• 中国精品科技期刊
  • 《中文核心期刊要目总览》收录期刊
  • RCCSE 中国核心期刊(5/114,A+)
  • Scopus收录期刊
  • 美国《化学文摘》(CA)收录期刊
  • WHO 西太平洋地区医学索引(WPRIM)收录期刊
  • 《中国科学引文数据库(CSCD)》核心库期刊 (C)
  • 中国科技核心期刊
  • 中国科技论文统计源期刊
  • 《日本科学技术振兴机构数据库(中国)》(JSTChina)收录期刊
  • 美国《乌利希期刊指南》(UIrichsweb)收录期刊
  • 中华预防医学会系列杂志优秀期刊(2019年)

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

银川市社区老年人尿中铁和砷浓度与血脂异常的关联性

何沛 王瑞 段思宇 王玉华 白梅丽 孙健 杨惠芳

何沛, 王瑞, 段思宇, 王玉华, 白梅丽, 孙健, 杨惠芳. 银川市社区老年人尿中铁和砷浓度与血脂异常的关联性[J]. 中华疾病控制杂志, 2023, 27(8): 877-882. doi: 10.16462/j.cnki.zhjbkz.2023.08.002
引用本文: 何沛, 王瑞, 段思宇, 王玉华, 白梅丽, 孙健, 杨惠芳. 银川市社区老年人尿中铁和砷浓度与血脂异常的关联性[J]. 中华疾病控制杂志, 2023, 27(8): 877-882. doi: 10.16462/j.cnki.zhjbkz.2023.08.002
HE Pei, WANG Rui, DUAN Siyu, WANG Yuhua, BAI Meili, SUN Jian, YANG Huifang. Association of urinary iron and arsenic levels with dyslipidemia in community-dwelling elderly in Yinchuan[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 877-882. doi: 10.16462/j.cnki.zhjbkz.2023.08.002
Citation: HE Pei, WANG Rui, DUAN Siyu, WANG Yuhua, BAI Meili, SUN Jian, YANG Huifang. Association of urinary iron and arsenic levels with dyslipidemia in community-dwelling elderly in Yinchuan[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(8): 877-882. doi: 10.16462/j.cnki.zhjbkz.2023.08.002

银川市社区老年人尿中铁和砷浓度与血脂异常的关联性

doi: 10.16462/j.cnki.zhjbkz.2023.08.002
基金项目: 

宁夏自然科学基金 2022AAC05028

宁夏医科大学特殊人才启动项目 XT2019013

详细信息
    通讯作者:

    杨惠芳,E-mail: joyceyhf@163.com

  • 中图分类号: R122.7

Association of urinary iron and arsenic levels with dyslipidemia in community-dwelling elderly in Yinchuan

Funds: 

Natural Science Foundation of Ningxia 2022AAC05028

Special Talents Start-up Project of Ningxia Medical University XT2019013

More Information
  • 摘要:   目的  探讨银川市社区老年人尿中铁(Iron,Fe)和砷(Arsenic,As)浓度与血脂异常之间的关联性,为血脂异常防治策略的制定提供科学的环境流行病学证据。  方法  选取银川市老年人慢性病队列基线调查人群为研究对象,通过问卷调查收集研究对象一般人口学信息,通过健康体检对研究对象血脂、血压和肝功能等指标进行测定,同时收集调查对象晨尿10 mL。使用电感耦合等离子体质谱仪测定研究对象尿中Fe和As的浓度,并用尿肌酐校正,通过条件logistic回归分析模型分析尿中Fe和As浓度与血脂异常患病率之间的关联。  结果  血脂异常组老年人尿中Fe浓度(115.97 μg/g肌酐)低于健康对照组(139.11 μg/g肌酐),差异有统计学意义(Z=3.405, P<0.001);血脂异常组老年人尿中As浓度(77.25 μg/g肌酐)高于健康对照组(67.45 μg/g肌酐),差异有统计学意义(Z=-3.269, P=0.001)。多因素logistic回归分析模型结果显示,校正协变量后,尿中Fe浓度与老年人血脂异常存在负相关(OR=0.68,95% CI:0.57~0.82,P < 0.001),尿中As浓度与老年人血脂异常存在正相关(OR=1.32,95% CI: 1.09~1.59,P=0.001),且呈现剂量-反应关系(均P趋势≤0.001)。按性别分层分析发现,在男性人群中,与Q1组相比,尿中Fe位于Q2、Q3和Q4组的血脂异常OR值(95% CI)分别为0.68(0.52~0.88)、0.70(0.54~0.92)和0.59(0.45~0.79),尿中As位于Q2、Q3和Q4组的血脂异常OR值(95% CI)分别为1.08(0.84~1.40)、1.31(1.01~1.71)和1.85(1.38~2.47);在女性人群中,与Q1组相比,仅尿中Fe位于Q4组的血脂异常OR值(95% CI)为0.76(0.59~0.98)。  结论  银川市社区老年人尿中较高浓度的Fe可能会降低血脂异常患病风险;尿中较高浓度的As可能会增加与血脂异常患病风险,这种关联在男性人群中更明显。
  • 表  1  研究对象基本情况

