PCDD/Fs and PCBs in hen eggs from a contaminated area in Italy: a 9 years spatio-temporal monitoring study

Factors affecting the polychlorinated biphenyl signatures in serum of adults living in a highly polluted area in eastern Slovakia

Introduction

Over the years eastern Slovakia has been subject to consistent monitoring of high levels of polychlorinated biphenyls (PCBs) in both the environment and human populations attributed to the former production of PCBs at the Chemko Strážske plant. We aimed to investigate the extent to which dietary habits and residential location could affect the concentrations of PCBs in the blood serum samples of subjects.

Methods

We enrolled 602 adult subjects from eastern Slovakia with an average age of 45.14 (±8.49) years. To determine the concentrations of 21 PCB congeners, we used solid phase extraction along with gas chromatography coupled with high-resolution mass spectrometry. Based on questionnaire data, the assessment of dietary habits and residential location was performed using score calculations and creating a map.

Results and discussion

Through principal component analysis, the 20 PCB congeners were classified into three groups: PC1, comprising highly chlorinated PCBs, and PC2 and PC3, consisting primarily of low chlorinated PCBs. Stepwise multivariate regression revealed positive and negative associations between PCB congeners represented by PC1-3 and scores related to the residential location and scores related to food consumption, respectively. We confirmed that levels of PCBs represented by PC1-3 increased with age. The geographical proximity to pollution sources proved to be a key contributing factor to the observed PCB levels in individuals residing in eastern Slovakia.

Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies

Introduction

Polychlorinated Biphenyls (PCBs) persist in the environment and accumulate in humans. Currently, there is a lack of understanding about the overall impact of PCBs on human health, and effective interventions for exposed populations are insufficient.

Methods

Our study aimed to assess the impact of PCBs on various diseases and mortality risks using data from the National Health and Nutrition Examination Survey, while proposing lifestyle adjustments, particularly dietary modifications, to mitigate mortality risk. Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models.

Results

Results indicated significant associations between 7 types of PCBs and 12 diseases (p < 0.05), with 6 diseases showing significant positive correlations (OR > 1, p < 0.05), along with listing the 25 most relevant diseases, such as asthma and chronic bronchitis (OR [95% CI] = 5.85 [4.37, 7.83], p < 0.0001), arthritis and osteoporosis (OR [95% CI] = 6.27 [5.23, 7.55], p < 0.0001). This suggested that PCBs may be intimately involved in the development and progression of multiple diseases. By constructing multidimensional machine learning models and conducting multiple iterations for precision and error measurement, PCBs may have the potential to become specific biomarkers for certain diseases in the future. Building upon this, we further suggested that controlling dietary intake to reduce dietary inflammatory index (DII) could lower mortality and disease risks.

Discussion

While PCBs were independent risk factors for mortality, substantial evidence suggested that adjusting DII might mitigate the adverse effects of PCBs to some extent. Further physiological mechanisms require deeper exploration through additional research.

Sex, environment, and death rate in a dementia cohort: a seven-years Bayesian survival analysis using medications data from a contaminated area in Italy

Introduction

Studies have analyzed the effects of industrial installations on the environment and human health in Taranto, Southern Italy. Literature documented associations between different variables and dementia mortality among both women and men. The present study aims to investigate the associations between sex, environment, age, disease duration, pandemic years, anti-dementia drugs, and death rate.

Methods

Data from the regional medication registry were used. All women and men with an anti-dementia medication between 2015 and 2021 were included and followed-up to 2021. Bayesian mixed effects logistic and Cox regression models with time varying exposures were fitted using integrated nested Laplace approximations and adjusting for patients and therapy characteristics.

