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Lin J, Zhang Y, Wang K, Xia H, Hua M, Lu K, Zheng W, Chen R. Long-term impact of PM 2.5 exposure on frailty, chronic diseases, and multimorbidity among middle-aged and older adults: insights from a national population-based longitudinal study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:4100-4110. [PMID: 38097844 DOI: 10.1007/s11356-023-31505-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/08/2023] [Indexed: 01/19/2024]
Abstract
Particulate Matter 2.5 (PM2.5) is a significant risk factor for frailty and chronic diseases. Studies on the associations between PM2.5 and frailty, chronic diseases, and multimorbidity are scarce, especially from large cohort studies. We aimed to explore the potential association between PM2.5 exposure and the risk of frailty, chronic diseases, and multimorbidity. We collected data from a national cohort (CHARLS) with a follow-up period of 11-18 years, totaling 13,366 participants. We obtained PM2.5 concentration data from the Atmospheric Composition Analysis Group at Dalhousie University. PM2.5 exposure is based on the average annual concentration in the prefecture-level city where residents live. We define frailty as the comprehensive manifestation of declining various body functions, characterized by a frailty index of 0.25 or greater, and multimorbidity as the presence of at least two or more chronic conditions. Cox proportional hazards regression was used to estimate the hazard ratio (HR) with its 95% confidence interval (95%CI). A 10-μg/m3 increase for PM2.5 was significantly associated with an increased risk of frailty (HR = 1.289, 95%CI = 1.257-1.322, P < 0.001). A 10-μg/m3 increase for PM2.5 was significantly associated with the elevated risk for most chronic diseases. Compared to those with no morbidity or only single morbidity, a 10-μg/m3 increase for PM2.5 was significantly associated with the elevated risk for multimorbidity (HR = 1.220, 95%CI = 1.181-1.260, P < 0.001). Ambient PM2.5 exposure is a significant risk factor for frailty, chronic diseases, and multimorbidity, and some measures need to be taken to reduce PM2.5 concentration and prevent frailty and chronic diseases.
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Affiliation(s)
- Junjie Lin
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yu Zhang
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Kunyi Wang
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Huilin Xia
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Minxia Hua
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Kexin Lu
- School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Weijun Zheng
- Department of Medical Statistics, School of Public Health, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou City, 310053, Zhejiang Province, China
| | - Rucheng Chen
- Department of Medical Statistics, School of Public Health, Zhejiang Chinese Medical University, 548 Binwen Road, Binjiang District, Hangzhou City, 310053, Zhejiang Province, China.
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Xiao H, Huang S, Yang W, Zhang W, Xiao H, Cai S. Causal association between air pollution and frailty: a Mendelian randomization study. Front Public Health 2023; 11:1288293. [PMID: 38026367 PMCID: PMC10662305 DOI: 10.3389/fpubh.2023.1288293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Backgrounds Frailty is a significant problem for older persons since it is linked to a number of unfavorable consequences. According to observational researches, air pollution may raise the risk of frailty. We investigated the causal association between frailty and air pollution (including PM2.5, PM2.5-10, PM10, nitrogen dioxide, and nitrogen oxides) using Mendelian randomization approach. Methods We conducted MR analysis using extensive publically accessible GWAS (genome-wide association studies) summary data. The inverse variance weighted (IVW) method was employed as the primary analysis method. The weighted median model, MR-Egger, simple model, and weighted model approaches were chosen for quality control. The Cochran's Q test was utilized to evaluate heterogeneity. Pleiotropy is found using the MR-Egger regression test. The MR-PRESSO method was used to recognize outliers. The leave-one-out strategy was used to conduct the sensitivity analysis. Results MR results suggested that PM2.5 was statistically significantly associated with frailty [odds ratio (OR) = 1.33; 95%confidence interval (CI) = 1.12-1.58, p = 0.001] in IVW method. We observed no statistical association between PM2.5-10(OR = 1.00, 95% CI = 0.79-1.28, p = 0.979), PM10(OR = 0.91, 95% CI = 0.75-1.11, p = 0.364), nitrogen dioxide (OR = 0.98, 95% CI = 0.85-1.12, p = 0.730), nitrogen oxides (OR = 1.15, 95% CI = 0.98-1.36, p = 0.086) and frailty. There was no pleiotropy in the results. The sensitivity analysis based on the leave-one-out method showed that the individual single nucleotide polymorphisms (SNPs) did not affect the robustness of the results. Conclusion The current MR investigation shows a causal association between PM2.5 and frailty. Frailty's detrimental progression may be slowed down with the help of air pollution prevention and control.
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Affiliation(s)
- Haixia Xiao
- Department of Obstetrics, Guangdong Women and Children Hospital, Guangzhou, China
| | - Shan Huang
- Department of MICU, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wei Yang
- Department of Internal Medicine, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wenni Zhang
- Department of MICU, Guangdong Women and Children Hospital, Guangzhou, China
| | - Huanshun Xiao
- Department of MICU, Guangdong Women and Children Hospital, Guangzhou, China
| | - Shuangming Cai
- Department of MICU, Guangdong Women and Children Hospital, Guangzhou, China
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Li W, Huang G, Tang N, Lu P, Jiang L, Lv J, Qin Y, Lin Y, Xu F, Lei D. Association between co-exposure to phenols, phthalates, and polycyclic aromatic hydrocarbons with the risk of frailty. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:105181-105193. [PMID: 37713077 DOI: 10.1007/s11356-023-29887-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
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 R2 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.
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Affiliation(s)
- Wenxiang Li
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Guangyi Huang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Ningning Tang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Peng Lu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Li Jiang
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Jian Lv
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Yuanjun Qin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Yunru Lin
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Fan Xu
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China
| | - Daizai Lei
- Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China.
- Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, 6 Taoyuan Road, Qingxiu District, Nanning, 530000, China.
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