PM10短期暴露對(duì)人群空腹血糖和血脂的影響

賀穎倩,王敏珍,鄭 山,趙亞楠,張露露,劉 婧,白亞娜

PM10短期暴露對(duì)人群空腹血糖和血脂的影響

賀穎倩,王敏珍*,鄭 山,趙亞楠,張露露,劉 婧,白亞娜

(蘭州大學(xué)公共衛(wèi)生學(xué)院,流行病與衛(wèi)生統(tǒng)計(jì)學(xué)研究所,甘肅 蘭州 730000)

為探析PM10短期暴露對(duì)不同糖代謝水平人群空腹血糖和血脂的影響,以“金昌隊(duì)列”為研究平臺(tái),收集金昌市2011～2017年污染物數(shù)據(jù)及氣象數(shù)據(jù).采用近鄰模型完成個(gè)體PM10暴露評(píng)估.運(yùn)用廣義估計(jì)方程分析PM10對(duì)血糖和血脂指標(biāo)的影響.采用廣義相加混合模型繪制暴露-反應(yīng)關(guān)系曲線(xiàn).結(jié)果表明,PM10濃度每增加一個(gè)四分位數(shù)間距,空腹血糖(FPG)、總膽固醇(TC)、低密度脂蛋白膽固醇(LDL-C)和高密度脂蛋白膽固醇(HDL-C)分別升高0.58mg/dL(95%CI:0.35,0.82)、0.38mg/dL(95%CI:0.25,0.52)、0.44mg/dL(95%CI:0.31,0.57)和0.34mg/dL(95%CI:0.29,0.40),甘油三酯(TG)降低0.67mg/dl(95%CI:-0.86,-0.47).隨著PM10濃度升高,FPG、TC、LDL-C和HDL-C均呈上升趨勢(shì),TG呈下降趨勢(shì). PM10對(duì)男性、年齡≥60歲者血糖和血脂指標(biāo)影響更顯著.因此,PM10與不同糖代謝狀態(tài)人群血糖和血脂水平異常密切相關(guān),男性和老年人應(yīng)加強(qiáng)自身防護(hù).

PM10；2型糖尿病；空腹血糖；血脂

隨著社會(huì)經(jīng)濟(jì)的發(fā)展,生活方式的改變和人口老齡化,糖尿病已經(jīng)成為威脅人類(lèi)生命健康的重要公共衛(wèi)生問(wèn)題之一.國(guó)際糖尿病聯(lián)合會(huì)(IDF)數(shù)據(jù)[1]顯示,2021年全球約5.37億成年人(20～79歲)患有糖尿病,預(yù)計(jì)到2030年該數(shù)字將上升至6.43億,到2045年將上升至7.83億;中國(guó)糖尿病現(xiàn)患人數(shù)為1.409億,位居世界第一.近年來(lái)有研究發(fā)現(xiàn),大氣顆粒物暴露可增加糖尿病患病率、發(fā)病率和死亡率[2-5].考慮到糖尿病是一種典型的慢性代謝性疾病,血脂和血糖水平異常與其發(fā)病密切相關(guān)[6-7].因此從這兩個(gè)方面探討大氣顆粒物暴露對(duì)糖尿病影響的作用機(jī)制具有一定的生物學(xué)意義.美國(guó)一項(xiàng)研究顯示PM10暴露可導(dǎo)致總膽固醇(Total cholesterol,TC)、甘油三酯(Triglyceride,TG)和低密度脂蛋白膽固醇(Low density lipoprotein cholesterol, LDL-C)顯著升高[8].泰國(guó)一項(xiàng)隊(duì)列研究結(jié)果表明,長(zhǎng)期暴露于PM10與LDL-C、空腹血糖(Fasting plasma glucose, FPG)呈正相關(guān),與高密度脂蛋白膽固醇(High density lipoprotein cholesterol, HDL-C)呈負(fù)相關(guān)[9].目前對(duì)于空氣污染與血糖和血脂關(guān)聯(lián)的縱向研究相對(duì)有限,且大多是在發(fā)達(dá)國(guó)家及我國(guó)東部地區(qū)進(jìn)行,而我國(guó)西部地區(qū)此類(lèi)研究相對(duì)較少.

