How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort

Chen, Meijin; Qin, Qiujun; Liu, Feifei; Wang, Yixuan; Wu, Chuanlong; Yan, Yunjun; Xiang, Hao

doi:10.1016/j.envres.2022.113158

Cited by 14 publications

(6 citation statements)

References 76 publications

(82 reference statements)

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“…We evaluated the chronic effect of ozone exposure on renal function based on baseline information from the Wuhan Chronic Disease Cohort Study (WCDCS) established in 2019. Detailed information for WCDCS has been described elsewhere ( Chen et al, 2022 , Wang et al, 2022b ). Briefly, we employed a multi-stage probability proportionate to size method to recruit a total of 10,253 participants.…”

Section: Methodsmentioning

confidence: 99%

Ambient ozone pollution impairs glucose homeostasis and contributes to renal function decline: Population-based evidence

Peng,

Chen,

et al. 2024

Ecotoxicology and Environmental Safety

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Section: Methodsmentioning

confidence: 99%

Ambient ozone pollution impairs glucose homeostasis and contributes to renal function decline: Population-based evidence

Peng,

Chen,

et al. 2024

Ecotoxicology and Environmental Safety

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“…The evidence theory, proposed by Dempster (2008), has been further developed by Shafer (Ai et al, 2022;He et al, 2022;Liu et al, 2022;Ren et al, 2022). Evidence theory applies fuzzy logic to handle uncertainty, via the following steps.…”

Section: Evidence Theorymentioning

confidence: 99%

“…Atmospheric pollution is an urgent issue globally, especially in regions undergoing rapid industrialization and urbanization. The detrimental effects of pollution on ecological and climatic stability and human health are increasingly evident (Murena, 2004;Chen and Zhu, 2014;Chen et al, 2022). With the increasing global focus on environmental protection and low-carbon development, research is focusing on how technological innovation, green finance, and policy design can reduce environmental pollution and foster sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Chao,

Guang Qiu

2024

Front. Environ. Sci.

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Background: Air pollution, characterized by complex spatiotemporal dynamics and inherent uncertainty, poses significant challenges in accurate air quality prediction, and current methodologies often fail to adequately address these complexities.Objective: This study presents a novel fuzzy modeling approach for estimating air pollution concentrations.Methods: This fuzzy evaluation method integrates an improved evidence theory with comprehensive weighting and the K-nearest neighbor (KNN) interval distance within the framework of the matter-element extension model. This involves generating the basic probability assignment (BPA) based on interval similarity, performing sequential fusion using the Dempster–Shafer evidence theory, enhancing the fusion results via comprehensive weighting, and conducting fuzzy evaluation of air pollution concentrations using the matter-element extension KNN interval distance.Results: Our method achieved significant improvements in monitoring air pollution concentrations, incorporating spatiotemporal factors and pollutant concentrations more effectively than existing methods. Implementing sequential fusion and subjective–objective weighting reduced the error rate by 38% relative to alternative methods.Discussion: Fusion of multi-source air pollution data via this method effectively mitigates inherent uncertainty and enhances the accuracy of the KNN method. It produces more comprehensive air pollution concentration fusion results, improving accuracy by considering spatiotemporal correlation, toxicity, and pollution levels. Compared to traditional air-quality indices, our approach achieves greater accuracy and better interpretability, making it possible to develop more effective air quality management strategies. Future research should focus on expanding the dataset to include more diverse geographical and meteorological conditions, further refining the model to integrate external factors like meteorological data and regional industrial activity, and improving computational efficiency for real-time applications.

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“…Air pollutants, such as PM10, PM2.5, and NO2, have been extensively researched due to their detrimental impact on glucose metabolism and insulin resistance, with long-term exposure associated with a heightened risk of T2D [ 112 , 113 ]. PM2.5 exposure, in particular, has been associated with glucose homeostasis abnormalities, which are attributed to inflammation, insulin resistance, mitochondrial alterations, and other cardio-metabolic disorders that may contribute to the development of T2D [ 113 ].…”

Section: Factors Leading To Development Of T2dmentioning

confidence: 99%

Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

Singh,

Kriti,

K.S.

et al. 2024

Metabolism Open

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How long-term air pollution and its metal constituents affect type 2 diabetes mellitus prevalence? Results from Wuhan Chronic Disease Cohort

Cited by 14 publications

References 76 publications

Ambient ozone pollution impairs glucose homeostasis and contributes to renal function decline: Population-based evidence

Ambient ozone pollution impairs glucose homeostasis and contributes to renal function decline: Population-based evidence

Air pollution concentration fuzzy evaluation based on evidence theory and the K-nearest neighbor algorithm

Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

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