Air Pollution/analysis* ; Environmental Exposure/analysis* ; China/epidemiology* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Monitoring

BACKGROUND: Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) has become a global epidemic, and air pollution has been identified as a potential risk factor. This study aims to investigate the non-linear relationship between ambient air pollution and MASLD prevalence. METHOD: In this cross-sectional study, participants undergoing health checkups were assessed for three-year average air pollution exposure. MASLD diagnosis required hepatic steatosis with at least 1 out of 5 cardiometabolic criteria. A stepwise approach combining data visualization and regression modeling was used to determine the most appropriate link function between each of the six air pollutants and MASLD. A covariate-adjusted six-pollutant model was constructed accordingly. RESULTS: A total of 131,592 participants were included, with 40.6% met the criteria of MASLD. "Threshold link function," "interaction link function," and "restricted cubic spline (RCS) link functions" best-fitted associations between MASLD and PM_2.5, PM₁₀/CO, and O₃ /SO₂/NO₂, respectively. In the six-pollutant model, significant positive associations were observed when pollutant concentrations were over: 34.64 µg/m³ for PM_2.5, 57.93 µg/m³ for PM₁₀, 56 µg/m³ for O₃, below 643.6 µg/m³ for CO, and within 33 and 48 µg/m³ for NO₂. The six-pollutant model using these best-fitted link functions demonstrated superior model fitting compared to exposure-categorized model or linear link function model assuming proportionality of odds. CONCLUSION Non-linear associations were found between air pollutants and MASLD prevalence. PM_2.5, PM₁₀, O₃, CO, and NO₂ exhibited positive associations with MASLD in specific concentration ranges, highlighting the need to consider non-linear relationships in assessing the impact of air pollution on MASLD.
Humans ; Nitrogen Dioxide ; Cross-Sectional Studies ; Air Pollution/analysis* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Liver Diseases ; Environmental Exposure/analysis*

Objective To explore the overall level,distribution characteristics,and differences in household fine particulate matter (PM_2.5) pollution caused by fuel burning in urban and rural areas in China. Methods The relevant articles published from 1991 to 2021 were retrieved and included in this study.The data including the average concentration of household PM_2.5 and urban and rural areas were extracted,and the stoves and fuel types were reclassified.The average concentration of PM_2.5 in different areas was calculated and analyzed by nonparametric test. Results The average household PM_2.5 concentration in China was (178.81±249.91) μg/m³.The mean household PM_2.5 concentration was higher in rural areas than in urban areas[(206.08±279.40) μg/m³ vs. (110.63±131.16) μg/m³;Z=-5.45,P<0.001] and higher in northern areas than in southern areas[(224.27±301.66) μg/m³ vs.(130.11±140.61) μg/m³;Z=-2.38,P=0.017].The north-south difference in household PM_2.5 concentration was more significant in rural areas than in urban areas[(324.19±367.94) μg/m³ vs.(141.20±151.05) μg/m³,χ²=-5.06,P<0.001].The PM_2.5 pollution level showed differences between urban and rural households using different fuel types (χ²=92.85,P<0.001),stove types (χ²=74.42,P<0.001),and whether they were heating (Z=-4.43,P<0.001).Specifically,rural households mainly used solid fuels (manure,charcoal,coal) and traditional or improved stoves,while urban households mainly used clean fuels (gas) and clean stoves.The PM_2.5 concentrations in heated households were higher than those in non-heated households in both rural and urban areas (Z=-4.43,P<0.001). Conclusions The household PM_2.5 pollution caused by fuel combustion in China remains a high level.The PM_2.5 concentration shows a significant difference between urban and rural households,and the PM_2.5 pollution is more serious in rural households.The difference in the household PM_2.5 concentration between urban and rural areas is more significant in northern China.PM_2.5 pollution in the households using solid fuel,traditional stoves,and heating is serious,and thus targeted measures should be taken to control PM_2.5 pollution in these households.
Humans ; Particulate Matter/analysis* ; Air Pollution, Indoor/analysis* ; Cooking ; Environmental Exposure/analysis* ; China ; Rural Population

