PM2.5暴露影响终止高血压饮食模式与中心性肥胖的相关性

张焕文, 乔婷婷, 陈珍, 罗涛, 张泽文, 王璐, 戴江红

张焕文, 乔婷婷, 陈珍, 罗涛, 张泽文, 王璐, 戴江红. PM2.5暴露影响终止高血压饮食模式与中心性肥胖的相关性[J]. 环境与职业医学, 2022, 39(11): 1262-1268. DOI: 10.11836/JEOM21502
引用本文: 张焕文, 乔婷婷, 陈珍, 罗涛, 张泽文, 王璐, 戴江红. PM2.5暴露影响终止高血压饮食模式与中心性肥胖的相关性[J]. 环境与职业医学, 2022, 39(11): 1262-1268. DOI: 10.11836/JEOM21502
ZHANG Huanwen, QIAO Tingting, CHEN Zhen, LUO Tao, ZHANG Zewen, WANG Lu, DAI Jianghong. Correlation between dietary approaches to stop hypertension pattern and central obesity affected by PM2.5 exposure[J]. Journal of Environmental and Occupational Medicine, 2022, 39(11): 1262-1268. DOI: 10.11836/JEOM21502
Citation: ZHANG Huanwen, QIAO Tingting, CHEN Zhen, LUO Tao, ZHANG Zewen, WANG Lu, DAI Jianghong. Correlation between dietary approaches to stop hypertension pattern and central obesity affected by PM2.5 exposure[J]. Journal of Environmental and Occupational Medicine, 2022, 39(11): 1262-1268. DOI: 10.11836/JEOM21502

PM2.5暴露影响终止高血压饮食模式与中心性肥胖的相关性

基金项目: 国家重点研发计划项目(2017YFC0907203);国家自然科学基金项目(82160640);新疆维吾尔自治区研究生创新项目(XJ2022G182)
详细信息
    作者简介:

    张焕文(1995—),女,硕士生; E-mail:1021551342@qq.com

    通讯作者:

    戴江红,E-mail:epi102@sina.com

  • 中图分类号: R12

Correlation between dietary approaches to stop hypertension pattern and central obesity affected by PM2.5 exposure

Funds: This study was funded.
More Information
  • 摘要:
    背景

    PM2.5及其组分暴露是否影响终止高血压饮食(DASH)模式与中心性肥胖之间的关系尚缺乏证据。

    目的

    探讨PM2.5及其组分暴露对DASH模式与中心性肥胖患病相关性的影响。

    方法

    数据来源于“新疆多民族自然人群队列建设及健康随访研究”乌鲁木齐市人群基线调查。根据8种食物组摄入频率计算DASH评分,推荐摄入的食物组摄入频率由低到高分别为1~5分,而限制摄入的食物组摄入频率由高到低分别为1~5分,总分即为DASH评分,评分越高,研究对象对DASH模式的依从性越好;对应用卫星获得的PM2.5和全球大气化学传输模式(GEOS-Chem)估计的有机碳(OC)、黑炭(BC)、硫酸盐(SO42−)、硝酸盐(NO3)、铵盐(NH4+)及扬尘进行暴露评估;以男性腰围≥90 cm或女性腰围≥85 cm定义为中心性肥胖(WS/T 428—2013《成人体重判定》)。采用logistic回归模型分析DASH模式、PM2.5及其组分对中心性肥胖的影响,采用分层分析探讨PM2.5及其组分对DASH模式和中心性肥胖关联的影响。

    结果

    研究共纳入9565名城市居民,年龄(62.30±9.42)岁,中心性肥胖患病率为60.75%。调整混杂因素后,DASH评分Q5组比Q1组中心性肥胖患病可能性降低17.5%(OR=0.825,95%CI:0.720~0.947)。PM2.5及其组分OC、BC、SO42−、NH4+、扬尘与中心性肥胖患病呈正相关,未见组分NO3暴露与中心性肥胖有关联。分层分析发现,暴露于低浓度PM2.5及其组分NO3、NH4+、扬尘的研究对象中,DASH评分Q5组中心性肥胖患病可能性降低,而暴露于高浓度PM2.5及其组分NO3、NH4+、扬尘的研究对象中,DASH模式对中心性肥胖的保护效应消失。

    结论

    PM2.5及其组分NO3、NH4+、扬尘暴露会削弱DASH模式对中心性肥胖的保护效应。

     

    Abstract:
    Background

    There is a lack of evidence on whether exposure to PM2.5 and its constituents would affect the relationship between the dietary approaches to stop hypertension (DASH) and central obesity.

    Objective

    To investigate the effect of exposure to PM2.5 and its constituents on the correlation between the DASH dietary pattern and the prevalence of central obesity.

    Methods

    The data were obtained from the baseline survey of the "Xinjiang Multi-Ethnic Natural Population Cohort Construction and Health Follow-Up Study" in Urumqi. A DASH score was calculated according to intake frequency of 8 food groups, and summed from intake frequency of recommended food groups scored from 1 to 5 from low to high, and intake frequency of restricted food groups scored from 1 to 5 from high to low. A higher DASH score indicates better compliance with the DASH dietary pattern. We estimated exposure using satellite-derived PM2.5 and a chemical transport model (GEOS-Chem) for its constituents, including organic carbon (OC), black carbon (BC), sulfate (SO42−), nitrate (NO3), ammonium (NH4+), and soil dust. Central obesity was defined by waist circumference: ≥90 cm for men or ≥85 cm for women according to Criteria of weight for adults (WS/T 428—2013). A logistic regression model was used to analyze the effects of the DASH dietary pattern as well as PM2.5 and its constituents on central obesity, and a stratified analysis was used to explore the effects of PM2.5 and its constituents on the association between the DASH dietary pattern and central obesity.

