Biological and Functional Biomarkers of Aging: Definition, Characteristics, and How They Can Impact Everyday Cancer Treatment

Abstract

Purpose of Review Recognize which are the elements that predict why a person is aging faster or slower and which intervention we can arrange to slow down the process, which permits to prevent or delay the progression of multimorbidity and disability. Recent Findings Aging is a complex process that leads to changes in all the systems of the body and all the functions of the person; however, aging develops at different rates in different people, and chronological age is not always consistent with biological age. Summary Gerontologists are focused not only on finding the best theory able to explain aging but also on identifying one or more markers, which are able to describe aging processes. These biomarkers are necessary to better define the aging-related pathologies, manage multimorbidity, and improve the quality of life. The aim of this paper is to review the most recent evidence on aging biomarkers and the clusters related to them for personalization of treatments.

Different Impact of Definitions of Sarcopenia in Defining Frailty Status in a Population of Older Women with Early Breast Cancer

“…This is a severe matter of concern when evidence-based guidelines are applied to older populations, with negative consequences on survival [7]. Thus, a personalized approach based on individual patients' clinical conditions and functionality rather than age [8][9][10][11] should be considered the standard of care for older women with breast cancer.…”

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

“…Our Oncogeriatric Unit implemented an innovative paradigm of care based on the combination of CGA with the longitudinal assessment of biological and functional biomarkers of ageing [ 29 ]. This approach is based on the integration of the multidomain nature of CGA with the new concept of geroscience, according to which perturbations in specific molecular pathways or “hallmarks of aging” are responsible for the development of age-related conditions [ 26 , 27 ]. The aim of this new care model is to capture elements (and associated biomarkers) which reflect age-related derangements in physiological systems with the objective to develop interventions to manage multimorbidity and to improve physical function and overall quality of life of older persons with cancer [ 28 ].…”

“…Machine Learning (ML) algorithms can help identifying clinically meaningful patterns by analyzing RWD (i.e., demographics, ICD v. 10 chronic diagnoses, prescribed drugs, and socio-economic status) from elderly populations (≥65 years) [ 42 ] to analyze the trajectories of mortality [ 43 ]. Genetic predisposition and blood-derived determinants should be investigated together with other well-established personal and contextual determinants of multimorbidity (i.e., sex, social and living environments, education, and physical function parameters) to design strategies to comprehensively target multimorbidity [ 27 ]. While the definition of molecular/genetic targets for multimorbidity is still in its infancy, a consensus panel of geriatricians and gerontologists recently proposed a conceptual framework to select circulating biomarkers to be used in “geroscience-guided” clinical trials [ 38 ].…”

“… Visual representation of the literature. AGING: [ 16 , 17 , 27 , 28 ]. AGING and CANCER: [ 16 , 17 , 18 , 26 , 29 ]; MULTIMORBIDITY: [ 15 , 27 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]; AI SOLUTIONS: [ 20 , 21 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

“…AGING: [ 16 , 17 , 27 , 28 ]. AGING and CANCER: [ 16 , 17 , 18 , 26 , 29 ]; MULTIMORBIDITY: [ 15 , 27 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]; AI SOLUTIONS: [ 20 , 21 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. AGING and GYNECOLOGICAL CANCER: [ 13 , 49 , 50 , 51 , 52 , 53 , 54 ]; MULTIMORBIDITY: [ 22 , 23 , 25 , 49 , 50 , 53 , 55 , 56 ]; AI SOLUTIONS: [ 24 , 57 , 58 ].…”