Due to the high dimensionality of omics datasets, the interpretation of results becomes especially important. By applying machine-learning techniques, different age predictors (epigenetics, transcriptomics, proteomics, metabolomics, etc.) can be developed. The prediction accuracy of such approaches increases with the increasing number of samples selected for modeling but only up to a limited extent. As already mentioned, people age differently, and their organ systems have different resistance to damage accumulation. Moreover, aging of one system may cause changes in other systems; for example, cardiovascular system decline can contribute to neurodegeneration, cognitive impairment, liver and kidney disease, complicating the interpretation of aging progression. If we want to improve biological age modeling, we should consider the personalized system-oriented trajectories of aging or, in other words, we need to formulate criteria for defining a personalized aging profile.

A particular area of interest is the metabolome of long-lived individuals. Cross-sectional metabolome profiling was carried out on humans from age 20 to 90 years old to identify potential markers associated with healthy aging [144]. Another study analyzed serum metabolites from more than 1,000 individuals aged 3-91 years old and identified 14 biomarkers that were significantly associated with chronological age [145]. These biomarkers were related to amino acid metabolism as well as energy metabolism.

Existing groups of biomarkers show distinct preferences for different stages of aging progression (Figure 2). As people age differently, the most accurate way to measure degeneration and predict future outcomes seems to be the use of integrative biomarker-based metrics able to define personalized trajectories of aging or aging profiles. Clinical settings' use of circulating systemic biomarkers, which are typically used in clinical practice for the diagnosis of medical conditions and diseases, is limited due to the labor-intensive and time-consuming modes of detection. Circulating systemic biomarkers, which are more relevant in the later stages of aging progression (Figure 2), are broadly incorporated in current models for biological age assessment and risk prediction. Classical anthropometric measurements, physical and cognitive functionality measurements, together with rising digital biomarkers, complement discrete molecular biomarkers in the evaluation of health at advanced ages.Persons' Aging Profile. Simple accumulation of biomarkers from all types of data (non-molecular, discrete molecular, and omics-pattern) would, if done, yield composite measurements of age-related changes. Genetic markers reflect predispositions to certain aging phenotypes characterized by the prevalence of specific pathological processes and age-related diseases. Nevertheless, there remains a strong influence of environmental factors that limits conclusions that can be made based on personalized genomic analyses. Molecular and cellular biomarkers, which reflect early-stage damage events, may be in demand for assessing cell functionality, albeit their applicability in the 1st generation metrics of aging is limited. The 1st generation metrics estimate biological age using a single dataset type and chronological age. The first version of epigenetic clocks, transcriptomic clocks, facial clocks were developed within this generation. The 2nd generation metrics of aging estimate biological age using a single dataset type, chronological age, and clinical biomarkers such as PhenoAge and GrimAge. Second-generation metrics integrate a single type of omics data with clinically relevant biomarkers and have greater predictive power while reflecting changes in individual health better. Integrative metrics can measure the rate of decline of organ systems rather than the biological age of the body as a whole. These metrics are being replaced by third-generation metrics that appeared in the last several years. Aging Profile is an example of a third-generation metric that estimates the biological age of organ systems by combining a single or multiple dataset types, chronological age, and system-specific sets of clinical and bioanalytical biomarkers (Figure 3). We aim to reveal aging profiles that are rather informative at the young adulthood and midlife stages. But once a person's profile is defined, it may be more reliable to apply predictive models personalized to each type of aging. Of course, this can only be implemented after training models on suitable datasets of appropriate size. Such personalized predictive models would present a powerful, third-generation tool for assessing aging. The authors are grateful to João Pedro de Magalhães and Yujia A. Chan for proofreading the manuscript and providing insightful comments. K.S.K., K.B., A.Y.K., N.S.V. work for Centaura AG, a company developing omics-based metrics of aging for the development of novel cellular interventions.

To date, and despite intense investigation, there is currently no single biomarker or single set of biomarkers that enable the reliable and accurate measurement of aging progression in humans. Due to the incredibly complex nature of aging, it is very likely that no such biomarker will ever be found. Instead of trying to find new good molecules, we need to focus on developing a new strategy for assessing aging in humans. Our main hopes lie in the area of omics approaches, which potentially cover the widest range of aging-related data. Taking into account the divergence in human aging scenarios, the modeling of personalized trajectories of aging or aging profiles by the integral application of all types of biomarkers will likely be the most accurate way to predict future outcomes and facilitate early stage cellular-based intervention aimed at slowing down aging.

Keywords: Aging, Aging Profiles, Biological Age, Biomarkers, Senescence.