    Results

    The study included 9 565 urban residents, aged (62.30±9.42) years, with a central obesity prevalence rate of 60.75%. After adjusting for selected confounders, the DASH score Q5 group had a 17.5% lower risk of central obesity than the Q1 group (OR=0.825, 95%CI: 0.720-0.947). PM2.5 and its constituents OC, BC, SO42−, NH4+, and soil dust were positively associated with the prevalence of central obesity, but no association was observed between constituent NO3 exposure and central obesity. The stratified analysis revealed that the prevalence of central obesity was reduced in the DASH score Q5 group in participants exposed to low concentrations of PM2.5 and its constituents NO3, NH4+, and soil dust, while the protective effect of the DASH pattern on central obesity disappeared in subjects exposed to high concentrations of PM2.5 and its constituents NO3, NH4+, and soil dust.

    Conclusion

    Exposure to PM2.5 and its constituents NO3, NH4+, and soil dust could attenuate the protective effect of the DASH pattern on central obesity.

     

  • 机体长期摄取过量的氟化物可透过血脑屏障蓄积在脑组织中,从而引起脑组织的形态学改变和功能异常,导致中枢神经系统损伤,表现为记忆衰退、认知低下等症状[1],研究证明炎症反应可能是氟致神经损伤的机制之一[2]。晚期糖基化终产物受体(receptor of advanced glycation end-products, RAGE)是一种多配体跨膜受体[3],属于免疫球蛋白基因超家族,其广泛分布于中枢神经系统中,对神经系统的发育调节起重要作用。当RAGE与其配体高迁移率族蛋白B1(high mobility group protein, HMGB1)相结合时,可以激活多条细胞内信号转导通路,其中就包括RAGE/p38丝裂原活化蛋白激酶(p38 mitogen-activated protein kinase, p38MAPK)/核因子κB(nuclear factor kappa-B, NF-κB)信号通路,且与炎症反应密切相关,进而释放大量炎症因子,如白介素-6(interleukin-6, IL-6)、肿瘤坏死因子-α(tumour necrosis factor-α, TNF-α)。RAGE阻断抑制剂(RAGE antagonist, FPS-ZM1)是2012年Deane等[4]首次发现的一种新型小分子RAGE阻断剂,其通过阻断RAGE通路可抑制阿尔茨海默病(Alzheimer's disease, AD)大鼠脑内β淀粉样蛋白(amyloid-β, Aβ)生成和炎症反应,明显改善认知功能;同时,研究显示中药银杏叶提取物(extract of Ginkgo biloba 761, EGb761)也具有阻断RAGE的作用,它是银杏叶提取物之一[5],主要成分是24%的银杏黄酮苷、6%的萜烯内酯、2.8%~3.4%的银杏内酯(A、B、C)和小于5 mg·L−1的银杏酸。Hu等[6]发现EGb761可降低大脑微血管内皮细胞中RAGE的表达,改善大脑的缺血性损伤。本研究通过建立亚慢性氟中毒动物模型,检测大鼠脑组织中RAGE/p38MAPK/NF-κB信号通路的变化,寻找氟致神经损伤的机制,同时探讨EGb761和FPS-ZM1对亚慢性氟中毒神经系统损伤的保护作用。

    兔抗多克隆RAGE和NF-κB抗体(英国Abcam),兔抗多克隆HMGB1、p38MAPK和磷酸化p38 MAPK(phospho-p38 MAPK, p-p38MAPK)抗体和鼠抗单克隆微管蛋白(tubulin antibody, Tublin)抗体(中国武汉三鹰),兔抗多克隆IL-6和TNF-α抗体(中国武汉爱博泰克),NaF(美国Sigma-Aldrich),EGb761药片(德国威玛舒培博士药厂),FPS-ZM1注射液(美国MCE公司),电泳槽和转印系统(中国北京百晶),电泳仪及曝光仪(美国Bio-Rad公司),正置显微镜(日本尼康),trizol试剂(美国Invitrogen),PrimeScript逆转录试剂盒(加拿大Genstar),SYBR Green Master Mix(加拿大Genstar)。

    选择1月龄雄性清洁级SD大鼠90只[贵州医科大学动物实验中心提供,动物合格证编号:SYXK(黔)2018-0001],已获得贵州医科大学实验动物伦理委员会批准(审批件编号:2200363),体重为95~120 g,适应性喂养1周后,按体重采用随机数字表法分为9组,每组10只。分为对照(C)组:自由饮用自来水(含氟量<0.5 mg·L−1);低剂量染氟(LF)组:自由饮用含氟量为10 mg·L−1的自来水;高剂量染氟(HF)组:自由饮用含氟量为50 mg·L−1的自来水;EGb761对照(CE)组:自由饮用自来水,同时给予EGb761灌胃;低剂量染氟+EGB761(LFE)组:自由饮用含氟量为10 mg·L−1的自来水,同时给予EGb761灌胃;高剂量染氟+EGb761(HFE)组:自由饮用含氟量为50 mg·L−1的自来水,同时给予EGb761灌胃;FPS-ZM1对照(CF)组:自由饮用自来水,造模结束前连续7 d腹腔注射FPS-ZM1;低剂量染氟+FPS-ZM1(LFF)组:自由饮用含氟量为10 mg·L−1的自来水,在造模结束前连续7 d腹腔注射FPS-ZM1;高剂量染氟+FPS-ZM1(HFE)组:自由饮用含氟量为50 mg·L−1的自来水,在造模结束前连续7 d腹腔注射FPS-ZM1。根据课题组前期研究[7],确定该实验的NaF暴露浓度为10、50 mg·L−1,造模周期为6个月,EGb761的干预浓度为100 mg·kg−1·d−1,每日灌胃,持续6个月;根据文献[8],确定FPS-ZM1的干预浓度为1 mg·kg−1·d−1,在染氟造模结束前7 d每天进行腹腔注射。实验结束后使用10%水合氯醛(3 mL·g−1)腹腔注射,从而麻醉大鼠,心脏取血,然后使用磷酸缓冲盐溶液(phosphate buffered saline, PBS)经主动脉进行灌注,低温下解剖取脑组织。

    水迷宫主体是一个圆柱形水箱,直径120 cm,高50 cm。将大鼠依次从第一、第二、第三、第四象限放入水面,使其自由游泳直到登上求生平台,设置大鼠自由寻找时间为90 s,设定时间内未寻找到平台的则引导其至平台上停留10 s。每日重复3次,训练5 d。结束训练后2 d,撤去求生平台,将大鼠自第三象限放入水中,记录大鼠逃避潜伏时间和穿过平台的次数。

    取新鲜大鼠脑组织中矢状面海马,置于固定液中,常规脱水包埋,切片,将切片置于焦油紫染色液中,56 ℃浸染1 h,随后去离子水冲洗,置于尼氏分化液中分化1 min,清水冲洗,迅速脱水、清水冲洗、二甲苯透明,中性树胶封片。因为其海马CA1区是脑内参与记忆储存的重要部位,故于显微镜下观察其CA1区的病理变化。

    处死大鼠时使用心脏取血法收集血液,离心提取上清液,放入−80 ℃冰箱,保存备用;取一部分大鼠脑组织,加入缓冲液制备匀浆,保存备用;盐酸或乙酸钠调节至近中性的总离子强度调节缓冲溶液,用实验室纯水稀释至标线,摇匀后倒入杯中,放入搅拌器,插入电极,搅拌待电位稳定后测量浓度为1、2、3、4、5、6 ng·L−1的NaF标准品溶液的电位。接下来依次测量各组大鼠的血清、脑组织匀浆的电位,绘制标准曲线从而计算出脑氟含量和血氟质量浓度(简称浓度)。

    取大鼠脑组织海马加入高效RIPA组织裂解液匀浆,4 ℃、12000×g离心40 min,取上清。二喹啉甲酸(bicinchoninic acid, BCA)法测定蛋白含量。经凝胶电泳分离蛋白,再将蛋白转移至聚偏二氟乙烯膜(polyvinylidene fluoride, PVDF)。分别加RAGE、p38MAPK、p-p38MAPK、NF-κB、HMGB1、IL-6、TNF-α和内参Tublin抗体(4 ℃孵育过夜),Tris-HCl+吐温20缓冲盐溶液(tris buffered saline with Tween 20, TBST)洗膜3次,每次10 min,再用辣根过氧化物酶(horseradish Peroxidase, HRP)标记二抗室温孵育1 h,与化学发光HPR底物反应,曝光仪曝光,最后使用Image J 1.44软件对条带进行灰度值分析。

    取大鼠脑组织海马20 mg,加入1 mL trizol试剂,经过研磨后提取总mRNA,PrimeScript逆转录试剂盒、SYBR Green Master Mix进行逆转录和实时荧光定量PCR。以甘油醛-3-磷酸脱氢酶(glyceraldehyde-3-phosphate dehydrogenase, GAPDH)为内参,2−∆∆Ct法计算HMGB1RAGEp38MAPK的mRNA相对表达量。实验所需引物由上海生工生物工程公司设计,见表1

    表  1  扩增基因引物序列和退火温度
    Table  1.  Primer sequences of amplified genes and annealing temperature
    基因序列(5'-3')退火温度/℃
    RAGE正向引物:CCGAGTCCGAGTCTACCGTAAGG61
    反向引物:ACACCAGGGCTAAGAGTCAAGGG
    HMGB1正向引物:AGGCTGACAAGGCTCGTTATGAAAG60
    反向引物:GGGCGGTACTCAGAACAGAACAAG
    p38MAPK正向引物:GATAAGAGGATCACAGCAGCCCAAG61
    反向引物:TCGTAGGTCAGGCTCTTCCATTCG
    GAPDH正向引物:GACATGCCGCCTGGAGAAAC60
    反向引物:AGCCCAGGATGCCCTTTAGT
    下载: 导出CSV 
    | 显示表格

    采用SPSS 22.0和GraphPad Prism 5.0软件分析数据,实验数据均以均数±标准差表示。多组间比较采用单因素方差分析,进一步组间两两比较,方差齐时采用Bonferroni检验,方差不齐时采用Tamhane's T2检验。检验水准α=0.05。

    与C组相比,LF组、HF组的血氟浓度和脑氟含量增加(P均<0.05),见表2。与C组相比,LF组、HF组的逃避潜伏时间均明显延长,与HF组相比,HFE组、HFF组的逃避潜伏时间均缩短;与C组相比,LF组、HF组穿越平台次数均减少,与HF组相比,HFE组、HFF组穿越平台次数均增加(P均<0.05)。见表2

    表  2  亚慢性氟中毒大鼠血氟浓度和脑氟含量以及水迷宫实验结果($\bar x \pm s $n=5)
    Table  2.  Blood fluoride concentration, brain fluoride content, and water maze test results of rats with chronic fluorosis ($\bar x \pm s $n=5)
    分组血氟/(ng·L−1)脑氟/(μg·g−1)逃避潜伏时间/s穿过平台次数
    C 0.02±0.01 4.58±0.33 8.32±1.53 2.60±1.14
    LF 0.09±0.00a 7.06±0.33a 21.94±3.28a 1.20±0.45a
    HF 0.18±0.01a 11.57±0.17a 43.37±6.96a 1.00±0.71a
    CE 0.03±0.00 3.96±0.40 11.48±0.86 3.00±1.00
    LFE 0.11±0.00 a 7.49±0.45 19.55±2.39 2.60±0.89
    HFE 0.15±0.01 a 10.23±0.21 a 24.76±1.62b 2.20±0.45b
    CF 0.03±0.00 5.94±0.16 6.70±1.97 2.60±0.55
    LFF 0.11±0.01 a 8.03±0.20 21.42±7.36 2.40±0.55
    HFF 0.16±0.01 a 9.49±0.25 a 27.35±5.28b 2.00±0.71b
    [注] a:与C组比较,P<0.05;b:与HF组比较,P<0.05。
    下载: 导出CSV 
    | 显示表格

    与C组相比,HF组海马CA1区神经元、尼氏体数量减少,着色较浅,神经细胞排列疏松、紊乱;与HF组相比,HFE组和HFF组海马CA1区神经元、尼氏体数量有所增加,着色较深,神经细胞排列较正常。见图1

    图  1  尼氏染色法检测大鼠海马组织病变情况
    A~I分别为C组、LF组、HF组、CE组 、LFE组、HFE组、CF组、LFF组、HFF组。箭头所示大鼠海马CA1区,与C组相比,HF组海马神经元、尼氏体数量减少,神经细胞排列紊乱;与HF组相比,HFE组和HFF组海马神经元、尼氏体数量有所增加,神经细胞排列较正常。
    Figure  1.  Pathological changes of rat hippocampal tissue detected by Nissl staining

    结果显示与C组相比,LF组RAGE、HMGB1、NF-κB、p-p38MAPK、IL-6蛋白表达水平上调,HF组大鼠的RAGE、HMGB1、NF-κB、p38MAPK、p-p38MAPK、IL-6、TNF-α蛋白表达水平上调;与LF组相比,LFE组和LFF组的RAGE、HMGB1蛋白表达水平下调(P均<0.05);与HF组相比,HFE组、HFF组RAGE、HMGB1、NF-κB、p38MAPK、p-p38MAPK、IL-6、TNF-α表达水平均下调(P均<0.05)。见图2

    图  2  大鼠脑组织RAGE、NF-κB、HMGB1、p38MAPK、p-p38MAPK、TNF-α和IL-6蛋白相对表达水平
    A~G:RAGE、HMGB1、NF-κB、p38MAPK、p-p38MAPK、IL-6和TNF-α的蛋白表达水平; H:电泳图,组别顺序与图A中一致。与C组相比,*:P<0.05,**:P<0.01;与LF组相比,&:P<0.05,&&:P<0.01;与HF组相比,#:P<0.05,##:P<0.01。
    Figure  2.  Protein expression levels of RAGE, NF-κB, HMGB1, p38MAPK, p-p38MAPK, TNF-α, and IL-6 in rat brain tissue

    与C组相比,HF组、LF组RAGEHMGB1的mRNA表达水平上调;与LF组相比,LFE组、LFF组RAGE的mRNA表达水平下调;与HF组相比,HFE组、HFF组RAGEHMGB1的mRNA表达水平下调(P均<0.05),其余结果无明显差异。见图3

    图  3  SD大鼠脑组织RAGE、HMGB1、p38MAPK的mRNA表达
    A~C:RAGEHMGB1p38MAPK的基因相对表达水平;与C组比较,*:P<0.05,**:P<0.01;与LF组相比,&&:P<0.01;与HF组比较,#:P<0.05,##:P<0.01。
    Figure  3.  mRNA expressions of RAGE, HMGB1, and p38MAPK in brain tissue of SD rats

    氟在自然界中广泛存在,如果长期摄入大量的氟,会严重影响人的身体健康[9]。已有研究表明[10],慢性氟中毒会引起儿童智力下降,认知功能减退等中枢神经系统功能性障碍。同时,在动物实验中也发现亚慢性氟中毒大鼠模型出现学习能力减退和记忆力衰退等现象[11]。在本实验的亚慢性氟中毒动物模型中,染氟大鼠血氟和脑氟含量均高于对照组,且随着染氟剂量的增加而增加,提示亚慢性氟中毒动物模型造模成功。尼氏染色结果显示,HF组大鼠的海马CA1区神经元、尼氏体数量减少,着色较浅,神经细胞排列疏松、紊乱;同时水迷宫实验结果显示与C组相比,染氟组逃避潜伏时间延长,平台穿越次数减少,说明过量的氟可引起大鼠学习记忆能力下降,提示氟中毒对神经系统有损伤作用。