Results

A total of 7,961 person-years were observed. Variables associated with lower prevalence of acetylcholinesterase inhibitors (AChEIs) medication were male sex (OR 0.63, 95% CrI 0.42-0.96), age 70-79 years (OR 0.17, 95% CrI 0.06-0.47) and ≥ 80 years (OR 0.08, 95% CrI 0.03-0.23), disease duration of 2-3 years (OR 0.43, 95% CrI 0.32-0.56) and 4-6 years (OR 0.21, 95% CrI 0.13-0.33), and pandemic years 2020 (OR 0.50, 95% CrI 0.37-0.67) and 2021 (OR 0.47, 95% CrI 0.33-0.65). Variables associated with higher mortality were male sex (HR 2.14, 95% CrI 1.75-2.62), residence in the contaminated site of national interest (SIN) (HR 1.25, 95% CrI 1.02-1.53), age ≥ 80 years (HR 6.06, 95% CrI 1.94-18.95), disease duration of 1 year (HR 1.50, 95% CrI 1.12-2.01), 2-3 years (HR 1.90, 95% CrI 1.45-2.48) and 4-6 years (HR 2.21, 95% CrI 1.60-3.07), and pandemic years 2020 (HR 1.38, 95% CrI 1.06-1.80) and 2021 (HR 1.56, 95% CrI 1.21-2.02). Variables associated with lower mortality were therapy with AChEIs alone (HR 0.69, 95% CrI 0.56-0.86) and in combination with memantine (HR 0.54, 95% CrI 0.37-0.81).

Discussion

Male sex, age, disease duration, and pandemic years appeared to be associated with lower AChEIs medications. Male sex, residence in the SIN of Taranto, age, disease duration, and pandemic years seemed to be associated with an increased death rate, while AChEIs medication seemed to be associated with improved survival rate.

Microplastics and environmental effects: investigating the effects of microplastics on aquatic habitats and their impact on human health

Microplastics (MPs) are particles with a diameter of <5 mm. The disposal of plastic waste into the environment poses a significant and pressing issue concern globally. Growing worry has been expressed in recent years over the impact of MPs on both human health and the entire natural ecosystem. MPs impact the feeding and digestive capabilities of marine organisms, as well as hinder the development of plant roots and leaves. Numerous studies have shown that the majority of individuals consume substantial quantities of MPs either through their dietary intake or by inhaling them. MPs have been identified in various human biological samples, such as lungs, stool, placenta, sputum, breast milk, liver, and blood. MPs can cause various illnesses in humans, depending on how they enter the body. Healthy and sustainable ecosystems depend on the proper functioning of microbiota, however, MPs disrupt the balance of microbiota. Also, due to their high surface area compared to their volume and chemical characteristics, MPs act as pollutant absorbers in different environments. Multiple policies and initiatives exist at both the domestic and global levels to mitigate pollution caused by MPs. Various techniques are currently employed to remove MPs, such as biodegradation, filtration systems, incineration, landfill disposal, and recycling, among others. In this review, we will discuss the sources and types of MPs, the presence of MPs in different environments and food, the impact of MPs on human health and microbiota, mechanisms of pollutant adsorption on MPs, and the methods of removing MPs with algae and microbes.

Causal associations between dietary factors and colorectal cancer risk: a Mendelian randomization study

Background

Previous epidemiological studies have found a link between colorectal cancer (CRC) and human dietary habits. However, the inherent limitations and inevitable confounding factors of the observational studies may lead to the inaccurate and doubtful results. The causality of dietary factors to CRC remains elusive.

Methods

We conducted two-sample Mendelian randomization (MR) analyses utilizing the data sets from the IEU Open GWAS project. The exposure datasets included alcoholic drinks per week, processed meat intake, beef intake, poultry intake, oily fish intake, non-oily fish intake, lamb/mutton intake, pork intake, cheese intake, bread intake, tea intake, coffee intake, cooked vegetable intake, cereal intake, fresh fruit intake, salad/raw vegetable intake, and dried fruit intake. In our MR analyses, the inverse variance weighted (IVW) method was employed as the primary analytical approach. The weighted median, MR-Egger, weighted mode, and simple mode were also applied to quality control. Heterogeneity and pleiotropic analyses were implemented to replenish the accuracy of the results.

