scholarly journals Accounting for measurement error to assess the effect of air pollution on omic signals

2018 ◽  
Author(s):  
Erica Ponzi ◽  
Paolo Vineis ◽  
Kian Fan Chung ◽  
Marta Blangiardo
Keyword(s):  
Air Pollution ◽  
Measurement Error ◽  
Error Term ◽  
Disease Risk ◽  
A Priori ◽  
Estimation Of Parameters ◽  
Bayesian Hierarchical ◽  
Short Term Exposure ◽  
Oxford Street ◽  
Made In

Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the “Oxford Street II Study”, a randomized crossover trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional MCMC simulative methods as well as integrated Laplace approximation. The inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution (TRAP) measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).

scholarly journals Relationship between Air Pollutant Exposure and Gynecologic Cancer Risk

2021 ◽  
Vol 18 (10) ◽  
pp. 5353
Author(s):  
Qiwei Yu ◽  
Liqiang Zhang ◽  
Kun Hou ◽  
Jingwen Li ◽  
Suhong Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cancer Risk ◽  
Air Pollutants ◽  
Gynecological Cancer ◽  
Gynecologic Cancer ◽  
Ambient Air ◽  
Air Pollutant ◽  
Ambient Air Pollution ◽  
Short Term Exposure ◽  
Increased Risk

Exposure to air pollution has been suggested to be associated with an increased risk of women’s health disorders. However, it remains unknown to what extent changes in ambient air pollution affect gynecological cancer. In our case–control study, the logistic regression model was combined with the restricted cubic spline to examine the association of short-term exposure to air pollution with gynecological cancer events using the clinical data of 35,989 women in Beijing from December 2008 to December 2017. We assessed the women’s exposure to air pollutants using the monitor located nearest to each woman’s residence and working places, adjusting for age, occupation, ambient temperature, and ambient humidity. The adjusted odds ratios (ORs) were examined to evaluate gynecologic cancer risk in six time windows (Phase 1–Phase 6) of women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the highest ORs were found in Phase 4 (240 days). Then, the higher adjusted ORs were found associated with the increased concentrations of each pollutant (PM2.5, CO, O3, and SO2) in Phase 4. For instance, the adjusted OR of gynecological cancer risk for a 1.0-mg m−3 increase in CO exposures was 1.010 (95% CI: 0.881–1.139) below 0.8 mg m−3, 1.032 (95% CI: 0.871–1.194) at 0.8–1.0 mg m−3, 1.059 (95% CI: 0.973–1.145) at 1.0–1.4 mg m−3, and 1.120 (95% CI: 0.993–1.246) above 1.4 mg m−3. The ORs calculated in different air pollution levels accessed us to identify the nonlinear association between women’s exposure to air pollutants (PM2.5, CO, O3, and SO2) and the gynecological cancer risk. This study supports that the gynecologic risks associated with air pollution should be considered in improved public health preventive measures and policymaking to minimize the dangerous effects of air pollution.

scholarly journals The Impact of Short-Term Exposure to Air Pollution on the Exhaled Breath of Healthy Adults

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2518
Author(s):  
Ariana Lammers ◽  
Anne H. Neerincx ◽  
Susanne J. H. Vijverberg ◽  
Cristina Longo ◽  
Nicole A. H. Janssen ◽  
...  
Keyword(s):  
Air Pollution ◽  
Ambient Air ◽  
Healthy Adults ◽  
Partial Least Square ◽  
Exhaled Breath ◽  
Short Term ◽  
Post Exposure ◽  
Short Term Exposure ◽  
Validation Set ◽  
The Impact

Environmental factors, such as air pollution, can affect the composition of exhaled breath, and should be well understood before biomarkers in exhaled breath can be used in clinical practice. Our objective was to investigate whether short-term exposures to air pollution can be detected in the exhaled breath profile of healthy adults. In this study, 20 healthy young adults were exposed 2–4 times to the ambient air near a major airport and two highways. Before and after each 5 h exposure, exhaled breath was analyzed using an electronic nose (eNose) consisting of seven different cross-reactive metal-oxide sensors. The discrimination between pre and post-exposure was investigated with multilevel partial least square discriminant analysis (PLSDA), followed by linear discriminant and receiver operating characteristic (ROC) analysis, for all data (71 visits), and for a training (51 visits) and validation set (20 visits). Using all eNose measurements and the training set, discrimination between pre and post-exposure resulted in an area under the ROC curve of 0.83 (95% CI = 0.76–0.89) and 0.84 (95% CI = 0.75–0.92), whereas it decreased to 0.66 (95% CI = 0.48–0.84) in the validation set. Short-term exposure to high levels of air pollution potentially influences the exhaled breath profiles of healthy adults, however, the effects may be minimal for regular daily exposures.