金昌市位于中國(guó)西北干旱和半干旱區(qū),屬大陸性溫帶干旱氣候,常年受沙塵暴天氣影響,其地形地貌復(fù)雜,南北海拔差達(dá)3000多米,氣候差異較大[10];

此外,金昌市因礦興企,金川集團(tuán)公司鎳產(chǎn)量占全國(guó)90%以上,該市是以金川集團(tuán)公司為依托而發(fā)展起來(lái)的新型礦業(yè)城市,且生產(chǎn)區(qū)緊鄰生活區(qū)[11].因此,金昌市因自然環(huán)境、地理特征和工業(yè)污染等因素,其空氣污染問(wèn)題較為突出.2005～2016年,金昌市首要空氣污染物從SO2轉(zhuǎn)變?yōu)镻M10,2010～2014年, PM10年均濃度值超過(guò)國(guó)家二級(jí)空氣質(zhì)量標(biāo)準(zhǔn), 2005～2016年間PM10濃度總體呈上升趨勢(shì)[11-12].

“金昌隊(duì)列”是以甘肅省金昌市金川集團(tuán)股份有限公司5萬(wàn)職工為基礎(chǔ)建立的大樣本前瞻性隊(duì)列[13].該隊(duì)列人群2型糖尿病和糖尿病前期粗患病率分別為8.5%和21.2%,是影響該人群健康的重要疾病之一.因此,本研究將依托“金昌隊(duì)列”平臺(tái),開(kāi)展大氣PM10暴露對(duì)人群血糖和血脂指標(biāo)的影響研究,為進(jìn)一步揭示PM10致糖尿病的作用機(jī)制提供數(shù)據(jù)支撐.

1 資料與方法

1.1 研究對(duì)象

“金昌隊(duì)列”于2011～2013年完成基線(xiàn)調(diào)查,隨后每?jī)赡赀M(jìn)行一次隨訪(fǎng).本研究以該隊(duì)列2011～ 2017年納入的人群為研究對(duì)象,剔除家庭住址信息不完整及糖脂代謝指標(biāo)缺失者,最終納入52274人次,其中基線(xiàn)納入24285人,第一次隨訪(fǎng)納入21703人,第二次隨訪(fǎng)納入6286人.本研究由蘭州大學(xué)公共衛(wèi)生學(xué)院倫理委員會(huì)批準(zhǔn)(倫理批準(zhǔn)代碼:2017-01).

1.2 現(xiàn)場(chǎng)調(diào)查

1.2.1 流行病學(xué)調(diào)查 采用自行設(shè)計(jì)的結(jié)構(gòu)化調(diào)查問(wèn)卷,由統(tǒng)一培訓(xùn)的調(diào)查員在獲得研究對(duì)象知情同意后,以面對(duì)面訪(fǎng)談形式開(kāi)展調(diào)查.主要信息包括一般人口社會(huì)學(xué)特征(性別、年齡、職業(yè)、婚姻狀況、文化程度、家庭收入等)、生活行為習(xí)慣(吸煙、飲酒、體育鍛煉等)、既往疾病史(心腦血管疾病史、癌癥史和內(nèi)分泌代謝性疾病史)及疾病家族史等.

1.2.2 健康體檢與生化指標(biāo)檢測(cè) 研究對(duì)象體格檢查與生化指標(biāo)檢測(cè)由金川公司職工醫(yī)院專(zhuān)業(yè)人員完成.在體檢當(dāng)天采集研究對(duì)象空腹靜脈血,由Hitachi日立公司全自動(dòng)生化分析儀(型號(hào):7600-020)對(duì)FPG、TC、TG、LDL-C、HDL-C指標(biāo)進(jìn)行檢測(cè).

1.3 環(huán)境數(shù)據(jù)來(lái)源

1.3.1 大氣污染和氣象要素 數(shù)據(jù)來(lái)源從金昌市環(huán)境監(jiān)測(cè)站收集四個(gè)環(huán)境監(jiān)測(cè)站點(diǎn)(新川苑、運(yùn)輸部、市科委和公司二招)2011年1月1日～2017年12月31日逐日PM10、SO2和NO2地面監(jiān)測(cè)數(shù)據(jù);同時(shí)從金昌市氣象局采集同期氣象觀(guān)測(cè)數(shù)據(jù),包括最高氣溫、最低氣溫、日平均氣溫、相對(duì)濕度等.采用環(huán)境監(jiān)測(cè)站鄰近點(diǎn)均值對(duì)缺失數(shù)據(jù)進(jìn)行填補(bǔ).以研究對(duì)象健康體檢時(shí)間為匹配變量,匹配體檢時(shí)間前7d大氣污染物及氣象要素?cái)?shù)據(jù)的平均值.

圖1 金昌市4個(gè)環(huán)境污染物監(jiān)測(cè)站點(diǎn)和研究對(duì)象家庭住址地理分布

1.3.2 個(gè)體PM10暴露水平估計(jì) 本研究應(yīng)用近鄰模型來(lái)評(píng)估個(gè)體暴露水平,該模型的原理是根據(jù)個(gè)體住宅最近監(jiān)測(cè)點(diǎn)測(cè)量的空氣污染水平來(lái)評(píng)估個(gè)體暴露水平[14-15].根據(jù)每一位研究對(duì)象家庭住址以及金昌市4個(gè)環(huán)境監(jiān)測(cè)站點(diǎn)位置,應(yīng)用Google地圖軟件批量查詢(xún)每個(gè)研究對(duì)象居住地點(diǎn)以及4個(gè)監(jiān)測(cè)站點(diǎn)所處經(jīng)緯度,利用ArcGIS10.3軟件分別計(jì)算每個(gè)研究對(duì)象居住點(diǎn)與4個(gè)監(jiān)測(cè)點(diǎn)的距離,選擇距離研究對(duì)象最近的監(jiān)測(cè)站點(diǎn),然后依次匹配環(huán)境監(jiān)測(cè)數(shù)據(jù),如果在最近的監(jiān)測(cè)站點(diǎn)未獲得相關(guān)測(cè)量數(shù)據(jù),則從第2相近的站點(diǎn)檢測(cè),直到獲得確切監(jiān)測(cè)數(shù)據(jù)來(lái)評(píng)估個(gè)體污染物的暴露[16].研究對(duì)象與監(jiān)測(cè)點(diǎn)的分布情況如圖1.

1.4 疾病診斷標(biāo)準(zhǔn)

糖尿病:根據(jù)美國(guó)糖尿病協(xié)會(huì)指南[17],將FPG37.0mmol/L或自述臨床診斷為糖尿病(需要患者提供具體診斷醫(yī)院)或正在使用降糖類(lèi)藥物定義為2型糖尿病.將空腹血糖受損(5.6mmol/L￡FPG< 7.0mmol/L)定義為糖尿病前期.

高血壓:根據(jù)《中國(guó)高血壓防治指南(2018修訂版)》[18],將高血壓定義為在未服用任何降壓藥物的情況下,收縮壓(SBP)3140mmHg和(或)舒張壓(DBP)390mmHg;或自述既往有高血壓病史;或正在服用降壓藥物.

血脂異常:根據(jù)《中國(guó)成人血脂異常防治指南(2016年修訂版)》[19], TC35.18mmol/L,TG31.70mmol/L,LDL-C33.37mmol/L,HDL-C<1.04mmol/L,以及自述臨床診斷為血脂異常,符合以上任何一項(xiàng)即診斷為血脂異常.

1.5 統(tǒng)計(jì)學(xué)分析

采用廣義估計(jì)方程(Generalized estimating equations, GEE)縱向分析PM10短期暴露對(duì)血糖、血脂指標(biāo)的效應(yīng)[20].模型1:納入年齡和性別因素;模型2:在模型1基礎(chǔ)上納入婚姻狀況、文化程度、家庭人均月收入、職業(yè)、吸煙飲酒狀況、體育鍛煉、高血壓、BMI、蔬菜水果攝入情況、糖尿病家族史、血脂異常家族史、降脂(糖)藥使用情況、血脂(TC、TG、LDL-C和HDL-C,對(duì)血糖的影響)/ FPG(對(duì)血脂指標(biāo)的影響)因素;模型3:模型2基礎(chǔ)上納入SO2、NO2、平均氣溫、平均相對(duì)濕度和季節(jié)因素.根據(jù)赤池信息準(zhǔn)則(Akaikes information criterion,AIC),采用自由度為4的自然樣條函數(shù)調(diào)整模型3中的平均氣溫及平均相對(duì)濕度從而控制其非線(xiàn)性影響[21].運(yùn)用廣義相加混合模型(Generalized additive mixed model, GAMM) ,調(diào)整混雜因素后繪制PM10與血糖和血脂指標(biāo)間的暴露-反應(yīng)關(guān)系曲線(xiàn)[22].此外,按照性別和年齡進(jìn)行分層分析.

