Background: Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among diabetics, and thus identification of potentially protective factors is particularly valuable. We aimed to explore whether long-term exposure to residential greenness was beneficial to DR.Methods: We used data from a large-scale, cross-sectional screening survey conducted in 129 cities of 27 provincial regions of China from 2018 to 2021 among diabetic patients. We measured residential greenness exposure as the 3-year average Normalized Difference Vegetation Index (NDVI) at a spatial resolution of 250 m. DR was assessed by ophthalmologists based on fundus photographs. Logistic regression was used to estimate the association between NDVI and prevalence of DR, DR severity status (i.e., nonproliferative and proliferative), hallmarks of retinal lesions and macular oedema. Restricted cubic splines were used to visualize the exposure-response relationships.Findings: A total of 484,380 adult diabetic patients were included in the current analysis, and 15.7% of them were diagnosed with DR. NDVI was inversely and linearly associated with DR prevalence, and an increment of 0.1 NDVI was associated with a 10% (9%–11%) decrease in DR prevalence. Significant and inverse associations were further found for nonproliferative DR, hallmarks of lesions and macular oedema. Participants who were obese, urban residents, had longer duration of diabetes or those did not take anti-diabetic medications may benefit more from greenness exposure. Interpretation: This large-scale nationwide study provides the first-hand epidemiological evidence on the protective effects of residential greenness against DR, especially its early stage and vision-threatening macular oedema. Our findings highlight the importance of residential greenness in alleviating DR risk especially in an era of aging and urbanization.Funding Information: National Natural Science Foundation of China (92143301, 81730026, 82003554, 82173613), the Shanghai Committee of Science and Technology (21TQ015), Shanghai Hospital Development Center (SHDC2020CR2040B, SHDC2020CR5014) and Scientific Project of Shanghai Municipal Health Commission (201940151, 202140018).Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: All participants provided written informed consent and the Institutional Review Board of National Clinical Research Center for Ophthalmic Diseases approved the study protocol (2022KY-120). This study was conducted in accordance with Declaration of Helsinki.
There have been relatively few opportunities to examine the cardiovascular effects of an extreme air pollution event in China. We aimed to examine the impact of the 2013 Eastern China Smog occurring from 2 to 9 December 2013, on outpatient visits for coronary heart diseases (CHD) in a typical hospital in Shanghai, China. We used the over-dispersed, generalized additive model to estimate the relative risk (RR) of the 2013 Eastern China Smog on the outpatient visits by comparing the smog period (2-9 December 2013; 8 days) to the non-smog period (1 November-1 December 2013, and 10 December-28 February 2014; 112 days). This model also controlled for time trends, days of the week, holidays, and meteorological factors. A stratification analysis was performed to estimate sex- and age-specific RRs. The daily average PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations during the smog period were 212 μg/m³, which were three times higher than during the non-smog period (76 μg/m³). The smog in Eastern China in 2013 was significantly associated with an increased risk of outpatient visits for CHD. For example, the RR was 1.18 (95% CI: 1.04, 1.32) on lag 0 day. There were similar effects on males and females. Our analyses provided preliminary evidence that smog constituted a significant risk factor of CHD in China.
Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.
Previous research has indicated that the cholinergic anti-inflammatory pathway (CAP) can regulate the duration and intensity of inflammatory responses. A wide range of research has demonstrated that PM2.5 exposure may induce various negative health effects via pulmonary and systemic inflammations. To study the potential role of the CAP in mediating PM2.5-induced effects, mice were treated with vagus nerve electrical stimulation (VNS) and diesel exhaust PM2.5 (DEP) instillation. Analysis of pulmonary and systemic inflammations in mice demonstrated that VNS significantly reduced the inflammatory responses triggered by DEP. The flow cytometry results showed that DEP may influence the CAP by altering the Th cell balance and macrophage polarization in spleen. To further confirm the effect of alpha7 nicotinic acetylcholine receptor (α7nAChR) in this pathway, mice were then treated with α7nAChR inhibitor (α-BGT) or agonist (PNU282987). Our results demonstrated that specific activation of α7nAChR with PNU282987 effectively alleviated DEP-induced pulmonary inflammation, while specific inhibition of α7nAChR with α-BGT exacerbated the inflammatory markers. The present study suggests that PM2.5 have an impact on the CAP, and CAP may play a critical function in mediating PM2.5 exposure-induced inflammatory response.
Abstract The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record‐hot or top‐10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID‐19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID‐19 cases and deaths. However, the associations between temperature and COVID‐19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID‐19 deaths during the summer of 2021 to examine the relationship between COVID‐19 deaths and temperature by applying a two‐stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID‐19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID‐19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID‐19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from −0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID‐19 deaths, which might help us to understand the underlying modulation of the COVID‐19 pandemic by meteorological variables and to develop epidemic policy response strategies.
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