Impacts Of Long-term Exposure To Air Pollution On The Risk Of MetS

Part One Evaluating the association of air pollution and metabolic syndrome based on a multilevel modelObjective:To evaluate the association of long-term exposure to air pollution with the risk of metabolic syndrome,and to explore the impact of air pollution on metabolic diseases.Methods:1.Study population:We enrolled individuals who were examined at one of three medical centres in Shijiazhuang from April 1,2018,to December 31,2018.And 6817 cases were selected as the research objects who strictly met the criteria,and all subjects came from 5 different administrative districts（Yuhua,Chang’an,Luquan,Xinhua,Qiaoxi）.Questionnaire was used to collect the basic information of research objects.2.Pollutants’data:The values of six air pollutants(PM_2.5,PM₁₀,NO₂,CO,SO₂,and O₃)of 51 pollutant monitoring stations in Shijiazhuang were collected reported by the official air quality release system of the Hebei Province Environmental Protection Department.Meteorological data came from the China Meteorological Administration,including the daily average temperature and relative humidity.Prediction of the individual exposure to air pollutants was evaluated using The Empirical Bayesian Kriging statistical model in Arc GIS 10.2 geographic information system.3.Definition of Metabolic syndrome:Metabolic syndrome was diagnosed according to the criteria of the Chinese Diabetes Society（CDS）（Joint Committee for the Development of Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China,2016）.All study subjects were divided into with and without Met S.4.Statistical description and univariate analysis:Quantitative data was described by Mean±Standard Deviation or Median(P₂₅,P₇₅),and categorical data was described by n（%）.The quantitative indicators between with and without metabolic syndrome were tested by Student’s t or Mann-Whitney U test,and the categorical indicators were tested by Chi-square test or Fisher’s exact test.5.Effect estimation:The multilevel model was used to evaluate the associations between six pollutants（per 10μg/m³increase）and metabolic syndrome for the purpose of improving statistical efficiency.Subjects were the first-level units,and the administrative regions were the second-level units.Intraclass correlation（ICC）was used to measure the aggregation of metabolic syndrome prevalence in the same region.In the multilevel model,the second-level（administrative region）was considered as random effects,while the exposure variables（six air pollutants）and all remaining adjustment factors including temperature,humidity,age,sex,nation,marital status,education level,examination month,smoking status,passive smoking,alcohol drinking status,respirator use,physical activity,night shift frequency were considered as fixed effects.6.Stratification analysis:The association between long-term exposure to air pollution and the risk of metabolic syndrome was stratified by age（>60,≤60）and gender（male,female）.7.Sensitivity analysis:Sensitivity analysis was performed by adjusting for different sets of covariates to estimate the robustness of the effects.Results:1.Among 6817 research subjects,1466 had metabolic syndrome,accounted for 21.5%.Except for physical activity,there were statistically significant differences in the remaining variables between the two groups with or without metabolic syndrome（P<0.05）.2.The results showed that the prevalence of metabolic syndrome in the same administrative region had a certain clustering（ICC>0.1,P<0.05）.3.After adjusting for all confounding factors,long-term exposure to PM_2.5,PM₁₀,NO₂,SO₂,O₃ was positively associated with the risk of Met S.The adjusted odds ratios and 95%CIs of metabolic syndrome per 10μg/m³increase in PM_2.5,PM₁₀,NO₂,SO₂,O₃ were 1.141（1.097,1.184）,1.122（1.010,1.154）,1.144（1.079,1.179）,1.055（1.036,1.105）,1.028（1.009,1.056）,respectively（P<0.05）.4.After stratified analyses by sex,our results suggested that males and the elderly were more sensitive to long-term air pollution exposure.5.Sensitivity analysis showed that the associations between long-term exposure to air pollutants(PM_2.5,PM₁₀,NO₂,SO₂,O₃)and the risk of metabolic syndrome was robust.Conclusions:1.This cross-sectional study concluded that the prevalence of metabolic syndrome had clustering.2.Long-term exposure to five air pollutants(PM_2.5,PM₁₀,NO₂,SO₂,O₃)was associated with an increased risk of metabolic syndrome.3.Males and older individuals may be more vulnerable to the adverse metabolic effects of air pollutants.Part Two Urban-rural disparities in the associations of long-term exposure to air pollution with the risk of Met S among adultsObjective:we