Environmental Health and Preventive Medicine最新文献

Background: Heavy metals such as lead (Pb) and cadmium (Cd) have been associated with adverse pregnancy and developmental outcomes, including congenital abnormalities. This study investigated the association between exposure to heavy metals and trace elements during fetal life and congenital limb abnormalities in infants.

Methods: This study is based on a prospective ongoing nationwide birth cohort from the Japan Environment and Children's Study (JECS). The concentrations of Cd, Pb, mercury (Hg), selenium (Se), and manganese (Mn) were measured in maternal blood collected during the mid-late trimesters. Inclusion criteria were available from questionnaires filled in during pregnancy, including information about congenital limb abnormalities at birth or at one month. To examine the associations with limb anomalies and individual chemicals, logistic regression models were applied following log-transformation or division into quartiles of Cd, Pb, Hg, Se, and Mn concentrations. To assess the associations with the heavy metals and trace elements mixture, quantile g-computation was employed. All models were adjusted for age, maternal smoking history, maternal alcohol intake, history of smoking, and infant sex.

Results: Data from 90,163 participants were included in the analysis, of whom 369 had congenital limb abnormalities in any of the collected information, and 89,794 had none. Among the 369 cases of congenital limb abnormalities, there were 185 and 142 cases of polydactyly and syndactyly, respectively. The median concentrations of Pb, Cd, Hg, Se, and Mn were 5.85, 0.66, 3.64, 168, and 15.3 ng/g, respectively. There were no associations between maternal blood concentrations of Pb [adjusted odd ratio = 0.83; 95% confidence interval = 0.61, 1.11], Cd [0.87; 0.68, 1.10], Hg [0.88; 0.73, 1.07], Se [1.07; 0.44, 2.59], and Mn [0.91; 0.64, 1.30] with congenital limb abnormalities. No significant association was observed between the mixture of heavy metals and trace elements [0.85; 0.72, 1.02] and any congenital limb abnormalities. Moreover, there was no association with all polydactylies and all syndactylies, or any type of abnormality as a subdivision.

Conclusion: At the maternal exposure levels of Cd, Pb, Hg, Se, and Mn assessed in the present study, no association was identified with the risk of developing congenital limb abnormalities in children.

Background: The acute effects of temperature and air pollution on mortality are well-known environmental factors that have been receiving more recognition lately. However, the health effects resulting from the interaction of air pollution and temperature remain uncertain, particularly in cities with low levels of pollution. This study aims to examine the modification effects of particulate matter with a diameter of 2.5 µm or less (PM_2.5) and ozone (O₃) on the association between temperature and mortality.

Methods: We collected the daily number of all-cause, cardiovascular, and respiratory mortality from 20 major cities in Japan from 2012-2018. We obtained meteorological data from the Japan Meteorological Agency and air pollution data from the National Institute for Environmental Studies. We conducted analyses using a quasi-Poisson regression model with a distributed lag non-linear model for temperature in each city and subsequently performed a random-effects meta-analysis to derive average estimates.

Results: We found that high levels of O₃ might positively modify the mortality risk of heat exposure, especially for cardiovascular diseases. Subgroups such as the elderly and females were susceptible. We did not observe consistent evidence of effect modification by PM_2.5, including effect modification on cold by both pollutants.

Conclusion: PM_2.5 and O₃ may positively modify the short-term association between heat and mortality in the urban areas of Japan. These results highlight the need for public health policies and interventions to address the collective impacts of both temperature and air pollution.

Background: The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies.

Method: We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure.

Result: Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle).

Conclusion: These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.

Background and aim: Remnant cholesterol (remnant-C) mediates the progression of major adverse cardiovascular events. It is unclear whether remnant-C, and particularly cumulative exposure to remnant-C, is associated with nonalcoholic fatty liver disease (NAFLD). This study aimed to explore whether remnant-C, not only baseline but cumulative exposure, can be used to independently evaluate the risk of NAFLD.

Methods: This study included 1 cohort totaling 21,958 subjects without NAFLD at baseline who underwent at least 2 repeated health checkups and 1 sub-cohort totaling 2,649 subjects restricted to those individuals with at least 4 examinations and no history of NAFLD until Exam 3. Cumulative remnant-C was calculated as a timeweighted model for each examination multiplied by the time between the 2 examinations divided the whole duration. Cox regression models were performed to estimate the association between baseline and cumulative exposure to remnant-C and incident NAFLD.