    Table  1.   Basic information of the research participants

    组别  Group 总人群[人数(占比/%)]
    Total [Number of people (proportion/%)] (n=3 785)
    健康对照组[人数(占比/%)]
    Healthy control [Number of people (proportion/%)] (n=1 928)
    血脂异常组[人数(占比/%)]
    Dyslipidemia [Number of people (proportion/%)] (n=1 857)
    χ2/t/Z值  value P
    value
    性别  Sex 1.714 0.190
      男  Male 1 769(46.74) 881(45.70) 888(47.82)
      女  Female 2 016(53.26) 1 047(54.30) 969(52.18)
    年龄组/岁  Age group/years -0.262 0.793
      60~<70 1 886(49.83) 966(50.10) 920(49.54)
      70~<80 1 616(42.69) 814(42.22) 802(43.19)
      ≥80 283(7.48) 148(7.68) 135(7.27)
    BMI/(kg·m-2) -9.390 <0.001
      < 18.5 83(2.19) 59(3.06) 24(1.29)
     18.5~<24.0 1 404(37.09) 826(42.84) 578(31.13)
     24.0~<28.0 1 621(42.83) 762(39.52) 859(46.26)
     ≥28.0 677(17.89) 281(14.58) 396(21.32)
    血压/mmHg, (x±s)  Blood pressure/mmHg, (x±s)
      DBP 76.98±9.51 75.91±8.71 78.10±10.16 -7.147 <0.001
      SBP 129.76±22.74 127.66±14.01 131.95±29.00 -5.826 <0.001
    锻炼频率  Exercise frequency 5.287 0.152
      每天 Everyday 2 847(75.22) 1 432(74.27) 1 415(76.20)
      每周1次以上  More than once a week 212(5.60) 101(5.24) 111(5.98)
      偶尔  Sometime 200(5.28) 105(5.45) 95(5.12)
      不锻炼  Does not exercise 526(13.90) 290(15.04) 236(12.70)
    吸烟情况  Smoking status 8.813 0.012
      从不  Never 3 092(81.69) 1 541(79.92) 1 551(83.52)
      已戒烟  Quit smoking 304(8.03) 175(9.08) 129(6.95)
      吸烟  Smoking 389(10.28) 212(11.00) 177(9.53)
    饮酒情况  Drinking 25.065 <0.001
      从不  Never 3 176(83.91) 1 562(81.02) 1 614(86.91)
      偶尔  Sometime 514(13.58) 312(16.18) 202(10.88)
      经常  Frequently 53(1.40) 29(1.50) 24(1.29)
      每天  Everday 42(1.11) 25(1.30) 17(0.92)
    饮食习惯  Dietary habit 1.004 0.605
      荤素均衡  Meat and vegetable balance 3 509(92.71) 1 786(92.63) 1 723(92.78)
      素食为主  Vegetarianism 262(6.92) 133(6.90) 129(6.95)
      荤食为主  Meat-based 14(0.37) 9(0.47) 5(0.27)
    重金属浓度/(μg·g-1肌酐), [M(IQR)]
    Heavy metal concentration /(μg·g-1 Creatinine), [M(IQR)]
      Fe 129.11(28.04, 346.37) 139.11(34.91, 367.80) 115.91(18.08, 310.90) 3.405 <0.001
      As 71.67(41.39, 138.52) 67.45(40.26, 129.71) 77.25(42.56, 147.68) -3.269 0.001
    注:BMI, 体质指数; DBP, 舒张压;SBP, 收缩压。
    Note: BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure.
    下载: 导出CSV

