3.5. Organ-Specific Markers

Among biomarkers identified to assess certain organ dysfunc- tion are well-known markers for the diagnosis and prognosis of heart failure: brain natriuretic peptide (BNP), N-terminal pro- BNP (NT-proBNP), and cardiac troponin[4]; the newly estab- lished marker of chronic kidney disease cystatin C, which is able to detect renal dysfunction at an earlier stage than serum creatinine[81]; classical biochemical biomarkers of liver dysfunc- tion such as albumin or alkaline phosphatase; and ApoE level in cerebrospinal fluid (CSF) as a marker of neurodegenerative disease.[22] The potential impact of these medical biomarkers in aging research and long-term risk prediction is yet to be estab- lished. For further reading on organ and tissue specific biomark- ers related to aging we suggest reviews by Engelfriet et al.[8] and Khan et al.[9]

3.6. Multiple Biomarkers Panel

4.1. Genomics

The simultaneous monitoring of various parameters indepen- dently associated with aging represents a more robust approach than tracking any single blood-based biomarker.[9,82] Combined panels of biomarkers demonstrated significantly better perfor- mance than individual ones in predicting future risk[64,82,83] and show better responsivity to geroprotective intervention, such as

Human aging is a very complex trait, affected by myriad factors aside from genetic predisposition. The magnitude of genetic con- tribution to human variation in aging is still debated, varying from around 15% to 30%,[91] but probably overestimated due to assortative mating in accordance to a recent suggestion[92]; the population specificity contributes to moderate convergence be-

lation clocks have been extensively studied and are considered to be the most accurate in predicting chronological age (Table S1, Supporting Information).

tween research results, since genes that affect human aging in one population may not be replicated in another.[93]

Genome-wide association studies (GWAS) are a powerful ap- proach to examine the genetic architecture of polygenic traits (as aging is) in the genome. The increasing number of ana- lyzed genomes of the elderly, in particular, centenarians, pro- vides insights into the genetic predisposition of exceptional longevity.[94–96] Heritability of longevity (survival beyond the old- est percentiles) increases sharply with survival beyond the age of 90,[97] suggesting the existence of specific genes variants that centenarians share.[98] However, only the APOE (apolipoprotein E protein), FOXO3 (stress-induced transcription factor forkhead box O3), and 5q33.3 (longevity locus on chromosome 5q33.3) loci are consistently associated with longer life span across studies.[99] These genes are implicated in various aspects of cellular and or- ganismal functioning.[100]

The original Horvath clock,[106] as well as analogs,[107] that predict a person’s age and risk of mortality, have been vali- dated in several independent cohorts of individuals across the lifespan.[108] These clocks can measure age in many human tis- sues, although tissue-specific effects exist.[104] Therefore, a spe- cific algorithm is necessary for optimal accuracy in each tissue. The second-generation clock “PhenoAge” demonstrates better predictive power for a variety of aging outcomes[109] and this was achieved by replacing chronological age with a surrogate measure called “phenotypic age,” estimated from a set of clinical biomark- ers. The most recently presented “GrimAge” clock further elabo- rates the “phenotypic age” concept, combining clinical biomark- ers with DNA methylation levels.[110]

Importantly, epigenetic age may respond relatively rapidly to anti-aging interventions, offering a unique opportunity for vali- dating strategies to delay aging, instead of conducting high-cost and long-term longitudinal trials. Recently, TRIIM (Thymus Re- generation, Immunorestoration, and Insulin Mitigation) treat- ment with a cocktail of substances, comprising human growth hormone, proto-hormone DHEA, and metformin, demonstrated epigenetic rejuvenation, reversing epigenetic age by 2.5 years in a one-year treatment window.[111] Despite these encouraging re- sults, little is known about which aging mechanisms epigenetic clocks reflect and whether the changes in the epigenome with age have a causal role.

Most recently, the largest GWAS on human lifespan to date (more than half a million participants) validated seven pre- viously identified loci, including APOE, FOXO3, and 5q33.3, and proposed five novel genomic regions implicated in lifes- pan heritability.[101] The polygenic risk score that 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 suggest- ing that further investigations using different populations and ethnic groups are needed.

Compared to life span, genetic variants that influence the health span, or disease-free survival, have less been studied at the whole-genome significance level. A recently performed GWAS n = in a relatively large human cohort ( 300 447) found 12 can- didate loci associated with health span.[96] Among them, three single nucleotide polymorphism located at or near CDKN2B (cyclin-dependent kinase inhibitor), LPA (apolipoprotein A), and HLA-DQA1 (leucocyte antigen DQA1) losi were identified which were previously reported to be also associated with the longevity phenotype.[95,102] Surprisingly, however, gene variants of APOE were not revealed. This is an important example of the differ- ences between the genetic signatures of the health span and lifes- pan, 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