Results: After multivariable adjustment, compared with the quintile 1 of baseline remnant-C, individuals with higher quintiles demonstrated significantly higher risks for NAFLD (hazard ratio [HR] 1.48, 95%CI 1.31-1.67 for quintile 2; HR 2.07, 95%CI 1.85-2.33 for quintile 3; HR 2.55, 95%CI 2.27-2.88 for quintile 4). Similarly, high cumulative remnant-C quintiles were significantly associated with higher risks for NAFLD (HR 3.43, 95%CI 1.95-6.05 for quintile 2; HR 4.25, 95%CI 2.44-7.40 for quintile 3; HR 6.29, 95%CI 3.59-10.99 for quintile 4), compared with the quintile 1.

Conclusion: Elevated levels of baseline and cumulative remnant-C were independently associated with incident NAFLD. Monitoring immediate levels and longitudinal trends of remnant-C may need to be emphasized in adults as part of NAFLD prevention strategy.

Background: Menopausal disorders include obscure symptomatology that greatly reduce work productivity among female workers. Quantifying the impact of menopause-related symptoms on work productivity is very difficult because no such guidelines exist to date. We aimed to develop a scale of overall health status for working women in the perimenopausal period.

Methods: In September, 2021, we conducted an Internet web survey which included 3,645 female workers aged 45-56 years in perimenopausal period. We asked the participants to answer 76 items relevant to menopausal symptomatology, that were created for this study and performed exploratory and confirmatory factor analyses for the scale development. Cronbach's alpha, receiver operating characteristic analysis, and logistic regression analysis were used to verify the developed scale.

Results: Approximately 85% participants did not have menstruation or disrupted cycles. Explanatory factor analysis using the maximum likelihood method and Promax rotation identified 21 items with a four-factor structure: psychological symptoms (8 items, α = 0.96); physiological symptoms (6 items, alpha = 0.87); sleep difficulty (4 items, alpha = 0.92); human relationship (3 items, alpha = 0.92). Confirmatory factor analyses found excellent model fit for the four-factor model (RMSR = 0.079; TLI = 0.929; CFI = 0.938). Criterion and concurrent validity were confirmed with high correlation coefficients between each of the four factors, previously validated menopausal symptom questionnaire, and Copenhagen Burnout Inventory scales, respectively (all ps < 0.0001). The developed scale was able to predict absenteeism with 78% sensitivity, 58% specificity, and an AUC of 0.727 (95%CI: 0.696-0.757). Higher scores of each factor as well as total score of the scale were more likely to be associated with work absence experience due to menopause-related symptoms even after adjusting for Copenhagen Burnout Inventory subscales (all ps < 0.0001).

Conclusion: We found that the developed scale has high validity and reliability and could be a significant indicator of absenteeism for working women in perimenopausal period.

Background: Cardiovascular disease (CVD) is the leading cause of death worldwide. Few studies have investigated the effects of mixed polycyclic aromatic hydrocarbon (PAH) exposure on CVD prevalence. We aimed to evaluate the association between mixed PAHs exposure and CVD and determine the extent to which these links are mediated by inflammatory indices.

Methods: We used National Health and Nutrition Examination Survey (NHANES) data from 2003 to 2016. Adults with a diagnosis of CVD and seven monohydroxylated PAH metabolites (OH-PAHs) in their urine samples were included. Multivariate logistic regression and Bayesian kernel machine regression (BKMR) models were used to estimate the association between single and mixed PAHs exposure and CVD. Mediation analysis was used to evaluate the mediating effect of inflammatory indices on the association between PAHs mixtures and CVD.

Results: Here, 9136 individuals were included and 10.5% had CVD. Multivariate logistic regression analysis with all the OH-PAHs included that 2-hydroxyfluorene was found positively associated with increased odds of CVD. The BKMR analysis revealed that the overall effect of the seven PAH mixtures was positively associated with CVD. The univariate exposure-response function showed that 2-hydroxyfluorene was positively associated with CVD. Moreover, mediation analysis demonstrated that neutrophil-to-lymphocyte ratio and systemic immune inflammation index mediated the association between PAHs and CVD.

Conclusions: Our findings highlight the complexity of the association between mixed PAHs exposure and CVD. At the same time, our study provides insight into the potential mechanisms of inflammation as a mediator between exposure to PAH mixtures and CVD.

Background: Low birth weight (LBW) is an important public health issue that affects development and health over a long period. However, there has been no sufficient decrease in the prevalence of LBW, and it is important to identify preventable factors for LBW which remain to be clarified. The purpose of this study was to clarify the association between Internet use for many hours during pregnancy and LBW.

Methods: The subjects were mothers who had submitted the pregnancy notification form in Matsue City between April 2016 and September 2017 and their children. The data provided by Matsue City authorities consisted of 2,465 records. We analyzed 2,089 records, excluding untraceable records, those with insufficient information, those on multiple pregnancy, and those on pregnant smokers. Logistic regression analysis was performed using LBW as a dependent variable, Internet use for many hours during pregnancy as an independent variable, and the child's sex, mother's age at the time of pregnancy, unmarried status on pregnancy, first childbirth, mother's job during pregnancy, and weeks of pregnancy on the notification as covariates.