數(shù)據(jù)分析采用SPSS24.0及R3.6.1軟件.采用雙側(cè)檢驗(yàn),檢驗(yàn)水準(zhǔn)為=0.05.

2 結(jié)果

2.1 基線(xiàn)特征

本研究共納入基線(xiàn)人群24285人,其中血糖正常17502人、糖尿病前期4717人、2型糖尿病患者2066人.糖尿病患者中男性1432人(占69.31%),女性634人(占30.69%).血糖正常、糖尿病前期與糖尿病患者平均年齡為(47.96±8.11),(51.81±8.84),(55.19±8.34)歲,平均BMI分別為(23.77±5.61),(25.22±6.69),(25.81±7.80)kg/m2,差異有統(tǒng)計(jì)學(xué)意義.三類(lèi)人群家庭人均月收入在5000元以下、文化程度在初中及以下、職業(yè)為工人的占比較高;糖尿病患者吸煙、飲酒、有糖尿病家族史以及患高血壓比例分別為55.52%、31.56%、22.60%和58.28%,均高于血糖正常與糖尿病前期人群,差異有統(tǒng)計(jì)學(xué)意義(<0.001).FPG、TC、TG、LDL-C和HDL-C在血糖正常者的平均值分別為89.42,85.21,32.25,52.88, 25.10mg/dL,在糖尿病前期人群中為107.13,87.99, 37.64,51.31,23.64mg/dL,在糖尿病人群中為163.84, 87.69,45.39,50.03,21.88mg/dL(表1).

表1 基線(xiàn)不同糖代謝水平人群的基本特征

續(xù)表1

2.2 PM10對(duì)血糖、血脂的影響

如表2所示,PM10可使人群FPG、TC、LDL-C和HDL-C水平升高,TG水平下降.調(diào)整混雜因素后,PM10濃度每增加一個(gè)四分位數(shù)間距(Inter-quartile range,IQR),總?cè)巳篎PG、TC、LDL-C和HDL-C分別升高0.58mg/dL (95%CI:0.35,0.82)、0.38mg/dL (95%CI:0.25,0.52)、0.44mg/ dL(95%CI: 0.31,0.57)和0.34mg/dL(95%CI:0.29,0.40),而TG降低0.67mg/dL (95%CI:-0.86,-0.47).

暴露-反應(yīng)關(guān)系圖顯示,在調(diào)整混雜因素后,總?cè)巳号c血糖正常人群中,隨著PM10濃度的升高, FPG、TC、LDL-C和HDL-C呈上升趨勢(shì),TG呈下降趨勢(shì),且PM10對(duì)TC的暴露-反應(yīng)關(guān)系曲線(xiàn)存在閾值,在PM10濃度為400μg/m3,TC出現(xiàn)較明顯拐點(diǎn);糖尿病前期人群中,TC、LDL-C和HDL-C呈上升趨勢(shì),TG呈下降趨勢(shì);糖尿病人群中,FPG、LDL-C和HDL-C呈上升趨勢(shì),TG呈下降趨勢(shì).

表2 PM10濃度每增加一個(gè)四分位數(shù)間距與血糖和血脂變化的關(guān)系

續(xù)表2

注:模型1:調(diào)整年齡、性別;模型2:模型1+婚姻、文化程度、家庭人均月收入、職業(yè)、吸煙飲酒、鍛煉、BMI、蔬菜水果攝入情況、高血壓、糖尿病家族史、血脂異常家族史、降脂(糖)藥使用情況、血脂(對(duì)血糖的影響)/FPG(對(duì)血脂的影響);模型3:模型2+氣溫、濕度、NO2、SO2和季節(jié).*<0.05,**<0.001. change: PM10濃度每增加一個(gè)IQR, FPG,TC,TG,LDL-C和HDL-C變化情況.

