AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

Abstract

Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex‐dependent role of anthropometry for the prediction of 10‐year all‐cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data‐driven approach pointed to sex‐specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all‐cause mortality (AUROC 0.876, 95%CI 0.864–0.887) and cause‐specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25–2.62) and comorbidity profiles. A simplified version of AnthropoAge (S‐AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause‐specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.

Multinational evaluation of anthropometric age (AnthropoAge) as a measure of biological age in the USA, England, Mexico, Costa Rica, and China: a population-based longitudinal study

“…AnthropoAge was previously developed and validated in the third and fourth National Health and Nutrition Examination Surveys (NHANES III and IV) by our team as a proxy of BA 17 . A detailed description on how AnthropoAge is calculated is available in Supplementary Methods.…”

“…For this study, we employed the simplified version of AnthropoAge, which uses CA in years, BMI and WHtR. To measure biological age acceleration, we estimated AnthropoAgeAccel by removing the effect of CA using the residuals from a linear model regressing AnthropoAge onto CA 17,23 and used this metric to define whether participants presented accelerated aging (see details in Statistical analysis).…”

“…Our team recently developed AnthropoAge, which is a non-invasive alternative to measure BA using anthropometry to capture body composition aging and mortality risk 17 , thus representing an accessible and potentially useful aging biomarker to assess population aging 5,16 . Although AnthropoAge was developed using nationally representative data of participants with diverse ethnical and racial backgrounds, this may not fully capture the wide range of diversity in body composition across ethnicities since all participants from the original study were from the U.S.…”

Circadian Rhythm Analysis Using Wearable-Based Accelerometry as a Digital Biomarker of Aging and Healthspan

“…To t e s t t h i s h y p o t h e s i s , we propose a method termed CosinorAge which allows us to estimate the circadian rhythm age using a Gompertz proportional hazard regression, a wellestablished statistical model to describe mortality in adults with multiple previous implementations 3,24 . Biological age estimators directly built with aging outcomes (e.g., mortality) also show more robust associations with mortality risk and aging traits than chronological age predictors 3,25 .…”

Maternal Prenatal Factors and Child Adiposity in Associations with Cardiometabolic Risk Factors in Term-Born Chinese Children at the Age of 2 Years

“…There are sex differences in the prevalence of overweight and obesity in Chinese children [13]; boys have higher overweight/obesity prevalence than girls [13]. It is known that body composition differs between adult men and women [14], whereby men on average have greater lean mass, while women have more fat mass as a proportion of body weight [14,15]. Body composition also differs by sex in prepubertal children, school-age children, and adolescents [12][13][14], and already at birth, boys have longer length and higher lean mass than girls [16].…”