In this article, we review the available evidence and explore the association between air pollution and insulin resistance (IR) using meta-analytic techniques. Cohort studies published before January 2018 were selected through English-language literature searches in nine databases. Six cohort studies were included in our sample, which assessed air pollutants including PM_2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm), NO₂(nitrogen dioxide), and PM₁₀ (particulate matter with an aerodynamic diameter less than 10 μm). Percentage change in insulin or insulin resistance associated with air pollutants with corresponding 95% confidence interval (CI) was used to evaluate the risk. A pooled effect (percentage change) was observed, with a 1 μg/m³ increase in NO₂ associated with a significant 1.25% change (95% CI: 0.67, 1.84; I² = 0.00%, p = 0.07) in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and a 0.60% change (95% CI: 0.17, 1.03; I² = 30.94%, p = 0.27) in insulin. Similar to the analysis of NO₂, a 1 μg/m³ increase in PM₁₀ was associated with a significant 2.77% change (95% CI: 0.67, 4.87; I² = 94.98%, p < 0.0001) in HOMA-IR and a 2.75% change in insulin (95% CI: 0.45, 5.04; I² = 58.66%, p = 0.057). No significant associations were found between PM_2.5 and insulin resistance biomarkers. We conclude that increased exposure to air pollution can lead to insulin resistance, further leading to diabetes and cardiometabolic diseases. Clinicians should consider the environmental exposure of patients when making screening and treatment decisions for them.

Background: Evidence in adults suggests that exposure to artificial light-at-night (ALAN) leads to obesity. However, little is known about whether this effect exists in children and adolescents. We aimed to investigate whether ALAN exposure was associated with overweight and obesity in school-aged children and adolescents and whether this association varied with socioeconomic status. Methods: Data on the height and weight of 129,500 children and adolescents aged 10–18 years from 72 cities were extracted from the 2014 Chinese National Survey on Students’ Constitution and Health (CNSSCH). The ALAN area percentage and average ALAN intensity were calculated using the Visible/Infrared Imager/Radiometer Suite. The subjects were separated into three categories based on the cities’ gross domestic product per capita (GDPPC). A mixed-effect logistic regression model and generalized additive model (GAM) were utilized to evaluate the association between ALAN exposure and overweight and obesity in children and adolescents stratified by municipal GDPPC. Results: Both ALAN area (OR = 1.194, 95% CI: 1.175–1.212) and ALAN intensity (OR = 1.019, 95% CI: 1.017–1.020) were positively associated with overweight and obesity in children and adolescents, and the associations remained robust after adjusting for covariates. ORs for overweight and obesity and ALAN area decreased as GDPPC level increased (first tertile: OR = 1.457, 95% CI: 1.335–1.590; second tertile: OR = 1.350, 95% CI: 1.245–1.464; third tertile: OR = 1.100, 95% CI: 1.081–1.119). Similar results were observed for ALAN intensity. In the GAM models, thresholds existed in almost all these spline trends, indicating that ALAN might have a nonlinear association with overweight and obesity. Conclusions: ALAN contributed to the development of overweight and obesity in children and adolescents and this effect differed with GDPPC. Future longitudinal studies should confirm the causal relationship between ALAN and obesity. Moreover, reducing unnecessary exposure to artificial light at night may have beneficial implications for controlling childhood and adolescent obesity, particularly in low-income areas.

