Geographic variation in Chinese children' forced vital capacity and its association with long-term exposure to local PM10: a national cross-sectional study

Long-term exposure to air pollution and lung function among children in China: Association and effect modification

Background

Children are vulnerable to the respiratory effects of air pollution, and their lung function has been associated with long-term exposure to low air pollution level in developed countries. However, the impact of contemporary air pollution level in developing countries as a result of recent efforts to improve air quality on children's lung function is less understood.

Methods

We obtained a cross-sectional sample of 617 schoolchildren living in three differently polluted areas in Anhui province, China. 2-year average concentrations of air pollutants at the year of spirometry and the previous year (2017-2018) obtained from district-level air monitoring stations were used to characterize long-term exposure. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), and forced expiratory flow between 25 and 75% of FVC (FEF_25-75) were determined under strict quality control. Multivariable regression was employed to evaluate the associations between air pollution level and lung function parameters, overall and by demographic characteristics, lifestyle, and vitamin D that was determined by liquid chromatography tandem mass spectrometry.

Results

Mean concentration of fine particulate matter was 44.7 μg/m³, which is slightly above the interim target 1 standard of the World Health Organization. After adjusting for confounders, FVC, FEV₁, and FEF_25-75 showed inverse trends with increasing air pollution levels, with children in high exposure group exhibiting 87.9 [95% confidence interval (CI): 9.5, 166.4] mL decrement in FEV₁ and 195.3 (95% CI: 30.5, 360.1) mL/s decrement in FEF_25-75 compared with those in low exposure group. Additionally, the above negative associations were more pronounced among those who were younger, girls, not exposed to secondhand smoke, non-overweight, physically inactive, or vitamin D deficient.

Conclusions

Our study suggests that long-term exposure to relatively high air pollution was associated with impaired lung function in children. More stringent pollution control measures and intervention strategies accounting for effect modification are needed for vulnerable populations in China and other developing countries.

Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images

PURPOSE

To develop a multimodal model to automate glaucoma detection DESIGN: Development of a machine-learning glaucoma detection model METHODS: We selected a study cohort from the UK Biobank data set with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multimodal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photographs, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma [PTG]).

RESULTS

Results show that a multimodal model that combines imaging with demographic and clinical features is highly accurate (area under the curve 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma-age and pulmonary function.

CONCLUSIONS

The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.

A 2-year longitudinal follow-up of performance characteristics in Chinese male elite youth athletes from swimming and racket sports

Training in elite sport aims at the optimization of the athletic performance, and to control the athletes`progress in physiological, anthropometrical and motor performance prerequisites. However, in most sports, the value of longitudinal testing is unclear. This study evaluates the longitudinal development and the influence of intense training over 2-years on specific physiological performance prerequisites, as well as certain body dimensions and motor abilities in elite youth athletes. Recruited between 11-13 years of age at Shanghai Elite Sport school, the sample of student-athletes (N = 21) was categorized as the swimming group (10 athletes), and the racket sports group (11 players: 7 table tennis and 4 badminton players). The performance monitoring took place over two years between September 2016 and September 2018 and included 5 test waves. In all the test waves, the athletes were assessed by means of three physiological measurements (vital capacity, hemoglobin concentration, heart rate at rest), three anthropometric parameters (body height, body weight, chest girth), and two motor tests (back strength, complex reaction speed). Seven out of eight diagnostic methods exhibit medium to high validity to discriminate between the different levels of performance development in the two sports groups. The investigated development of the performance characteristics is attributed partly to the inherited athletic disposition as well as to the different sport-specific training regimens of the two sports groups.