in clinical populations of older adults. The integrative review was conducted using PubMed, CINAHL, Web of Science, PsycInfo, and ProQuest Dissertations and Theses. From the total 185 articles retrieved, review of titles and key words were conducted. Following the initial review, 168 articles did not meet the inclusion criteria for association of frailty and cognition. Of the 18 fulltext articles reviewed, 11 articles met the inclusion criteria; these articles were reviewed in-depth to determine validity and reliability of the cognitive frailty measures. Predictive validity was established by the studies reviewed in four main areas: frailty and type of dementia MCI (OR 7.4, 95% CI 4.2-13.2), vascular dementia (OR 6.7, 95% CI 1.6-27.4) and Alzheimer's dementia (OR 3.2, 95% CI 1.7-6.2), frailty and vascular dementia (VaAD) is further supported by the rate of change in frailty x macroinfarcts (r = 0.032, p < 0.001); frailty and the individual domains of cognitive function established with the relationship of neurocognitive speed and change in cognition using regression coefficients; individual components of frailty and individual domains of cognitive function associations inculded slow gait and executive function (β -0.20, p < 0.008 ), attention (β -0.25 p < 0.008), processing speed (β -0.16, p < 0.008), word recall (β - 0.18, p = 0.02), and logical memory (β = 0.04, p =0.04). Weak grip was predictive for changes in executive function (β - 0.16, p =0.008). Physical activity was associated with changes in executive function (β = -0.18, p= 0.02) and word recall (β = 0.17, p= 0.02), individual components of frailty and global cognitive function were found in several studies which included grip strength (r = - 0.51, p < 0.001), gait speed (r = - 0.067, p < 0.001), and exhaustion (β - 0.18, p < 0.008). This paper presents the first-known review of the measurement properties for the cognitive frailty construct since the published results from the International Consensus Group (I.A.N.A/I.A.G.G). Evidence presented in this review continues to support the link between physical frailty and cognition with developing validity to support distinct relationships between components of physical frailty and cognitive decline. Results call attention to inconsistencies in reporting of reliability, validity, and heterogeneity in the measurements and operational definition for cognitive frailty. Further research is needed to establish an operational definition and develop psychometrically appropriate clinical measures to construct an understanding of the relationship between physical frailty and cognitive decline.

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.

Previous research using genome-wide association studies has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases. We utilized genome-wide association studies, expression quantitative trait locus mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data-driven process. Expression quantitative trait locus analyses combined with genome-wide association studies identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood-based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex. GRN expression mediates neuroinflammation function related to multiple neurodegenerative diseases. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.

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. Results: A significant association was found between ACB and cognitive decline (p = .02), frailty (p < .001), and cognitive frailty (p < .001). The odds of cognitive impairment increased by 1.21 (95% confidence interval [CI] = 1.06–1.37, p < .001), odds of being frail increased by 1.33 (95% CI = 1.18–1.50, p < .001), and odds of having cognitive frailty increased by 1.36 (95% CI = 1.21–1.54, p < .001). Population modeling results indicated ACB score as one of the stronger predictors for cognitive impairment, physical frailty, and cognitive frailty with area under the curves ranging from 0.81 to 0.88. Conclusions: Anticholinergic medications are a potentially modifiable risk factor for the prevention of cognitive and physical decline. Identification of reversible causes for cognitive and physical impairment is critical for the aging population. These findings encourage new research that may lead to effective interventions for deprescribing programs for the prevention of cognitive and physical decline in older adults.

Overlapping symptoms and co-pathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms and genome-wide association study summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Lewy body dementia and frontotemporal dementia). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic aetiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs.