Association of urinary iron and arsenic levels with dyslipidemia in community-dwelling elderly in Yinchuan
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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。 Abstract:Objective To explore the relationship between urinary iron and arsenic content and dyslipidemia in community-dwelling elderly people in Yinchuan City, and to provide scientific environmental epidemiological evidence for the formulation of dyslipidemia prevention strategies. Methods The baseline population of the Yinchuan Chronic Disease Cohort for the Elderly was selected as the study population. The general demographic information was collected through questionnaires, and blood lipids, blood pressure and liver function were measured through health checkups. We also collected 10 mL of morning urine at the baseline survey. The concentration of iron and arsenic in the urine of the study subjects was determined using an inductively coupled plasma- mass spectrometry and the anhydride was corrected with urine muscles. The association between urinary iron and arsenic levels and the prevalence of dyslipidemia was analyzed by conditional logistic regression. Results Urinary iron concentration in elderly people in the dyslipidemia group (115.97 μg/g creatinine) was lower than that in the healthy control group (139.11 μg/g creatinine), and the difference was statistically significant (Z=3.405, P < 0.001); urinary arsenic concentration in elderly people in the dyslipidemia group (77.25 μg/g creatinine) was higher than that in the healthy control group (67.45 μg/g creatinine), and the difference was statistically significant (Z=-3.269, P=0.001). Multiple logistic regression analysis showed that after adjusting for covariates, urinary Fe (OR=0.68, 95% CI: 0.57-0.82, P < 0.001) concentration was negatively associated with dyslipidemia in the elderly, and urinary As (OR=1.32, 95% CI: 1.09-1.59, P=0.001) concentration was positively associated with dyslipidemia in the elderly and showed a dose-response relationship (all Ptrend≤0.001). Stratified analysis by gender found that in the male population, compared with the Q1 group, the ORs and their 95% CIs for dyslipidemia were 0.68 (0.52-0.88), 0.70 (0.54-0.92), and 0.59 (0.45-0.79) for urinary iron in the Q2, Q3, and Q4 groups, respectively, and urinary arsenic in the Q2, Q3, and Q4 groups ORs for dyslipidemia and their 95% CIs were 1.08 (0.84-1.40), 1.31 (1.01-1.71), and 1.85 (1.38-2.47), respectively. For the female population, the ORs for dyslipidemia and their 95% CIs were 0.76 (0.59-0.98) for the group with urinary iron at the Q4 group only compared with the Q1 group. Conclusions Higher urinary iron concentrations in community-dwelling older adults may reduce the risk of dyslipidemia in Yinchuan; higher urinary arsenic concentrations may increase the risk of dyslipidemia, and this association is more pronounced in the male population. -
Key words:
- Heavy metals /
- Iron /
- Arsenic /
- Aged /
- Dyslipidemia
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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值
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.
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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趋势值①
Ptrend value ①线性模型②
Linear Model ②Q1组
GroupQ2组
GroupQ3组
GroupQ4组
GroupFe/(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.
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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趋势值
Ptrend valueOR值(95% CI)
OR value (95% CI)P趋势值
Ptrend valueOR值(95% CI)
OR value (95% CI)P趋势值
Ptrend valueOR值(95% CI)
OR value (95% CI)P趋势值
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.
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