网格化大气污染物暴露与代谢综合征患者心脏代谢指标的相关性分析

发布时间：2018-04-17 12:50

本文选题：大气污染 + 代谢综合征　；参考：《北京协和医学院》2016年博士论文

【摘要】：背景空气污染与心血管病发病及死亡的相关性已在很多研究中得到确认,不管在发展中国家还是发达国家,不管短期还是长期暴露均有相似的结果。然而,空气污染的心血管效应的机制尚未明确,目前研究表明血压升高、心率变异性下降、内皮功能障碍等可能作为空气污染所致心血管效应的机制。目的本研究旨在评估代谢综合征人群暴露于直径2.5μm的颗粒物(PM2.5)、直径10μm的颗粒物(PM10)、二氧化氮(NO2)、二氧化硫(SO2)对心血管相关指标的影响,探讨大气污染物介导心血管效应的可能机制。具体为(1)探讨研究期间估算的受试者居住地址的大气污染物暴露浓度与北京市地面监测点实际记录的大气污染物浓度的变化趋势；(2)比较患者两次随访的结局变量和网格化大气污染物浓度的差异；(3)通过线性混合效应模型评估网格化污染物浓度对受试者血压、中心动脉压、脉搏波流速、内皮功能、心率变异性及心脏代谢指标的影响；(4)对随访期间的数据以横断面的研究方式评估短期中期(=90天)网格化污染物浓度暴露对受试者上述指标的影响。方法采用固定群组追踪研究设计,连续入组2012年9月至2013年2月在北京协和医院心血管内科门诊就诊的非吸烟且居住在北京的代谢综合征(MetS)患者59例(42～77岁)。MetS患者符合国际糖尿病联盟(IDF)针对亚洲人群的诊断标准。获取受试者基线资料,包括具体居住地址、人口测量学特征等,按照说明书完成动脉血压监测(24h、日间及夜间血压、心率)、5-min动态心电图监测(短程时域和频域指标)、中心动脉压(中心动脉血压、增强压AP、增强指数AIx、中心动脉脉压APP)、脉搏波流速(PWV)、内皮功能监测(反应性充血指数,RHI)、胰岛素抵抗(稳态模式胰岛素抵抗评估法,HOMA-IR)及心脏代谢指标(瘦素、高分子脂联素)监测,获取相应的数据。2013年1月～2013年7月完成随访,两次均参与的受试者人数为59例,平均随访间隔时间为128天,重复测量以上指标。获取所有受试者居住详细地址信息及其经纬度和北京市2012年6月～2013年7月所有空气质量监测点的日均大气污染物数据(包括PM10、NO2、802),获取2012年10月至2013年7月所有监测点的PM2.5日均数据,通过ArcGIS10.1中的克里金插值法将北京市网格化(分辨率为0.05°,≈5.5km)获取受试者居住地址的大气污染物暴露水平作为个体化暴露,并从中国气象局获取同时期日平均温度、相对湿度和气压。重复测量数据采用线性混合效应模型分析污染物与结局变量之间相关性,因获取的PM10、NO2、SO2可覆盖整个研究期间,因此对于PM10、N02、S02的模型中,受试者人数为59例,但是获取PM2.5数据是从2012年10月开始的,因此在PM2.5的模型中,两次随访均参与的受试者人数为50例。对于短中期(=90天)污染物暴露的横断面分析数据采用多元线性回归完成。所有模型中均调整年龄、性别、体重指数、疾病状态、服药情况、活动情况、温度、相对湿度和气压。效应值表示为污染物每增加10μg/m3,结局变量变化情况。结果一般情况分析：共入组59例受试者,平均年龄62.53岁,其中83.1%的受试者患有高血压,25.4%患有糖尿病,66.1%患有血脂异常,基线血压、血糖、血脂控制尚可。81%的受试者分布在东城、西城、朝阳、海淀、丰台区。基线及随访各项指标比较：与基线相比,随访时24h平均DBP明显下降(P0.05),其他指标无明显变化(P0.05)。污染物暴露方面：从2012年6月～2013年7月,PM2.5、PM10、N02、及S02呈相似的变化趋势,从2012年11月开始升高,在2013年1月-2月份时最高,持续至4月份呈明显下降。所有受试者基线时结局指标测量当日PM10、N02、S02、PM2.5的平均浓度为126.72、70.00、34.23、110.35μg/m3,随访时的浓度分别为129.49、53.14、24.60、78.25μg/m3,两次的平均温度分别为7.44、17.59℃,比较差异有统计学意义(P0.05)。线性混合效应分析结果：(1)网格化污染物与血压：短期暴露PM2.5、PM10、NO2与24h平均DBP、静息SBP、静息DBP相关性明显,其中,滞后1天、累积2天PM2.5效应最明显,每升高10μg/m3,24h平均DBP升高0.32、0.48 mmHg(P0.05)；滞后8天PM10对24h平均DBP效应最明显,升高0.32mmHg(P0.05);累积效应分析结果表明随着PM10累积时间延长,静息SBP、DBP均呈上升趋势,最大效应为累积暴露11天,DBP升高0.59mmHg(P0.05)；SO2累积2天时对静息SBP、DBP的影响更明显,每升高10μg/m3,分别升高2.12、1.25mmHg(P0.05)。滞后效应分析中NO2滞后10天对24h平均DBP、静息SBP、静息DBP影响最大,分别升高0.73、1.00、0.54 mmHg;而累积效应分析发现NO2累积3天与静息DBP升高0.63 mmHg相关(P=0.046)。(2)网格化污染物与中心动脉压、脉搏波流速：不同滞后天数的网格化污染物均与中心动脉SBP、DBP、AP或APP呈正相关,尤其以PM10、NO2、SO2的累积效应最为明显,其中前两者的最大效应出现在累积11天,与中心动脉SBP升高1.27、1.71mmHg相关,与中心动脉DBP升高0.67、1.03mmHg相关,而SO2的最大效应出现在累积2天时,中心动脉SBP和DBP分别升高2.992.00mmHg。仅发现滞后3、4天(lag3、lag4)SO2暴露与PWV分别升高0.19、0.14m/s相关,但我们并没有发现其他污染物与PWV的相关性。(3)网格化污染物与其他指标：滞后和累积效应分析结果并没有发现网格化污染物与RHI、HOMA-IR、瘦素、高分子脂联素的相关性(P0.05)。短中期网格化污染物与心血管相关指标的横断面分析结果：滞后0天PM2.5每升高10μg/m3对24h平均DBP影响最明显,升高2.33mmHg (P0.05)。从累积2天到累积6天PM2.5与24h平均DBP升高明显相关(P0.05),最大效应出现在累积6天时,24h平均DBP升高4.35mmHg(P0.05)。然而,超过6天后并没有发现PM2.5与24h平均DBP的相关性(P0.05)。在双污染物模型中分别加入NO2、SO2,累积6天PM2.5对24h平均DBP较单污染物模型更明显(P0.05)。累积16天PM2.5和PM10暴露每增加10μg/m3,RHI分别下降0.87和0.72(P均0.05),然而并未发现气体污染物与RHI的相关性(P0.05),在横断面分析中亦未发现短中期网格化污染物暴露与其他心血管相关指标的相关性。结论网格化大气污染物暴露与代谢综合征患者血压、中心动脉压升高、内皮功能受损相关,这些病理生理学改变可能部分解释空气污染导致心血管疾病发生和死亡风险增加的机制,但并未发现与心率变异性、胰岛素抵抗及心脏代谢指标的相关性。
