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Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65 years) diabetic patients between 2014 and 2019 in Beijing. A time‐stratified case‐crossover design based on negative‐control exposure was used to assess causal associations of short‐term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7 days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10 μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1 mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), −0.08 (−0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), −0.04 (−0.06, −0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short‐term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients. Plain Language Summary: Although researches for cardiovascular disease (CVD) and respiratory diseases (RD) with air pollutants are widely reported, there is little evidence for causal associations of air pollution with CVD combined with diabetes mellitus (CVD‐DM) and RD combined with diabetes mellitus (RD‐DM) in elder patients. We included elder hospital admissions (aged ≥65 years) on comorbid CVD‐DM and RD‐DM patients between 2014 and 2019 in Beijing, China, to assess associations and causal inferences between air pollutants with CVD‐DM and RD‐DM. Random forest regression model was used to calculate the contribution magnitude of air pollutants. We found that exposure to air pollution increased admissions of CVD‐DM and RD‐DM in the elderly. Gaseous pollutants had a greater contribution to CVD‐DM and RD‐DM. The finding may lead to policy changes to reduce air pollution concentration, particularly gaseous pollutants to decrease hospital admissions. Key Points: Investigated causal associations of air pollutants on comorbid elderly populationA stable and accurate effect of air pollution was gained base on large sample sizesThe effects of gaseous pollutants are stronger than those of PM for the elderly [ABSTRACT FROM AUTHOR]

Long-term exposure to ambient particulate pollutants (PM2.5 and PM10) may increase the risk of chronic kidney disease (CKD), but the results of previous research were limited and inconsistent. The purpose of this study was to assess the relationships of PM2.5 and PM10 with CKD. This study was a cohort study based on the physical examination data of 2082 Beijing residents from 2013 to 2018 in the Beijing Health Management Cohort (BHMC). A land-use regression model was used to estimate the individual exposure concentration of air pollution based on the address provided by each participant. CKD events were identified based on self-report or medical evaluation (estimated glomerular filtration rate, eGFR less than 60 ml/min/1.73 m2). Finally, the associations of PM2.5 and PM10 with CKD were calculated using univariate and multivariate logistic regression models. During the research period, we collected potentially confounding information. After adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with an 84% (OR: 1.84; 95% CI: 1.45, 2.33) and 37% (OR: 1.37; 95% CI: 1.15, 1.63) increased risk of CKD. Adjusting for the four common gaseous air pollutants (CO, NO2, SO2, O3), the effect of PM2.5 and PM10 on CKD was significantly enhanced, but the effect of PM10 was no longer significant in the multi-pollutant model. The results of the stratified analysis showed that PM2.5 and PM10 were more significant in males, middle-aged and elderly people over 45 years old, smokers, drinkers, BMI ≥ 24 kg/m2, and abnormal metabolic components. In conclusion, long-term exposure to ambient PM2.5 and PM10 was associated with an increased risk of CKD. [ABSTRACT FROM AUTHOR]

Air pollution and ischemic stroke (IS) are both vital factors affecting the health of Beijing citizens. This study aims at exploring the associations between air pollution, meteorology, and the hospital admission of IS (IS HA). Information on 476,659 IS inpatients in secondary and higher hospitals in Beijing from 2013 to 2018 were collected. A time-stratified case-crossover design with the generalized additive model and the distributed lag nonlinear model were used. In the single-pollutant models, an inter-quartile range increase in O3, SO2, CO, and NO2 resulted in a significant highest increase in IS HA by 2.23% (95% CI: 1.56%, 2.90%), 1.53% (95% CI: 1.12%, 1.95%), 1.05% (95% CI: 0.70%, 1.40%), and 0.51% (95% CI: 0.24%, 0.79%) on the day of pollution, so did PM2.5 and PM10 by 1.13% (95% CI: 0.68%, 1.59%) and 1.19% (95% CI: 0.74%, 1.64%) at a lag of 0–5 days. There was a nonlinear relationship between meteorology and IS HA. In the multivariate model, the cumulative relative risks with a maximum lag time of 21 days of PM2.5 and NO2 were 1.11 (95% CI: 1.04, 1.19) and 0.88 (95% CI: 0.82, 0.94), while the effects of SO2, O3, and meteorology were insignificant. The findings suggested that particulate pollutants could increase the risk of IS, and the elderly were more sensitive to it, while the results of gaseous pollutants are still discordant. The control of air pollution and the protection of susceptible populations should receive higher attention from policymakers. [ABSTRACT FROM AUTHOR]

Air pollutants' effect on ischemic stroke (IS) has been widely reported. But the effect of high-level concentrations during people's outdoor periods among hypertension patients was unknown. Peak-hour concentrations were defined considering air pollutants' high concentrations as well as people's outdoor periods. We conducted a time-series study and used the generalized additive model to analyze peak-hour concentrations' acute effect. A total of 315,499 IS patients comorbid with hypertension were admitted to secondary and above hospitals in Beijing from 2014 to 2018. A 10 µg/m3 (CO: 1 mg/m3) increase of the peak-hour concentrations was positively associated with IS hospital admissions among hypertension patients. The maximum effect sizes were as follows: for PM2.5, 0.17% (95% confidence interval [CI]: 0.10–0.24%) at Lag0 and 0.22% (95% CI: 0.12–0.33%) at Lag0–5; for PM10, 0.09% (95% CI: 0.05–0.13%) at Lag5 and 0.17% (95% CI: 0.09–0.26%) at Lag0–5; for SO2, 0.87% (95% CI: 0.46–1.29%) at Lag5; for NO2, 0.83% (95% CI: 0.62–1.04%) at Lag0 and 0.86% (95% CI: 0.59–1.13%) at Lag0–1; for CO 1.23% (95% CI: 0.66–1.80%) at Lag0 and 1.33% (95% CI: 0.33–2.35%) at Lag0–5; for O3 0.23% (95% CI: 0.12–0.35%) at Lag0 and 0.20% (95% CI: 0.05–0.34%) at Lag0–1. The effect sizes of PM2.5, NO2, and O3 remained significant after adjusting daily mean. Larger effect sizes were observed for PM2.5 and PM10 in cool season and for O3 in warm season. As significant exposure indicators of air pollution, peak-hour concentrations exposure increased the risk of IS hospital admissions among hypertension patients and it is worthy of consideration in relative environmental standard. It is suggested for hypertension patients to avoid outdoor activity during peak hours. More relevant searches are required to further illustrate air pollutant's effect on chronic disease population. [ABSTRACT FROM AUTHOR]

Background. The relationship between the IgG glycan panel and type 2 diabetes remains unclear in Chinese population. We aimed to investigate the association of the IgG glycan profile and glycan score with type 2 diabetes. Methods. In the discovery population, 162 individuals diagnosed with type 2 diabetes and 162 matched controls from Beijing health management cohort were included. We analyzed the IgG glycan profile and composed a glycan score for type 2 diabetes. Findings were validated in the replication population from Beijing Xuanwu community cohort (280 cases and 508 controls). Area under curve (AUC) using 10-fold and bootstrap validation, net reclassification index (NRI), and integrated discrimination index (IDI) were calculated for the glycan score. Results. In the discovery population, 5 initial IgG glycans and 7 derived traits were significantly associated with type 2 diabetes after Bonferroni correction and Lasso selection, which were validated in the replication population subsequently. The glycan score composed of these IgG glycans and traits showed a strong association with type 2 diabetes (combined odds ratio (OR): 3.78) and its risk factors. In the replication population, AUC of the model involving clinical traits improved from 0.74 to above 0.90, and the values of NRI and IDI were 0.35 and 0.42, respectively, with the glycan score added. Conclusions. IgG glycosylation profiles were associated with type 2 diabetes and the glycan score may be a novel indicator for diabetes which reflected a proinflammatory status. [ABSTRACT FROM AUTHOR]

Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM 2.5 and PM 10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM 2.5 corresponded to 1.91% (95% CI: 1.33–2.49%), 2.16% (95% CI: 1.08–3.25%), and 1.92% (95% CI: 1.46–2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0–8 days, lag 8 days, and lag 8 days. The effect size of PM 2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM 10 were 3.86% (95% CI: 2.48–5.27%), 3.73% (95% CI: 1.72–5.79%), and 3.92% (95% CI: 2.65–5.21%), respectively, at lag 0–13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM 10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM 2.5 was higher in patients with T2D than those without T2D. [Display omitted] • Short-term PM exposures were associated with increased admissions for RD and for RD with and without T2D. • The effect of PM 2.5 on RD was significantly higher in patients with T2D than those without T2D. • Significant difference could be found between three different RD groups for effects of PM 10 on RD without T2D. • E-R curves varied by sex, age and T2D conditions for the associations between PM and RD. [ABSTRACT FROM AUTHOR]

The brightness of pixels in nighttime light images (NTL) has been regarded as the proxy of the urban dynamics. However, the great difference between the pixel values of NTL from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS) poses obstacles to analyze economic and social development with NTL in a continuous temporal sequence. This research proposes a methodology to align the pixel values of both NTL by calibrating annual DMSP images between the years 1992–2013 with a robust regression algorithm with a quadratic polynomial regression model and simulating annual DMSP images with VIIRS images between years 2012 and 2018 with a model consisting of a power function and a Gaussian low pass filter. As a result, DMSP annual images between years 1992–2018 can be produced. Case study of Beijing and Yiwu are conducted and evaluated with local gross domestic product (GDP). Compared with the values of DMSP and VIIRS annual composites, the Pearson correlation coefficients of DMSP and simulated DMSP annual composites in 2012 and in 2013 increase significantly, while the root mean square error (RMSE) decrease evidently. In addition, the correlation of the sum of light of NTL and local GDP is enhanced with a simulation process. These results demonstrate the feasibility of the proposed method in narrowing the gap between DMSP and VIIRS NTL in pixel values. [ABSTRACT FROM AUTHOR]

