Biomarkers of ageing: Current state‐of‐art, challenges, and opportunities

Abstract

Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of "biological age" has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and exploration into antiageing interventions. Numerous potential biomarkers of ageing have been proposed, spanning from molecular changes and imaging characteristics to clinical phenotypes. In this review, we will start off with a discussion of the development of ageing biomarkers, then we will provide a comprehensive summary of currently identified ageing biomarkers in humans, discuss the rationale behind each biomarker and highlight their accuracy and clinical value with a contemporary perspective. Additionally, we MedComm -Future Medicine.

Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review

“…The introductory study, highlighting the development of the model, revealed a significant association of a 2% increase in mortality risk for each one-year increase in RAG [Hazards Risk (HR) = 1.02, 95% CI 1.00-1.03, p=0.020] (Zhu, Shi, et al, 2023). Beyond the risk stratification for mortality, several prospective studies have highlighted associations for each one-year increase in RAG with a 10% increase of Parkinson’s disease [HR=1.10, 95% CI: 1.01-1.20, p=0.023] (Hu et al, 2022), a 4% increase of stroke [HR=1.04, 95% CI: 1.00-1.08, p=0.029] (Zhu, Hu, et al, 2022), a 3% increase of incident cardiovascular disease [HR = 1.03, 95% CI: 1.00-1.05, p=0.019] (Zhu, Chen, et al, 2022), a 10% increase in risk of incident kidney failure [HR = 1.10, 95% CI: 1.03-1.17, p=0.003] (Zhang et al, 2022), and a 7% increased risk of diabetic retinopathy in diabetic patients [HR = 1.07, 95% CI: 1.02-1.12, p=0.004] (Chen, Zhang, Hu, et al, 2023). Several cross-sectional studies utilising the ‘Retinal Age’ model explored the associations between certain lifestyle diseases and RAG.…”

“…The introduction of the retinal age gap (RAG), defined as the difference between calculated retinal age and chronological age, provides a valuable metric for assessing deviations from normal ageing. When compared to traditional biomarker approaches, often criticised for their cost, invasiveness, time-consuming nature, and suboptimal accuracy, the application of retinal age models provides a cost-effective, non-invasive, and readily accessible way of estimating biological age (Butler et al, 2004; Chen, Wang, et al, 2023). This makes it particularly suitable for large scale population studies.…”

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

“…Our model was validated using an independent dataset and demonstrated robustness, the ability to reflect the progressive nature of aging, and improved predictive accuracy compared to the recently reported approaches for BA prediction using retinal age ( 8 ). While previous studies have demonstrated facial or retinal age to be a biomarker of aging ( 8 , 10 , 11 , 24 ), our study expanded this potential by combining and integrating retinal, tongue, and facial images to gain a more complete portrait of BA. Our AI model achieved comparable BA prediction on retinal age to previous studies (around 2.5 y versus CA).…”

Histone mark age of human tissues and cells

“…Aging is marked by noticeable changes mainly at cellular and organismal levels, encompassing phenomena like epigenetic disturbances, genomic instability, proteostasis loss, nutrient-sensing deregulation, and dysbiosis [1,2]. This understanding has spawned a variety of omics age predictors in fields such as epigenetics, transcriptomics, proteomics, metabolomics, and microbiotics [3,4]. Most studies focus on on blood chemistry, transcriptomics, and DNA methylation, revealing several aging biomarkers, including those based on DNA methylation, telomere length, and proteomics [3,5].…”

“…This understanding has spawned a variety of omics age predictors in fields such as epigenetics, transcriptomics, proteomics, metabolomics, and microbiotics [3,4]. Most studies focus on on blood chemistry, transcriptomics, and DNA methylation, revealing several aging biomarkers, including those based on DNA methylation, telomere length, and proteomics [3,5]. Blood tests, facilitated by deep neural networks, offer notable accuracy with a median absolute error of about five to six years [6,7].…”