    表  2  老年人尿中重金属浓度与血脂异常关系的多因素分析

    Table  2.   Multivariate analysis of the relationship between the concentration of heavy metals in urine and dyslipidemia in the elderly

    模型  Model 血脂异常[OR值(95% CI)]  Dyslipidemia[OR value (95% CI)] P趋势
    Ptrend value
    线性模型
    Linear Model
    Q1组
    Group
    Q2组
    Group
    Q3组
    Group
    Q4组
    Group
    Fe/(μg·g-1肌酐)  Fe/(μg·g-1 Creatinine) <28.04 28.04~<129.11 129.11~<346.37 ≥346.37
      模型1  Model 1 1.00 0.77(0.64~0.92) 0.79(0.66~0.95) 0.68(0.57~0.82) <0.001 0.90(0.85~0.95)
      模型2  Model 2 1.00 0.76(0.63~0.92) 0.78(0.65~0.94) 0.68(0.57~0.82) <0.001 0.89(0.84~0.95)
      模型3  Model 3 1.00 0.77(0.64~0.93) 0.79(0.65~0.95) 0.68(0.57~0.82) <0.001 0.89(0.84~0.95)
    As/(μg·g-1肌酐)  As/(μg·g-1 Creatinine) <41.39 41.39~<71.67 71.67~<138.52 ≥138.52
      模型1  Model 1 1.00 0.97(0.81~1.17) 1.18(0.99~1.42) 1.32(1.10~1.58) <0.001 1.11(1.05~1.17)
      模型2  Model 2 1.00 0.99(0.83~1.20) 1.21(1.01~1.46) 1.34(1.11~1.62) <0.001 1.11(1.05~1.18)
      模型3  Model 3 1.00 0.99(0.82~1.19) 1.21(1.00~1.59) 1.32(1.09~1.59) 0.001 1.11(1.04~1.18)
    注:1. Fe, 铁; As, 砷。
    2. 模型1, 原始模型,未调整; 模型2, 调整性别、年龄(连续变量)、血压(连续变量)、BMI(连续变量); 模型3, 在模型2的基础上,调整吸烟、饮酒、锻炼频率、饮食习惯。
    ①代表将每个分位的中位数进行对数转化后作为连续变量纳入多因素logistic回归分析模型。②代表将通过四分位间距转换后的金属浓度纳入回归模型,表示尿金属浓度每增加一个四分位间距对应增血脂异常患病风险的OR值(95% CI)。
    Note: 1. Fe, Iron; As, Arsenic.
    2. Model 1, Original model, not adjusted; Model 2, Adjusting for gender, age (continuous variable), blood pressure (continuous variable), BMI (continuous variable); Model 3, On the basis of Model 2, smoking, drinking, exercise frequency, and eating habits were adjusted.
    ① means that the median of each quantile is converted logarithmically and incorporated into the multiple logistic regression analysis model as a continuous variable. ② represents the inclusion of metal concentration after interquartile interval conversion into the regression model, indicating that each increase of urinary metal concentration by interquartile interval corresponds to the OR value (95% CI) of hyperlipidemia.
    下载: 导出CSV

    表  3  尿中金属浓度对血脂异常影响关联按性别分层logistic回归分析模型分析

    Table  3.   Sex-stratified logistic regression analysis of the effect of urinary metal concentrations on dyslipidemia

