Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers DOI

Youyi Wu,

Guoliang Li, Ming Dong

et al.

Journal of Pharmaceutical and Biomedical Analysis, Journal Year: 2025, Volume and Issue: 259, P. 116761 - 116761

Published: Feb. 19, 2025

Language: Английский

Public health concerns of multifaceted exposures to metal and metalloid mixtures: a systematic review DOI
Godswill J. Udom,

David Iyaye,

Benjamin Oritsemuelebi

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(5)

Published: April 2, 2025

Language: Английский

Citations

1

Mediating role of systemic immune-inflammation index between heavy metal exposure and hepatic steatosis/hepatic fibrosis: evidence from NHANES 2005–2020 DOI Creative Commons
Ningning Wang, Xuying Li, Rui He

et al.

Frontiers in Nutrition, Journal Year: 2025, Volume and Issue: 12

Published: May 21, 2025

Background Moderate heavy metals can lead to the occurrence of liver injury, but specific mechanism remains unclear. Methods This study, based on data from National Health and Nutrition Examination Survey (NHANES), analyzed associations between 10 hepatic injury in 5,613 adults, with a focus mediating role Systemic Immune-Inflammation Index (SII). Partial correlation analysis, weighted linear regression, quantile sum (WQS) mediation effect models were used study. Results SII showed significant negative correlations fibrosis markers (FIB-4: r = −0.290; NFS: −0.382, both P < 0.001) no association steatosis indices. Arsenic (As), cobalt (Co), cesium (Cs) identified as critical linking indicators SII. As mediated its pro-fibrotic effects by completely suppressing (OR 0.0220–0.0581), while Co promoted NFS risk through complete (Q2 vs. Q1 OR 1.26). Conversely, Cs exhibited anti-fibrotic protectionvia positive Conclusion The findings demonstrate that Heavy differentially regulate immune-inflammatory pathways influence progression, providing new evidence for mechanisms environmental exposure-induced injury.

Language: Английский

Citations

0

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers DOI

Youyi Wu,

Guoliang Li, Ming Dong

et al.

Journal of Pharmaceutical and Biomedical Analysis, Journal Year: 2025, Volume and Issue: 259, P. 116761 - 116761

Published: Feb. 19, 2025

Language: Английский

Citations

0