Objective: To investigate the association of mixed exposure to greenness and nitrogen dioxide(NO₂) and hypertension among the older adults aged 65 years and over in China. Methods: The study subjects were from the Chinese Longitudinal Healthy Longevity Survey from 2017 to 2018. A total of 15 423 older adults aged 65 years and over meeting the criteria were finally included in the study. A questionnaire survey was used to collect information on demographic characteristics, lifestyle habits and self-reported prevalence of hypertension. Blood pressure values were obtained through physical examination. The level of normalized difference vegetation index(NDVI) was measured by the Medium-resolution Imaging Spectral Radiator(MODIS) of the National Aeronautics and Space Administration(NASA). The concentration of NO₂ was from China's surface air pollutant data set. Meteorological data was from NASA MERRA-2. The exposure to NDVI and NO₂ for each study subject was calculated based on the area within a 1 km radius around their residence. The association between mixed exposure of NDVI and NO₂ as well as their interaction and hypertension in older adults was analyzed by using the multivariate logistic regression model. The restrictive cubic spline(RCS) function was used to explore the exposure-response relationship between greenness and NO₂ and the risk of hypertension in study subjects. Results: The mean age of 15 423 older adults were (85.6±11.6). Women accounted for 56.3%(8 685/15 423) and 55.6%(8 578/15 423) lived in urban areas. The mean time of residence was (60.9±28.5) years. 59.8% of participants were with hypertension. The mean NDVI level was 0.41±0.13, and the mean NO₂ concentration was (32.18±10.36) μg/cm³. The results of multivariate logistic regression analysis showed that NDVI was inversely and linearly associated with the hypertension in older adults, with the OR(95%CI) value of 0.959(0.928-0.992). Compared with the T₁ group of NDVI, the risk of hypertension was lower in the T₃ group, with the OR(95%CI) value of 0.852(0.769-0.944), and the trend test was statistically significant(P<0.05). Compared with the T₁ group of NO₂, the risk of hypertension was higher in the T₂ and T₃ groups, with OR(95%CI) values of 1.160(1.055-1.275) and 1.244(1.111-1.393), and the trend test was statistically significant (P<0.05). The result of the RCS showed that NDVI was inversely and linearly associated with hypertension in older adults. NO₂ was nonlinearly associated with hypertension in older adults. The interaction analysis showed that NDVI and NO₂ had a negative multiplicative interaction on the risk of hypertension, with OR(95%CI) value of 0.995(0.992-0.997). Conclusion: Exposure to greenness and NO₂ are associated with hypertension in older adults.
Aged ; Humans ; Female ; Nitrogen Dioxide ; Air Pollution ; Prevalence ; Hypertension/epidemiology* ; China/epidemiology* ; Particulate Matter/analysis*

Objective: To analyze the short-term effect of individual atmospheric PM_2.5 exposure on the diversity, enterotype, and community structure of gut microbiome in healthy elderly people in Jinan, Shandong province. Methods: The present panel study recruited 76 healthy elderly people aged 60-69 years old in Dianliu Street, Lixia District, Jinan, Shandong Province, and followed them up five times from September 2018 to January 2019. The relevant information was collected by questionnaire, physical examination, precise monitoring of individual PM_2.5 exposure, fecal sample collection and gut microbiome 16S rDNA sequencing. The Dirichlet multinomial mixtures (DMM) model was used to analyze the enterotype. Linear mixed effect model and generalized linear mixed effect model were used to analyze the effect of PM_2.5 exposure on gut microbiome α diversity indices (Shannon, Simpson, Chao1, and ACE indices), enterotype and abundance of core species. Results: Each of the 76 subjects participated in at least two follow-up visits, resulting in a total of 352 person-visits. The age of 76 subjects was (65.0±2.8) years old with BMI (25.0±2.4) kg/m². There were 38 males accounting for 50% of the subjects. People with an educational level of primary school or below accounted for 10.5% of the 76 subjects, and those with secondary school and junior college or above accounting for 71.1% and 18.4%. The individual PM_2.5 exposure concentration of 76 subjects during the study period was (58.7±53.7) μg/m³. DMM model showed that the subjects could be divided into four enterotypes, which were mainly driven by Bacteroides, Faecalibacterium, Lachnospiraceae, Prevotellaceae, and Ruminococcaceae. Linear mixed effects model showed that different lag periods of PM_2.5 exposure were significantly associated with a lower gut α diversity index (FDR<0.05 after correction). Further analysis showed that PM_2.5 exposure was significantly associated with changes in the abundances of Firmicutes (Megamonas, Blautia, Streptococcus, etc.) and Bacteroidetes (Alistipes) (FDR<0.05 after correction). Conclusion: Short-term PM_2.5 exposure is significantly associated with a decrease in gut microbiome diversity and changes in the abundance of several species of Firmicutes and Bacteroidetes in the elderly. It is necessary to further explore the underlying mechanisms between PM_2.5 exposure and the gut microbiome, so as to provide a scientific basis for promoting the intestinal health of the elderly.
Aged ; Humans ; Male ; Middle Aged ; Feces/microbiology* ; Gastrointestinal Microbiome ; Particulate Matter ; RNA, Ribosomal, 16S/genetics* ; Female