Results

MR consequences revealed that alcoholic drinks per week [odds ratio (OR): 1.565, 95% confidence interval (CI): 1.068-2.293, p = 0.022], non-oily fish intake (OR: 0.286; 95% CI: 0.095-0.860; p = 0.026), fresh fruit intake (OR: 0.513; 95% CI: 0.273-0.964; p = 0.038), cereal intake (OR: 0.435; 95% CI: 0.253-0.476; p = 0.003) and dried fruit intake (OR: 0.522; 95% CI: 0.311-0.875; p = 0.014) was causally correlated with the risk of CRC. No other significant relationships were obtained. The sensitivity analyses proposed the absence of heterogeneity or pleiotropy, demonstrating the reliability of the MR results.

Conclusion

This study indicated that alcoholic drinks were associated with an increased risk of CRC, while non-oily fish intake, fresh fruit intake, cereal intake, and dried fruit were associated with a decreased risk of CRC. This study also indicated that other dietary factors included in this research were not associated with CRC. The current study is the first to establish the link between comprehensive diet-related factors and CRC at the genetic level, offering novel clues for interpreting the genetic etiology of CRC and replenishing new perspectives for the clinical practice of gastrointestinal disease prevention.

The relationship between heavy metals and metabolic syndrome using machine learning

Background

Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method.

Methods

The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model.

Results

11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS.

Conclusion

Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.

Heavy metal contamination in duck eggs from a mercury mining area, southwestern China

Objective

Mercury (Hg) contamination in the environment around mercury mines is often accompanied by heavy metal contamination.

Methods

Here, we determined concentrations of chromium (Cr), zinc (Zn), strontium (Sr), barium (Ba), and lead (Pb) in duck eggs from a Hg mining area in Southwest China to assess the contamination and health risk.

Results

Duck eggs obtained from the mining area exhibit higher concentrations of Cr, Zn, Sr, Ba, and Pb compared to those from the background area, with egg yolks containing higher metal levels than egg whites. Specifically, the mean Cr, Zn, Sr, Ba, and Pb concentrations of duck eggs from the Hg mining area are 0.38, 63.06, 4.86, 10.08, and 0.05 μg/g, respectively, while those from the background area are only 0.21, 24.65, 1.43, 1.05, and 0.01 μg/g. Based on the single-factor contamination index and health risk assessment, heavy metal contamination in duck eggs poses an ecological risk and health risk.

Conclusion

This study provides important insight into heavy metal contamination in duck eggs from Hg mining areas.

The association between polychlorinated dibenzo-p-dioxin exposure and cancer mortality in the general population: a cohort study

Introduction

Earlier research has indicated that being exposed to polychlorinated dibenzo-p-dioxins (PCDDs) in the workplace can heighten the likelihood of cancer-related deaths. Nevertheless, there is limited information available regarding the connection between PCDD exposure and the risk of cancer mortality in the general population (i.e., individuals not exposed to these substances through their occupation).

Methods

The National Health and Nutrition Examination Survey (NHANES) detected PCDDs in the general population, and the death data were recently updated as of December 31, 2019. We conducted Cox regression analysis and controlled for covariates including age, gender, ethnicity, educational attainment, physical activity, alcohol intake, NHANES survey period, BMI category, cotinine concentration, and household earnings.

Results

After accounting for confounding factors, the findings indicated that for each incremental rise of 1 log unit in 1,2,3,4,6,7,8,9-octachlorodibenzo-p-dioxin, there was a 76% rise in the likelihood of death from any cause, with a p value of 0.003. An increase of 1 log unit in the concentration of 1,2,3,4,6,7,8-heptachlorodibenzofuran could potentially lead to a 90% higher risk of cancer mortality, as indicated by a p value of 0.034 and a 95% confidence interval of 0.05-2.43. As the concentrations of 1,2,3,4,6,7,8-heptachlorodibenzofuran increased, the dose-response curve indicated a proportional rise in the risk of cancer mortality, accompanied by a linear p value of 0.044. The sensitivity analysis demonstrated that our findings were resilient.

Discussion

In the general population, an elevated risk of cancer mortality was observed in PCDDs due to the presence of 1,2,3,4,6,7,8-heptachlorodibenzofuran. Mechanistic research is required to further confirm it.

