Are Environmental Contaminants Responsible
for ‘Globesity’?

Filipiak‐Florkiewicz, Agnieszka; Topolska, Kinga; Florkiewicz, Adam; Cieśłik, Ewa

doi:10.15244/pjoes/65427

Cited by 4 publications

(3 citation statements)

References 91 publications

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“…An escalating global epidemic of obesity is taking over many parts of the world [ 1 ]. China has experienced a sharp increase in economic growth since 1978 and has become the country with the largest number of children with obesity in the world [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Childhood obesity inequality in northeast China: joint effect of social economic status and school neighborhood environment

et al. 2023

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Background Obesogenic environment is important in driving obesity epidemic. Children spend large amount of their time in schools. School neighborhood environment, as well as its interaction with socioeconomic status (SES) needs to be explored to provide evidence for children obesity prevention policies. Methods Objective anthropometric measurement, a household structured questionnaire, and school geospatial analyses were carried out on 3670 children (aged 9–12 years) of 26 schools in northeast China. Interaction between SES inter-categorical intersectionality group and school neighborhood environment was tested for the effect on children’s body mass index z scores (z-BMI) and waist–hip ratio z scores (z-WHR), following formulation of SES inter-categorical intersectionality group based on household wealth, parental education, and parental occupation. Results SES groups formed by household wealth, parental education and parental occupation was associated with z-BMI and z-WHR for girls. Those from moderate wealth & self-employed (M&S) families had the highest adjusted z-BMI and z-WHR among all SES groups. School neighborhood environment factors interacted with SES groups in association with WHR for girls. Number of school neighborhood supermarkets and residential sites were negatively associated with z-WHR for girls from M&S families (β= -0.45 (95%CI: -0.76, -0.15) for supermarkets; β= -0.01 (95%CI: -0.03, 0.00) for residential sites). Number of school neighborhood convenience stores and public transport stops were positively associated with z-WHR for girls from M&S families (β = 0.02 (95%CI: 0.00, 0.03) for convenience stores; β = 0.23 (95%CI: 0.15, 0.31) for public transport stops). While non-significant association was found for number of vegetable stores. Conclusion Girls from moderate wealth & self-employed families may be the group susceptible to school neighborhood environment. Local policies targeted at improving the school neighborhood environment may be one avenue for reducing socioeconomic disparities in obesity especially for girls.

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Section: Introductionmentioning

confidence: 99%

Childhood obesity inequality in northeast China: joint effect of social economic status and school neighborhood environment

et al. 2023

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“…People with heart and lung disease, diabetics, the elderly and children are vulnerable to health effects related to air pollution. In addition, these pollutants and their derivatives can cause many adverse effects on the environment [3][4][5], including visibility impairment, acid deposition, global climate change, and water quality degradation. This shows that efficient and accurate air quality prediction models are important for early warning of susceptible populations and preventing diseases induced by exposure to air pollutants.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Air Quality Prediction Based on Data Decomposition

et al. 2021

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Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method.

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“…However, heavy metals are indestructible, and therefore can concentrate in the food chain and eventually accumulate in the human body [ 2 , 12 ]. Filipiak-Florkiewicz et al [ 13 ] mentioned that neuroendocrine disruptions caused by heavy metals and other organic pollutants can be associated with their neurotoxicity and obesity prevalence.…”

Section: Introductionmentioning

confidence: 99%

Heavy Metals in Notifications of Rapid Alert System for Food and Feed

Pigłowski

2018

IJERPH

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Heavy metals represent the fourth most often notified hazard category in the Rapid Alert System for Food and Feed (RASFF) from 1980–2016. The goal of the study was to examine the similarities in notifications of particular heavy metals within the RASFF year, product category, notifying country, country of origin, notification basis, notification type, distribution status, risk decision, and action taken, taking into account the particular product type, such as food, food contact material, and feed. The data originated from the RASFF database. Cluster analysis on pivot tables was applied using joining and two-way joining methods. Most notifications concerned food, in which the highest number were related to mercury, cadmium, chromium, lead, arsenic, and nickel. Notifications were mainly related to fish and food contact materials, in addition to fruits and vegetables, seafood, and dietetic food. The number of notifications decreased in 2015 and 2016. The majority of products were notified by Italy, Spain, Germany, and France. The notified products originated mainly from China and Spain. The notification was usually based on official controls on the market, as well as border controls. The notification types were mainly information, alert, and border rejections. Products were not frequently distributed due to distribution restriction to the notifying country or the possibility of distribution to the market. A risk decision was not usually made. The taken actions included re-dispatch of products, withdrawal from the market, or destruction. The data on heavy metals from the RASFF database can help European and national authorities in shaping public health.

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Are Environmental Contaminants Responsible for ‘Globesity’?

Cited by 4 publications

References 91 publications

Childhood obesity inequality in northeast China: joint effect of social economic status and school neighborhood environment

Childhood obesity inequality in northeast China: joint effect of social economic status and school neighborhood environment

A Hybrid Model for Air Quality Prediction Based on Data Decomposition

Heavy Metals in Notifications of Rapid Alert System for Food and Feed

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