[Abstract]:Background air pollution and cardiovascular disease morbidity and mortality correlation has been confirmed in many studies, whether in developing countries or developed countries, whether short-term or long-term exposure had similar results. However, the mechanism of cardiovascular effects of air pollution is not yet clear, the present study showed that elevated blood pressure, heart rate variability decreased, endothelial dysfunction may be as the mechanism of air pollution caused by cardiovascular effects. The purpose of this study was to evaluate the metabolic syndrome patients exposed to 2.5 m diameter particles (PM2.5), particle diameter of 10 mu m (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) effects on cardiovascular related indicators, to explore the possible mechanism of atmospheric pollutants mediated cardiovascular effects. (1) to investigate the specific air pollutants during the study subjects to estimate residential address exposure concentration and Beijing ground monitoring actual record The change trend of air pollutant concentrations recorded; (2) compared with atmospheric pollutant outcome variables and grid concentration two follow-up; (3) through the linear mixed effects model to assess blood pressure concentration of pollutants on the grid subjects, central arterial pressure, pulse wave velocity, endothelial function and heart rate variability the heart and metabolism index; (4) the follow-up data to study cross-sectional evaluation of short term (=90 days) grid pollutant exposure effect on subjects. The index method with fixed group tracking study design, continuous group in September 2012 to February 2013 in the non - smoking Peking Union Medical College Hospital cardiovascular outpatients living in Beijing and the metabolic syndrome (MetS) patients, 59 cases (42~77) of.MetS patients with the International Diabetes Federation (IDF) according to the diagnostic criteria of the Asian population. For subjects The baseline data, including the specific address of residence, population measurement characteristics, in accordance with the instructions to complete the monitoring of arterial blood pressure (24h, daytime and nighttime blood pressure, heart rate), 5-min dynamic electrocardiogram (short time domain and frequency domain index), central arterial pressure (central arterial pressure, increased AP, enhanced index AIx, central pulse pressure APP) and the pulse wave velocity (PWV), endothelial function monitoring (reactive hyperemia index, insulin resistance (RHI), homeostasis model assessment of insulin resistance, and cardiac index (HOMA-IR) metabolism of leptin, adiponectin polymer) monitoring data obtained from.2013 in January to July 2013 to complete the follow-up, the number of participants two times in the 59 cases, the average follow-up interval of 128 days, repeated measurements of these index. For all