blood oxygen levels (by noninvasive pulse oximeter). These mea- surements complement and can even replace traditional physio- logical tests.[141]

Biochemical sensors require body fluids for analysis and can be noninvasive in the case of collecting secreted fluids (saliva, tears, sweat, skin interstitial fluids, urine, or stools), or invasive (blood, biopsies), when sampling is performed by the penetration of the body through injection or incision.[142] Biochemical sen- sors provide measurements of pH, blood oxygen saturation, pro- tein concentration, amino acids, lipids, electrolytes and metabo- lites, hormones, and pathogenic bacteria. The incorporation of the automated dosing systems allows not only monitoring of biomarkers concentration in the body, but also automatically con- trolled drug release, such as in the artificial pancreas device sys- tem, where a glucose sensor placed under the skin is linked to an insulin infusion pump.[143]

Substantial progress in omics approaches provides an oppor- tunity for more multilayer assessment of aging in humans.[90] Omics-based biomarkers can be equally applicable throughout the human lifespan, and at all levels of biological organization, from single cell profiling to systemic phenotyping (Figure 2). The advantage of omics approaches over systemic biomarkers is that they help to reveal underlying degenerative processes at the ear- liest stages when no strong correlation between single molecular biomarkers and associated degenerative processes exist.

Wearable devices with wireless modules are particularly valu- able in remote patient monitoring, allowing real-time consul- tation and immediate medical interventions in life-threatening conditions. They offer advantages over standard functional tests in the clinical, as they generate continuous data, providing a de- termination of an individual’s behavior in normal settings and longitudinally.

Due to the high dimensionality of omics datasets, the inter- pretation of results becomes especially important. By applying machine-learning techniques, different age predictors (epigenet- ics, transcriptomics, proteomics, metabolomics, etc.) can be de- veloped. The prediction accuracy of such approaches increases with the increasing number of samples selected for modeling, but only up to a limited extent. As it has been already men- tioned, people age in different ways, and their organ systems have different resistance to damage accumulation. Moreover, ag- ing of one system may cause changes in other systems, for example, cardiovascular system decline can contribute to neu- rodegeneration, 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 person’s aging profile. Simple accumulation of biomarkers from all types of data (non-molecular, discrete molecular, and omics-pattern) would, if

6. Future Perspective of Aging Assessment in Human

Distinct aging biomarkers measure different aspects of aging progression, and there is a clear trend toward composite mea- surements of age-related changes. Genetic markers reflect pre- dispositions to certain aging phenotypes, characterized by the prevalence of specific pathological processes and age-related dis- eases; nevertheless, there remains a strong influence of environ- mental factors, limiting conclusions that can be made based on personalized genomic analyses. Molecular and cellular biomark- ers, which reflect early-stage damage events may be in demand for assessment of cell functionality, albeit their applicability in

  1st generationmetrics of agingEstimate biological age usinga single dataset typeand chronological age|First version of epigenetic clocks,transcriptomic clocks, facial clocks  2nd generationmetrics of agingEstimate biological age usinga single dataset type, chronologicalage and clinical biomarkers|PhenoAge, GrimAge  3rd generationmetrics of agingEstimate biological age of organsystems by combining a single ormultiple dataset types, chronologicalage, and system-specific setsof clinical and bioanalyticalbiomarkers|Aging Profile  

Figure 3. Evolution of metrics of biological aging. First-generation metrics of aging were based on one type of dataset (DNA methylation,[106] tomic data,[86] facial images,[144] etc.) correlated with chronological age. These estimators, or aging clocks, measured the deviation of predicted from actual age (biological age acceleration), which was found to be correlated with many age-related diseases and conditions. Now, metrics of aging are being replaced by second-generation metrics that have appeared in the last several years[109,110]. Second-generation metrics aging integrate single type of omics data with clinically relevant biomarkers and, as a result, have greater predictive power and better reflect changes individual health. Integrative metrics able to measure the rate of decline of organ systems, rather than the biological age of the body as a whole,

Acknowledgements

we aim to reveal aging profiles, be rather informative at the young adulthood and midlife stages. But once a person’s profile is de- fined, it may be more reliable to apply predictive models person- alized to each type of aging. Of course, this can only be imple- mented after training of models on suitable datasets of appro- priate size. Such personalized predictive models would present a powerful, third-generation tool for assessing aging (Figure 3).

The authors are grateful to João Pedro de Magalhães and Yujia A. Chan proofreading of the manuscript and insightful comments.

Conflict of Interest

K.S.K., K.B., A.Y.K., N.S.V. work for Centaura AG, a company omics-based metrics of aging for development of novel cellular tions.

7. Conclusions