Objective: To explore the risk intensity and related influencing factors of post-traumatic stress disorder (PTSD) among high-stress rescue workers, and to provide effective tools for the risk assessment of PTSD in military rescue workers. Method: From June to August 2022, cluster sampling was used to select the high-stress rescue personnel of an Army department as the survey subjects. The acute Stress reaction (ASD) scale and PTSD checklist were used to evaluate the risk of PTSD in military rescue personnel. Multivariate logistic regression were used to analyze the influencing factors of PTSD. Results: The age of 4 460 subjects was (24.38±4.072) years old, including 4 396 males (98.6%). The positive rate of initial screening for ASD was 2.85% (127/4 460). The positive rate of PTSD was 0.67% (30/4 460). Multivariate logistic regression model analysis showed that female, older age, recent trauma exposure history, passive smoking and alcohol consumption were at higher risk of ASD, the values of OR (95%CI) were 4.183 (1.819-9.618), 6.278 (1.363-28.912), 3.094 (1.500-6.379), 2.059 (1.298-3.267) and 2.607 (1.614-4.211), respectively; Lower education level was associated with lower risk of ASD, OR (95%CI) was 0.593 (0.359-0.978); People who are older, thinner, have a history of mental illness, and drink alcohol were at higher risk for PTSD, the values of OR (95%CI) were 20.144 (2.459-165.043), 10.287 (2.218-47.700), 91.104 (8.592-965.980) and 2.866 (1.144-7.180), respectively. Conclusion: Gender, age, education level, passive smoking, alcohol consumption, past history of mental illness and body mass index may be related to the potential risk of PTSD in rescue workers,passive smoking, alcohol consumption, and weight controlling should be focused on to reduce potential risks of PTSD.
Male ; Humans ; Female ; Young Adult ; Adult ; Stress Disorders, Post-Traumatic/prevention & control* ; Tobacco Smoke Pollution ; Risk Assessment ; Military Personnel ; Alcohol Drinking

Objective: To understand the performance of pulmonary function tests in people aged ≥40 years and its changes in China, and provide evidence for the evaluation of the effect of chronic obstructive pulmonary disease (COPD) prevention and control in China. Methods: The subjects of the survey were from COPD surveillance during 2014-2015 and during 2019-2020, which covered 31 provinces (autonomous regions and municipalities) in China. The survey used multi-stage stratified cluster random sampling method, the trained investigators conducted face-to-face interview to know whether subjects had previous pulmonary function testing or not. Complex sampling weighting was used to estimate the rate of pulmonary function testing in people aged ≥40 years, and the pulmonary function testing rates of the two COPD surveillance periods were compared. Results: A total of 148 427 persons were included in the analysis, including 74 591 persons during 2014-2015 and 73 836 persons during 2019-2020. In 2019-2020, the pulmonary function testing rate in Chinese residents aged ≥40 years was 6.7% (95%CI: 5.2%-8.2%), the rate in men (8.1%, 95%CI: 6.7%-9.6%) was higher than that in women (5.4%, 95%CI: 3.7%- 7.0%), and the rate in urban residents (8.3%, 95%CI: 6.1%-10.5%) was higher than that in rural residents (4.4%, 95%CI: 3.8%-5.1%). The rate of pulmonary function testing increased with the increase of education level. During 2019-2020, the residents with history of chronic respiratory diseases had the highest rate of pulmonary function testing (21.2%, 95%CI: 16.8%-25.7%), followed by the residents with respiratory symptoms (15.1%, 95%CI: 11.8%-18.4%) , the pulmonary function testing rate in those who knew the name of chronic respiratory disease was higher than that in those who did not knew the name of respiratory disease, and the pulmonary function testing rate in former smokers was higher than that in current smokers and non-smokers. Those exposed to occupational dust and/or harmful gases had a higher rate of pulmonary function testing compared with those who were not exposed, and those who used polluted fuels indoors had a lower rate of pulmonary function testing than those who did not use polluted fuels indoors (all P<0.05). Compared with 2014-2015, the pulmonary function testing rate in residents aged ≥40 years in China increased by 1.9 percentage points during 2019-2020, and the rate of pulmonary function testing in groups with different characteristics all increased, and the rates of pulmonary function testing increased by 7.4 percentage points and 7.1 percentage points in residents with respiratory symptoms and in those with history of chronic respiratory diseases (all P<0.05). Conclusions: Compared with 2014-2015, the rate of pulmonary function testing increased in China during 2019-2020 and the increase in residents with history of chronic respiratory diseases and respiratory symptoms was relatively obvious, but the overall pulmonary function testing rate was still at a low level. Effective measures should be taken to further increase the rate of pulmonary function testing.
Female ; Humans ; Male ; Asian People ; China ; Dust ; Pulmonary Disease, Chronic Obstructive ; Respiratory Function Tests ; Adult

