Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors DOI Creative Commons
Ying Zhang, Cheng Zhao, Lei Yu

et al.

Lupus Science & Medicine, Journal Year: 2024, Volume and Issue: 11(2), P. e001311 - e001311

Published: Nov. 1, 2024

Objective Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health quality life. While hereditary elements are essential in the onset SLE, external environmental influences also significant. Currently, there few predictive models for SLE that takes into account impact occupational living exposures. Therefore, we collected basic information, background exposure data from patients with to construct model facilitates easier intervention. Methods We conducted study comparing 316 individuals diagnosed 851 healthy volunteers case–control design, collecting their history data. Subjects were randomly allocated training validation groups using 70/30 split. Using three-feature selection methods, constructed four multivariate logistic regression. Model performance clinical utility evaluated via receiver operating characteristic, calibration decision curves. Leave-one-out cross-validation further validated models. The best was used create dynamic nomogram, visually representing predicted relative risk onset. Results ForestMDG demonstrated strong ability, area under curve 0.903 (95% CI 0.880 0.925) set 0.851 0.809 0.894) set, as indicated evaluation. Calibration curves accurate results along practical value. confirmed had accuracy (0.8338). Finally, developed nomogram use, which accessible following link: https://yingzhang99321.shinyapps.io/dynnomapp/ . Conclusion created user-friendly predicting based on Trial registration number ChiCTR2000038187.

Language: Английский

Per- and Polyfluoroalkyl Substances and Female Health Concern: Gender-based Accumulation Differences, Adverse Outcomes, and Mechanisms DOI
Xin Li, Minmin Hou, Feng Zhang

et al.

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

The deleterious health implications of perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely recognized. Females, in contrast to males, exhibit unique pathways for PFAS exposure excretion, leading complex outcomes. status females is largely influenced by hormone-related processes. have been reported be associated with various aspects female health, including reproductive system disorders pregnancy-related diseases. In this article, we provide insights into the correlations between female-prevalent Current epidemiological toxicological evidence has demonstrated that adverse effects on primarily attributed disruption hypothalamic-pituitary-gonadal (HPG) axis hormonal homeostasis. However, these findings do not sufficiently elucidate intricate associations specific Furthermore, autoimmune disorders, another category more prevalent women compared men, require additional investigation. Immune biomarkers pertinent observed exposure, although insufficient substantiate relations. Further thorough exploration encompassing studies essential elucidating inherent influence human pathologies. Additionally, comprehensive investigations issues beyond their functions essential.

Language: Английский

Citations

2
Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors DOI Creative Commons
Ying Zhang, Cheng Zhao, Lei Yu

et al.

Lupus Science & Medicine, Journal Year: 2024, Volume and Issue: 11(2), P. e001311 - e001311

Published: Nov. 1, 2024

Objective Systemic lupus erythematosus (SLE) is an autoimmune disease characterised by a loss of immune tolerance, affecting multiple organs and significantly impairing patients’ health quality life. While hereditary elements are essential in the onset SLE, external environmental influences also significant. Currently, there few predictive models for SLE that takes into account impact occupational living exposures. Therefore, we collected basic information, background exposure data from patients with to construct model facilitates easier intervention. Methods We conducted study comparing 316 individuals diagnosed 851 healthy volunteers case–control design, collecting their history data. Subjects were randomly allocated training validation groups using 70/30 split. Using three-feature selection methods, constructed four multivariate logistic regression. Model performance clinical utility evaluated via receiver operating characteristic, calibration decision curves. Leave-one-out cross-validation further validated models. The best was used create dynamic nomogram, visually representing predicted relative risk onset. Results ForestMDG demonstrated strong ability, area under curve 0.903 (95% CI 0.880 0.925) set 0.851 0.809 0.894) set, as indicated evaluation. Calibration curves accurate results along practical value. confirmed had accuracy (0.8338). Finally, developed nomogram use, which accessible following link: https://yingzhang99321.shinyapps.io/dynnomapp/ . Conclusion created user-friendly predicting based on Trial registration number ChiCTR2000038187.

Language: Английский

Citations

0