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AimsTo explore the effect of long-term exposure to particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) on childhood obesity based on a cohort study in Chongqing.MethodsA total of 4,284 children aged 6–8 years at baseline were enrolled from the Chongqing Children Health Cohort in 2014–2015 and were followed up in 2019. A stratified cluster sampling was applied to select the participants. A Mixed-effects linear regression model was used to examine the effect of long-term exposure to PM2.5 on the growth curve of obesity indicators [including body mass index (BMI), BMI Z-score (BMIz), and waist-to-height ratio (WHtR)]. A mixed-effects logistic regression model was used to study the dose relationship between PM2.5 exposure and the risk of obesity indicators.ResultsA higher level of accumulating exposure to PM2.5 was associated with an increased childhood obesity index, and the effect was the most significant for WHtR than BMI and BMIz. This effect was more pronounced in boys than in girls except for WHtR, and it was the most significant under the PM2.5 exposure period from pregnancy to 6 years old. Compared the annual average PM2.5 exposure level of <60 μg/m3, the WHtR and BMI were increased by 0.019 [(95% CIs): 0.014, 0.024] and 0.326 [(95% CIs): 0.037, 0.616] Kg/m2 for participants living with the PM2.5 exposure level of 70–75 μg/m3, respectively. For every 5 μg/m3 increase in PM2.5 levels (from pregnancy to 6 years old), the risk of central obesity was increased by 1.26 {odds ratio [OR] (95% CIs): 1.26 (1.16, 1.37), p < 0.001} times.ConclusionsThis study confirmed a dose-response relationship between PM2.5 exposure and childhood obesity, especially central obesity, suggesting that controlling ambient air pollution can prevent the occurrence of obesity in children and adolescents.

ObjectiveIncreased blood pressure (BP) is a major risk factor for cardiovascular disease (CVD) in adults. Regular consumption of nuts may improve some BP in adults whereas evidence in children is relatively lacking. This study aimed to determine the efficacy of nuts intake on BP in children.MethodsStratified cluster sampling was performed to include a total of 15,268 primary school children aged 6–12 years in urban and rural areas in Southwest China. The daily nuts intake dosage was collected by questionnaires, and generalized linear model (GLM) and logistic regression were used to analyze the relationship between nuts intake and BP.ResultsFor the total subjects, 11,130 (72.9%) participants consumed <35 g/day of nuts, 1,145 (7.5%) participants consumed 35 g/day ≤ nut <50 g/day of nuts, 2,053 (13.4%) participants consumed 50~100 g/day of nuts, and 940 (6.2%) participants consumed over 100 g/day of nut. For sex subgroup, 1,074 (13.53%) boys and 979 (13.35%) girls consumed 50~100 g/day of nuts. Compared with the 50~100 g/day of nuts intake group, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were significantly different in <35 g/day, 35g/day ≤ nut <50 g/day, and >100 g/day nuts intake groups (all p < 0.001). The logistic regression showed that compared with the 50~100 g/day group, the other three groups are more likely associated with childhood hypertension (all p < 0.001). Therefore, a U-shaped relationship between nuts intake and BP level was identified.ConclusionsThe finding suggests that intake of 50~100 g/day nuts is the recommended dose of nuts intake to control childhood hypertension, as well as for cardioprotection purposes.

Childhood hypertension has become a global public health issue due to its increasing prevalence and association with cerebral‐cardiovascular disease in adults. In this study, we developed a predictive model for childhood hypertension based on environmental and genetic factors to identify at‐risk individuals. Eighty children diagnosed with childhood hypertension and 84 children in the control group matched by sex and age from an established cohort were included in a nested case–control study. We constructed a multiple logistic regression model to analyze the factors associated with hypertension and applied the 10‐fold cross‐validation method to verify the prediction stability of the model. Childhood hypertension was found positively correlated with triglyceride level ≥150 mg/dL; low‐density lipoprotein cholesterol level ≥110 mg/dL; body mass index Z score; waist‐to‐height ratio Z score; and red blood cell count (all P < .01) and negatively correlated with the relative expression level of retinol acyltransferase; relative expression level of vitamin D receptor; and dietary intake of fiber, vitamin C and copper (all P < .05) in this study. BMI Z score, triglyceride ≥150 mg/dL, RBC count, VDR/β‐actin and LRAT/β‐actin ratios were used to construct the predictive model. The area under the receiver operating characteristic curve was 94.45% (95% CI = 89.35%∼98.65%, P < .001). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were all above 80% in both the training and validation sets. In conclusion, this model can predict the risk of childhood hypertension and could provide a theoretical basis for early prevention and intervention to improve the cardiovascular health of children.