Kuo CL, Chen Z, Liu P, Pilling LC, Atkins JL, Fortinsky RH, Kuchel GA, Diniz BS. Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults. Aging Cell. 2024 May 15:e14195. doi: 10.1111/acel.14195. Epub ahead of print. PMID: 38747160.
We developed a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
Users are required to download and install R but no R package is needed to calculate the PAC proteomic age.
The input file, as shown in "pac_example_data.csv", should have the first column designated as the ID column, with any arbitrary column name. This should be followed by columns containing age and the required proteins. The input file may include additional proteins beyond those needed. The protein column names will be converted to lowercase.
The R code below shows you how to load the "pac_proteomic_age" function in "pac_proteomic_age.R" to calculate PAC proteomic ages for five subjects with input data in "pac_example_data.csv".
source("pac_proteomic_age.R")
pac_input=read.csv("pac_example_data.csv")
pac_proteomic_age(pac_input)