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Three-dimensional human facial morphologies as robust aging markers.

Weiyang Chen1, Wei Qian1, Gang Wu2

  • 11] Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China [2] University of Chinese Academy of Sciences, Beijing 100049, China.

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This summary is machine-generated.

Human 3D facial features can reliably predict biological aging, outperforming blood markers. This research maps the aging facial phenome, identifying individuals aging faster or slower than their chronological age.

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Area of Science:

  • Biogerontology
  • Medical Imaging
  • Facial Morphometry

Background:

  • Aging is linked to numerous complex diseases, necessitating reliable aging markers for risk assessment.
  • Current aging biomarkers lack reliability, hindering accurate prediction of age-associated disease risks.
  • Human 3D facial imaging offers a novel, non-invasive approach to assess biological aging.

Purpose of the Study:

  • To investigate the potential of 3D facial imaging features as reliable aging biomarkers.
  • To develop a comprehensive map of the aging human facial phenome.
  • To compare the efficacy of facial features versus blood profiles in predicting aging and health status.

Main Methods:

  • Collected over 300 3D human facial images and blood profiles from individuals aged 17 to 77.
  • Analyzed facial morphological profiles to create a comprehensive aging facial phenome map.
  • Developed a robust age predictor using quantitative facial features, such as eye slopes.

Main Results:

  • Identified quantitative facial features, like eye slopes, strongly correlating with chronological age.
  • Developed a facial age predictor revealing average age discrepancies of ±6 years, increasing post-40.
  • Identified slow and fast agers, validated by distinct health indicator levels, with facial features proving more reliable than blood profiles.

Conclusions:

  • 3D facial imaging features serve as more reliable aging biomarkers than blood profiles.
  • Facial age prediction offers a superior reflection of general health status compared to chronological age.
  • This study establishes facial morphometry as a promising tool for assessing biological aging and health outcomes.