    RAGE是一种多配体跨膜受体,广泛分布于多种细胞,如平滑肌细胞、肝细胞、神经元等[12]。生理状态下,RAGE在体内呈低水平表达,但是在机体受到相关刺激下,会使RAGE表达水平增高,从而引起氧化应激水平增高和炎症反应。RAGE与其配体结合可激活细胞内多条信号转导通路[13-14],参与细胞迁移和炎症的发生[15]。其中RAGE/p38MAPK/NF-κB信号通路是与炎症发生密切相关的一条信号通路[16-19]。HMGB1是RAGE的配体之一,其是一种强有力的致炎因子,也是参与全身炎症反应的“晚期”介质[20-21]。在本研究中,与C组相比,HF组的RAGE、HMGB1、NF-κB、p38MAPK、p-p38MAPK、IL-6、TNF-α蛋白表达水平增加,HF组的RAGE、HMGB1的mRNA表达水平同时增加。结果表明,当大量的氟进入大鼠中枢神经系统后,可能会引起RAGE/p38MAPK/NF-κB信号通路表达上调,从而导致炎症因子表达上调,继而造成神经系统的损伤,学习记忆能力的减退。这种改变可能是氟致神经损伤的机制之一。

    已有研究表明FPS-ZM1经腹腔注射后,能直接穿过血脑屏障,通过阻断RAGE通路,抑制AD鼠脑内Aβ生成和炎症反应,改善认知障碍,有望成为一种有前景的RAGE阻断剂[22]。同时研究显示部分中药提取物也具有缓解炎症损伤的作用,其中EGb761是银杏叶提取物之一[23],已有研究表明EGb761可抑制炎症因子和神经毒性因子的产生与释放[24-25],其中Kim等[26]发现EGb761可通过降低大脑微血管内皮细胞中RAGE的表达,提升大脑血流量,从而改善大脑的病理损伤。为了进一步证实氟致神经损伤与该信号通路表达上调有关以及寻找可能的治疗途径,本实验中加入RAGE特异阻断剂FPS-ZM1和银杏叶提取物(EGb761),结果显示,经过FPS-ZM1干预和EGb-761的治疗后,相较于HF组,HFE组和HFF组的RAGE、HMGB1、NF-κB、p-38MAPK、p-p38MAPK、IL-6、TNF-α表达水平均有不同程度的下调;伴随RAGEHMGB1的mRNA水平下调。同时水迷宫实验结果显示,HFE组和HFF组的大鼠的逃避潜伏时间比HF组明显减少,穿越平台次数明显增加;尼氏染色结果显示,相比于HF组,HFE组和HFF组的大鼠海马神经元CA1区尼氏体数量增加,着色较深,神经细胞排列较规律。结合以上结果表明,通过FPS-ZM1和EGb761的干预之后,RAGE/p38MAPK/NF-κB信号通路蛋白有不同程度的下调,大鼠的学习记忆能力有所改善,再次证实亚慢性氟中毒可引起RAGE/p38MAPK/NF-κB信号通路的改变,同时也说明FPS-ZM1、EGb761对亚慢性氟中毒引起的神经损伤可能起到一定的保护作用。

  • 表  1   不同人口学特征研究对象间DASH评分比较

    Table  1   Comparison of DASH score among participants grouped by different basic characteristics

    基本特征变量赋值人数(构成比/%)DASH评分
    $ \overline {{x}} \pm s $t/FP
    性别10.767<0.001
     女05812(60.76)30.33±3.51
     男13753(39.24)29.54±3.53
    年龄/岁2.0010.045
     <6504188(43.78)30.10±3.44
     ≥6515377(56.22)29.96±3.61
    职业10.755<0.001
     农民/工人12769(28.95)29.69±3.90
     行政/专业技术人员2840(8.78)30.57±3.66
     服务行业3749(7.83)30.21±4.08
     家务/待业42711(28.34)30.06±3.05
     其他52494(26.07)30.09±3.36
    婚姻3.3420.035
     已婚18352(87.32)30.04±3.54
     丧偶/分居/离婚21153(12.05)29.80±3.53
     未婚356(0.59)30.68±3.65
    吸烟5.2510.005
     从不吸烟18139(85.14)30.04±3.53
     过去吸烟2361(3.78)29.43±3.67
     目前吸烟31059(11.08)30.07±3.53
    空气污染防护措施4.854<0.001
     无防护措施0125(1.31)29.74±4.21
     减少外出11972(20.62)29.76±3.63
     佩戴口罩22406(25.15)29.98±3.63
     紧闭门窗34477(46.81)30.15±3.49
     开启空气净化器等设备4482(5.04)30.24±2.80
     其他5103(1.08)29.32±3.73
    体育锻炼125.871<0.001
     从不或几乎从不参加13321(34.72)29.27±3.67
     每月1~3次2693(7.25)29.08±3.46
     每周1~2次3511(5.34)29.23±3.59
     每周3~5次4750(7.84)29.86±3.12
     每天或几乎每天54277(44.72)30.88±3.31
    睡眠29.828<0.001
     适宜06072(63.60)30.15±3.51
     不足12980(31.21)29.94±3.62
     过多2495(5.18)28.91±3.07
    中心性肥胖6.4960.011
     否03754(39.25)30.13±3.55
     是15811(60.75)29.94±3.53
    下载: 导出CSV

