Ultrasensitive profiling of > 300 plasma proteins for predicting biological age

Biological aging underlies almost all major chronic diseases such as cancer, autoimmune diseases and Alzheimer"s disease. Elucidating the biological mechanisms of aging and monitoring the aging process noninvasively holds great promise for the early detection and ultimately the prevention of aging-related diseases. Current approaches to quantify biological age have mostly focused on epigenetic changes in the genome. Advances in proteomic technologies can provide an alternative and arguably more actionable measure of biological aging at the protein level. Here we apply the recently developed Nucleic acid-linked immunosandwich assay (NULISA) to measure >300 proteins to assess age-related changes in plasma protein levels.

In this study, we profiled a cohort of 500 individuals spanning over 7 decades of chronological age from a biobank maintained by a major metropolitan academic medical center. The cohort consisted of an even balance of male and female subjects and is generally representative of the racial/ethnic distribution of the collection site. Plasma samples from this cohort were assayed with the NULISAseq 250-plex Inflammation and 120-plex CNS Disease Panels targeting key inflammation-related cytokines and chemokines and proteins involved in all major hallmarks of neurodegeneration such as pTau and β-amyloid proteins.

Overall target detectability in plasma samples from this cohort was 98.8% with the Inflammation Panel and 96.8% with the CNS Disease Panel. Preliminary bioinformatics analyses identified >150 targets from the Inflammation Panel that were increased with age including proinflammatory markers such as IL6, TNFα, LIF, TGFβ and IL18. Analysis with the CNS markers further identified age-dependent increases in pTau-217, pTau-181, pTau-231, Aβs (38, 40 and 42) as well as NEFL and NEFH among many other markers implicated in neurodegeneration. Additional analyses are planned to build a protein-based age predictor and assess its correlation with clinical outcomes to predict mortality and common diseases.

In conclusion, this study applied the recently developed NULISA platform to profile key inflammation and neurodegeneration-related proteins in plasma in a well curated clinical cohort. The large number of proteins identified to be associated with age indicates that this dataset may provide a rich and high-quality dataset for the aging research community.