    变量  Variable Fe As
    男  Male 女  Female 男  Male 女  Fmeale
    OR值(95% CI)
    OR value (95% CI)
    P趋势
    Ptrend value
    OR值(95% CI)
    OR value (95% CI)
    P趋势
    Ptrend value
    OR值(95% CI)
    OR value (95% CI)
    P趋势
    Ptrend value
    OR值(95% CI)
    OR value (95% CI)
    P趋势
    Ptrend value
    未调整模型  Unadjusted model
      Q1 1.00 1.00 1.00 1.00
      Q2 0.68(0.53~0.88) < 0.003 0.87(0.67~1.13) 0.291 1.09(0.85~1.40) 0.490 0.85(0.65~1.11) 0.239
      Q3 0.74(0.57~0.96) 0.022 0.86(0.67~1.11) 0.242 1.30(1.01~1.68) 0.041 1.06(0.82~1.38) 0.645
      Q4 0.61(0.46~0.80) < 0.001 0.77(0.60~0.98) 0.033 1.87(1.42~2.47) < 0.001 1.05(0.82~1.35) 0.678
    调整模型a  Adjust model a
      Q1 1.00 1.00 1.00 1.00
      Q2 0.66(0.51~0.86) < 0.002 0.87(0.67~1.14) 0.316 1.09(0.85~1.41) 0.500 0.88(0.67~1.15) 0.337
      Q3 0.71(0.54~0.93) 0.012 0.87(0.67~1.12) 0.276 1.34(1.03~1.74) 0.029 1.08(0.83~1.40) 0.584
      Q4 0.59(0.45~0.79) < 0.001 0.75(0.59~0.97) 0.026 1.93(1.45~2.58) < 0.001 1.03(0.80~1.33) 0.816
    调整模型b  Adjust model b
      Q1 1.00 1.00 1.00 1.00
      Q2 0.68(0.52~0.88) 0.003 0.88(0.68~1.50) 0.345 1.08(0.84~1.40) 0.546 0.89(0.68~1.16) 0.382
      Q3 0.70(0.54~0.92) 0.011 0.87(0.68~1.13) 0.301 1.31(1.01~1.71) 0.042 1.10(0.84~1.43) 0.484
      Q4 0.59(0.45~0.79) < 0.001 0.76(0.59~0.98) 0.031 1.85(1.38~2.47) < 0.001 1.03(0.80~1.34) 0.792
    注:1. Fe, 铁; As, 砷。
    2. 调整模型a调整性别、年龄(连续变量)、血压(连续变量)、BMI(连续变量); 调整模型b在模型a的基础上,调整吸烟、饮酒、锻炼频率、饮食习惯。
    Note: 1. Fe, Iron; As, Arsenic.
    2. Adjustment Model a adjusts for gender, age (continuous variable), blood pressure (continuous variable), BMI (continuous variable); On the basis of Model a, smoking, drinking, exercise frequency and eating habits were adjusted for Model b.
    下载: 导出CSV
  • [1] 诸骏仁, 高润霖, 赵水平, 等. 中国成人血脂异常防治指南(2016年修订版)[J]. 中华健康管理学杂志, 2017, 11(1): 7-28. DOI: 10.3760/j.issn.1674-0815.2017.01.003.

    Zhu JR, Gao RL, Zhao SP, et al. Guidelines for the Prevention and Treatment of Dy-slipidemia in Adults in China (Revised Edition 2016)[J]. Chin J Health Manage, 2017, 11(1): 7-28. DOI: 10.3760/j.issn.1674-0815.2017.01.003.
    [2] 李立明, 饶克勤, 孔灵芝, 等. 中国居民2002年营养与健康状况调查[J]. 中华流行病学杂志, 2005(7): 478-484. DOI: 10.3760/j.issn:0254-6450.2005.07.004.