    表  2   DASH评分对中心性肥胖的影响

    Table  2   Effects of DASH score on central obesity

    DASH评分模型1模型2
    OR(95%CI)POR(95%CI)P
    每增加10分0.860(0.765~0.966)0.0110.878(0.777~0.992)0.038
    Q11.0001.000
    Q20.972(0.855~1.105)0.6630.997(0.874~1.138)0.968
    Q30.952(0.843~1.075)0.4300.970(0.855~1.100)0.635
    Q40.990(0.872~1.125)0.8831.013(0.889~1.154)0.846
    Q50.818(0.717~0.934)0.0030.825(0.720~0.947)0.006
    P趋势0.0180.045
    [注]模型1为未校正模型,模型2校正了性别、年龄、职业、婚姻、吸烟、空气污染防护措施、体育锻炼、睡眠;P趋势为趋势性检验P值。
    下载: 导出CSV

    表  3   PM2.5及组分对中心性肥胖的影响

    Table  3   Effects of PM2.5 and its constituents on central obesity

    PM2.5及组分模型1模型2
    OR(95%CI)POR(95%CI)P
    PM2.51.071(1.022~1.122)0.0041.055(1.006~1.107)0.029
    OC1.078(1.010~1.150)0.0231.069(1.001~1.143)0.048
    BC1.085(1.026~1.147)0.0041.079(1.020~1.142)0.008
    SO42-1.106(1.043~1.172)0.0011.109(1.045~1.178)0.001
    NO3-1.062(1.003~1.125)0.0391.055(0.995~1.119)0.073
    NH4+1.078(1.021~1.138)0.0071.076(1.018~1.137)0.009
    扬尘1.055(1.028~1.083)<0.0011.060(1.032~1.089)<0.001
    [注]模型1为未校正模型,模型2校正了性别、年龄、职业、婚姻、吸烟、空气污染防护措施、体育锻炼、睡眠。
    下载: 导出CSV

    表  4   按PM2.5及其组分分层的DASH模式与中心性肥胖的关系[OR(95%CI)]

    Table  4   Associations of the DASH dietary pattern with the risk for central obesity stratified by PM2.5 and its constituents [OR(95%CI)]

    分组DASH模式评分P交互
    Q1Q2Q3Q4Q5
    PM2.50.010
     低浓度组1.0001.104(0.902~1.352)0.987(0.815~1.194)0.982(0.807~1.195)0.773(0.626~0.954)
     高浓度组1.0000.914(0.767~1.089)0.921(0.780~1.087)1.001(0.841~1.191)0.848(0.708~1.016)
    OC0.345
     低浓度组1.0001.018(0.844~1.228)0.939(0.784~1.125)0.892(0.741~1.074)0.717(0.590~0.871)
     高浓度组1.0000.941(0.777~1.141)0.976(0.816~1.167)1.164(0.964~1.407)0.953(0.782~1.162)
    BC0.084
     低浓度组1.0000.997(0.823~1.208)0.898(0.748~1.079)0.888(0.735~1.071)0.684(0.562~0.832)
     高浓度组1.0000.958(0.796~1.152)0.970(0.816~1.154)1.096(0.913~1.316)0.959(0.791~1.164)
    SO42−0.093
     低浓度组1.0000.997(0.822~1.210)0.925(0.767~1.114)0.891(0.736~1.080)0.702(0.573~0.861)
     高浓度组1.0000.957(0.795~1.153)0.989(0.832~1.175)1.141(0.951~1.368)0.954(0.791~1.152)
    NO3-0.031
     低浓度组1.0001.049(0.866~1.269)0.964(0.804~1.157)0.908(0.753~1.095)0.691(0.567~0.841)
     高浓度组1.0000.936(0.777~1.127)0.963(0.808~1.147)1.124(0.936~1.351)0.976(0.805~1.184)
    NH4+0.012
     低浓度组1.0001.001(0.826~1.214)0.945(0.785~1.137)0.908(0.750~1.099)0.702(0.574~0.859)
     高浓度组1.0000.957(0.795~1.152)0.962(0.809~1.144)1.113(0.928~1.335)0.959(0.793~1.159)
    扬尘0.012
     低浓度组1.0001.043(0.863~1.262)0.999(0.832~1.199)1.002(0.831~1.207)0.752(0.618~0.916)
     高浓度组1.0000.990(0.821~1.195)0.974(0.818~1.160)1.086(0.902~1.309)0.950(0.782~1.154)
    [注]模型校正性别、年龄、职业、婚姻、吸烟、空气污染防护措施、体育锻炼、睡眠;P交互为交互作用P值。
    下载: 导出CSV
  • [1]

    BENDALL C L, MAYR H L, OPIE R S, et al. Central obesity and the Mediterranean diet: a systematic review of intervention trials[J]. Crit Rev Food Sci Nutr, 2018, 58(18): 3070-3084. doi: 10.1080/10408398.2017.1351917

    [2]

    TIAN Y, JIANG C, WANG M, et al. BMI, leisure-time physical activity, and physical fitness in adults in China: results from a series of national surveys, 2000-14[J]. Lancet Diabetes Endocrinol, 2016, 4(6): 487-497. doi: 10.1016/S2213-8587(16)00081-4

    [3]

