Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies

Abstract

In recent years, different machine learning algorithms have been developed for the estimation of Biological Age (BA), defined as the hypothetical underlying age of an organism. BA can be computed based on different circulating and non-circulating biomarkers. In this perspective, identifying biomarkers with a prominent influence on BA and developing reliable models for its estimation is of fundamental importance for monitoring healthy aging, and could provide new tools to screen health status and the risk of clinical events in the general population. Here, we briefly review the different machine learning (ML) approaches used for BA estimation, focusing on those methods with potential application to the Moli-sani study, a prospective population-based cohort study of 24,325 subjects (35–99 years). In particular, we discuss the potential of BA estimation based on blood biomarkers, which likely represents the easiest and most immediate way to compute organismal BA. Similarly, we describe ML methods for the estimation of brain age based on structural neuroimaging features. For each method, we discuss the relation with epidemiological variables (e.g., mortality), genetic and environmental factors, and common age-related diseases (e.g., Alzheimer disease), to examine the potential as aging biomarker in the general population. Finally, we hypothesize new approaches for BA estimation, both at the single organ and at the whole organism level. Overall, here we trace the road ahead in the Big Data era for our and other prospective general population cohorts, presenting ways to exploit the notable amount of data available nowadays.

Age and life expectancy clocks based on machine learning analysis of mouse frailty

“…There are some limitations of these complex models, however, including a lack of interpretability of the weighting and interactions of the variables. Some previous studies have also used machine learning approaches for the development of aging biomarkers, including deep neural networks of standard blood biomarkers 51,52 and deep learning of brain imaging data 53 , with promising results 54,55 . These have been exclusively humans studies, and our findings suggest that future studies exploring biological age biomarkers in mice could benefit from incorporating machine learning approaches such as neural networks or gradient boosting machine algorithms.…”

Modeling the human aging transcriptome across tissues, health status, and sex

“…Thus, in addition to methylation and clinical data, aging clocks over varying accuracies can be constructed from transcriptomes, repeat elements, microRNAs, and protein abundance measures, revealing specific biological signatures across diverse data types. Various machine learning models can also be employed, including deep learning algorithms (Gialluisi et al, 2019; Zhavoronkov et al, 2019).…”

Deep biomarkers of aging and longevity: from research to applications

“…In addition, the realization that age is a universal feature present in all biological and non-biological objects triggered the interest of the artificial intelligence researchers interested in the study of causality and making the deep neural networks (DNNs) more interpretable. This leads to the convergence of aging research and deep learning [3, 16, 28, 29].…”

“…With the first DNN-based aging clocks published in 2016 by Zhavoronkov laboratory [33], significant progress has been made the past few years in deep learned biomarkers of human aging [28, 29]. The first DL clock was constructed using 41 blood test values of over 50,000 individuals.…”