    Li LM, Rao KQ, Kong LZ, et al. A description on the Chinese national nutrition and health survey in 2002[J]. Chin J Epidemiol, 2005, 26(7): 478-484. DOI: 10.3760/j.issn:0254-6450.2005.07.004.
    [3] Moran A, Gu D, Zhao D, et al. Future cardiovascular disease in china: markov model and risk factor scenario projections from the coronary heart disease policy model-china[J]. Circ Cardiovasc Qual Outcomes, 2010, 3(3): 243-252. DOI: 10.1161/CIRCOUTCOMES.109.910711.
    [4] Kah M, Levy L, Brown C. Potential for effects of land contamination on human health. 1. The case of cadmium[J]. J Toxicol Environ Health B Crit Rev, 2012, 15(5): 348-363. DOI: 10.1080/10937404.2012.705107.
    [5] Briffa J, Sinagra E, Blundell R. Heavy metal pollution in the environment and their toxicological effects on humans[J]. Heliyon, 2020, 6(9): e04691. DOI: 10.1016/j.heliyon.2020.e04691.
    [6] González-Domínguez á, Visiedo-García FM, Domínguez-Riscart J, et al. Iron Metabolism in Obesity and Metabolic Syndrome[J]. Int J Mol Sci, 2020, 21(15): 5529. DOI: 10.3390/ijms21155529.
    [7] Zhao HQ, Wu H, Meng R, et al. Distribution of serum transferrin, and its associations with metabolic disorders among Chinese: a nation-wide, health and nutrition survey[J]. Mol Nutr Food Res, 2015, 59: 1535-1540. DOI: 10.1002/mnfr.201500038.
    [8] Zhu Y, He B, Xiao Y, et al. Iron metabolism and its association with dyslipidemia risk in children and adolescents: a cross-sectional study[J]. Lipids Health Dis, 2019, 18(1): 50. DOI: 10.1186/s12944-019-0985-8.
    [9] Shi Y, Zou Y, Shen Z, et al. Trace Elements, PPARs, and Metabolic Syndrome[J]. Int J Mol Sci, 2020, 21(7): 2612. DOI: 10.3390/ijms21072612.
    [10] Straif K, Benbrahim-Tallaa L, Baan R, et al. A review of human carcinogens-Part C: metals, arsenic, dusts, and fibres[J]. Lancet Oncol, 2009, 10(5): 453-454. DOI: 10.1016/s1470-2045(09)70134-210.
    [11] Jomova K, Jenisova Z, Feszterova M, et al. Arsenic: toxicity, oxidative stress and human disease[J]. J Appl Toxicol, 2011, 31: 95-107. DOI: 10.1002/jat.1649.
    [12] 李鑫, 陶小娥, 徐畅, 等. 中国60岁及以上老年人群血脂异常流行现状的Meta分析[J]. 职业卫生与病伤, 2021, 36(6): 343-350. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYWB202106003.htm

    Li X, Tao XE, Xu C, et al. A meta-analysis of the prevalence of dyslipidemia in elderly people aged 60 and above in China[J]. Occupational Health and Injury, 2021, 36(6): 343-350. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYWB202106003.htm
    [13] Gade M, Comfort N, Re DB. Sex-specific neurotoxic effects of heavy metal pollutants: Epidemiological, experimental evidence and candidate mechanisms[J]. Environ Res, 2021, 201: 111558. DOI: 10.1016/J.ENVRES.2021.111558.
    [14] Lindberg AL, Kumar R, Goessler W, et al. Metabolism of low-dose inorganic arsenic in a central European population: influence of sex and genetic polymorphisms[J]. Environ Health Perspect, 2007, 115(7): 1081-1086. DOI: 10.1289/ehp.10026.
    [15] Galy B, Ferring-Appel D, Sauer SW, et al. Iron regulatory proteins secure mitochondrial iron sufficiency and function[J]. Cell Metab, 2010, 12: 194-201. DOI: 10.1016/j.cmet.2010.06.007.
    [16] Habib A, Finn AV. The role of iron metabolism as a mediator of macrophage inflammation and lipid handling in atherosclerosis[J]. Front Pharmacol, 2014, 5: 195. DOI: 10.3389/fphar.2014.00195.
    [17] Auer JW, Berent R, Weber T, et al. Iron metabolism and development of atherosclerosis[J]. Circulation, 2002, 106(2): e7. DOI: 10.1161/01.cir.0000019984.69119.4f.
    [18] Smith AH, Hopenhayn-Rich C, Bates MN, et al. Cancer risks from arsenic in drinking water[J]. Environ Health Perspect, 1992, 97: 259-267. DOI: 10.1289/ehp.9297259.
    [19] Farzan SF, Eunus HM, Haque SE, et al. Arsenic exposure from drinking water and endothelial dysfunction in Bangladeshi adolescents[J]. Environ Res, 2022, 208: 112697. DOI: 10.1016/J.ENVRES.2022.112697.
    [20] 冯伟. 社区人群金属暴露与早期心血管损伤的关联性研究[D]. 武汉: 华中科技大学, 2014.

    Feng W. Correlation between metal exposure and early cardiovascular injury in community populations[D]. Wuhan: Huazhong University of Science and Technology, 2014.
  • 加载中
表(3)
计量
  • 文章访问数:  189
  • HTML全文浏览量:  146
  • PDF下载量:  48
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-05-15
  • 修回日期:  2022-07-28
  • 网络出版日期:  2023-09-02
  • 刊出日期:  2023-08-10

目录

    /

    返回文章
    返回