aimed to contrast the effect estimates of air pollutants on Met S between urban and rural areas among Chinese adult population.Methods:1.Study population:The research subjects came from four cities with different GDP levels officially released by Hebei Province.They all received physical examination or medical treatment from April 1,2018 to December 31,2018.According to strict inclusion and exclusion criteria,10,917 cases were finally included.7012 cases were urban residents and 3905 cases were rural residents.Questionnaire or telephone interview were used to collect the basic information of research objects.2.Pollutants’data:The values of six air pollutants(PM_2.5,PM₁₀,NO₂,CO,SO₂,and O₃)were collected reported by the official air quality release system of the Hebei Province Environmental Protection Department,and including the daily average concentration data at all monitoring sites in each city.Meteorological data came from the China Meteorological Administration,including the daily average temperature and relative humidity in each city.Prediction of the Individual exposure to air pollutants concentration was evaluated using The Empirical Bayesian Kriging statistical model in Arc GIS10.2 geographic information system.3.Definition of Metabolic syndrome:Metabolic syndrome was diagnosed according to the criteria of the Chinese Diabetes Society（CDS）（Joint Committee for the Development of Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China,2016）.4.Statistical description and univariate analysis:If quantitative data was normal distribution,it is described by Mean±Standard Deviation.If quantitative data was not normal distribution,it is described by Median(P₂₅,P₇₅).Categorical data were described with n（%）.Differences of quantitative indicators between urban and rural residents were tested by Student’s t test or Mann-Whitney U rank-sum test,and categorical indicators between two groups were tested by Chi-square test or Fisher’s exact test.5.Effect estimation:The generalized linear model was used to evaluate the associations of six air pollutants(PM_2.5,PM₁₀,NO₂,SO₂,CO,O₃)and the risk of metabolic syndrome in general population,urban,and rural residents,respectively.The difference of risks between urban and rural areas was compared using z-test.Adjustment factors included temperature,humidity,age,gender,ethnicity,marital status,education level,smoking status,passive smoking,drinking status,physical activity,and respirator use.6.Stratified analysis:We performed stratified analyses by urban and rural areas separately in both age（>60 years,≤60 years）,sex（male,female）classification.The differences were also examined by using z-test.7.Sensitivity analysis:Sensitivity analysis is performed by including different sets of covariates to test the robustness of the effect estimations.Results:1.A total of 3905（35.7%）participants were from rural areas,and 7012（64.3%）were urban residents.The average age of rural residents was54.42±14.83 years old,higher than that of urban residents was 47.86±14.46years old.The differences of all indicators between rural and urban residents were statistically significant.2.After adjusting for all confounding factors,long-term exposure to PM_2.5,PM₁₀,NO₂,SO₂,and O₃ was positively associated with the increased risk of metabolic syndrome,and NO₂ had the greatest impact.The adjusted odds ratios and 95%CIs of metabolic syndrome per 10μg/m³ increase in PM_2.5,PM₁₀,NO₂,SO₂,O₃were 1.131（1.117,1.154）,1.112（1.103,1.124）,1.154（1.139,1.169）,1.115（1.106,1.126）,1.122（1.107,1.136）（P<0.05）.In addition,the results showed that the concentration of particulate matter(PM_2.5,PM₁₀)in rural areas was lower than that in urban areas,but the association of PM_2.5 and PM₁₀ with metabolic syndrome in rural residents was higher than that in urban residents(PM_2.5:P=0.010;PM₁₀:P=0.025).And the association of NO₂,SO₂,O₃ with metabolic syndrome was no statistical difference between rural and urban residents（NO₂:P=0.078;SO₂:P=0.053;O₃:P=0.051）.3.The urban-rural differences of association were compared using z test in sex（male,female）and age（≤60 years old,>60 years old）,respectively.Compared with rural males,long-term exposure to PM_2.5,PM₁₀,NO₂ had greater effects on urban men（P<0.05）;Compared with urban women,long-term exposure to PM_2.5,PM₁₀,NO₂and O₃ had greater effects on rural women（P<0.05）;The effects of long-term exposure to PM_2.5,PM₁₀,SO₂,NO₂and O₃ had no significant difference between urban and rural residents aged less than or equal to 60（P>0.05）;Compared with urban elders（more than 60 years）,long-term exposure to PM_2.5,PM₁₀,NO₂ has a greater impact on rural elderly residents（P<0.05）4.Sensitivity