3. Systemic Biomarkers

Going further up in the hierarchical structure of the human body, the aging process can be ultimately observed at the system level as well. The functional decline in specific tissues and organs due to the accumulation of aged cells, complicated by intrasystem/intersystem interactions and environmental factors (e.g., diet, lifestyle) may be best measured by systemic molecular biomarkers. Although these biomarkers reflect deleterious consequences of aging rather than primary causes such as molecular damage, they provide essential input to developing prediction models for biological age comprising information from multiple systems throughout the body.[61]

3.3. Metabolic Markers

Altered energy metabolism is another major feature of aging. At the cellular level, it occurs predominantly due to the disruption inConsequent impairment in cellular utilization of metabolic substrates, especially glucose, leads to decreased energy production. The level of blood glucose is primarily determined by insulin secretion, which, in turn, is affected by gradual decline of beta-cell function and mass. [68] Glucose uptake and oxidation are often reduced later in life with the onset and progression of peripheral insulin resistance, particularly in skeletal muscle and adipose tissue, which can lead to weight loss and frailty observed in the elderly. [34] Population-based studies revealed that centenarians have higher insulin sensitivity and preserved beta-cell function than younger controls. [69] This suggests the importance of normal energy metabolism for healthy aging. Biomarkers of dysregulated glucose metabolism, especially glycated haemoglobin HbA1C, despite being typically 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]

3.1. Classical Blood-Based Biomarkers

Biochemical, and especially blood-based biomarkers are probably the most investigated group due to a large amount of data accumulated in clinical trials. A number of parameters routinely 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 targeting aging with caloric restriction or exercise. [63,84] In this regard, the methodology for extracting information with predictive value from a set of biomarkers is particularly important. Several mathematical algorithms 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 57 years (Table S1, Supporting Information); these clocks also show high heterogeneity of predictions between human populations. [86,87]

3.4. Hormones

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 decrease from the third decade of life [72] and serve as prospective 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 the liver in response to GH, is referred to as the somatopause. These hormones affect 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.

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