scholarly journals Systemic Inflammation (C-Reactive Protein) in Older Chinese Adults Is Associated with Long-Term Exposure to Ambient Air Pollution

Author(s):  
Mona Elbarbary ◽  
Artem Oganesyan ◽  
Trenton Honda ◽  
Geoffrey Morgan ◽  
Yuming Guo ◽  
...  

There is an established association between air pollution and cardiovascular disease (CVD), which is likely to be mediated by systemic inflammation. The present study evaluated links between long-term exposure to ambient air pollution and high-sensitivity C reactive protein (hs-CRP) in an older Chinese adult cohort (n = 7915) enrolled in the World Health Organization (WHO) study on global aging and adult health (SAGE) China Wave 1 in 2008–2010. Multilevel linear and logistic regression models were used to assess the associations of particulate matter (PM) and nitrogen dioxide (NO2) on log-transformed hs-CRP levels and odds ratios of CVD risk derived from CRP levels adjusted for confounders. A satellite-based spatial statistical model was applied to estimate the average community exposure to outdoor air pollutants (PM with an aerodynamic diameter of 10 μm or less (PM10), 2.5 μm or less (PM2.5), and 1 μm or less (PM1) and NO2) for each participant of the study. hs-CRP levels were drawn from dried blood spots of each participant. Each 10 μg/m3 increment in PM10, PM2.5, PM1, and NO2 was associated with 12.8% (95% confidence interval; (CI): 9.1, 16.6), 15.7% (95% CI: 10.9, 20.8), 10.2% (95% CI: 7.3, 13.2), and 11.8% (95% CI: 7.9, 15.8) higher serum levels of hs-CRP, respectively. Our findings suggest that air pollution may be an important factor in increasing systemic inflammation in older Chinese adults.

2020 ◽  
Vol 7 (1) ◽  
pp. e000684
Author(s):  
Mona Elbarbary ◽  
Artem Oganesyan ◽  
Trenton Honda ◽  
Patrick Kelly ◽  
Ying Zhang ◽  
...  

BackgroundLong-term exposure to ambient air pollution leads to respiratory morbidity and mortality; however, the evidence of the effect on lung function and chronic obstructive pulmonary disease (COPD) in older adult populations is inconsistent.ObjectiveTo address this knowledge gap, we investigated the associations between particulate matter (PM), nitrogen dioxide (NO2) exposure and lung function, as well as COPD prevalence, in older Chinese adults.MethodsWe used data from the WHO Study on global AGEing and adult health (SAGE) China Wave 1, which includes 11, 693 participants from 64 townships in China. A cross-sectional analysis explored the association between satellite-based air pollution exposure estimates (PM with an aerodynamic diameter of ≤10 µm [PM10], ≤2.5 µm [PM2.5] and NO2) and forced expiratory volume in one second (FEV1), forced vital capacity (FVC), the FEV1/FVC ratio and COPD (defined as post-bronchodilator FEV1/FVC <70%). Data on lung function changes were further stratified by COPD status.ResultsHigher exposure to each pollutant was associated with lower lung function. An IQR (26.1 µg/m3) increase in PM2.5 was associated with lower FEV1 (−71.88 mL, 95% CI –92.13 to –51.64) and FEV1/FVC (−2.81, 95% CI −3.37 to –2.25). For NO2, an IQR increment of 26.8 µg/m3 was associated with decreases in FEV1 (−60.12 mL, 95% CI –84.00 to –36.23) and FVC (−32.33 mL, 95% CI –56.35 to –8.32). A 31.2 µg/m3 IQR increase in PM10 was linked to reduced FEV1 (−8.86 mL, 95% CI −5.40 to 23.11) and FEV1/FVC (−1.85, 95% CI −2.24 to –1.46). These associations were stronger for participants with COPD. Also, COPD prevalence was linked to higher levels of PM2.5 (POR 1.35, 95% CI 1.26 to 1.43), PM10 (POR 1.24, 95% CI 1.18 to 1.29) and NO2 (POR 1.04, 95% CI 0.98 to 1.11).ConclusionAmbient air pollution was associated with lower lung function, especially in individuals with COPD, and increased COPD prevalence in older Chinese adults.


Author(s):  
Mona Elbarbary ◽  
Trenton Honda ◽  
Geoffrey Morgan ◽  
Yuming Guo ◽  
Yanfei Guo ◽  
...  

Background: Health effects of air pollution on anaemia have been scarcely studied worldwide. We aimed to explore the associations of long-term exposure to ambient air pollutants with anaemia prevalence and haemoglobin levels in Chinese older adults. Methods: We used two-level linear regression models and modified Poisson regression with robust error variance to examine the associations of particulate matter (PM) and nitrogen dioxide (NO2) on haemoglobin concentrations and the prevalence of anaemia, respectively, among 10,611 older Chinese adults enrolled in World Health Organization (WHO) Study on global AGEing and adult health (SAGE) China. The average community exposure to ambient air pollutants (PM with an aerodynamic diameter of 10 μm or less (PM10), 2.5 μm or less (PM2.5), 1 μm or less (PM1) and nitrogen dioxide (NO2)) for each participant was estimated using a satellite-based spatial statistical model. Haemoglobin levels were measured for participants from dried blood spots. The models were controlled for confounders. Results: All the studied pollutants were significantly associated with increased anaemia prevalence in single pollutant model (e.g., the prevalence ratios associated with an increase in inter quartile range in three years moving average PM10 (1.05; 95% CI: 1.02–1.09), PM2.5 (1.11; 95% CI: 1.06–1.16), PM1 (1.13; 95% CI: 1.06–1.20) and NO2 (1.42; 95% CI: 1.34–1.49), respectively. These air pollutants were also associated with lower concentrations of haemoglobin: PM10 (−0.53; 95% CI: −0.67, −0.38); PM2.5 (−0.52; 95% CI: −0.71, −0.33); PM1 (−0.55; 95% CI: −0.69, −0.41); NO2 (−1.71; 95% CI: −1.85, −1.57) respectively. Conclusions: Air pollution exposure was significantly associated with increased prevalence of anaemia and decreased haemoglobin levels in a cohort of older Chinese adults.


Diabetes Care ◽  
2012 ◽  
Vol 36 (3) ◽  
pp. 625-630 ◽  
Author(s):  
M. A. Khafaie ◽  
S. S. Salvi ◽  
A. Ojha ◽  
B. Khafaie ◽  
S. S. Gore ◽  
...  

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Bo-Yi Yang ◽  
Zhengmin Min Qian ◽  
Michael G. Vaughn ◽  
Steven W. Howard ◽  
John Phillip Pemberton ◽  
...  

2021 ◽  
Vol 195 ◽  
pp. 110804
Author(s):  
Hao Zheng ◽  
Zhiwei Xu ◽  
QingQing Wang ◽  
Zhen Ding ◽  
Lian Zhou ◽  
...  

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