scholarly journals Effect of short-term exposure to particulate air pollution on heart rate variability in normal-weight and obese adults

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Luyi Li ◽  
Dayu Hu ◽  
Wenlou Zhang ◽  
Liyan Cui ◽  
Xu Jia ◽  
...  
Keyword(s):  
Heart Rate ◽  
Air Pollution ◽  
Heart Rate Variability ◽  
High Frequency ◽  
Normal Weight ◽  
High Frequency Power ◽  
Short Term ◽  
Particulate Air Pollution ◽  
Short Term Exposure ◽  
Frequency Power

Abstract Background The adverse effects of particulate air pollution on heart rate variability (HRV) have been reported. However, it remains unclear whether they differ by the weight status as well as between wake and sleep. Methods A repeated-measure study was conducted in 97 young adults in Beijing, China, and they were classified by body mass index (BMI) as normal-weight (BMI, 18.5–24.0 kg/m2) and obese (BMI ≥ 28.0 kg/m2) groups. Personal exposures to fine particulate matter (PM2.5) and black carbon (BC) were measured with portable exposure monitors, and the ambient PM2.5/BC concentrations were obtained from the fixed monitoring sites near the subjects’ residences. HRV and heart rate (HR) were monitored by 24-h Holter electrocardiography. The study period was divided into waking and sleeping hours according to time-activity diaries. Linear mixed-effects models were used to investigate the effects of PM2.5/BC on HRV and HR in both groups during wake and sleep. Results The effects of short-term exposure to PM2.5/BC on HRV were more pronounced among obese participants. In the normal-weight group, the positive association between personal PM2.5/BC exposure and high-frequency power (HF) as well as the ratio of low-frequency power to high-frequency power (LF/HF) was observed during wakefulness. In the obese group, personal PM2.5/BC exposure was negatively associated with HF but positively associated with LF/HF during wakefulness, whereas it was negatively correlated to total power and standard deviation of all NN intervals (SDNN) during sleep. An interquartile range (IQR) increase in BC at 2-h moving average was associated with 37.64% (95% confidence interval [CI]: 25.03, 51.51%) increases in LF/HF during wakefulness and associated with 6.28% (95% CI: − 17.26, 6.15%) decreases in SDNN during sleep in obese individuals, and the interaction terms between BC and obesity in LF/HF and SDNN were both statistically significant (p <  0.05). The results also suggested that the effects of PM2.5/BC exposure on several HRV indices and HR differed in magnitude or direction between wake and sleep. Conclusions Short-term exposure to PM2.5/BC is associated with HRV and HR, especially in obese individuals. The circadian rhythm of HRV should be considered in future studies when HRV is applied. Graphical abstract

scholarly journals Air pollution and out-of-hospital cardiac arrest risk

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Moderato ◽  
D Lazzeroni ◽  
A Biagi ◽  
T Spezzano ◽  
B Matrone ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cardiac Arrest ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
External Temperature ◽  
Pm 2.5 ◽  
Pm10 And Pm2.5 ◽  
Short Term Exposure ◽  
The Impact ◽  
Hospital Cardiac Arrest

