Associations between residential greenness and air pollution and the incident metabolic syndrome in a Thai worker cohort.

Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM10, CO, SO2, NO2, and O3) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM10 (9.5 µg/m3), SO2 (0.9 ppb), and CO (0.3 ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM10 at concentrations more than 50 µg/m3. In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes.

Residential greenness and kidney function: A cohort study of Thai employees.

Higher residential greenness is associated with a lower risk of chronic kidney disease, but evidence on the association between greenness exposure and kidney function has not been conducted. Using cohort data from Electricity Generating Authority of Thailand (EGAT) employees, we investigated the association between long-term exposure to greenness and kidney function using estimated glomerular filtration rate (eGFR) in Bangkok Metropolitan Region (BMR), Thailand. We analyzed data from 2022 EGAT workers (aged 25-55 years at baseline) from 2009 to 2019. The level of greenness was calculated using the satellite-derived Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). From 2008 to 2019, the average concentration of each air pollutant (PM10, O3, NO2, SO2, and CO) at the sub-district level in BMR was generated using the Kriging method. Long-term exposure for each participant was defined as the 1-year average concentrations before the date of the physical examination in 2009, 2014, and 2019. We employed linear mixed effects models to evaluate associations of NDVI and EVI with eGFR. The robustness of the results was also tested by including air pollutants in the models. After relevant confounders were controlled, the interquartile range increase in NDVI was associated with higher eGFR [1.03% (95%CI: 0.33, 1.74)]. After PM10 and SO2 were included in the models, the associations between NDVI and eGFR became weaker. The additions of O3, NO2, and CO strengthened the associations between them. In contrast, we did not find any association between EVI and eGFR. In conclusion, there was a positive association between NDVI and eGFR, but not for EVI. Air pollutants had a significant impact on the relationship between NDVI and eGFR. Additional research is needed to duplicate this result in various settings and populations to confirm our findings.

Analysis of land surface temperature with land use and land cover and elevation from NASA MODIS satellite data: a case study of Bali, Indonesia.

The Moderate Resolution Imaging Spectroradiometer (MODIS) of the National Aeronautics and Space Administration (NASA) offers numerous land products of the Earth's datasets. On the other hand, researchers find it difficult to retrieve this data for specific places. The methods for extracting and analyzing land surface temperature (LST), land use and land cover (LULC), and elevation are presented in this study. The R commands provided make the time-consuming process of extracting data for specific places much more accessible. As a result, a statistical study of LST over Bali is shown as an example. Over the 15 regions of Bali, a quadratic polynomial identified five possible warming patterns, while a logistic regression model assessed the probability of warming. The findings suggest that 25.2% of Bali has warmed during the last two decades, with temperatures being highest in urban and built-up areas and deciduous forests and inversely associated with elevation. Global warming has sparked a lot of academic interest and has become a serious climate problem. The techniques proposed in this work simplify the extraction of LST, LULC, and elevation data from MODIS satellites. These approaches can also be used on other datasets with identical topologies, such as the normalized difference vegetation index (NDVI), aerosol optical depth (AOD), and night light data.

Long-term air pollution exposure and decreased kidney function: A longitudinal cohort study in Bangkok Metropolitan Region, Thailand from 2002 to 2012.

BACKGROUND: Kidney dysfunction is considered a cardiovascular risk factor. However, few longitudinal studies have examined the effects of air pollution on kidney function. We evaluated associations between long-term air pollution exposure and estimated glomerular filtration rate (eGFR) using data from a cohort of the Electricity Generating Authority of Thailand (EGAT) study in Bangkok Metropolitan Region, Thailand. METHODS: This longitudinal study included 1839 subjects (aged 52-71 years in 2002) from the EGAT1 cohort study during 2002-2012. eGFR, based on creatinine, was measured in 2002, 2007, and 2012. Annual mean concentrations of air pollutants (i.e., particulate matter with an aerodynamic diameter ≤10 µm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)) prior to a measurement of creatinine were assessed with the ordinary kriging method. Mixed-effect linear regression models were used to assess associations between air pollutants and eGFR, while controlling for potential covariates. eGFR values are expressed as percent change per interquartile range (IQR) increments of each pollutant. RESULTS: Lower eGFR was associated with higher concentrations of PM10 (-1.99%, 95% confidence interval (CI): -3.33, -0.63), SO2 (-4.89%, 95%CI: -6.69, -3.07), and CO (-0.97%, 95%CI: -1.96, 0.03). However, after adjusting for temperature, relative humidity, PM10, and SO2, no significant association was observed between CO and eGFR. CONCLUSIONS: Our findings support the hypothesis that long-term exposure to high concentrations of PM10 and SO2 is associated with the progression of kidney dysfunction in subjects of the EGAT cohort study.

Diagnostic and prognostic value of serum miR-145 and vascular endothelial growth factor in non-small cell lung cancer.