Results: The results of analysis showed that Internet use for many hours during pregnancy accounted for 4.4%, and that LBW accounted for 7.2%. Internet use for many hours during pregnancy was associated with LBW (adjusted odds ratio = 2.16 (95%CI: 1.13-4.13)).

Conclusions: This study suggested that there is an association between Internet use for many hours during pregnancy and LBW. It is necessary to provide appropriate support to pregnant women who use the Internet for many hours during pregnancy after confirming the presence or absence of risk factors for LBW.

The extensive number of publications on suicide risk factors presents a practical challenge for clinicians, policy makers, and researchers to translate the research findings from academia to the improvement of individual suicide predictions and collective prevention. The Suicide Risk-Factor - Data Query Tool (SURF-DTQ) is a web application that collates organized data and helps with meta-analysis of suicidality data. Through a process of systematic review of literature according to PRISMA standards, screening and extracting studies with designs of a high level of evidence, and multi-level data synthesis, information is presented in the SURF database in a summarized, structured, and immediately relevant form. SURF allows users to search by risk factor, population, date, and indicator filters, enabling users to quickly access the most up to date and empirically grounded general or specific knowledge about suicide-related risk factors.

Background: An increased risk of diabetes after COVID-19 exposure has been reported in Caucasians during the early phase of the pandemic, but the effects across viral variants and in non-Caucasians have not been evaluated.

Methods: To address this gap, survival analyses were performed for five outbreak periods. From an anonymized health insurance database REZULT for the employees and their dependents of large companies or government agencies in Japan, 5 matched cohorts were generated based on age, sex, area of residence (47 prefectures), and 7 ranges of medical bills (COVID-19 exposed:unexposed = 1:4). Observation of each matching group began on the same day. Incident diabetes type 1 (T1D) and type 2 (T2D) were defined as the first claim during the target period, including at least 1 year before the start of observation.

Results: T1D accounted for 0.8% of incident diabetes after the first COVID-19 exposure, similar to the non-exposed cohort. Most T2D in the COVID-19 cohort was observed within a few weeks. After further adjustment for the number of days from the start of observation to hospitalization (a time-dependent variable), the hazard ratio for incident T2D ranged from 14.1 to 20.0, with 95% confidence intervals (95%CI) of 8.7 to 32.0, during the 2-month follow-ups from the original strain outbreak to the Delta variant outbreak (by September 2021), and decreased to 2.0, with a 95%CI of 1.6 to 2.5, during the Omicron outbreak (by March 2022). No association was found during the BA.4/5 outbreak (until September 2022). Males had a higher risk, and the trend toward higher risk in older age groups was inconsistent across the periods.

Conclusions: Our large dataset, covering 2019-2023, reports for the first time the impact of COVID-19 on incident diabetes in non-Caucasians. The risk intensity and attributes of post-COVID-19 T2D were inconsistent across outbreak periods, suggesting diverse biological effects of different SARS-CoV-2 variants.

Background: Atrazine (ATR), a commonly used herbicide, is linked to dopaminergic neurotoxicity, which may cause symptoms resembling Parkinson's disease (PD). This study aims to reveal the molecular regulatory networks responsible for ATR exposure and its effects on dopaminergic neurotoxicity based on an integration strategy.

Methods: Our approach involved network toxicology, construction of protein-protein interaction (PPI) networks, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as molecular docking techniques. Subsequently, we validated the predicted results in PC12 cells in vitro.

Results: An integrated analysis strategy indicating that 5 hub targets, including mitogen-activated protein kinase 3 (Mapk3), catalase (Cat), heme oxygenase 1 (Hmox1), tumor protein p53 (Tp53), and prostaglandin-endoperoxide synthase 2 (Ptgs2), may play a crucial role in ATR-induced dopaminergic injury. Molecular docking indicated that the 5 hub targets exhibited certain binding activity with ATR. Cell counting kit-8 (CCK8) results illustrated a dose-response relationship in PC12 cells. Real-time quantitative polymerase chain reaction (RT-qPCR) displayed notable changes in the expression of hub targets mRNA levels, with the exception of Mapk3. Western blotting results suggested that ATR treatment in PC12 cells resulted in an upregulation of the Cat, Hmox1, and p-Mapk3 protein expression levels while causing a downregulation in Tp53, Ptgs2, and Mapk3.

Conclusion: Our findings indicated that 5 hub targets identified could play a vital role in ATR-induced dopaminergic neurotoxicity in PC12 cells. These results provide preliminary support for further investigation into the molecular mechanism of ATR-induced toxicity.