圖2 PM10與總?cè)巳貉呛脱谋┞斗磻?yīng)關(guān)系

調(diào)整因素同表2模型3

圖3 PM10與血糖正常人群血糖和血脂的暴露反應(yīng)關(guān)系

調(diào)整因素同表2模型3

圖4 PM10與糖尿病前期人群血糖和血脂的暴露反應(yīng)關(guān)系

調(diào)整因素同表2模型3

圖5 PM10與糖尿病人群血糖和血脂的暴露反應(yīng)關(guān)系

調(diào)整因素同表2模型3

2.3 亞組分析

性別分層分析顯示,調(diào)整潛在混雜因素后, PM10濃度每增加一個(gè)IQR,男性人群FPG、TC、LDL-C和HDL-C分別升高0.58mg/dL(95%CI: 0.23, 0.92)、0.37mg/dL(95%CI:0.19,0.54)、0.51mg/ dL(95%CI: 0.35, 0.68)和0.35mg/dL(95%CI:0.28, 0.41), TG降低0.80mg/dL(95%CI:-1.08%,-0.52%);女性人群FPG、TC、LDL-C和HDL-C分別升高0.54mg/dL(95%CI:0.26,0.82)、0.42mg/dL(95%CI: 0.20,0.64)、0.27mg/dL(95%CI:0.07,0.47)和0.30mg/ dL(95%CI:0.21,0.39),TG則降低0.40mg/dL(95%CI: -0.63,-0.18)(表3).

年齡分層分析顯示:調(diào)整潛在混雜因素后,PM10濃度每增加一個(gè)IQR,年齡<60歲人群FPG、TC、LDL-C和HDL-C分別升高0.58mg/dL(95%CI:0.33, 0.83)、0.22mg/dL(95%CI:0.08,0.36)、0.19mg/dL (95%CI:0.05,0.32)和0.30mg/dL(95%CI:0.24,0.36), TG則降低0.76mg/dL(95%CI:-0.98,-0.55);年齡360歲人群TC、LDL-C和HDL-C分別升高0.96mg/dL (95%CI:0.56,1.36)、0.66mg/dL(95%CI:0.31,1.01)和0.39mg/dL(95%CI:0.25,0.52),FPG與TG的變化無(wú)統(tǒng)計(jì)學(xué)意義(表3).

表3 PM10濃度每增加一個(gè)四分位間距與不同亞組人群血糖和血脂變化的關(guān)系

注:調(diào)整因素同表2模型3;*<0.05,**<0.001,#:亞組間差異有統(tǒng)計(jì)學(xué)意義. change: PM10濃度每增加一個(gè)IQR, FPG,TC,TG,LDL-C和HDL-C變化情況.

3 討論

本研究以金昌隊(duì)列人群為研究對(duì)象,采用縱向研究揭示了PM10短期暴露可引起不同血糖代謝狀態(tài)人群空腹血糖和血脂水平紊亂,其中與FPG、TC、LDL-C及HDL-C呈正相關(guān),與TG呈負(fù)相關(guān),男性和360歲者易感性較高.

3.1 PM10可導(dǎo)致人群血糖和血脂水平異常

我國(guó)一項(xiàng)基于開(kāi)灤隊(duì)列的研究數(shù)據(jù)表明,PM10濃度每增加100μg/m3,FPG增加0.11mmol/L (95% CI: 0.07,0.15)[20];歐洲Lifelines隊(duì)列研究結(jié)果顯示,PM10濃度與FPG水平升高顯著相關(guān)[23];這與本研究結(jié)果一致.臺(tái)灣一項(xiàng)研究表明,PM10每增加一個(gè)IQR,TG升高2.96mg/dL(95%CI:-0.07,5.99), HDL-C降低0.90mg/dL(95%CI:-1.46,-0.34)[24].河南省農(nóng)村的一項(xiàng)隊(duì)列研究表明,較高濃度的PM10暴露導(dǎo)致 TC和LDL-C升高,使TG和HDL-C降低[25].伊朗一項(xiàng)研究結(jié)果顯示,空氣質(zhì)量指數(shù)(AQI)與TC、LDL-C和TG呈顯著正相關(guān),與HDL-C呈負(fù)相關(guān)[26].以色列南部的一項(xiàng)回顧性隊(duì)列研究發(fā)現(xiàn),PM10中期暴露致使FPG升高0.30%(95%CI:0.15,0.45),LDL-C升高2.32%(95%CI:2.15,2.49),TG升高0.23% (95%CI: 0.02,0.42),HDL-C降低1.13%(95%CI:-1.23,-1.03);但急性暴露于PM10與FPG、TG、LDL-C及HDL-C之間沒(méi)有關(guān)聯(lián)[27].一項(xiàng)對(duì)兒童和青少年的研究表明,PM10暴露與TC、HDL-C水平呈正相關(guān)[28].交通相關(guān)污染物與LDL-C和TG之間有顯著正向關(guān)聯(lián)[29].以上研究在TC與LDL-C的變化趨勢(shì)上與本研究結(jié)果一致,但TG與HDL-C的變化存在差異.