Gender differences, environmental pressures, tumor characteristics, and death rate in a lung cancer cohort: a seven-years Bayesian survival analysis using cancer registry data from a contaminated area in Italy

Introduction

In Taranto, Southern Italy, adverse impacts on the environment and human health due to industrial installations have been studied. In the literature, associations have been reported between gender, environmental factors, and lung cancer mortality in women and men. The aim of this study was to investigate the relationships between gender, residence in areas with high environmental pressures, bronchus/lung cancer characteristics, and death rate.

Methods

Data from the Taranto Cancer Registry were used, including all women and men with invasive bronchus/lung cancer diagnosed between 1 January 2016 and 31 December 2020 and with follow-up to 31 December 2022. Bayesian mixed effects logistic and Cox regression models were fitted with the approach of integrated nested Laplace approximation, adjusting for patients and disease characteristics.

Results

A total of 2,535 person-years were observed. Male gender was associated with a higher prevalence of histological grade 3 (OR 2.45, 95% CrI 1.35-4.43) and lung squamous-cell carcinoma (OR 3.04, 95% CrI 1.97-4.69). Variables associated with higher death rate were male gender (HR 1.24, 95% CrI 1.07-1.43), pathological/clinical stage II (HR 2.49, 95% CrI 1.63-3.79), III (HR 3.40, 95% CrI 2.33-4.97), and IV (HR 8.21, 95% CrI 5.95-11.34), histological grade 3 (HR 1.80, 95% CrI 1.25-2.59), lung squamous-cell carcinoma (HR 1.18, 95% CrI 1.00-1.39), and small-cell lung cancer (HR 1.62, 95% CrI 1.31-1.99). Variables associated with lower death rate were other-type lung cancer (HR 0.65, 95% CrI 0.44-0.95), high immune checkpoint ligand expression (HR 0.75, 95% CrI 0.59-0.95), lung localization (HR 0.73, 95% CrI 0.62-0.86), and left localization (HR 0.85, 95% CrI 0.75-0.95).

Discussion

The results among patients with lung cancer did not show an association between residence in the contaminated site of national interest (SIN) and the prevalence of the above mentioned prognostic factors, nor between residence in SIN and death rate. The findings confirmed the independent prognostic values of different lung cancer characteristics. Even after adjusting for patients and disease characteristics, male gender appeared to be associated with a higher prevalence of poorly differentiated cancer and squamous-cell carcinoma, and with an increased death rate.

Environmental pressures, tumor characteristics, and death rate in a female breast cancer cohort: a seven-years Bayesian survival analysis using cancer registry data from a contaminated area in Italy

Introduction

In Taranto, Southern Italy, adverse impacts on the environment and human health due to industrial installations have been studied. In the literature, few associations have been reported between environmental factors and breast cancer mortality in women. The aim of this study was to investigate the relationships between residence in areas with high environmental pressures, female breast cancer characteristics, and death rate.

Methods

Data from the Taranto Cancer Registry were used, including all women with invasive breast cancer diagnosed between 01 January 2015 and 31 December 2020 and with follow-up to 31 December 2021. Bayesian mixed effects logistic and Cox regression models were fitted with the approach of integrated nested Laplace approximation, adjusting for patients and disease characteristics.

Results

A total of 10,445 person-years were observed. Variables associated with higher death rate were residence in the contaminated site of national interest (SIN) (HR 1.22, 95% CrI 1.01-1.48), pathological/clinical stage III (HR 2.77, 95% CrI 1.93-3.97) and IV (HR 17.05, 95% CrI 11.94-24.34), histological grade 3 (HR 2.50, 95% CrI 1.20-5.23), Ki-67 proliferation index of 21-50% (HR 1.42, 95% CrI 1.10-1.83) and > 50% (HR 1.81, 95% CrI 1.29-2.55), and bilateral localization (HR 1.65, 95% CrI 1.01-2.68). Variables associated with lower death rate were estrogen and/or progesterone receptor positivity (HR 0.61, 95% CrI 0.45-0.81) and HER2/neu oncogene positivity (HR 0.59, 95% CrI 0.44-0.79).

Discussion

The findings confirmed the independent prognostic values of different female breast cancer characteristics. Even after adjusting for patients and disease characteristics, residence in the SIN of Taranto appeared to be associated with an increased death rate.