Correlative Analysis Of Cardiac Metabolic Indexes In Patients With Metabolic Atmospheric Pollutant Exposure And Metabolic Syndrome

BackgroundThe association between short-term or long-term exposure to air pollutants and cardiovascular morbidity and mortality has been confirmed in most studies either in developed or developing countries. However, the mechanisms have been uncertain. Current evidence has shown that elevated blood pressure (BP), reduced heart rate variability (HRV) and endothelial dysfunction may patially explain the mechanisms of air pollution-media ted cardiovascular effects.ObjectiveThe repeated-measure study was conducted to detect the relationship of PM2.5, PM10, SO2 and NO2 with cardiovascular related outcomes. (1) To evaluate the trends of the concentration of actual and predicted measurements during the study period; (2)To compare the difference of outcomes and the predicted air pollutants concentration between the two visits; (3)To detect the effect of air pollutants on cardiovascular outcomes using linear mixed effect model in a repeated measure analysis; (4) To evaluate the effect of short-term or middle-term exposure to air pollutants on ambulatory BP and RHI in a cross-sectional analysis based on the data from the second visit.MethodsIn the panel study, there were 59 patients aged 62.53 years (age range:42-77 years) with metabolic syndrome enrolled in the study during September 2012 to February 2013. Mets patients fulfilled the International Diabetes Federation (IDF) diagnostic criteria for the Asian population.The baseline data were obtained, including residential address and demographic characteristics. In addition, the ambulatory BP monitoring (24-hour, daytime and nighttime mean BP),5-minute holter monitoring (time domain and frequency domain), central hemodynamics (aortic BP, augmentation pressure (AP), aortic pulse pressure(APP), augmentation index(AIx)), endothelial function(reactive hyperemia index(RHI)), and insulin resistance(homeostasis model assessment of insulin resistance(HOMA-IR)) and cardio-metabolic indices(leptin, high molecular weight adiponectin(HMW-ADP)) were performed for all the patients, the corresponding data of which were obtained. The patients were followed from January 2013 to July 2013, for an average interval of 128 days. The outcomes above were repeated during the follow-up. We got daily mean concentration for PM10, NO2, SO2 between Jun 1,2012 and Jul 4,2013, for PM2.5 between Oct 1,2012 and Jul 4,2013 from Beijing Municipal Environmental Monitoring Center (BMEMC). The latitude and longitude were obtained by Google maps according to the detailed residential address for every subject. Kriging interpolation using ArcGIS10.1 was conducted based on latitude and longitude to get the grid concentration of six pollutants at the resolution of 0.05 degree (about 5.5km). Kriging is a geostatistical interpolation technique that considers both the distance and the degree of varation between known data points when estimating values in unknown areas.Then modeled or predicted individual exposure concentration was obtained for every subject. In addition, we got meteorological data from the China Meteorological Administration for the whole study period. Linear mixed effect models and multiple linear regression models were separately used to analyze the relationship between the air pollutants and cardiovascular outcomes in the repeated measure analysis and in the cross-sectional analysis. In the linear mixed effect models for PM10, NO2, SO2, the number of subjects who completed the two visits was 59, while there were 50 subjects who participated in the two visits for PM2.5 models. The factors as follows were adjusted in the models, such as age, gender, BMI, disease status, drug medication, activity, and meteorological data. Estimates of the effects of air pollutants were scaled to 10 μg/m3 increments for PM2.5, PM10, SO2 and NO2. Statistical significance was achieved with a two-tailed value of P<0.05.ResultsGeneral information:Fifty-nine participants were recruited for the study. The mean age of the population was 62.53 years. Forty-nine (83.1%) of the participants suffered from hypertension,25.4% had diabetes and 66.1% had dyslipidemia. More than About 80% of the patients live in Dongcheng, Xicheng, Chaoyang, Haidian and Fengtai districts. Compared with the baseline data, twenty-four-hour mean diastolic BP during the follow-up was lower than that of the first visit (P<0.05), while other outcomes had nonsignificant difference (P>0.05). Exposure:From June 2012-July 2013, PM2.5, PM10, NO2 and SO2 showed a similar trend, which began to increase from November 2012 in January, reached the highest in January and February, declined from April.The mean of predicted air pollutants for all subjects in the current day of outcome measure was 108.17 μg/m3 for PM2.5,125.38 μg/m3 for PM10,69.57 μg/m3 for NO2,33.99 μg/m3 for SO2 for the first visit,78.22,129.58,24.97 and 52.93 μg/m3 for the follow-up; the mean temperature for the two visit was 7.32、17.84 ℃(P<0.05).Linear mixed effect analysis:(1) Air pollutants and BP:Short-term exposure to PM2.5, PM10, and NO2 was associated with 24-hour mean DBP, resting SBP and DBP. The greatest effect on 24-hour DBP was observed for lag0 and lag01 PM2.5, for lag 8 PM10, increase of 0.32,0.48, and 0.32mmHg (P<0.05). Over the exposure time of PM10, the effect on resting SBP and DBP was gradually greater, with the greatest effect occurring in 10-day average before measurement of 0.59mmHg increase in resting DBP (P<0.05). lag01 SO2 was significantly associated with resting SBP, DBP, with the increase of 2.12,1.25mmHg, separately. NO2 for 10-day lag was associated with 24-hour mean DBP, resting SBP or DBP, increase of 0.73,1.00 or 0.54mmHg, while lag02 NO2 with 0.63mmHg increase in resting DBP(P<0.05). (2) Air pollutants and central hemodynamics/PWV:Predicted individual air pollutants for different lag patterns was associated with aortic SBP, aortic DBP, AP or APP, which is more significant for the cumulative effect of PM10, NO2 and SO2. The greatest effect of PM10 and NO2 occurred in 10-day average, with increase of 1.27,1.71 mmHg in aortic SBP,0.67,1.03mmHg increase in aortic DBP, while the greatest effect of SO2 occurred in 1-day average, with aortic SBP or DBP increase of 2.99,2.00mmHg (P<0.05). Only SO2 for 3-day,4-day lag was associated with PWV (P<0.05). (3) Air pollutants and other cardiovascular outcomes:No significant association was found between predicted individual air pollutants and RHI, HOMA-IR, leptin and HMW-ADP (P>0.05).Multiple linear analysis:PM2.5 for lag0 was significantly associated with 24-hour mean DBP, with increase of 2.33mmHg (P<0.05). Over the exposure time from 1-day to 5-day average, the effect of PM2.5 on 24-hour mean DBP was gradually increasing, with the greatest effect of 4.35mmHg for 5-fay average (P<0.05). In the two-pollutant model adding NO2 or SO2, the effect of PM2.5 for 5-day average on 24-hour mean DBP was greater than that in single-pollutant model (P<0.05). Multiple linear analysis has shown that decrease by 0.87 and 0.72 in RHI were associated with each 10 μg/m3 increase in PM2.5 and PM10 for 15-day moving average (all P<0.05). No significant association was found RHI and gaseous air pollutants (all P>0.05).ConclusionShort-term exposure to air pollutants was associated with elevated ambulatory BP, central hemodynamics, impaired endothelial function in patients with metabolic syndrome. The way could, in part, account for the increases in cardiovascular disease morbidity and mortality seen in prior studies. However, the association between air pollutants and HRV, cardio-metabolic indices was not observed in the study.