空氣污染導(dǎo)致血糖和血脂指標(biāo)異常的生物學(xué)機(jī)制尚不十分清楚,但有研究提出了以下幾種可能的途徑.一些研究表明,吸入空氣顆粒物會(huì)引發(fā)炎癥反應(yīng)、氧化應(yīng)激及自主神經(jīng)失衡,進(jìn)而影響胰島素抵抗水平、脂質(zhì)代謝及氧化,最終導(dǎo)致血脂代謝紊亂和高血糖[26,30-33].實(shí)驗(yàn)研究也發(fā)現(xiàn)空氣污染物還可能通過(guò)降低DNA甲基轉(zhuǎn)移酶的活性而導(dǎo)致異常的 DNA甲基化,從而影響脂質(zhì)代謝與炎癥反應(yīng)[34-35].

3.2 不同血糖代調(diào)整混雜因素后,謝狀態(tài)對(duì)PM10暴露效應(yīng)存在差異

本研究發(fā)現(xiàn),調(diào)整混雜因素后,血糖正常人群中PM10暴露對(duì)血糖及血脂指標(biāo)均有影響,糖尿病前期人群中PM10與FPG無(wú)關(guān)聯(lián),糖尿病人群中PM10對(duì)FPG、TG和LDL-C的效應(yīng)均無(wú)統(tǒng)計(jì)學(xué)意義;NHIS-NSC隊(duì)列研究表明[36],PM2.5暴露對(duì)FPG或LDL-C水平異常者的FPG和LDL-C影響無(wú)統(tǒng)計(jì)學(xué)意義,但在指標(biāo)正常者中PM2.5與FPG和LDL-C水平顯著相關(guān).可能是因?yàn)樘悄虿∏捌诩疤悄虿∪巳捍嬖诓煌潭忍谴x與脂代謝紊亂,從而對(duì)外環(huán)境暴露的敏感性降低.

3.3 男性和老年人的易感性較高

男性、年齡≥60歲者更容易受到PM10的不良影響.相關(guān)隊(duì)列研究meta分析結(jié)果顯示,長(zhǎng)期暴露于空氣污染中,女性患2型糖尿病的風(fēng)險(xiǎn)高于男性[37].石家莊一項(xiàng)研究結(jié)果表明,在老年人群中空氣污染對(duì)血脂水平異常的影響更明顯[38].美國(guó)一項(xiàng)研究表明,年齡沒(méi)有顯著改變 PM10與TC、TG的關(guān)聯(lián),PM10與TG和TC相關(guān)性在男性中更強(qiáng)[8].S?rensen等[39]研究發(fā)現(xiàn),在年齡和性別分層分析中,PM2.5對(duì)TC的影響沒(méi)有差異.由此可見(jiàn),大氣顆粒物暴露對(duì)不同性別和年齡人群的影響存在異質(zhì)性.男性不良健康生活方式的暴露相比女性更加顯著,如吸煙、飲酒、缺乏鍛煉、攝入高糖、高脂的食物等,這可能會(huì)引發(fā)全身炎癥和氧化應(yīng)激[40];此外,本研究中男性工人占比較高,工人從事重體力活動(dòng)可增加肺活量,在相同的環(huán)境濃度下,體力活動(dòng)者的肺組織對(duì)顆粒物的暴露水平更高.老年人群的生理機(jī)能不斷退化,代謝性疾病、心血管疾病與呼吸系統(tǒng)疾病患病率高,因此更易受到空氣污染的影響;此外,老年人群的某些社會(huì)學(xué)特征能間接影響空氣污染物的健康效應(yīng),研究顯示[41],老年人因缺乏健康素養(yǎng),導(dǎo)致醫(yī)療衛(wèi)生服務(wù)利用水平較低,可能會(huì)加劇空氣污染物對(duì)血脂和血糖代謝的不利影響.

