Importantly, epigenetic age may respond relatively rapidly to anti-aging interventions, offering a unique opportunity for validating strategies to delay aging, instead of conducting high-cost and long-term longitudinal trials. Recently, TRIIM (Thymus Regeneration, Immunorestoration, and Insulin Mitigation) treatment with a cocktail of substances comprising human growth hormone, proto-hormone DHEA, and metformin demonstrated epigenetic rejuvenation. It reversed epigenetic age by 2.5 years in a one-year treatment window [111]. Despite these encouraging results, little is known about which aging mechanisms epigenetic clocks reflect and whether the changes in the epgenomic level. A large-scale GWAS study has revealed genomic regions that are implicated in lifespan heritability.[101] The polygenic risk score, which predicts human predisposition to a longer life, is established based on weighted contributions from these relevant genetic variants.[101] However, even such a large-scale GWAS failed to completely explain the magnitude of heritability of both lifespan and longevity, suggesting that further investigations using different populations and ethnic groups are needed. Compared to lifespan, genetic variants that influence health span or disease-free survival have been less studied at the whole-genome significance level. A recently performed GWAS in a relatively large human cohort (n = 300,447) found 12 candidate loci associated with health span.[96] Among them, three single nucleotide polymorphisms located at or near CDKN2B (cyclin-dependent kinase inhibitor), LPA (apolipoprotein A), and HLA-DQA1 (leukocyte antigen DQA1) loci were identified, which were previously reported to be associated with the longevity phenotype.[95,102] Surprisingly, gene variants of APOE were not revealed. This is an important example of the differences between the genetic signatures of health span and lifespan or, in other words, between late-life severe diseases leading to death and early age pathological conditions corresponding to human health with age.[96]

4.3 Transcriptomics

The transcriptome is the entirety of RNA transcripts, which can be divided into coding and noncoding RNAs, the latter acting as regulatory elements.[112] Similar to the epigenome, the transcriptome changes significantly during aging.[90] Due to the rise of technologies such as NGS and microarrays (DNA chips), the field of transcriptomics has significantly evolved, making it possible to study gene alterations with age on large sample sets. Transcriptomic studies have already been performed for many types of human cells and tissues, and age-related changes in transcriptome were shown to be highly tissue-specific.[90,113] The first large-scale meta-analysis (n = 14,983) of human whole-blood gene expression revealed a compendium of genes (1497) differentially expressed with chronological age.[114] Identified genes were consistent with well-known aging mechanisms, including dysregulation of transcription and translation, ribosome and mitochondria dysfunction, DNA damage accumulation, immune senescence, and altered metabolism. A principal transcriptomic age predictor was established based on 11,908 significantly expressed genes with the average absolute difference between predicted age and chronological age of 7.8 (Table S1, Supporting Information).

4.2 Epigenomics

Epigenetic modifications including DNA methylation as well as histone methylation and acetylation are highly dynamic processes that occur over the human lifespan and are influenced by environmental and genetic factors.[103] These modifications also change during aging, making them a potential biomarker. While the total DNA methylation level slowly decreases with age, cytosine methylation at specific loci containing CpG dinucleotides (referred to as CpG sites) changes differently with age, becoming both hyper- or hypomethylated in different genomic locations.[104,105] A significant breakthrough in biological age measurement came with the development of well-known epigenetic clocks based on a correlation between chronological age and methylation status of selected CpG sites. Methy- Integrative metabolomics and transcriptomics were later applied to show global metabolic adaptations occurring in epidermal skin during aging.[115] Microarray analysis of the transcriptome was performed on epidermal tissue samples from 24 young and 24 old donors, of which 23 donors of each group were also included in the metabolomics analysis. The study's results revealed age-dependent reduction of coenzyme Q10 levels, as well as altered activity in upper glycolysis and glycerolipid biosynthesis or decreased protein and polyamine biosynthesis.[115] Importantly, the identified compendium of age-dependent genes (1053 transcripts with decreased and 932 transcripts with increased levels) had no significant overlap with reported genes predicted to be involved in aging in humans in other tissues. This indicates that each tissue of our body has its own age-dependent gene expression pattern.