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and has serious implications for the health of mothers and their offspring. In recent years, studies have confirmed that air pollution is one of the main risk factors for diabetes, and there is increasing evidence that air pollution exposure is closely related to the occurrence of gestational diabetes. However, current studies on the association between air pollutant exposure and the incidence of gestational diabetes are inconsistent, and the window period of pollutant exposure is still unclear. Limited mechanistic studies suggest that airborne particulate matter and gaseous pollutants may affect GDM through multiple mechanisms, including inflammation, oxidative stress, disruption of adipokine secretion, and imbalance of intestinal flora. This review summarizes the relationship between air pollutant exposure and the incidence of GDM in recent years, as well as the possible molecular mechanism of the occurrence and development of GDM caused by air pollutants, in order to provide scientific basis for preventing pollutant exposure, reducing the risk of GDM, improving maternal and fetal outcomes and improving the quality of the birth population.
Pregnancy ; Female ; Humans ; Diabetes, Gestational/epidemiology* ; Air Pollution/analysis* ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Risk Factors ; Maternal Exposure/adverse effects*

Objective: To investigate the association between physical exercise and non-alcoholic fatty liver disease (NAFLD) in people infected with HBV. Methods: The information about the 3 813 participants infected with HBV, including the prevalence of NAFLD, prevalence of physical exercise and other covariates, were collected from the National Science and Technology Major Project of China during 2016-2020. The logistic regression model was used to evaluate the association between physical exercise and NAFLD in HBV infected patients, and subgroup analysis was performed to identify the effect modifiers. Results: A total of 2 259 HBV infected participants were included in the final analysis and 454 (20.10%) had NAFLD. After adjusting for covariates, we found that moderate physical exercise was a protective factor for NAFLD (OR=0.66, 95%CI: 0.46-0.94). Subgroup analysis suggested that the protective effect of moderate physical exercise on NAFLD might be stronger in women (OR=0.61, 95%CI: 0.36-1.01), those <45 years old (OR=0.24, 95%CI: 0.06-0.80), those who had low education level (OR=0.16, 95%CI: 0.04-0.49), those who had low annual income (OR=0.39, 95%CI: 0.16-0.89 for <30 000 yuan RMB; OR=0.64, 95%CI: 0.40-1.00 for 30 000-80 000 yuan RMB), those who had hypertension (OR=0.45, 95%CI: 0.21-0.88), those with BMI ≥24.0 kg/m² (OR=0.66, 95%CI: 0.43-1.01), those who had more daily fruit or vegetable intake (OR=0.61, 95%CI: 0.38-0.97), those who had more daily meat intake (OR=0.49, 95%CI: 0.23-0.97), and those who had no smoking history (OR=0.66, 95%CI: 0.45-0.95) or passive smoking exposure (OR=0.61, 95%CI: 0.37-0.97). Conclusions: Among HBV infected patients, moderate physical exercise was negatively associated with the prevalence of NAFLD. Women, young people, those who had low education level, those who had low annual income, those with hypertension, those with high BMI, those who had more daily fruit or vegetable and meat intakes, and those who had no smoking history or passive smoking exposure might be more sensitive to the protective effect.
Humans ; Female ; Adolescent ; Middle Aged ; Non-alcoholic Fatty Liver Disease/epidemiology* ; Hepatitis B virus ; Risk Factors ; Tobacco Smoke Pollution ; Exercise ; Hypertension

In China, the level of ambient fine particulate matter (PM 2.5 ) pollution far exceeds the air quality standards recommended by the World Health Organization. Moreover, the health effects of PM 2.5 exposure have become a major public health issue. More than half of PM 2.5 -related excess deaths are caused by cardiopulmonary disease, which has become a major health risk associated with PM 2.5 pollution. In this review, we discussed the latest epidemiological advances relating to the health effects of PM 2.5 on cardiopulmonary diseases in China, including studies relating to the effects of PM 2.5 on mortality, morbidity, and risk factors for cardiovascular and respiratory diseases. These data provided important evidence to highlight the cardiopulmonary risk associated with PM 2.5 across the world. In the future, further studies need to be carried out to investigate the specific relationship between the constituents and sources of PM 2.5 and cardiopulmonary disease. These studies provided scientific evidence for precise reduction measurement of pollution sources and public health risks. It is also necessary to identify effective biomarkers and elucidate the biological mechanisms and pathways involved; this may help us to take steps to reduce PM 2.5 pollution and reduce the incidence of cardiopulmonary disease.
Humans ; Particulate Matter/analysis* ; Air Pollution/adverse effects* ; Risk Factors ; Respiratory Tract Diseases ; China/epidemiology* ; Environmental Exposure/adverse effects*