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Area of Science:

  • Biomedical Science
  • Artificial Intelligence
  • Aging Research

Background:

  • Smoking is linked to cancer, cardiovascular disease, and mortality.
  • Current methods for assessing smoking's impact on biological aging are lacking.
  • Affordable and informative tests are needed to quantify smoking-induced aging.

Purpose of the Study:

  • To determine if blood biochemistry and cell counts can predict smoking status using AI.
  • To quantify the acceleration of biological aging in smokers compared to non-smokers.
  • To explore AI's potential in assessing lifestyle factors' effects on aging.

Main Methods:

  • Utilized supervised deep learning techniques for age-prediction models.
  • Analyzed blood biochemistry and cell count data.
  • Employed AI models to compare biological aging rates between smokers and non-smokers.

Main Results:

  • Smoking status was predicted using blood test results and AI.
  • Smokers exhibited significantly higher biological aging rates than non-smokers.
  • Female smokers showed twice the predicted age, males 1.5 times, compared to chronological age.

Conclusions:

  • Deep learning analysis of routine blood tests can predict smoking status.
  • AI-driven analysis quantifies accelerated biological aging due to smoking.
  • This approach may enhance or replace self-reporting and assess other environmental factors.