subjects living with address information and the latitude and longitude and the city of Beijing from June 2012 to July 2013 all air quality monitoring points The average daily air pollution data (including PM10, NO2802, PM2.5) for daily data from October 2012 to July 2013 of all monitoring points, using Kriging interpolation in ArcGIS10.1 Beijing city grid (resolution of 0.05 degrees, about 5.5km) to obtain atmospheric pollutants address subjects as the exposure level of individual exposure, and get on the average temperature at the same time from the China Meteorological Bureau, relative humidity and air pressure. The repeated measurement data using linear mixed effects model analysis the correlation between pollutants and the outcome variable for PM10, NO2, SO2 can cover the entire study period, so for PM10, N02, S02 model, the number of subjects was 59 cases, but to obtain PM2.5 data from the beginning of October 2012, so in the PM2.5 model, the number of two subjects were involved in the follow-up of 50 cases. For the short and medium term (=90 days) exposure to pollutants into cross section Analysis of data using multiple linear regression. All models were adjusted for age, sex, body mass index, disease status, medication, activity, temperature, relative humidity and air pressure effect. Values are expressed as pollutants each increase of 10 g/m3, the outcome variable changes. The general situation analysis: 59 cases were divided into two groups the subjects, the average age of 62.53 years, of which 83.1% of the subjects had hypertension, 25.4% had diabetes, 66.1% had dyslipidemia, baseline blood pressure, blood glucose, blood lipid control subjects.81% is located in Dongcheng, Xicheng, Chaoyang, Haidian, Fengtai District. In comparison to the baseline and follow-up indicators: Compared with baseline, follow-up the average 24h DBP decreased significantly (P0.05), no significant changes in other indicators (P0.05). The pollutant exposure: from June 2012 to July 2013, PM2.5, PM10, N02, and S02 showed a similar tendency, from November 2012 began to increase in 2013 In January -2 month when the highest until April was significantly decreased. All subjects at baseline outcome measurement day on PM10, N02, S02, the average concentration of PM2.5 is 126.72,70.00,34.23110.35 g/m3, the concentration of follow-up were 129.49,53.14,24.60,78.25 g/m3, two times the average temperature of 7.44,17.59 DEG C, there was significant difference the difference (P0.05). The analysis of linear mixed effect results: (1) grid pollutants and blood pressure: short term exposure to PM2.5, PM10, NO2 and 24h average DBP, resting SBP, resting DBP obvious correlation, the lag of 1 days, the cumulative 2 days PM2.5 the most obvious effect, every increase 10 g/m3,24h average increase of DBP 0.32,0.48 mmHg (P0.05); PM10 24h on average 8 days lag DBP the most obvious effect, increased 0.32mmHg (P0.05); the cumulative effect analysis results show that with the PM10 accumulation time, resting SBP, DBP increased, the maximum effect for the accumulation of violence Open 11 days, increased DBP 0.59mmHg (P0.05); SO2 cumulative 2 days of resting SBP, DBP effect is more obvious, every increase of 10 g/m3, respectively. 