Recent increases in nitrogen dioxide (NO₂) and ozone (O₃), two highly reactive and oxidative pollutants, have raised concerns about their potential impact on adolescent mental health. This study leveraged data from the Chinese National Survey on Students' Constitution and Health (CNSSCH) in 2019, a nationally representative cross-sectional survey of Chinese adolescents. A total of 149,697 adolescents aged 10-18 years were included in this study. NO₂ and O₃ were sourced from the ChinaHighAirPollutants dataset, and the combined oxidative potential (O_X) was subsequently calculated using the concentrations of NO₂ and O₃. The study quantitively examined the association of NO₂, O₃, and O_X with adolescent mental health using the Dual Factor Model of Mental Health (DFM), which was derived from a questionnaire. According to the DFM, mental health status was divided into four groups: complete mental health, vulnerable, symptomatic but content, and troubled. Greenness around schools, measured by the Normalized Difference Vegetation Index (NDVI), was examined for its potential effect modification on the association between NO₂, O₃, O_X, and mental health. Each IQR (Interquartile Range) increase in O_X was related to adverse mental health outcomes, with ORs of 1.17 (95 % CI: 1.06, 1.29) for being vulnerable, 1.20 (95 % CI: 1.12, 1.28) for being more symptomatic but content, and 1.15 (95 % CI: 1.07, 1.23) for being troubled. Similar findings emerged in relation to O₃ exposure. A positive association was observed between NO₂ exposure and being vulnerable (OR = 1.07; 95 % CI: 1.01, 1.16). Additionally, the impact of exposure to NO₂, O₃, and O_X on mental health outcomes was significantly stronger in the low-level NDVI group compared to the high-level NDVI group (P for interaction < 0.05). The joint effects analysis revealed that adolescents exposed to high levels of air pollutants and low NDVI had the highest odds of adverse mental health outcomes. These results highlight the complex relationship between NO₂, O₃, O_X, and mental health, with particular emphasis on the underexplored role of O_X. Notably, the findings suggest that reducing pollution and increasing greenery could offer significant benefits for improving adolescent mental health.

Background: Additional metabolic indicators ought to be combined as outcome variables when exploring the impact of breastfeeding on obesity risk. Given the role of a healthy lifestyle in reducing obesity, we aimed to assess the effect of breastfeeding duration on different obesity phenotypes according to metabolic status in children and adolescents, and to explore the offsetting effect of healthy lifestyle factors on the associations between breastfeeding duration and obesity phenotypes. Methods: A total of 8208 eligible children and adolescents aged 7–18 years were recruited from a Chinese national cross-sectional study conducted in 2013. Anthropometric indicators were measured in the survey sites, metabolic indicators were tested from fasting blood samples, and breastfeeding duration and sociodemographic factors were collected by questionnaires. According to anthropometric and metabolic indicators, obesity phenotypes were divided into metabolic healthy normal weight (MHNW), metabolic unhealthy normal weight (MUNW), metabolic healthy obesity (MHO), and metabolic unhealthy obesity (MUO). Four common obesity risk factors (dietary consumption, physical activity, screen time, and sleep duration) were used to construct a healthy lifestyle score. Scores on the lifestyle index ranged from 0 to 4 and were further divided into unfavorable lifestyles (zero or one healthy lifestyle factor), intermediate lifestyles (two healthy lifestyle factors), and favorable lifestyle (three or four healthy lifestyle factors). Multinomial logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (95% CI) for the associations between breastfeeding duration and obesity phenotypes. Furthermore, the interaction terms of breastfeeding duration and each healthy lifestyle category were tested to explore the offsetting effect of lifestyle factors. Results: The prevalence of obesity among Chinese children and adolescents aged 7–18 years was 11.0%. Among the children and adolescents with obesity, the prevalence of MHO and MUO was 41.0% and 59.0%, respectively. Compared to the children and adolescents who were breastfed for 6–11 months, prolonged breastfeeding (≥12 months) increased the risks of MUNW (OR = 1.35, 95% CI: 1.19–1.52), MHO (OR = 1.61, 95% CI: 1.27–2.05), and MUO (OR = 1.46, 95% CI: 1.20–1.76). When stratified by healthy lifestyle category, there was a typical dose–response relationship between duration of breastfeeding over 12 months and MUNW, MHO, and MUO, with an increased risk of a favorable lifestyle moved to an unfavorable lifestyle. Conclusions: Prolonged breastfeeding (≥12 months) may be associated with increased risks of MUNW, MHO, and MUO, and the benefits of breastfeeding among children and adolescents may begin to wane around the age of 12 months. The increased risks may be largely offset by a favorable lifestyle.