Biomarkers selection and mathematical modeling in biological age estimation

Abstract

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual’s BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Exploring Multifunctional Markers of Biological Age in Farmed Gilthead Sea Bream (<em>Sparus aurata</em>). A Transcriptomic and Epigenetic Interplay for an Improved Fish Welfare Assessment Approach

“…The result is a reduced list of 10 potential biological age markers that require to be validated in independent studies. Afterwards, the generated knowledge (i.e., the gene expression data from these biomarkers) might be applied through an appropriate estimation algorithm (e.g., multiple linear regression) [123,124] to compute individual's biological age at a field scale as part of an improved welfare assessment system for auditing farmed fish welfare.…”

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

“…Biological aging clocks can be constructed based on a wide range of biological features/biomarkers as described above, and selected examples are summarized in Table 3. For recent exhaustive reviews on currently available biological aging clocks, see Moqri et al and Bafei and Shen [285,425]. [335], Metabolomic age (metabolomics markers) [33], iAge (immune protein markers) [36], GlycanAge (glycobiology markers) [37] * Indirectly trained on clinical blood markers.…”

Machine learning for human biological age estimation based on clinical blood analysis

“…This approach showed satisfactory results at the time, although it had vulnerabilities to multicollinearity and the biomarker paradox. In the [3] discusses a method for addressing the issue of multicollinearity through the use of the Z-score.…”

“…Another method, also discussed in the article [3], namely the principal component analysis method, was employed for the same task. To determine a set of biomarkers that were later orthogonally transformed into linearly uncorrelated data, several methods such as correlation analysis, stability analysis, and dimensionality analysis were utilized.…”