Association of long-term exposure to PM2.5 with hypertension and diabetes among the middle-aged and elderly people in Chinese mainland: a spatial study

Associations between long-term exposure to air pollution, diabetes, and hypertension in metropolitan Iran: an ecologic study

Epidemiological studies on air pollution, diabetes, and hypertension conflict. This study examined air pollution, diabetes, and hypertension in adults in 11 metropolitan areas of Iran (2012-2016). Local environment departments and the Tehran Air Quality Control Company provided air quality data. The VIZIT website and Stepwise Approach to Chronic Disease Risk Factor Surveillance study delivered chronic disease data. Multiple logistic regression and generalized estimating equations evaluated air pollution-related diabetes and hypertension. In Isfahan, Ahvaz, and Tehran, PM2.5 was linked to diabetes. In all cities except Urmia, Yasuj, and Yazd, PM2.5 was statistically related to hypertension. O3 was connected to hypertension in Ahvaz, Tehran, and Shiraz, whereas NO2 was not. BMI and gender predict hypertension and diabetes. Diabetes, SBP, and total cholesterol were correlated. Iran's largest cities' poor air quality may promote diabetes and hypertension. PM2.5 impacts many cities' outcomes. Therefore, politicians and specialists have to control air pollution.

Spatial-temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

OBJECTIVE

This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.

METHODS

This study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.

RESULTS

PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.

CONCLUSION

PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

Association between PM2.5 and hypertension among the floating population in China: a cross-sectional study

Few studies have investigated the association between PM2.5 and hypertension among floating populations. We therefore examined the relationship using binary logistic regression. Each grade of increment in the annual average PM2.5 (grade one: ≤15 µg/m3; grade two: 15-25 µg/m3; grade three: 25-35 µg/m3 [Excluding 25]; grade four: ≥35 µg/m3) was associated with an increased risk of hypertension (odds ratio [OR] = 1.081, 95% confidence interval (CI): 1.034-1.129). Among the female floating population (OR = 1.114, 95% CI: 1.030-1.204), those with education level of primary school and below (OR = 1.140, 95% CI: 1.058-1.229), construction workers (OR = 1.228, 95% CI: 1.058-1.426), and those living in the eastern region of China (OR = 1.241, 95% CI: 1.145-1.346) were more vulnerable to PM2.5. These results indicate that PM2.5 is positively associated with hypertension in floating populations. Floating populations who are female, less educated, construction workers, and living in the eastern region of China are more vulnerable to the adverse impacts of PM2.5.

Mendelian randomization study supports the causal effects of air pollution on longevity via multiple age-related diseases

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may reduce life expectancy; however, the causal pathways of PM2.5 exposure affecting life expectancy remain unknown. Here, we assess the causal effects of genetically predicted PM2.5 concentration on common chronic diseases and longevity using a Mendelian randomization (MR) statistical framework based on large-scale genome-wide association studies (GWAS) (>400,000 participants). After adjusting for other types of air pollution and smoking, we find significant causal relationships between PM2.5 concentration and angina pectoris, hypercholesterolaemia and hypothyroidism, but no causal relationship with longevity. Mediation analysis shows that although the association between PM2.5 concentration and longevity is not significant, PM2.5 exposure indirectly affects longevity via diastolic blood pressure (DBP), hypertension, angina pectoris, hypercholesterolaemia and Alzheimer's disease, with a mediated proportion of 31.5, 70.9, 2.5, 100, and 24.7%, respectively. Our findings indicate that public health policies to control air pollution may help improve life expectancy.

Overview of PM2.5 and health outcomes: Focusing on components, sources, and pollutant mixture co-exposure

PM_2.5 varies in source and composition over time and space as a complicated mixture. Consequently, the health effects caused by PM_2.5 varies significantly over time and generally exhibit significant regional variations. According to numerous studies, a notable relationship exists between PM_2.5 and the occurrence of many diseases, such as respiratory, cardiovascular, and nervous system diseases, as well as cancer. Therefore, a comprehensive understanding of the effect of PM_2.5 on human health is critical. The toxic effects of various PM_2.5 components, as well as the overall toxicity of PM_2.5 are discussed in this review to provide a foundation for precise PM_2.5 emission control. Furthermore, this review summarizes the synergistic effect of PM_2.5 and other pollutants, which can be used to draft effective policies.

Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy

Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with T2DM were retrospectively studied. These patients were hospitalized between 1 January 2017 and 31 December 2020 in the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China. A total of 901 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China who were hospitalized between 1 January 2019 and 31 December 2020 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were performed to identify independent predictors and establish a nomogram to predict the occurrence of DPN. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and a decision curve analysis (DCA). Findings: Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) were used to establish a nomogram model for predicting the risk of DPN. In the training and validation cohorts, the areas under the curve of the nomogram constructed from the above six factors were 0.8256 (95% CI: 0.8104–0.8408) and 0.8608 (95% CI: 0.8376–0.8840), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: This study has developed and externally validated a nomogram model which exhibits good predictive ability in assessing DPN risk among the type 2 diabetes population. It provided clinicians with an accurate and effective tool for the early prediction and timely management of DPN.

The association of long-term ambient fine particulate matter exposure with blood pressure among Chinese adults

Previous studies indicated that long-term exposure to high level of fine particulate matter (PM_2.5) was associated with elevated blood pressure (BP) and hypertension, but most of them were conducted in high-income countries with low PM_2.5 level. Therefore, we aimed to evaluate the adverse impacts of long-term exposure to PM_2.5 on BP and hypertension in China with high concentration. A total of 99,084 adults aged ≥18 years old were included from three cohorts among the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China. PM_2.5 concentrations during 2000-2015 at 1 × 1 km spatial resolution were evaluated using satellite-based spatiotemporal models. Generalized estimating equation was applied to assess the impact of three-year average PM_2.5 concentrations on BP level and hypertension. We also examined whether health status and lifestyles modified the effects of PM_2.5 on BP and hypertension. Generally, high concentration of PM_2.5 was associated with increased BP level and higher risk of hypertension. With each 10 μg/m³ increment in PM_2.5 concentration, systolic BP (SBP) and diastolic BP (DBP) increased by 1.67 [95% confidence interval (CI): 1.48, 1.86] mmHg and 0.45 (95% CI: 0.35, 0.56) mmHg, and the prevalence of hypertension increased by 29% [odds ratio (OR): 1.29, 95% CI: 1.26, 1.32]. In comparison with the first quartile of PM_2.5 concentration, SBP, DBP and prevalence of hypertension in the fourth quartile were increased by 8.26 (95% CI: 7.73, 8.80) mmHg, 2.85 (95% CI: 2.55, 3.15) mmHg, and 133% (OR: 2.33, 95% CI: 2.21, 2.47), respectively, in the fully adjusted model. However, the relationships of PM_2.5 with BP might be non-linear, as BP level started to decline when PM_2.5 exceeded 75 μg/m³. In conclusion, long-term PM_2.5 exposure could elevate BP level and prevalence of hypertension. People living in high-polluted areas should strengthen their awareness of prevention.