3.1. Classical Blood-Based Biomarkers

Biochemical, and especially blood-based biomarkers are proba- bly the most investigated group due to a large amount of data accumulated in clinical trials. A number of parameters rou- tinely measured in a comprehensive metabolic panel (glucose, albumin, creatinine, alkaline phosphatase, bilirubin), complete blood count (red blood cell counts, hemoglobin, hematocrit), and lipid profile (total cholesterol, low- and high-density lipoprotein cholesterol, triglyceride concentration) were found to correlate with age, age-related disease, and mortality.[8,62] For instance, lipid fractions, glucose, and albumin are among the recently identified predictors of long-term all-cause mortality.[63] But generally, the accuracy of aging prediction by routine clinical chemistries is poor,[64] and for this reason, such markers were omitted from consideration when the Targeting Aging with MEt-

caloric restriction or exercise.[63,84] In this regard, the methodol- ogy for extracting information with predictive value from a set of biomarkers is particularly important. Several mathematical algo- rithms have been proposed for calculating biological age from multiparameter data, including the multiple linear regression (MLR), the principal component analysis (PCA), and the Klemera and Doubal method.[85] Recently created “blood aging clocks” based on various machine learning methods have a mean average error ranging between 5–7 years (Table S1, Supporting Informa- tion); these clocks also show high heterogeneity of predictions between human populations.[86,87]

used for the diagnosis of diabetes mellitus, can also be applied as prognostic biomarkers for non-diabetics[70]; however, given the complexity of regulation of glucose and insulin levels, metabolic measures can be difficult to interpret.[8]

3.4. Hormones

in hormone production. Declines in sex hormones, estrogen, and testosterone (menopause and andropause, respectively) trigger downstream fading effects, such as muscle mass loss and physical frailty.[71] Dehydroepiandrosterone (DHEA) and its ester metabolite dehydroepiandrosterone sulfate (DHEA-S) are essential steroid precursors for sex hormones that profoundly decreases from the third decade of life[72] and serve as prospec- tive candidate biomarkers of human aging.[73] The gradual and progressive decline in growth hormone (GH), secreted by the pituitary gland, and insulin-like growth factor 1 (IGF-1), produced mainly in liver in response to GH, is referred to as the somatopause. These hormones affects insulin secretion and sensitivity in adults,[8] and it has been shown that individuals with exceptional longevity demonstrate decreased levels of systemic GH[74] and IGF-1,[75] and are enriched in gene variants causing disturbance of somatotropic axis.[76] Among hormones of the thyroid gland, low levels of triiodothyronine are associated with an increased risk for morbidity and mortality.[77,78] Age- dependent elevation of circulating metabolic hormones such as adipokines (leptin, adiponectin) are linked to increased risk of metabolic syndrome and mortality in older adults.[79] Vitamin D deficiency is associated with increased overall morbidity and cognitive impairment.[77,80]

However, some indicators of human aging demonstrate non- linear age-related changes and therefore cannot be interpreted using standard linear regression, which is commonly applied to composite panels of biomarkers.[7] Moreover, certain biomarkers can increase in one tissue and decrease in another at the same time,[88] suggesting that not only people, but even different body systems age at different rates. The concept of a biological signature of aging[83] offers potential compromise or alternative ways of using a panel of biomarkers to track individual aging trajectories. Human signatures (i.e., patterns of blood-based biomarker levels) associated with more or less intensive aging and/or increased risk of a specific disease have been found. The main advantage of such an approach is its ability to capture the interdependence between aging and age-associated diseases that would be difficult to reveal using standard regression modeling.

4. Omics-Based Molecule-Pattern Biomarkers

Over the past decade, numerous attempts have been made to un- ravel aging mechanisms via omics approaches. High throughput sequencing, mass-spectrometry, and other technologies opened opportunities to quantify aging not just with a small number of well-studied and carefully picked biomarkers, but with anal- ysis of many thousands of genes, gene products, epigenetic modifications, and/or metabolites. These technologies permit the identification of distinct aging signatures and have tremen- dous 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 the integration of single omics data into one network. Given the high hetero- geneity of omics data and multifactorial nature of aging, this is not as straightforward as it seems.[89,90] The robustness of omics-derived results also needs consideration. Curse of dimen- sionality (high dimension, low-sample size data), heterogene- ity of technologies leading to “batch effects,” high invasiveness of retrieving samples from some of the organs, lack of lon- gitudinal data—are all problems typical to omics-based aging biomarkers.

3.5. Organ-Specific Markers