author	title
Bryce Bartlett	R package to estimate biological age training parameters using the Klemera-Doubal algorithm.

Introduction

Overview

What does it do? Estimate "biological age" from a range of biomarkers using the Klemera Doubal algorithm (2006).

Why? Estimating biological age gives better leverage on aging, senescence, and disease process than chronological age alone.

Installation

This package is still in development, but you can install and use it from github using the R library devtools. Here is the code-block:

install.packages('devtools')
library(devtools)
install_github('bjb40/bioage')

Example

#Train biological age parameters
train = kdm_calc(nhanes,agevar='age',
  biomarkers=c('sysbp','totchol','bun','cmv','mcv'))

#Use training data to calculate out-of-sample biological ages 
biocalc = kdm_calc(data,agevar='age',
  biomarkers=c('sysbp','totchol','bun','cmv','mcv'),
  fit=train$fit)

#combine biological ages and training data
data$bioage = extract_data(biocalc)[,'bioage']

After installing, you can view a more detailed vignette using the following code, and clicking on the HTML result.

browseVignettes('bioage')

Description of Algorithm:

Klemera P, Doubal S. 2006. A new approach to the concept and computation of biological age. Mechanisms of Ageing and Development. 127(3):240-48