    PHILLIPS C M, HARRINGTON J M, PERRY I J. Relationship between dietary quality, determined by DASH score, and cardiometabolic health biomarkers: a cross-sectional analysis in adults[J]. Clin Nutr, 2019, 38(4): 1620-1628. doi: 10.1016/j.clnu.2018.08.028

    [4]

    JIA L, LU H, WU J, et al. Association between diet quality and obesity indicators among the working-age adults in Inner Mongolia, Northern China: a cross-sectional study[J]. BMC Public Health, 2020, 20(1): 1165. doi: 10.1186/s12889-020-09281-5

    [5]

    RODRÍGUEZ-LÓPEZ C P, GONZÁLEZ-TORRES M C, AGUILAR-SALINAS C A, et al. DASH diet as a proposal for improvement in cellular immunity and its association with metabolic parameters in persons with overweight and obesity[J]. Nutrients, 2021, 13(10): 3540. doi: 10.3390/nu13103540

    [6]

    SUN X, LIU C, LIANG H, et al. Prenatal exposure to residential PM2.5 and its chemical constituents and weight in preschool children: a longitudinal study from Shanghai, China[J]. Environ Int, 2021, 154: 106580. doi: 10.1016/j.envint.2021.106580

    [7]

    CHEN Y, CHEN R, CHEN Y, et al. The prospective effects of long-term exposure to ambient PM2.5 and constituents on mortality in rural East China[J]. Chemosphere, 2021, 280: 130740. doi: 10.1016/j.chemosphere.2021.130740

    [8]

    LIU M, TANG W, ZHANG Y, et al. Urban-rural differences in the association between long-term exposure to ambient air pollution and obesity in China[J]. Environ Res, 2021, 201: 111597. doi: 10.1016/j.envres.2021.111597

    [9]

    ZHANG N, WANG L, ZHANG M, et al. Air quality and obesity at older ages in China: the role of duration, severity and pollutants[J]. PLoS One, 2019, 14(12): e0226279. doi: 10.1371/journal.pone.0226279

    [10]

    LIU X, TU R, QIAO D, et al. Association between long-term exposure to ambient air pollution and obesity in a Chinese rural population: the Henan rural cohort study[J]. Environ Pollut, 2020, 260: 114077. doi: 10.1016/j.envpol.2020.114077

    [11]

    WEI Y, ZHANG J J, LI Z, et al. Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: findings from a natural experiment in Beijing[J]. FASEB J, 2016, 30(6): 2115-2122. doi: 10.1096/fj.201500142

    [12]

    World Health Organization. 9 out of 10 people worldwide breathe polluted air, but more countries are taking action[EB/OL]. [2018-09-16]. https://www.who.int/news-room/detail/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action.

    [13]

    BARAK F, FALAHI E, KESHTELI A H, et al. Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet in relation to obesity among Iranian female nurses[J]. Public Health Nutr, 2015, 18(4): 705-712. doi: 10.1017/S1368980014000822

    [14]

    JOHNSTON B C, KANTERS S, BANDAYREL K, et al. Comparison of weight loss among named diet programs in overweight and obese adults: a meta-analysis[J]. JAMA, 2014, 312(9): 923-933. doi: 10.1001/jama.2014.10397

    [15]

    LI C, LIU Y, SHI G, et al. Cohort profile: regional ethnic cohort study in Northwest China[J]. Int J Epidemiol, 2022, 51(2): e18-e26. doi: 10.1093/ije/dyab212

    [16]

    LIESE A D, BORTSOV A, GÜNTHER A L B, et al. Association of DASH diet with cardiovascular risk factors in youth with diabetes mellitus: the SEARCH for Diabetes in Youth study[J]. Circulation, 2011, 123(13): 1410-1417. doi: 10.1161/CIRCULATIONAHA.110.955922

    [17]

    FUNG T T, CHIUVE S E, MCCULLOUGH M L, et al. Adherence to a DASH-Style diet and risk of coronary heart disease and stroke in women[J]. Arch Intern Med, 2008, 168(7): 713-720. doi: 10.1001/archinte.168.7.713

    [18]

    MENG X, LIU C, ZHANG L, et al. Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016[J]. Remote Sens Environ, 2021, 253: 112203. doi: 10.1016/j.rse.2020.112203

    [19]

    VAN DONKELAAR A, MARTIN R V, BRAUER M, et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors[J]. Environ Sci Technol, 2016, 50(7): 3762-3772. doi: 10.1021/acs.est.5b05833

    [20] 成人体重判定: WS/T 428—2013[S]. 北京: 中国标准出版社, 2013.

    Criteria of weight for adults: WS/T 428—2013[S]. Beijing: Standards Press of China, 2013.