Previous studies have reported the diagnostic and prognostic value of serum microRNA (miR)-145 and vascular endothelial growth factor (VEGF) levels in various types of cancer; however, their clinical use in non-small cell lung cancer (NSCLC) remains unclear. The present study included 215 patients, 106 with NSCLC and 109 with other lung diseases (OLDs). miR-145 expression levels were determined using reverse transcription-quantitative PCR (RT-qPCR) and VEGF levels were measured using an ELISA. The diagnostic performance was assessed using a receiver operating characteristic curve and area under the curve (AUC) analysis. A Kaplan-Meier survival curve and Cox regression analysis were employed to evaluate the prognostic significance of the markers. The biological function of miR-145 was examined in A549 and H1792 cell lines. The effects of miR-145 on cell proliferation of NSCLC cells were evaluated by flow cytometry, and the expression levels of miR-145 and cell cycle-related genes were determined by RT-qPCR. The results revealed that miR-145 and VEGF exhibited fair diagnostic performance [AUC, 0.61 (95% CI, 0.55-0.68) and AUC, 0.64 (95% CI, 0.57-0.71), respectively]. Combining age and smoking status with miR-145 and VEGF provided the best model for differentiating patients with NSCLC from those with OLDs (AUC, 0.76; 95% CI, 0.69-0.83). Furthermore, low serum miR-145 levels were associated with poor overall survival [hazard ratio (HR), 0.48; 95% CI, 0.27-0.85], whereas high VEGF levels were not associated with poor overall survival (HR, 1.47; 95% CI, 0.81-2.68). In addition, the results of the in vitro experiments indicated that miR-145 decreased cell proliferation via the induction of cell cycle arrest. In conclusion, the findings of the present study suggested that combining miR-145 and VEGF levels with clinical risk factors may be a potential diagnostic scheme for NSCLC. In addition, serum miR-145 may be used as a prognostic marker. These results indicated that miR-145 may function as a tumor suppressor in NSCLC.

Effects of long-term air pollution exposure on ankle-brachial index and cardio-ankle vascular index: A longitudinal cohort study using data from the Electricity Generating Authority of Thailand study.

BACKGROUND: Ankle-brachial index (ABI) and cardio-ankle vascular index (CAVI) are surrogate measures of atherosclerosis based on the functional performance of vessels, and are highly related to cardiovascular events. However, only a few longitudinal studies have been conducted on their associations with long-term air pollution exposure. OBJECTIVE: This study aimed to examine whether long-term air pollution exposure is associated with ABI and CAVI in workers of the Electricity Generating Authority of Thailand (EGAT) in the Bangkok Metropolitan Region (BMR). METHODS: This longitudinal study included 1261 participants (age range, 57-76 years as of 2007) of the EGAT study (2007-2017). ABI and CAVI were measured in 2007, 2012, and 2017. Annual mean concentrations of particulate matter ≤10 µm in diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) were estimated by ordinary kriging using data from 22 background and 7 traffic monitoring stations in BMR between 2002 and 2017. Linear mixed-effects models were used to assess associations between air pollution (expressed as 1-year, 3-year, and 5-year average concentration) and ABI and CAVI (expressed as percent changes per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO). We also applied the mixed-effect ordinal logistic models to calculate odds ratios (ORs) of having high or moderate CAVI per an IQR increase in air pollution. RESULTS: After controlling for potential confounders, 1-year average CO was negatively associated with ABI, but not significantly (-0.48%, 95% CI: -1.03, 0.07). Three-year average NO2 was positively associated with CAVI (6.67%, 95% CI: 0.21, 13.1). In contrast, 1-year average PM10 was inversely associated with CAVI although the association was not significant. Although not significantly, 1-year average NO2 and CO were positively associated with prevalence of high or moderate CAVI. CONCLUSIONS: Although not statistically significant, long-term NO2 and CO exposure was associated with ABI and CAVI in the participants of the EGAT study.

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

BACKGROUND: Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand. METHODS: This longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ≤10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005-2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO. RESULTS: After controlling for potential confounders, we found that an IQR increase in PM10 was significantly associated with self-reported high blood pressure (HR = 1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR = 1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR = 1.05, 95%CI: 0.91, 1.21). SO2 was also positively associated with self-reported high blood pressure (HR = 1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR = 1.20, 95%CI: 1.11, 1.30), and diabetes (HR = 1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR = 1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O3 and NO2. CONCLUSIONS: Long-term exposure to air pollution, especially for PM10 and SO2 was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases cardiovascular disease risk factors for younger population.

Serum miR-339-3p as a potential diagnostic marker for non-small cell lung cancer.

Objective: MicroRNA (miRNA), a short noncoding RNA, is claimed to be a potential blood-based biomarker. We aimed to identify and evaluate miRNAs as diagnostic biomarkers for non-small cell lung cancer (NSCLC). Methods: Profiles of 745 miRNAs were screened in the serum of 8 patients with NSCLC and 8 age- and sex-matched controls using TaqMan low-density arrays (TLDAs) and validated in 25 patients with NSCLC and 30 with other lung diseases (OLs) as well as in 19 healthy persons (HPs). The diagnostic performance of the candidate miRNAs was assessed in 117 cases of NSCLC and 113 OLs using quantitative real-time polymerase chain reaction (qRT-PCR). Differences in miRNA expression between patients with NSCLC and controls were assessed using the Mann-Whitney U test. The area under receiver operating characteristic (ROC) curve (AUC) was obtained based on the logistic regression model. Results: Ten miRNAs were found to be differentially expressed between patients with NSCLC and controls, including miR-769, miR-339-3p, miR-339-5p, miR-519a, miR-1238, miR-99a#, miR-134, miR-604, miR-539, and miR-342. The expression of miR-339-3p was significantly higher in patients with NSCLC than in those with OLs (P < 0.001) and HPs (P = 0.020). ROC analysis revealed an miR-339-3p expression AUC of 0.616 [95% confidence interval (CI): 0.561-0.702]. The diagnostic prediction was increased (AUC = 0.706, 95% CI: 0.649-0.779) in the model combining miR-339-3p expression and other known risk factors (i.e., age, smoking status, and drinking status). Conclusions: MiR-339-3p was significantly upregulated in patients with NSCLC compared with participants without cancer, suggesting a diagnostic prediction value for high-risk individuals. Therefore, miR-339-3p expression could be a potential blood-based biomarker for NSCLC.