Association between co-exposure to phenols, phthalates, and polycyclic aromatic hydrocarbons with the risk of frailty

• Published in: Environmental science and pollution research international Vol. 30; no. 48; pp. 105181 - 105193
• Main Authors: Li, Wenxiang, Huang, Guangyi, Tang, Ningning, Lu, Peng, Jiang, Li, Lv, Jian, Qin, Yuanjun, Lin, Yunru, Xu, Fan, Lei, Daizai
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023; Springer Nature B.V
• Subjects: 2-Methylisoborneol; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Contaminants; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Exposure - analysis; Environmental factors; Environmental Health; Exposure; Frailty; Frailty - epidemiology; Humans; Machine learning; Nutrition Surveys; Organic contaminants; Phenols; Phenols - analysis; Phthalate esters; Phthalates; Polycyclic aromatic hydrocarbons; Polycyclic Aromatic Hydrocarbons - analysis; Prediction models; Regression analysis; Regression models; Research Article; Surveys; Synergistic effect; Waste Water Technology; Water Management; Water Pollution Control; Phenols; Interpretable machine learning; Frailty; Polycyclic aromatic hydrocarbons; Co-exposure; Phthalates
• DOI: 10.1007/s11356-023-29887-7
• ISSN: 1614-7499; 0944-1344

The phenomenon of population aging has brought forth the challenge of frailty. Nevertheless, the contribution of environmental exposure to frailty remains ambiguous. Our objective was to investigate the association between phenols, phthalates (PAEs), and polycyclic aromatic hydrocarbons (PAHs) with frailty. We constructed a 48-item frailty index using data from the National Health and Nutrition Examination Survey (NHANES). The exposure levels of 20 organic contaminants were obtained from the survey circle between 2005 and 2016. The association between individual organic contaminants and the frailty index was assessed using negative binomial regression models. The combined effect of organic contaminants was examined using weighted quantile sum (WQS) regression. Dose–response patterns were modeled using generalized additive models (GAMs). Additionally, an interpretable machine learning approach was employed to develop a predictive model for the frailty index. A total of 1566 participants were included in the analysis. Positive associations were observed between exposure to MIB, P02, ECP, MBP, MHH, MOH, MZP, MC1, and P01 with the frailty index. WQS regression analysis revealed a significant increase in the frailty index with higher levels of the mixture of organic contaminants (aOR, 1.12; 95% CI, 1.05–1.20; p  < 0.001), with MIB, ECP, COP, MBP, P02, and P01 identified as the major contributors. Dose–response relationships were observed between MIB, ECP, MBP, P02, and P01 exposure with an increased risk of frailty (both with p  < 0.05). The developed predictive model based on organic contaminants exposure demonstrated high performance, with an R 2 of 0.9634 and 0.9611 in the training and testing sets, respectively. Furthermore, the predictive model suggested potential synergistic effects in the MIB-MBP and P01-P02 pairs. Taken together, these findings suggest a significant association between exposure to phthalates and PAHs with an increased susceptibility to frailty.