analysis:Different sets of covariates were included in the model for sensitivity analysis,and the results showed that the association between air pollutants and the risk of metabolic syndrome and the differences in urban-rural were robust.Conclusion:1.Long-term exposure to PM_2.5,PM₁₀,NO₂,SO₂,and O₃ were all positively correlated with the increased risk of metabolic syndrome,2.There were differences between urban and rural areas in the association between exposure to particulate matter(PM_2.5,PM₁₀)and metabolic syndrome.3.Males in urban,females in rural,over 60 years old in rural were more vulnerable to the adverse effects of air pollutants.Part Three The interaction effect of blood glucose levels on the association between air pollution and dyslipidemiaObjective:We aimed to test whether abnormal FBG could enhance the associations between long-term individual exposure to air pollution and dyslipidemia prevalence in general Chinese adult population.Methods:1.Study population:We enrolled 8917 physical examination population from 4 cities of Hebei province during April 1,2018 to December31,2018.Subjects were grouped into normal,prediabetes,diabetes according to FBG levels.2.Pollutants’data:The values of six air pollutants(PM_2.5,PM₁₀,NO₂,CO,SO₂,and O₃)were collected reported by the official air quality release system of the Hebei Province Environmental Protection Department,and including the daily average concentration data at all monitoring sites in each city.Meteorological data came from the China Meteorological Administration,including the daily average temperature and relative humidity in each city.Prediction of the Individual exposure to air pollutants concentration using the Empirical Bayesian Kriging statistical model in Arc GIS 10.2 geographic information system.3.Definition of dyslipidemia:the diagnosis of dyslipidemia was based on the Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China（2016 revision）.4.Statistical description and univariate analysis:Mean±Standard Deviation was presented to describe the continuous variables of normal distribution,and Median(P₂₅,P₇₅)for skewed distribution.n（%）was used to describe categorical variables.The contrasts in baseline characteristics between normal blood lipids and dyslipidemia groups were tested by univariate analyses.Student’s t-test or Mann–Whitney U test（if the variables were not normal or homogeneous）was used for continuous variables,and the Chi-square test was used for categorical variables.5.Effect estimation:Odds ratio（OR,95%CI）was calculated to estimate the risk of air pollution on dyslipidemia prevalence and FBG on dyslipidemia morbidity.OR_inter（95%CI）was calculated to assess the interaction of air pollution and FBG on dyslipidemia.Interaction plots were used to describe ORs trend with increasing FBG level.6.We performed stratified analyses by age（<65years,≥65years）,sex（male,female）,BMI（<25 kg/m²,25–28 kg/m²,≥28 kg/m²）.7.A series of sensitivity analyses adjusted by different sets of covariates were performed to evaluate the robustness of our results.Results:1.Among 8917 subjects,3295 were normal FBG,3122 were prediabetic,and 2500 were diabetic.3908 subjects were diagnosed with dyslipidemia,accounting for 43.83%.Except for sex,marriage status,nation,and passive smoking status,the differences of other variables between normal blood lipids and dyslipidemia were all statistically significant（P<0.05）.2.After adjusting for all covariates,long-term exposure to PM_2.5,PM₁₀,NO₂ and SO₂ in prediabetic and diabetic subjects had higher effects on the risk of dyslipidemia than in subjects with normal FBG.The interaction effect of air pollutants(PM_2.5,PM₁₀,NO₂,SO₂)and FBG was statistically significant,the adjusted OR_inter（95%CIs）were 1.171（1.162,1.189）,1.119（1.111,1.127）,1.124（1.115,1.130）,1.107（1.098,1.115）.3.The results of subgroup analysis showed that sex,age,BMI were risk factors for dyslipidemia caused by air pollution(PM_2.5,PM₁₀,NO₂,SO₂,CO,O₃).4.The modifying effects of FBG on the association of air pollution with dyslipidemia were stronger among male,people less than or equal to 65 years old,and overweight/obesity.5.Sensitivity analysis shows that the results are more robust after adding different sets of adjustment variables into the model.Conclusion:1.The results of this study suggested that high FBG levels could appreciably enhance the risk of air pollutants(PM_2.5,PM₁₀,NO₂,SO₂)on dyslipidemia.2.The higher the FBG level,the higher the risk of dyslipidemia caused by air pollution.3.The modifying effects of FBG on the association of air pollution with dyslipidemia were stronger among male,people less than 65 years old,and overweight/obesity.