The forecasted increase in the number of older people for this century will be accompanied by an increase of those with disabilities. Disability is usually preceded by a condition named frailty which is still a non-reversible condition when compared with
disability. Recent studies stress the relevance of testing the clinical utility of the existing definition of frailty by using combinations of clinical criteria (current definition) and lab Biomarkers (BMs).In Frailomic we aimed to characterize, both
biologically and clinically, frailty by profiling more than 30000 blood and urine derived -Omic signatures in four different European cohorts. In all cohorts, we combined the omic information with existing clinical data that included existing relevant
markers such as disability, co-morbidity or depression among others.
The analysis was conducted as a three-stage workflow. In a first stage, we identified those signatures per omic type and per cohort type that were significantly associated with frailty, using a non-parametric approach that included as covariates known frailty covariates such as age or depression among others. In a second stage, we identified using Machine Learning techniques and per cohort, the minimal models of omic and non-omic signatures that better predicted frailty diagnosis. In a third stage, we investigated the robustness of the minimal models and the possible use in combination with existing clinical classifications of frailty.
As a result, we quantified the value of -omic improving the clinical definition of frailty, but also gained frailty-related functional information at the level of blood and urine metabolites and non-coding RNAs.
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