Co-exposure to priority-controlled metals mixture and blood pressure in Chinese children from two panel studies

Liu, Miao; Li, Meng; Guo, Wenting; Zhao, Lei; Yang, Huihua; Yu, Jie; Liu, Linlin; Fang, Qin; Lai, Xuefeng; Yang, Liangle; Zhu, Kejing; Dai, Weiyu; Mei, Wenhua; Zhang, Xiaomin

doi:10.1016/j.envpol.2022.119388

Cited by 10 publications

(5 citation statements)

References 72 publications

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“…Moreover, heavy metals showed pertinence for special populations. The study suggested that increased Manganese during pregnancy might be a potential risk factor for inducing pregnancy hypertension, 43 and urinary Antimony was consistently and dose‐responsively associated with increased blood pressure and hypertension, of which Antimony was the major contributor among children 44 …”

Section: Discussionmentioning

confidence: 99%

“…The study suggested that increased Manganese during pregnancy might be a potential risk factor for inducing pregnancy hypertension, 43 and urinary Antimony was consistently and dose-responsively associated with increased blood pressure and hypertension, of which Antimony was the major contributor among children. 44 When it comes to the analysis and explanation of particular features, experts will benefit in the future from constant tracking because it will help them come to logical conclusions rather than simply accepting the algorithm's predictions. Further studies could also focus on validating the model's performance by expanding the database and increasing the participation of clinicians' judgment.…”

Section: Discussionmentioning

confidence: 99%

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The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Xu,

Sun

2024

J of Clinical Hypertension

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There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013–2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model‐Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best‐performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772–0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (−0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA‐XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Xu,

Sun

2024

J of Clinical Hypertension

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“…Generally, the above variables have been proved to affect phthalate exposure levels, metabolite rate, and thyroid function. , Urinary creatinine was added as an independent continuous variable given that creatinine levels could affect the statistical significance of other variables . Additionally, the school variable (kindergartens/primary schools) was adjusted as random intercept to account for within-school correlations among the multiple measurements . In addition, we further explored the relations between urinary mPAEs and thyroid function stratified by gender and age according to LME model results.…”

Section: Methodsmentioning

confidence: 99%

“…33 The parental education level relied on the relatively higher degree of education in both parents, and then it was classified into two groups by the cutoff point of high school. The physical activity was calculated by summing up regular physical education classes and extracurricular activity duration (e.g., running, playing ball games, dancing, swimming, biking, and so on), 34 and the latter should be taken for ≥2 times/month and lasting for ≥20 min/time. Physical activity was classified as <1 h/day, 1−2 h/day, and >2 h/day.…”

Section: Data Collectionmentioning

confidence: 99%

“…38 Additionally, the school variable (kindergartens/primary schools) was adjusted as random intercept to account for within-school correlations among the multiple measurements. 34 In addition, we further explored the relations between urinary mPAEs and thyroid function stratified by gender and age according to LME model results.…”

Section: Serum Thyroid Function Hormone and Cytokinementioning

confidence: 99%

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Associations of Urinary Phthalate Metabolites with Thyroid Function and the Mediated Role of Cytokines: A Panel Study of Healthy Children

Yang

Zheng

Lai

et al. 2023

Environ. Sci. Technol.

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Evidence on joint association of a phthalate mixture with thyroid function among children and its underlying mechanism is largely unknown. We aimed to explore the associations of 10 urinary phthalate metabolites (mPAEs), either as individuals or as a mixture, with thyroid function indicators [free thyroxine, free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH)] in 144 children aged 4–12 years with up to 3 repeated visits across 3 seasons. Significant and positive associations were observed for mono-(2-ethylhexyl) phthalate (MEHP), mono-iso-butyl phthalate (MiBP), and mono-n-butyl phthalate (MnBP) with TSH, as well as monobenzyl phthalate (MBzP) with FT3 in dose–response manners. The relationship between MEHP and TSH remained robust in multiple-phthalate models. Bayesian kernel machine regression (BKMR) models revealed overall linear associations of the 10 mPAE mixture with higher TSH and FT3 levels, and MEHP and MBzP were major contributors. Meanwhile, MEHP, MiBP, and MnBP were linked to the elevation of multiple cytokines including CCL 27, CCL3, CXCL1, and IL-16. Among them, IL-16 mediated the relationships of MEHP and MiBP with TSH, and the mediated proportions were 24.16% and 24.27%, respectively. Our findings suggested that mPAEs dominated by MEHP were dose-responsively associated with elevated TSH among healthy children and mediated by IL-16.

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Association of urinary thallium with hypertension in children and adolescents aged 8–17 years: NHANES 2005–2018

Li,

Pan,

Wang

et al. 2023

Environ Sci Pollut Res

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Co-exposure to priority-controlled metals mixture and blood pressure in Chinese children from two panel studies

Cited by 10 publications

References 72 publications

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method

Associations of Urinary Phthalate Metabolites with Thyroid Function and the Mediated Role of Cytokines: A Panel Study of Healthy Children

Association of urinary thallium with hypertension in children and adolescents aged 8–17 years: NHANES 2005–2018

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