Molecular and physiological manifestations and measurement of aging in humans

Abstract

SummaryBiological aging is associated with a reduction in the reparative and regenerative potential in tissues and organs. This reduction manifests as a decreased physiological reserve in response to stress (termed homeostenosis) and a time‐dependent failure of complex molecular mechanisms that cumulatively create disorder. Aging inevitably occurs with time in all organisms and emerges on a molecular, cellular, organ, and organismal level with genetic, epigenetic, and environmental modulators. Individuals with the same chronological age exhibit differential trajectories of age‐related decline, and it follows that we should assess biological age distinctly from chronological age. In this review, we outline mechanisms of aging with attention to well‐described molecular and cellular hallmarks and discuss physiological changes of aging at the organ‐system level. We suggest methods to measure aging with attention to both molecular biology (e.g., telomere length and epigenetic marks) and physiological function (e.g., lung function and echocardiographic measurements). Finally, we propose a framework to integrate these molecular and physiological data into a composite score that measures biological aging in humans. Understanding the molecular and physiological phenomena that drive the complex and multifactorial processes underlying the variable pace of biological aging in humans will inform how researchers assess and investigate health and disease over the life course. This composite biological age score could be of use to researchers seeking to characterize normal, accelerated, and exceptionally successful aging as well as to assess the effect of interventions aimed at modulating human aging.

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

“…The simultaneous monitoring of various parameters independently associated with aging represents a more robust approach than tracking any single blood‐based biomarker. [ 9,82 ] Combined panels of biomarkers demonstrated significantly better performance than individual ones in predicting future risk [ 64,82,83 ] and show better responsivity to geroprotective intervention, such as caloric restriction or exercise. [ 63,84 ] In this regard, the methodology for extracting information with predictive value from a set of biomarkers is particularly important.…”

“…In this review, we summarize data on all existing groups of biomarkers and biomarker-based age predictors (as other reviews have done in refs. [2,3,[4][5][6][7][8][9]), but with a focus on up-and-coming omics-based biomarkers, and outline our vision for aging assessment moving forward.…”

Physical fitness in community‐dwelling older adults is linked to dietary intake, gut microbiota, and metabolomic signatures

“…Throughout the course of aging, physical impairment and changes in body composition may arise along with a number of lifestyle-associated disorders influencing physical decline and ultimately frailty (Holm et al, 2014;Xue, 2011). Aging inevitably occurs in all organisms with genetics, epigenetics, and environmental exposures (e.g., diet, physical activity) being modulators of the bodily deterioration caused by biological age (Khan, Singer, & Vaughan, 2017). A number of guidelines toward dietary and daily physical activity recommendations are currently available; however, adherence remains a significant challenge (Gopinath, Russell, Kifley, Flood, & Mitchell, 2016).…”

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

“…Interestingly, individuals with the same chronological age (ChA) exhibit different trajectories of age-related biological deterioration, as measured by biomarkers of functional performance, tissue integrity and metabolic health 2,3 . This mismatch reflects two different concepts for ageing.…”

“…We next regressed out the motion time courses, the average cerebrospinal fluid (CSF) signal, the average white-matter signal and the average global signal. Finally, the functional data was spatially normalized to the MNI152 brain template, with a voxel size of 3*3*3 mm 3 . In addition to head motion correction, we performed scrubbing, by which time points with framewise displacement higher than 0.5 were interpolated by a cubic spline…”