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Multi-omics approaches to human biological age estimation.

Ilya Solovev1, Mikhail Shaposhnikov2, Alexey Moskalev3

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Multi-omics research integrates diverse biological data to understand aging. This approach aids in developing accurate biomarkers for predicting biological age and advancing translational gerontology.

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Area of Science:

  • Gerontology and Molecular Biology
  • Biogerontology and Systems Biology

Background:

  • Aging research faces challenges in distinguishing causal factors from effects.
  • Understanding the complex nature of aging requires holistic approaches.

Purpose of the Study:

  • To review the application of multi-omics in biogerontology for aging biomarker development.
  • To highlight innovations in bioinformatics for predicting human biological age.

Main Methods:

  • Comprehensive literature and patent review.
  • Analysis of multi-omics datasets (metabolome, methylome, transcriptome, proteome, metagenome).
  • Focus on bioinformatics innovations for biological age prediction.

Main Results:

  • Multi-omics enables the evaluation of complex quantitative and phenotypic aging biomarkers.
  • Identified patents, datasets, and studies utilizing multi-omics for aging research.
  • Bioinformatics tools are crucial for integrating multi-omics data to predict biological age.

Conclusions:

  • Multi-omics provides a holistic view of the aging landscape, uncovering new mechanisms.
  • The integration of diverse omics data is essential for robust aging biomarker design.
  • Findings support translational applications for clinicians in gerontology.