Epidemiological evidence for the effect of environmental heavy metal exposure on the immune system in children

Zheng, Keyang; Zeng, Zhijun; Tian, Qianwen; Huang, Jintao; Zhong, Qin; Huo, Xia

doi:10.1016/j.scitotenv.2023.161691

Cited by 67 publications

(22 citation statements)

References 142 publications

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“…A vast amount of research has been performed on trace element exposure and the development of inflammatory responses. A recent review reveals epidemiological evidence on the effect of heavy metal exposure on childhood immune function from multiple perspectives [ 41 ]. Cu can also catalyze the generation of reactive species/radicals, potentially damaging macromolecules: Proteins, lipids, and nucleic acids [ 42 ].…”

Section: Resultsmentioning

confidence: 99%

PM2.5 Extracts Induce INFγ-Independent Activation of CIITA, MHCII, and Increases Inflammation in Human Bronchial Epithelium

Jirau-Colón,

Jiménez-Vélez

2024

Toxics

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The capacity of particulate matter (PM) to enhance and stimulate the expression of pro-inflammatory mediators has been previously demonstrated in non-antigen-presenting cells (human bronchial epithelia). Nonetheless, many proposed mechanisms for this are extrapolated from known canonical molecular pathways. This work evaluates a possible mechanism for inflammatory exacerbation after exposure to PM2.5 (from Puerto Rico) and CuSO4, using human bronchial epithelial cells (BEAS-2B) as a model. The induction of CIITA, MHCII genes, and various pro-inflammatory mediators was investigated. Among these, the phosphorylation of STAT1 Y701 was significantly induced after 4 h of PM2.5 exposure, concurrent with a slight increase in CIITA and HLA-DRα mRNA levels. INFγ mRNA levels remained low amidst exposure time, while IL-6 levels significantly increased at earlier times. IL-8 remained low, as expected from attenuation by IL-6 in the known INFγ-independent inflammation pathway. The effects of CuSO4 showed an increase in HLA-DRα expression after 8 h, an increase in STAT1 at 1 h, and RF1 at 8 h We hypothesize and show evidence that an inflammatory response due to PM2.5 extract exposure in human bronchial epithelia can be induced early via an alternate non-canonical pathway in the absence of INFγ.

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Section: Resultsmentioning

confidence: 99%

PM2.5 Extracts Induce INFγ-Independent Activation of CIITA, MHCII, and Increases Inflammation in Human Bronchial Epithelium

Jirau-Colón,

Jiménez-Vélez

2024

Toxics

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“…Kontaminasi logam berat di dalam ekosistem perairan merupakan sebuah masalah yang serius pada tingkat global karena menimbulkan dampak yang besar, tidak hanya terhadap ekosistem tetapi juga bagi kesehatan manusia (Pinzón-Bedoya et al, 2020;Monchanin et al, 2021;Takarina et al, 2021;Melake et al, 2022;Zheng et al, 2023). Beberapa studi melaporkan adanya kontaminasi logam berat di dalam biota akuatik yang dikaitkan dengan tingginya konsentrasi logam berat yang terkandung di dalam ekosistem perairan (Islam et al, 2020;Agah, 2021;dan Pan et al, 2022).…”

Section: Pendahuluanunclassified

Analisis Pencemaran Logam Berat Pada Sedimen Permukaan Di Dasar Perairan Laut Pulau Pasaran

2023

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Kawasan muara merupakan salah satu ekosistem perairan yang sering mengalami kontaminasi logam berat seperti nikel (Ni), cadmium (Cd), timbal (Pb), dan merkuri (Hg). Kontaminasi tersebut biasanya berasal dari aktivitas manusia seperti kegiatan rumah tangga, aktivitas perkapalan, dan kegiatan industri. Salah satu wilayah perairan yang terletak di sekitar kawasan muara adalah perairan laut di sekitar Pulau Pasaran. Kawasan ini terkenal sebagai salah satu sentra pembuatan ikan asin di Provinsi Lampung, Indonesia. Kawasan tersebut juga digunakan sebagai tempat penangkapan dan budi daya sumber daya kelautan seperti ikan dan kerang. Penelitian ini dilakukan dengan tujuan untuk menganalisis tingkat pencemaran logam berat jenis Ni, Cd, Pb, dan Hg yang terdapat pada sedimen permukaan di dasar laut Pulau Pasaran. Metode penelitian ini menggunakan pendekatan deskriptif kuantitatif dengan menganalisis tingkat pencemaran menggunakan Geoaccumulation Index (Igeo). Hasil penelitian menunjukkan bahwa sedimen permukaan di dasar laut Pulau Pasaran tidak terindikasi mengalami pencemaran Ni, Cd, dan Hg. Meski demikian, hasil penghitungan Igeo mengindikasikan adanya kontaminasi Pb dengan tingkat pencemaran berat hingga tercemar sangat berat.

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“…Risk factors for hypertension include obesity, lack of physical exercise, alcohol consumption, etc 2 . According to epidemiological data, exposure to environmental metals is associated with hypertension 3 …”

Section: Introductionmentioning

confidence: 99%

“…2 According to epidemiological data, exposure to environmental metals is associated with hypertension. 3 Genetics, diet, and lifestyle are currently well-established risk factors for hypertension, 4 while previous researches have suggested that metal exposure may also be the cause of hypertension's etiology. 5 Metals can get into people's bodies in a variety of ways, such as air inhalation, skin contact, and digestion.…”

Section: Introductionmentioning

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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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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Epidemiological evidence for the effect of environmental heavy metal exposure on the immune system in children

Cited by 67 publications

References 142 publications

PM2.5 Extracts Induce INFγ-Independent Activation of CIITA, MHCII, and Increases Inflammation in Human Bronchial Epithelium

PM2.5 Extracts Induce INFγ-Independent Activation of CIITA, MHCII, and Increases Inflammation in Human Bronchial Epithelium

Analisis Pencemaran Logam Berat Pada Sedimen Permukaan Di Dasar Perairan Laut Pulau Pasaran

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

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