Date.[122] The extremely wide concentration range of plasma proteins (up to 12 orders of magnitude) as well as the presence of highly abundant proteins such as serum albumin, complement factors, and immunoglobulins, which constitute more than 99% of the total protein mass,[123] impede the discovery and validation of novel biomarkers in clinical samples. Mass spectrometry-based proteomics remains the gold standard for protein biomarker screening, yet it lacks throughput in the validation phase.[124] The dynamic range problem could be partly overcome due to the emergence of affinity-based or targeted proteomics technologies. Thus, SOMAscan assay, a Slow Off-rate Modified Aptamer (SOMHuman sub-proteomes were analyzed by Fleischer and co-workers, who used large genome-wide RNA-seq data from human dermal fibroblasts of 133 individuals aged 1 to 94 years old to reveal a transcriptomic signature of biological age. Their ensemble machine learning method, based on linear discriminant analysis classifiers (i.e., transcriptomic clock), was shown to predict chronological age with a median absolute error of 4 years, outperforming previous epigenetic and transcriptomic age estimators, despite using one to two orders of magnitude fewer samples (Table S1, Supporting Information). The proteomics study of plasma proteins using SOMAscan assay discovered 13 proteins associated with chronological age and n = age-related phenotypes in a cohort of female twins (202), and of these proteins, ten were also replicated in the independent sex-mixed cohort (n = 677). Further validation using RNAseq data (n = 384) gave a total of 11 proteins replicated at the protein level or with consistent association with age at gene expression level. The estimated heritability of these proteins was low to moderate, confirming the proposition that human aging and longevity have only a modest genetic contribution, instead being strongly influenced by environmental factors. The strongest association with age was shown for chordin-like protein 1 (CHRDL1), which is involved in bone morphogenic protein signaling, retinal angiogenesis, and brain plasticity. Levels of CHRDL1 increase in older individuals, correlating with lower cardiovascular risk and higher birth weight (a well-known determinant of aging trajectories), suggesting its potential as a biomarker of healthy aging. Surprisingly, the discovered set of 11 proteins does not include broadly accepted aging biomarkers, such as IL-6 or CRP, which is explainable by insufficient proteome coverage and specificity.

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

MicroRNAs (abbreviated miRNAs) are small (from 21 to 23 nucleotides) noncoding RNA molecules that participate in the regulation 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. 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. Given their specific functions, which are loosely predictable by their sequence, circulating miRNAs have emerged as novel noninvasive indicators that reflect molecular mechanisms underlying age-related pathologies. In humans, numerous miRNAs have been identified to be up- or down-regulated during aging in whole blood, serum, or PBMCs, and the first microRNA age prediction model based on whole blood microRNA expression profiling has been established. In addition to prognostic usage, miRNA transfection and/or overexpression has utility as a therapeutic strategy for precise attenuation of age-related signaling pathways with potential impacts on human health span and lifespan.