Abstract Introduction Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide; it accounts for up to 50% of all cardiovascular deaths.It is well established that ambient air pollution triggers fatal and non-fatal cardiovascular events. However, the impact of air pollution on OHCA is still controversial. The objective of this study was to investigate the impact of short-term exposure to outdoor air pollutants on the incidence of OHCA in the urban area of Piacenza, Italy, one of the most polluted area in Europe. Methods From 01/01/2010 to 31/12/2017 day-by-day PM10 and PM2.5 levels, as well as climatic data, were extracted from Environmental Protection Agency (ARPA) local monitoring stations. OHCA were extracted from the prospective registry of Community-based automated external defibrillator Cardiac arrest “Progetto Vita”. OHCA data were included: audio recordings, event information and ECG tracings. Logistic regression analysis was used to estimate the association between the risk of OHC, expressed as odds ratios (OR), associated with the PM10 and PM2.5 levels. Results Mean PM10 levels were 33±29 μg/m3 and the safety threshold (50 μg/m3) recommended by both WHO and Italian legislation has been exceeded for 535 days (17.5%). Mean PM 5 levels were 33±29 μg/m3. During the follow-up period, 880 OHCA were recorded on 750 days; the remaining 2174 days without OHCA were used as control days. Mean age of OHCA patients was 76±15 years; male gender was prevalent (55% male vs 45% female; &lt;0.001). Concentration of PM10 and PM 2.5 were significantly higher on days with the occurrence of OHCA (PM10 levels: 37.7±22 μg/m3 vs 32.7±19 μg/m3; p&lt;0.001; PM 2.5 levels: 26±16 vs 22±15 p&lt;0.001). Risk of OHCA was significantly increased with the progressive increase of PM10 (OR: 1.009, 95% CI 1.004–1.015; p&lt;0.001) and PM2.5 levels (OR 1.012, 95% CI 1.007–1.017; p&lt;0.001). Interestingly, the above mentioned results remain independent even when correct for external temperature or season (PM 2.5 levels: p=0.01 – PM 10 levels: p=0.002), Moreover, dividing PM10 values in quintiles, a 1.9 fold higher risk of cardiac arrest has been showed in the highest quintile (Highest quintile cut-off: &lt;48μg/m3) Conclusions In large cohort of patients from a high pollution area, both PM10 and PM2.5 levels are associated with the risk of Out-of-hospital cardiac arrest. PM10 and PM2.5 levels and risk of OHCA Funding Acknowledgement Type of funding source: None

scholarly journals Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhan Ren ◽  
Xingyuan Liu ◽  
Tianyu Liu ◽  
Dieyi Chen ◽  
Kuizhuang Jiao ◽  
...  
Keyword(s):  
Air Pollution ◽  
Hospital Admission ◽  
Hospital Admissions ◽  
Conditional Logistic Regression ◽  
Warm Season ◽  
Central China ◽  
Air Pollution Control ◽  
Short Term ◽  
Short Term Exposure ◽  
Increased Risk

Abstract Background Positive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error. Methods This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season. Results A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m3 increase in PM2.5 (lag0–2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01–1.45%) and 1.95% (95% CI 1.63–2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season. Conclusions This study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.

scholarly journals Short term exposure to air pollution and stroke: systematic review and meta-analysis

BMJ ◽  
2015 ◽  
pp. h1295 ◽  
Author(s):  
Anoop S V Shah ◽  
Kuan Ken Lee ◽  
David A McAllister ◽  
Amanda Hunter ◽  
Harish Nair ◽  
...  
Keyword(s):  
Systematic Review ◽  
Air Pollution ◽  
Meta Analysis ◽  
Short Term ◽  
Short Term Exposure

Nanomechanical Biosensor Using Polymer Membranes

2004 ◽  
Author(s):  
Srinath Satyanarayana ◽  
Daniel T. McCormick ◽  
Arun Majumdar
Keyword(s):  
Surface Stress ◽  
A Priori ◽  
Detection Systems ◽  
Stress Sensors ◽  
Design Flexibility ◽  
High Elasticity ◽  
Sensor Geometry ◽  
Specific Reactions ◽  
Higher Sensitivity ◽  
Made In

In recent years several surface stress sensors based on microcantilevers have been developed for biosensing [1–4]. Since these sensors are made using standard microfabrication processes, they can be easily made in an array format, making them suitable for high-throughput multiplexed analysis. Specific reactions occurring on one surface (enabled by selective modification of the surface a priori) of the sensor element change the surface stress, which in turn causes the sensor to deflect. The magnitude and the rate of deflection are then used to study the reaction. The microcantilevers in these sensors are usually fabricated using material like silicon and its oxides or nitrides. The high elasticity modulus of these materials places limitations on the sensitivity and sensor geometry. Alternately polymers, which have a much lower elastic modulus when compared to silicon or its derivatives, offers greater design flexibility, i.e. allow the exploration of innovative sensor configurations that can have higher sensitivity and at the same time are suitable for integration with microfluidics and electrical detection systems.

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