    [21] 健康中国行动推进委员会. 健康中国行动(2019—2030年): 总体要求、重大行动及主要指标[J]. 中国循环杂志, 2019, 34(9): 846-858. doi: 10.3969/j.issn.1000-3614.2019.09.003

    Promoting the Health of China's Action Committee. Healthy China initiative (2019-2030): overall requirements, major actions and key indicators[J]. Chin Circ J, 2019, 34(9): 846-858. doi: 10.3969/j.issn.1000-3614.2019.09.003

    [22] 周蔚, 夏蒨, 李香亭, 等. 上海成年居民膳食模式与超重/肥胖和中心型肥胖的关系[J]. 环境与职业医学, 2020, 37(9): 846-852. doi: 10.13213/j.cnki.jeom.2020.20197

    ZHOU W, XIA Q, LI X T, et al. Dietary patterns and their associations with overweight/obesity and central obesity among adult residents in Shanghai[J]. J Environ Occup Med, 2020, 37(9): 846-852. doi: 10.13213/j.cnki.jeom.2020.20197

    [23]

    ZHANG L, WANG Z, WANG X, et al. Prevalence of abdominal obesity in China: results from a cross-sectional study of nearly half a million participants[J]. Obesity (Silver Spring), 2019, 27(11): 1898-1905. doi: 10.1002/oby.22620

    [24]

    SOLTANI S, SHIRANI F, CHITSAZI M J, et al. The effect of dietary approaches to stop hypertension (DASH) diet on weight and body composition in adults: a systematic review and meta-analysis of randomized controlled clinical trials[J]. Obes Rev, 2016, 17(5): 442-454. doi: 10.1111/obr.12391

    [25]

    CHUANG C C, MCINTOSH M K. Potential mechanisms by which polyphenol-rich grapes prevent obesity-mediated inflammation and metabolic diseases[J]. Annu Rev Nutr, 2011, 31: 155-176. doi: 10.1146/annurev-nutr-072610-145149

    [26]

    NANI A, MURTAZA B, SAYED KHAN A, et al. Antioxidant and anti-inflammatory potential of polyphenols contained in Mediterranean diet in obesity: molecular mechanisms[J]. Molecules, 2021, 26(4): 985. doi: 10.3390/molecules26040985

    [27]

    CASAS-AGUSTENCH P, LÓPEZ-URIARTE P, BULLÓ M, et al. Acute effects of three high-fat meals with different fat saturations on energy expenditure, substrate oxidation and satiety[J]. Clin Nutr, 2009, 28(1): 39-45. doi: 10.1016/j.clnu.2008.10.008

    [28]

    CAO S, GUO Q, XUE T, et al. Long-term exposure to ambient PM2.5 increase obesity risk in Chinese adults: a cross-sectional study based on a nationwide survey in China[J]. Sci Total Environ, 2021, 778: 145812. doi: 10.1016/j.scitotenv.2021.145812

    [29]

    ZHANG Z, DONG B, CHEN G, et al. Ambient air pollution and obesity in school-aged children and adolescents: a multicenter study in China[J]. Sci Total Environ, 2021, 771: 144583. doi: 10.1016/j.scitotenv.2020.144583

    [30]

    HAN B, XU J, ZHANG Y, et al. Associations of exposure to fine particulate matter mass and constituents with systemic inflammation: a cross-sectional study of urban older adults in China[J]. Environ Sci Technol, 2022, 56(11): 7244-7255. doi: 10.1021/acs.est.1c04488

    [31]

    LIANG S, ZHAO T, XU Q, et al. Evaluation of fine particulate matter on vascular endothelial function in vivo and in vitro[J]. Ecotoxicol Environ Saf, 2021, 222: 112485. doi: 10.1016/j.ecoenv.2021.112485

    [32] 郭伟丽. PM2.5诱发的大鼠心血管毒性效应及鱼油和维生素E的干预作用[D]. 新乡: 新乡医学院, 2017.

    GUO W L. PM2.5-induced cardiovascular toxic effects and intervention of fish oil and vitamin E[D]. Xinxiang: Xinxiang Medical University, 2017.

    [33]

    DENG T, LYON C J, BERGIN S, et al. Obesity, inflammation, and cancer[J]. Annu Rev Pathol Mech Dis, 2016, 11: 421-449. doi: 10.1146/annurev-pathol-012615-044359

    [34]

    LIN H, GUO Y, DI Q, et al. Ambient PM2.5 and stroke: effect modifiers and population attributable risk in six low- and middle-income countries[J]. Stroke, 2017, 48(5): 1191-1197. doi: 10.1161/STROKEAHA.116.015739

    [35]

    LIN H, GUO Y, DI Q, et al. Consumption of fruit and vegetables might mitigate the adverse effects of ambient PM2.5 on lung function among adults[J]. Environ Res, 2018, 160: 77-82. doi: 10.1016/j.envres.2017.09.007

    [36]

    SHAN M, YANG X, EZZATI M, et al. A feasibility study of the association of exposure to biomass smoke with vascular function, inflammation, and cellular aging[J]. Environ Res, 2014, 135: 165-172. doi: 10.1016/j.envres.2014.09.006

    [37]

    HOU J, LIU X, TU R, et al. Long-term exposure to ambient air pollution attenuated the association of physical activity with metabolic syndrome in rural Chinese adults: a cross-sectional study[J]. Environ Int, 2020, 136: 105459. doi: 10.1016/j.envint.2020.105459

    [38] 孙长颢. 关于营养学的几点前瞻性思考[J]. 中华预防医学杂志, 2018, 52(2): 121-123. doi: 10.3760/cma.j.issn.0253-9624.2018.02.001

    SUN C H. Prospective thoughts of nutrition landscape[J]. Chin J Prev Med, 2018, 52(2): 121-123. doi: 10.3760/cma.j.issn.0253-9624.2018.02.001

  • 期刊类型引用(2)

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出版历程
  • 收稿日期:  2021-10-16
  • 录用日期:  2022-09-24
  • 网络出版日期:  2023-02-14
  • 刊出日期:  2023-02-14

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