Predicting Alzheimer’s Disease Phenotypes With Aging Clocks: An Exploratory Analysis
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Due to the continuous aging of the world’s population, early diagnosis of age-related diseases, such as Alzheimer’s disease (AD), has become a priority and should be improved by accessible, minimally invasive, comprehensive approaches.
Methods
In this study, we explored the potential of epigenetic and proteomic aging clocks as complementary biomarkers to established cognitive tests and AD blood-based biomarkers (BBBs). Omics data were generated from blood samples of 153 cognitively unimpaired individuals, aged on average 62 years. BBBs and cognitive functioning were measured approximately nine years later. We used Generalized Estimating Equation models to investigate associations between biological aging and AD-related phenotypes, and how dementia risk factors modulate these.
Findings
Proteomic mortality-based clocks showed more significant associations with cognitive measures and BBBs than epigenetic clocks. Accelerated systemic and brain-specific proteomic aging was linked to lower cognitive functioning and higher plasma levels of neurofilament light chain (NfL). Multiple organ-specific proteomic models were positively associated with plasma NfL, indicating that changes in biological processes and cellular components in these organs, captured by these models, may be implicated in neurodegeneration and AD pathogenesis. Interaction analysis showed that negative associations of proteomic aging with cognitive scores are stronger in individuals with lower genetic liability for type II diabetes and high levels of fasting glucose.
Interpretation
In our modest-sized sample, proteomic clocks better captured variability in cognition and AD-related biomarkers than epigenetic clocks, and may have higher potential as auxiliary biomarkers of AD. Thus, our study highlights the importance of investigating proteomic aging in AD.
Funding
EH-Epi data used in the analysis is deposited in the Biobank of the Finnish Institute for Health and Welfare ( https://thl.fi/en/web/thl-biobank/forresearchers ). It is available to researchers after written application and following the relevant Finnish legislation.
