Project description:Frailty is defined as a clinical syndrome. It reflects a decrease in physiological reserve capacities that alters the mechanisms of adaptation to stress. Its clinical expression is modulated by biological, physiological, social and environmental changes. Thus, the accumulation of declines in these multiple systems leads to vulnerability to adverse outcomes (Fried et al., 2001; Nourhashémi et al., 2001). Indeed, frailty increases the risk of disability, falls, hospitalization and death. Age is currently a major determinant of frailty but does not explain this syndrome alone. It has been estimated that the prevalence of frailty is 4% in people aged 65-69 years and increases to 26% in people aged over 85 years (Clegg et al., 2013). However, this parameter is not sufficient to understand why long-lived people can preserve their physical function even at extreme ages (Ayers et al., 2017). The characterization of frailty has therefore become a priority in order to put in place actions to reduce or delay its harmful consequences. Early diagnostic methods are needed to identify those at risk. The search for biomarkers is part of this approach. As a result, the search for biomarkers of frailty has led to the identification of several types of molecules linked to the inflammatory response (e.g. IL-6, CRP, TNF-α and homocysteine), to nutritional status (e.g. vitamin D, albumin) or even endocrine. metabolism (e.g. IGF-1 and testosterone) (Picca et al., 2022; Mailliez et al., 2020; Alvarez-Sanchez et al, 2020). The development of new analytical techniques now allows multiplexed analysis rather than an individual biomarker to characterize a biological profile of this complex and dynamic frailty syndrome (Picca et al., 2022). Proteomic analysis is the technique of choice for studying the complete protein content of a biological sample. This technique has already proven itself in the discovery of new biomarkers in age-related diseases such as Parkinson's disease (Raghunathan et al., 2022) and Alzheimer's disease (Bai et al., 2021). Several studies have already described the plasma proteomic profile of frailty (Shamsi et al., 2012; Lin et al., 2017; Landino et al., 2021; Sathyan et al., 2020; Verghese et al., 2021), highlighting the importance of biological pathways associated with frailty: immune response, coagulation pathway or lipid metabolism. On the other hand, the determination of the level of frailty of the patients thanks to a proteomic analysis could also make it possible to improve the prediction of this syndrome as well as its phenotyping. In a first work on plasma samples, Sathyan et al. constructed a frailty prediction model that was more strongly correlated with chronological age than observed clinical frailty (Sathyan et al., 2020). Cognitive impairment plays a key role in determining vulnerability to adverse outcomes (Cesari et al., 2013), leading to the proposal in 2013 of a concept and operational definition of “cognitive frailty”. (Sugimoto et al., 2022). Proteomic analysis of cerebrospinal fluid (CSF) is therefore an approach of choice for understanding brain responses to aging and frailty syndrome. Its analysis could allow the identification of new biomarkers linked to the frailty syndrome. Indeed, many of the CSF proteins are released by the various cells of the central nervous system (CNS). This biofluid thus reflects the molecular content of neuronal and glial cells. For this reason, any change in the composition of the CSF reflects the pathophysiological state of the brain (inflammation, infection, neurodegeneration...). CSF proteomics is therefore a technique of choice to comprehensively understand the cerebral pathogenic pathways linked to frailty. The PROLIPHYC cohort is composed of patients suspected of normal pressure hydrocephalus (NPH). We have previously shown that there is an association in these patients between an alteration of the biomechanical properties of the CNS and the frailty index, and that homocysteine is the only plasma macromolecule associated with these two clinical and biomechanical parameters (Vallet et al., 2020; Guillotin et al., 2022). In this new study, we sought to characterize the protein composition of CSF related to frailty by quantitative proteomic analysis. The second objective was to develop a model for predicting frailty from the abundance of CSF proteins. This model was then compared to the frailty estimated by the frailty index. Together, our results enabled us to highlight a possible link between inhibition of the expression of proteins involved in neurogenesis and the frailty index, then to propose a first proteomic profile of frailty in the CSF and a model for predicting frailty based on the biological signature of the CNS.

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CircRNA in Frailty

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