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October 18, 2025Open Access

Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations

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Authors

SXSihao XiaoBLBowen LiuMAM. Austin Argentieri

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Overview

Machine learning analysis reveals smoking's impact on mortality and chronic diseases in varied populations, highlighting proteomic potential.

Key Points

  • The proteomic Smoking INdex (pSIN) showed a high accuracy for identifying smokers, achieving an AUC of 0.95.
  • pSIN significantly predicts all-cause mortality and the incidence of 18 chronic diseases, including cancers and heart diseases.
  • Genetic and environmental factors further modify pSIN, showing complexities beyond self-reported smoking history.
  • pSIN identifies former smokers at high risk for chronic diseases similar to current smokers, emphasizing the importance of proteomics.

Cite This Study

Xiao et al. (2025) studied this question.

synapsesocial.com/papers/68f35bfc73f0a7d050f47d18https://doi.org/10.21203/rs.3.rs-4998867/v1
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