4.correction: 

Many thousands of genes, gene products, epigenetic modifications, and/or metabolites are involved in the aging process. These technologies allow for the identification of distinct aging signatures and have tremendous potential for revealing age-associated changes in the very early stages when significant increases in known biomarkers are not yet observable. The next logical step is to integrate single omics data into one network. However, given the high heterogeneity of omics data and the multifactorial nature of aging, this is not as straightforward as it seems [89, 90]. The robustness of omics-derived results also needs to be taken into consideration. Problems typical to omics-based aging biomarkers include curse of dimensionality (high dimension, low-sample size data), heterogeneity of technologies leading to batch effects, high invasiveness of retrieving samples from some organs, lack of longitudinal data [89, 90].

Organ-specific markers are among the biomarkers identified to assess certain organ dysfunction. Well-known markers for the diagnosis and prognosis of heart failure include brain natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), and cardiac troponin [4]. Cystatin C is a newly established marker of chronic kidney disease that is able to detect renal dysfunction at an earlier stage than serum creatinine [81]. Classical biochemical biomarkers of liver dysfunction include albumin or alkaline phosphatase. ApoE level in cerebrospinal fluid (CSF) serves 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 established. For further reading on organ and tissue-specific biomarkers related to aging, we suggest reviews by Engelfriet et al. [8] and Khan et al. [9].

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 have demonstrated significantly better performance than individual ones in predicting future risk [64, 82, 83]. They show better responsiveness to geroprotective intervention, such as calorie restriction [83]. 

Human aging is a very complex trait affected by myriad factors aside from genetic predisposition. The magnitude of genetic contribution to human variation in aging is still debated, varying from around 15% to 30% [91], but probably overestimated due to assortative mating according to a recent suggestion [92]. The population specificity contributes to moderate convergence between 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 approach to examine the genetic architecture of polygenic traits (such as aging) in the genome. The increasing number of analyzed genomes of the elderly, in particular centenarians, provides insights into the genetic predisposition of exceptional longevity [94-96]. Heritability of longevity (survival beyond the oldest percentiles) increases sharply with survival beyond the age of 90 [97], suggesting the existence of specific gene 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 lifespan across studies [99]. These genes are implicated in various aspects of cellular and organismal functioning [100].

The original Horvath clock [106], as well as analogs [107], predict a person's age and risk of mortality and have been validated in several independent cohorts of individuals across the lifespan [108]. These clocks can measure age in many human tissues, although tissue-specific effects exist [104]. Therefore, a specific 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]. This was achieved by replacing chronological age with a surrogate measure called phenotypic age, estimated from a set of clinical biomarkers. The most recently presented GrimAge clock further elaborates the phenotypic age concept, combining clinical biomarkers with DNA methylation levels [110].