AnthropoAgeR

Authors

Omar Yaxmehen Bello-Chavolla, Carlos Alberto Fermín-Martínez https://bellolab.org

Instituto Nacional de Geriatría, Mexico City, Mexico

---

The AnthropoAgeR package cfacilitate estimation of second-generation biological aging measures derived from body-composition measures (AnthropoAge, S-AnthropoAge) and PhenoAge in R. This package also allows for estimation of sex-stratified accelerated aging measures PhenoAgeAccel and AnthropoAgeAccel. These metrics are derived from work by Fermín-Martínez CA et al (Aging Cell, 2023) and Levine M et al (Aging, 2018).

AnthropoAge is a second-generation biological aging biomarker developed to estimate 10-year mortality years expressed as age using anthropometric measurements. The full version of AnthropoAge uses chronological age, sex, ethnicity, weight, height, waist circumference, subscapular and triceps skinfold thickness (for women), and arm and thigh circunference (for men). The simplified version of AnthropoAge uses the body-mass index and the waist-to-height ratio. The full version of AnthropoAge can be calculated using the anthropoage() function, whilst the simplified version can be calculated using the s_anthropoage() function.

PhenoAge is a second-generation aging biomarker developed to estimate 10-year mortality years expressed as age using blood biomarker measurements. Phenoage uses chronological age, fasting plasma glucose, serum creatinine, alkaline phosphatase, lymphocyte percentage, red cell distribution width, mean cell volume, serum albumin, and c-reactive protein. PhenoAge can be calculated using the phenoage() function.

Installation

You can install the development version of AnthropoAgeR from Github

# install.package("remotes")   #In case you have not installed it.
remotes::install_github("oyaxbell/AnthropoAgeR")

Alternatively, you will soon be able install the released version of AnthropoAge from CRAN with:

# not approved yet
# install.packages("AnthropoAgeR")

Example

AnthropoAge calculation

To calculate AnthropoAge use the anthropoage() function. The following code provides an example for a calculation for males and females using AnthropoAge. The function can also be used to calculate AnthropoAge in a large cohort.

library(AnthropoAgeR)

## Male
age1<-anthropoage(Age=31, Sex="Men", Weight=75,
              Height=1.73, Ethnicity="Mexican-American",
              Thigh_circumference=49.5, Arm_circumference=27.7,
              Subscapular_skinfold=17, Triceps_skinfold=17.2)

age1

## Female

age2<-anthropoage(Age=24, Sex="Women", Weight=61,
              Height=1.62, Ethnicity="Mexican-American",
              Subscapular_skinfold=17, Triceps_skinfold=17.2,
              Thigh_circumference=49.5, Arm_circumference=27.7)

age2

S- AnthropoAge calculation

To calculate S-AnthropoAge use the s_anthropoage()function. The following code provides an example for a calculation for males and for a datset using AnthropoAge. The function can also be used to calculate AnthropoAge in a large cohort; for this latter purpose thes_anthropoage_fast() function is recommended.

library(AnthropoAgeR)

## Calculate s_anthropoage in a single individual ##

age1<-s_anthropoage(Age=31, Sex="Men", Weight=75,
              Height=1.73, Ethnicity="Mexican American")

age1

## Calculate  AnthropoAge in a dataset ##

df<-data.frame(Age=c(24, 31, 27), Sex=c("Women", "Men", "Men"),
              Weight=c(61, 73, 68), Height=c(1.61, 1.73, 1.68),
              Waist=c(76, 82, 91), Ethnicity=c("Mexican-American",
              "Mexican-American","Mexican-American"))

df$AnthropoAge<-s_anthropoage_fast(Age=df$Age, Sex=df$Sex, Weight=df$Weight,
              Height=df$Height, Ethnicity=df$Ethnicity)

df$AnthropoAge

PhenoAge calculation

To calculate PhenoAge use the phenoage()function. The following code provides an example for a calculation of PhenoAge in a single individual.

library(AnthropoAgeR)

## Calculate phenoage in a single individual ##

age1<-phenoage(Age=40, CRP=0.02, Lymph=23.9, Glu=95,
                  Alb=4.4, MCV=93.6, RCDW=12, AP=52,
                  WBC=5.7, Cr=0.7)

age1

Age acceleration

To calculate accelerated aging metrics derived from AnthropoAge, S-AnthropoAge and PhenoAge, the age_accel() function must be used. This provides a sex-based estimate of accelerated aging for all metrics. AnthropoAgeAccel is interpreted as the average deviation of biological from chronological age and is specific for each population. Values ≥0 indicate accelerated aging rates, whilst values<0 indicate non-accelerated aging rates.

library(AnthropoAgeR)

## Calculate age acceleration in a single individual ##

accel<-age_accel(Age=31, BA=26.2, Sex="Men")

accel


## Calculate age acceleration in a dataset ##

data<-data.frame(Age=c(23,34,45,33,56,76,61,41,32,27),
                 BA=c(25.2,30.6,44.2,33.5,61.2,81.1,57.2,40.7,35.2,26.1),
                 Sex=c("Men", "Women", "Women", "Men", "Women", "Men", 
                       "Men", "Men", "Women", "Women"))

data$accel<-age_accel(Age=data$Age, BA=data$BA, Sex=data$Sex)

data$accel

hist(data$accel)

References

Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018 Apr 18;10(4):573-591. doi: https://doi.org/10.18632/aging.101414. PMID: 29676998; PMCID: PMC5940111.

Fermín-Martínez CA, Márquez-Salinas A, Guerra EC, Zavala-Romero L, Antonio-Villa NE, Fernández-Chirino L, Sandoval-Colin E, Barquera-Guevara DA, Campos Muñoz A, Vargas-Vázquez A, Paz-Cabrera CD, Ramírez-García D, Gutiérrez-Robledo LM, Bello-Chavolla OY. AnthropoAge, a novel approach to integrate body composition into the estimation of biological age. Aging Cell. 2023 Jan;22(1):e13756. doi: https://doi.org/10.1111/acel.13756. Epub 2022 Dec 22. PMID: 36547004; PMCID: PMC9835580.