[Display omitted] • The first comprehensive description of the relationship between air pollution and hospital admissions, length of stay, and costs for patients with cardiovascular disease with comorbid diabetes mellites. • First application of WHO 2021 air quality standards to a real-world study. • PM 2.5 and PM 10 are positively associated with hospital admissions, hospital cost and length of hospital stay for patients with cardiovascular disease with comorbid diabetes mellites. • Policy changes to reduce air pollutants exposure may reduce cardiovascular disease with comorbid diabetes mellites admissions and substantial savings in health care spending and hospital stay. Evidence on the effects of the air pollutants on the hospital admissions, hospital cost and length of stay (LOS) among patients with comorbidities remains limited in China, particularly for patients with cardiovascular diseases and comorbid diabetes mellitus (CVD-DM). We collected daily data on CVD-DM patients from 242 hospitals in Beijing between 2014 and 2019. Generalized additive model was employed to quantify the associations between admissions, LOS, and hospital cost for CVD-DM patients and air pollutants. We further evaluated the attributable risk posed by air pollutants to CVD-DM patients, using both Chinese and WHO air quality guidelines as reference. Per 10 ug/m3 increase of particles with an aerodynamic diameter < 2.5 μm (PM 2.5), particles with an aerodynamic diameter < 10 μm (PM 10), sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbonic oxide (CO) and ozone (O 3) corresponded to a 0.64% (95% CI : 0.57 to 0.71), 0.52% (95% CI : 0.46 to 0.57), 0.93% (95% CI : 0.67 to 1.20), 0.98% (95% CI : 0.81 to 1.16), 1.66% (95% CI : 1.18 to 2.14) and 0.53% (95% CI : 0.45 to 0.61) increment for CVD-DM patients' admissions. Among the six pollutants, particulate pollutants (PM 2.5 and PM 10) in most lag days exhibited adverse effects on LOS and hospital cost. For every 10 ug/m3 increase in PM 2.5 and PM 10 , the absolute increase with LOS will increase 62.08 days (95% CI : 28.93 to 95.23) and 51.77 days (95% CI :22.88 to 80.66), respectively. The absolute increase with hospital cost will increase 105.04 Chinese Yuan (CNY) (95% CI : 49.27 to 160.81) and 81.76 CNY (95% CI : 42.01 to 121.51) in PM 2.5 and PM 10 , respectively. Given WHO 2021 air quality guideline as the reference, PM 2.5 had the maximum attributable fraction of 3.34% (95% CI : 2.94% to 3.75%), corresponding to an avoidable of 65,845 (95% CI : 57,953 to 73,812) patients with CVD-DM. PM 2.5 and PM 10 are positively associated with hospital admissions, hospital cost and LOS for patients with CVD-DM. Policy changes to reduce air pollutants exposure may reduce CVD-DM admissions and substantial savings in health care spending and LOS. [ABSTRACT FROM AUTHOR]

Exposure to ambient particulate matter with a diameter of <2.5 μm (PM 2.5) has been linked to increases in blood pressure. The aim of this study was to assess the effects of short-term exposure to PM 2.5 on blood pressure in office workers in Beijing, China. A total of 4801 individuals aged 18–60 years underwent an annual medical examination between 2013 and 2017. Levels of air pollutants were obtained from 35 fixed monitoring stations and correlated with the employment location of each participant to predict personal exposure via kriging interpolation. Linear mixed-effects models were used to estimate the changes in blood pressure associated with PM 2.5 exposure at various lag times. After adjusting for personal characteristics and other potential confounders, each interquartile range increase in PM 2.5 was associated with a 0.413-mmHg (95% confidence interval [CI]: 0.252–0.573), 0.171-mmHg (95% CI: 0.053–0.288), 0.278-mmHg (95% CI: 0.152–0.404), and 0.241-mmHg (95% CI: 0.120–0.362) increase in systolic blood pressure, diastolic blood pressure, pulse pressure, and mean arterial pressure, respectively (p < 0.05). Men, individuals previously diagnosed with hypertension, and subjects working in the northern districts of Beijing had larger changes in blood pressure, and the effect sizes were 0.477-mmHg (95% CI: 0.286–0.669), 0.851-mmHg (95% CI: 0.306–1.397, and 0.672-mmHg (95% CI: 0.405–0.940). The findings suggested that exposure to PM 2.5 had adverse effects on blood pressure, especially among males and hypertensive patients. Image 1 • Individual PM 2.5 exposure was measured by Kriging interpolation method. • Blood pressure increased significantly with elevations in PM 2.5 for lag 4–6 days. • Stronger effects were found among males and hypertensive patients. [ABSTRACT FROM AUTHOR]