Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models

Mei Li, Liting Sheng, Zige Ding, Guangsheng Yu, Yichu Chen, Qitian Chen, Wei Shao, Bingxin Liu, Mulong Du, Dongying Gu, Silu Chen, Junyi Xin, Meilin Wang

Open source

DOI
10.1007/s00204-026-04454-4
Published
2026-06-02
Container
Archives of Toxicology
Publisher
Springer Science and Business Media LLC
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1007/s00204-026-04454-4,
  title = {Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models},
  author = {Mei Li and Liting Sheng and Zige Ding and Guangsheng Yu and Yichu Chen and Qitian Chen and Wei Shao and Bingxin Liu and Mulong Du and Dongying Gu and Silu Chen and Junyi Xin and Meilin Wang},
  year = {2026},
  journal = {Archives of Toxicology},
  doi = {10.1007/s00204-026-04454-4},
  url = {https://doi.org/10.1007/s00204-026-04454-4}
}

RIS

TY  - JOUR
TI  - Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models
AU  - Mei Li
AU  - Liting Sheng
AU  - Zige Ding
AU  - Guangsheng Yu
AU  - Yichu Chen
AU  - Qitian Chen
AU  - Wei Shao
AU  - Bingxin Liu
AU  - Mulong Du
AU  - Dongying Gu
AU  - Silu Chen
AU  - Junyi Xin
AU  - Meilin Wang
PY  - 2026
JO  - Archives of Toxicology
DO  - 10.1007/s00204-026-04454-4
UR  - https://doi.org/10.1007/s00204-026-04454-4
ER  - 

APA

Li, M., Sheng, L., Ding, Z., Yu, G., Chen, Y., Chen, Q., Shao, W., Liu, B., Du, M., Gu, D., Chen, S., Xin, J., & Wang, M. (2026). Associations of serum per- and polyfluoroalkyl substances (PFAS) mixture with the risk of chronic diseases: evidence from single‑ , multi-pollutant, and machine learning models. Archives of Toxicology. https://doi.org/10.1007/s00204-026-04454-4

Source records