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Biological age estimation using circulating blood biomarkers.

Jordan Bortz1,2, Andrea Guariglia3,4, Lucija Klaric3

  • 1Humanity Inc, Humanity, 177 Huntington Ave, Ste 1700, Humanity Inc - 91556, Boston, MA, 02115, USA. jordan.bortz@humanity.email.

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Summary
This summary is machine-generated.

This study developed a more accurate biological age estimation using machine learning and 25 blood biomarkers from the UK Biobank. The new method improves mortality risk prediction compared to existing models, offering a practical tool for assessing physiological aging.

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Area of Science:

  • Biomedical Science
  • Gerontology
  • Computational Biology

Background:

  • Biological age, reflecting physiological decline, is a better indicator of healthspan than chronological age.
  • Blood biomarkers offer a promising avenue for estimating biological age and guiding interventions.
  • Existing biological age models, like PhenoAge, have limitations in predictive accuracy.

Purpose of the Study:

  • To enhance biological age estimation using machine learning and a comprehensive set of circulating biomarkers.
  • To develop a more accurate predictor of mortality risk than current blood-biomarker based models.
  • To establish a practical and cost-efficient method for biological age assessment.

Main Methods:

  • Utilized a dataset of 306,116 individuals from the UK Biobank with 60 circulating biomarkers.
  • Implemented an Elastic-Net derived Cox model to select 25 key biomarkers for mortality risk prediction.
  • Estimated biological age based on an individual's mortality risk relative to the same-sex population.

Main Results:

  • The developed model achieved a C-Index of 0.778, outperforming the PhenoAge model (C-Index = 0.750).
  • This represents an 11% relative increase in predictive value for mortality risk.
  • Using common clinical assay panels with imputation did not significantly reduce predictive accuracy.

Conclusions:

  • A practical and cost-efficient machine learning model for biological age estimation has been developed.
  • This improved measure of biological age, ranging from 20 years younger to 20 years older than chronological age, reveals significant aging signals in blood.
  • The method is accessible for general population use, aiding in healthspan research and interventions.