2.12,1.25mmHg (P0.05). The lag effect analysis of NO2 24h DBP on average 10 days lag, resting SBP, resting DBP impact, 0.73,1.00,0.54 mmHg respectively. But the cumulative effect; analysis showed that NO2 accumulated 3 days and resting DBP increased 0.63 mmHg (P=0.046). (2) grid pollutants and central arterial pressure, pulse wave velocity, different lag grid days were pollutants and central aortic SBP, DBP, was positively related to AP or APP, especially PM10, NO2. The cumulative effect of SO2 was the most obvious, one of the biggest effect both in the cumulative 11 days, 1.27,1.71mmHg related with central artery SBP increased, 0.67,1.03mmHg and DBP increased while the maximum central artery, the effect of SO2 in cumulative 2 days, central artery SBP and DBP were increased 2.99 2.00mmHg. found that only 3,4 days lag (Lag3, lag4) SO2 and PWV were associated with elevated 0.19,0.14m/s exposure, but we found no correlation between other pollutants and PWV. (3) grid pollutants and other indicators: lag and cumulative effect analysis results and found no grid pollutants and RHI, HOMA-IR, leptin, adiponectin molecular correlation the cross section (P0.05). Grid pollutants and cardiovascular related indicators analysis results: the short term lag of 0 days PM2.5 per 10 g/m3 on the effects of elevated 24h on average DBP significantly increased 2.33mmHg (P0.05). From the accumulation of 2 days to 6 days and the average cumulative PM2.5 24h DBP increased significantly (P0.05), maximum correlation the cumulative effects appear in 6 days, the average increase of DBP 24h 4.35mmHg (P0.05). However, after more than 6 days and found no correlation between PM2.5 and average 24h DBP (P0.05). In the double pollutant model respectively into NO2, SO2, cumulative 6 days PM2.5 The average DBP of 24h more obviously than single pollutant model (P0.05). The cumulative 16 days PM2.5 and PM10 exposure for each additional 10 g/m3, RHI were decreased by 0.87 and 0.72 (P 0.05), but did not find a correlation between air pollutants and RHI (P0.05), short period of exposure in correlation with other cardiovascular related pollutants in grid the index was not found in cross-sectional analysis. Conclusion the grid air pollutant exposure and blood pressure in patients with metabolic syndrome and central arterial pressure, endothelial dysfunction, these pathophysiological changes may explain in part due to air pollution mechanism and increased risk of death from cardiovascular disease, but did not find a correlation between insulin and heart rate variability. Resistance and cardiac metabolism.

【学位授予单位】：北京协和医学院
【学位级别】：博士
【学位授予年份】：2016
【分类号】：R589;R122

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