More recently, the first muscle tissue-specific transcriptomic aging clock was proposed by applying supervised machine learn- ing algorithms on gene expression data from young and old healthy donors (Table S1, Supporting Information)[117]; several candidate biomarkers of human muscle aging, including those which were not yet studied in this context, were reported.

4.4. miRNA Omics

MicroRNAs (abbreviated miRNAs) are small (from 21 to 23 nu- cleotides) noncoding RNA molecules that participate in regu- lation of gene expression by base pairing to complementary mRNA sequences triggering mRNA cleavage and translational inhibition. miRNAs-mediated RNA degradation contributes to a lack of concordance between transcript levels and their protein products.[118] Despite being predominant within the cell where they are expressed, miRNAs can also be found circulating in blood serum, urine or saliva, within exosomes/microvesicles or bound to protein or lipoprotein factors.[119] Given their specific functions, which are loosely predictable by their sequence, cir- culating miRNAs have emerged as novel noninvasive indica- tors that reflect molecular mechanisms underlying age-related pathologies.[120] In humans numerous miRNAs have been iden- tified to be up- or down-regulated during aging in whole blood, serum, or PBMCs,[121] and the first microRNA age prediction model based on whole blood microRNA expression profiling has been established.[120] In addition to prognostic usage, miRNAs transfection and/or overexpression has utility as a therapeutic strategy for precise attenuation of age-related signaling pathway with potential impacts on human health span and life span.

The SOMAscan-based proteomic measurements of 1301 plasma proteins in a population of 240 healthy men and women (22–93 years old) identified growth differentiation factor 15 (GDF15) as the strongest biomarker, positively associated with age.[130] The proteomic age predictor was developed based on rel- ative concentrations of 76 proteins that highly correlated with chronological age using elastic net regression models (Table S1, Supporting Information).

Continually improving technologies should lead to better age predictors as more proteins become identifiable. The newest study on 2925 plasma proteins from 4263 young adults to nona- genarians (18–95 years old) revealed specific waves of changes in the proteome in the fourth, seventh, and eighth decades of life, and identified a highly accurate plasma proteomic clock consist- ing of 373 proteins (Table S1, Supporting Information).[131]

Besides the blood proteome, other clinical samples can be an- alyzed. The first broad application of SOMAscan technology to CSF from 90 cognitive healthy donors of different ages revealed 82 age-associated proteins.[88] Most of them were novel biomark- ers of aging that are prominently associated with immune sys- tem activation or response to injury. Despite the fact that the sub-

4.5. Proteomics

The number of proteins is at least two orders of magnitudes higher than the number of genes, because of alternative splic- ing and post-translational protein modifications.[90] In human plasma, more than 10 000 proteins have been identified to



jects included in this study were judged to be cognitively normal, the probability remains that some of elderly patients could have had undetected, preclinical neurodegenerative disease, suggest- ing that some of identified age-correlated biomarkers are instead disease-correlated biomarkers.

out on several cohorts of centenarians[135] and offsprings of nonagenarians[136]; metabolite profilescaptured earlierinlife and associated with the future ability to achieve longevity were also studied.[137] Centenarians were revealed to have metabolic pheno- types distinct from that of elderly individuals, with alterations in lipid and fatty acid, nucleic acid, and citric acid cycle metabolism; however, metabolic patterns on different cohorts had only limited overlap.

The most recent proteome-wide study using mass- n = spectrometry-based quantification in a discovery ( 104) and n = replication cohort ( 39) of initially cognitively unimpaired, longitudinally assessed older-adult post-mortem brain donors, revealing 579 proteins associated with cognitive decline, among which 38 replicated proteins were associated independently of 𝛽 traditional neuropathologies, such as -amyloid plaques and neurofibrillary tangles.[132] Notably, individuals with cognitive stability demonstrated increased neuronal mitochondrial activity and decreased inflammation and apoptosis, suggesting that proteomics could reveal not only novel protein targets but even novel age-related pathways.

5. Non-Molecular Biomarkers

5.1. Biomarkers of Physical Capability and Physiological Function

Anthropometric measurements and noninvasive physiological tests represent one clear way to quantify the age-related changes that occur over time. Handgrip strength and gait speed, the two most common measures of physical capability, reflect the gen- eralized weakness and frailty caused by the progressive loss in skeletal muscle mass and strength, known as sarcopenia. Despite the apparent simplicity, these tests predict the risk of many health outcomes.[138] The functional decline of certain organs can be re- vealed by physiological tests, such as pulmonary function tests, cognitive tests, or blood pressure and pulse-wave velocity for as- sessment of cardiovascular system performance. Altered body composition and bone health can be measured by dual-energy X- ray absorptiometry (DEXA); as a result, the risk of osteoporosis or fracture can be evaluated.[139] The decrease in skin’s strength and elasticity, as well as diminution of subdermal fat, can be mon- itored by photographic imaging approaches, such as 2D or 3D facial imaging. Established 3D facial image-based age predictors estimate age with a high level of accuracy (Table S1, Supporting Information).[140]