Background: Air pollution nowadays has seriously threatened the health of the Chinese population, especially in the vulnerable groups of fetuses, infants and toddlers. In particular, the effects of air pollution on children’s neurobehavioral development have attracted widespread attention. Moreover, the early detection of a sensitive period is very important for the precise intervention of the disease. However, such studies focusing on hyperactive behaviors and susceptible window identification are currently lacking in China. Objectives: The study aims to explore the correlation between air pollution exposure and hyperactive behaviors during the early life stage and attempt to identify whether a susceptible exposure window exists that is crucial for further precise intervention. Methods: Based on the Longhua Child Cohort Study, we collected the basic information and hyperactivity index of 26,052 children using a questionnaire conducted from 2015 to 2017, and the Conners’ Parent Rating Scale-revised (CPRS-48) was used to assess hyperactive behaviors. Moreover, the data of air pollution concentration (PM₁₀, PM_2.5, NO₂, CO, O₃ and SO₂) were collected from the monitoring station between 2011 to 2017, and a land-use random forest model was used to evaluate the exposure level of each subject. Furthermore, Distributed lag non-linear models (DLNMs) were applied for statistic analysis. Results: The risk of child hyperactivity was found to be positively associated with early life exposure to PM₁₀, PM_2.5 and NO₂. In particular, for an increase of per 10 µg/m³ in PM₁₀, PM_2.5 and NO₂ exposure concentration during early life, the risk of child hyperactivity increased significantly during the seventh month of pregnancy to the fourth month after birth, with the strongest association in the ninth month of pregnancy (PM₁₀: OR = 1.043, 95% CI: 1.016–1.071; PM_2.5: OR = 1.062, 95% CI: 1.024–1.102; NO₂: OR = 1.043, 95% CI: 1.016–1.071). However, no significant associations among early life exposure to CO, O₃ and SO₂ and child hyperactive behaviors were observed. Conclusions: Early life exposure to PM₁₀, PM_2.5 and NO₂ is associated with an increased risk of child ADHD-like behaviors at the age around 3 years, and the late-prenatal and early postnatal periods might be the susceptible exposure windows.

This study sought to investigate the relationship between meteorological factors and outpatient visits for herpes zoster. In this time-series analysis, we used data from two major hospitals in Hefei, collected between 2015 and 2019, to evaluate the impact of meteorological factors on the risk of herpes zoster. After controlling for confounders, we adopted a distributed lag nonlinear model to probe the relationship between meteorological factors and outpatient visits for herpes zoster. The analysis was stratified according to age (<40 years, ≥40 years) and sex (male, female). A total of 43,547 cases of herpes zoster were reported, and compared with the median value, a high temperature and high relative humidity had a significant risk effect on the incidence of herpes zoster. The maximum harmful effect of high temperature on herpes zoster occurred on the lag0 (RR: 1.027, 95% CI: 1.002–1.053) and further declined over the following days. The cumulative effect increased with the extension of lag days, and the cumulative RR was the largest on the sixth day of lag (RR1.031, 95% CI: 1.006–1.056) when the relative humidity was 85.7% (77.0% as the reference). The stratified analysis results reveal that females and the elderly (≥40 years) were more susceptible to temperature and relative humidity. This study shows that high-temperatures may lead to herpes zoster, indicating that those infected with varicella zoster virus need to take measures over the course of several days when not exposed to the best appropriate temperature conditions.

Phthalates are positioned as potential risk factors for health-related diseases. However, the effects of exposure to phthalates on accelerated aging and the potential modifications of physical activity remain unclear. A total of 2317 participants containing complete study-related information from the National Health and Nutrition Examination Survey 2007-2010 were included in the current study. We used two indicators, the Klemera-Doubal method biological age acceleration (BioAgeAccel) and phenotypic age acceleration (PhenoAgeAccel), to assess the accelerated aging status of the subjects. Multiple linear regression (single pollutant models), weighted quantile sum (WQS) regression, Quantile g-computation, and Bayesian kernel machine regression (BKMR) models were utilized to explore the associations between urinary phthalate metabolites and accelerated aging. Three groups of physical activity with different intensities were used to evaluate the modifying effects on the above associations. Results indicated that most phthalate metabolites were significantly associated with BioAgeAccel and PhenoAgeAccel, with effect values (β) ranging from 0.16 to 0.21 and 0.16-0.37, respectively. The WQS indices were positively associated with BioAgeAccel (0.33, 95% CI: 0.11, 0.54) and PhenoAgeAccel (0.50, 95% CI: 0.19, 0.82). Quantile g-computation indicated that phthalate mixtures were associated with accelerated aging, with effect values of 0.15 (95% CI: 0.02, 0.28) for BioAgeAccel and 0.39 (95% CI: 0.12, 0.67) for PhenoAgeAccel respectively. The BKMR models indicated a significant positive association between the concentrations of urinary phthalate mixtures with the two indicators. In addition, we found that most phthalate metabolites showed the strongest effects on accelerated aging in the no physical activity group and that the effects decreased gradually with increasing levels of physical activity (P < 0.05 for trend). Similar results were also observed in the mixed exposure models (WQS and Quantile g-computation). This study indicates that phthalates exposure is associated with accelerated aging, while physical activity may be a crucial barrier against phthalates exposure-related aging.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder with complex pathogenesis, and conventional biomarkers often fail to capture disease-associated metabolic changes. A more comprehensive understanding of the metabolic alterations in RA may provide deeper insights into disease mechanisms. This study aimed to identify serum metabolic changes associated with RA using non-targeted metabolomics and lipidomics, and to explore the potential impact of these metabolic alterations on RA pathophysiology. A case-control study was conducted using baseline data from the Inflammatory Immune Disease Cohort at the First Affiliated Hospital of Anhui Medical University (October 2021 to June 2023). A total of 91 RA patients and 125 healthy controls were included. Serum samples were analyzed by non-targeted liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were identified using orthogonal partial least squares discriminant analysis (OPLS-DA), followed by filtration with Student's t-test (P < 0.05). Logistic regression was applied to identify potential metabolic alterations for further investigation. Pathway enrichment analysis identified significant metabolic pathways associated with RA. The median age of both groups was 50.0 years, with no significant demographic differences (P > 0.05). Metabolomics and lipidomics identified 41 differential metabolites, including 16 small molecules and 25 lipids. Pathway analysis revealed disturbances in the urea cycle, sphingolipid metabolism, arginine and proline metabolism, and glycine and serine metabolism. These findings suggest systemic metabolic alterations in RA and provide new insights into the disease's metabolic profile. The study identified significant metabolic alterations in RA, contributing to a better understanding of the disease's pathophysiology. These findings highlight the importance of metabolic reprogramming in RA and offer directions for further investigation into metabolic pathways involved in disease progression.

Evidence suggests that physical activity (PA) may mitigate chronic inflammation, while sedentary behavior (SB) could exacerbate gut dysbiosis—key mec