3.4 局限性

盡管本研究基于大樣本隊(duì)列人群,探討短期暴露于大氣PM10對(duì)人群血糖和血脂的影響,發(fā)現(xiàn)了有意義的研究結(jié)果,但仍存在一定的局限性,首先,未納入PM2.5與O3作為混雜因素.其次,因?yàn)榭諝馕廴緮?shù)據(jù)收集的局限性,沒(méi)有考慮人群流動(dòng)性所造成的暴露,只是根據(jù)個(gè)體住宅最近監(jiān)測(cè)點(diǎn)測(cè)量的空氣污染水平來(lái)評(píng)估個(gè)體暴露水平,無(wú)法測(cè)量工作環(huán)境和室外活動(dòng)的顆粒物暴露,這可能導(dǎo)致PM10的健康效應(yīng)被低估,需要在未來(lái)實(shí)施進(jìn)一步的研究.最后,盡管本研究調(diào)整了潛在混雜因素,但是仍存在一些無(wú)法衡量的殘余混雜,包括綠化狀況、交通相關(guān)污染物和噪聲暴露等.

4 結(jié)論

4.1 大氣PM10每增加一個(gè)四分位數(shù)間距,可導(dǎo)致總?cè)巳篎PG、TC、LDL-C及HDL-C分別升高0.58mg/dL、0.38mg/dL、0.44mg/dL和0.34mg/dL,TG降低0.67mg/dL;血糖正常人群FPG、TC、LDL-C及HDL-C分別升高0.22mg/dL、0.33mg/dL、0.38mg/dL和0.36mg/dL,TG降低0.55mg/dL;糖尿病前期人群TC、LDL-C及HDL-C分別升高0.40mg/ dL、0.35mg/dL和0.25mg/dL,TG降低0.85mg/dL;糖尿病人群HDL-C升高0.34mg/dL,TG降低1.65mg/dL.

4.2 性別與年齡分層分析結(jié)果提示,男性、年齡>60歲者對(duì)PM10暴露較為敏感.

4.3 暴露-反應(yīng)關(guān)系曲線(xiàn)表明,隨著PM10濃度升高,FPG、TC、LDL-C及HDL-C呈上升趨勢(shì),TG呈下降趨勢(shì).因此,加強(qiáng)環(huán)境保護(hù)和治理,減輕空氣污染對(duì)人體的危害,降低與空氣污染有關(guān)的糖尿病發(fā)病率具有重要意義.

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Effects of short-term exposure to PM10on fasting plasma glucose and blood lipids.

HE Ying-qian, WANG Min-zhen*, ZHENG Shan, ZHAO Ya-nan, ZHANG Lu-lu, LIU Jing, BAI Ya-na

(Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China)., 2022,42(6)：2911～2920

This study was designed to explore the effects of short-term exposure to PM10on fasting plasma glucose and blood lipids in population with different glucose metabolism levels based on the platform of "Jinchang Cohort". The pollutant and meteorological data in Jinchang city from 2011 to 2017 were collected. Individual exposure levels were estimated by the nearest neighbor model. Generalized estimating equations were employed to analyze relationships between PM10, blood glucose and blood lipids. The exposure-response relationship curves were drawn by generalized additive mixed model. For every IQR increase in PM10concentration, it was found that FPG, TC, LDL-C and HDL-C increased by 0.58mg/dL (95%CI: 0.35, 0.82), 0.38mg/dL (95%CI: 0.25, 0.52), 0.44mg/dL (95%CI: 0.31, 0.57) and 0.34mg/dL (95%CI: 0.29, 0.40), respectively, TG decreased by 0.67mg/dL (95%CI:-0.86,-0.47). With the increase of PM10concentration, FPG, TC, LDL-C and HDL-C all showed an upward trend, while TG showed a downward trend. The adverse effects of PM10on blood glucose and lipid indicators were greater in male and elder people. Findings suggest that PM10was associated with changed fasting plasma glucose and blood lipid levels among population with different blood glucose states. Male and elder people should pay more attention to personal safety protection.

PM10；Type 2 diabetes；fasting plasma glucose；blood lipid

X503.1

A

1000-6923(2022)06-2911-10

賀穎倩(1998-),女,四川達(dá)州人,蘭州大學(xué)碩士研究生,主要從事環(huán)境流行病學(xué)研究.

2021-11-15

國(guó)家自然科學(xué)基金資助項(xiàng)目(41705122,41505095)

* 責(zé)任作者, 副教授, wangmzh@lzu.edu.cn