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A Representative Example of Present Approaches to the Development of Biomarkers of Aging


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Posted 12 August 2021 - 10:11 AM


The open access paper noted here is interesting as a representative example of the way in which researchers presently go about the development of biomarkers of aging that measure biological age, the burden of damage and change that leads to disease and mortality. There is now a great deal of work out there in the literature to build upon, and so much of present progress takes the form of incremental advances that add to the foundation of an established biomarker. It remains the case that all too little exploration is aimed at understand what exactly is being measured, and how the assessed differences are caused by underlying mechanisms of aging.

In this study, we aimed to improve biological age algorithms through a population-risk-based framework. Recent work has introduced a novel measure of 'phenotypic age', developed and validated on NHANES III data. A Gompertz proportional hazards regression was applied to account for the hazard of mortality when selecting clinical biomarkers in the training dataset. This approach is unlike most of the previous biological age algorithms, which largely aimed to model chronological age as the dependent variable. As a result, it is able to capture the association between accelerated biological age and elevated risk of age-related comorbidity.

However, the "phenotypic age" algorithm was unable to establish a significant link between accelerated aging and elevated risk for dementia. We hypothesized that this caveat was not due to the population-risk-framework itself, but because the "phenotypic age" was trained in the NHANES data, which has a relatively younger age distribution than is common for neurological outcomes. Furthermore, we investigated whether by including markers of neurodegeneration, we could improve the prediction of neurological outcomes in advanced-aged cohorts.

Our approach to improve the biological age models comprised of two steps. First, we validated the "phenotypic age" algorithm in the Rotterdam Study. Second, we developed and validated new biological age algorithms in the Rotterdam Study using the same Gompertz proportional hazards regression framework plus neurodegenerative markers (neurofilament light chain, total-tau, amyloid beta-40 and amyloid beta-42). Two variants of a phenotypic blood-based algorithm that either excludes (BioAge1) or includes (BioAge2) neurofilament light chain as a neurodegenerative marker were assessed. BioAge1 and BioAge2 predict dementia equally well, as well as lifespan and healthspan. Each one-year increase in BioAge1/2 was associated with 11% elevated risk of mortality and 7% elevated risk of first morbidities.

Link: https://doi.org/10.1...598-021-95425-5


View the full article at FightAging




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