Effects of short-term exposure to PM10 on biomarkers of diabetes
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摘要: 为探讨PM10对糖尿病发病相关生物标志物的效应,以“金昌队列”为平台,采用随机抽样方法在基线人群中选择2型糖尿病、糖尿病前期和血糖正常组共420人,用近邻模型完成个体PM10暴露评估。运用酶联免疫吸附法对炎症反应指标(IL-6、VCAM-1)、氧化损伤指标(8-iso-PGF2α)、胰岛素功能指标(INS)进行检测,运用多重线性回归模型从以上3方面评价PM10的效应。结果显示:糖尿病前期人群中,滞后6 d时PM10浓度每升高10 μg/m3,IL-6升高0.45%(95%CI:0.19%—0.88%),当天的PM10与VCAM-1关联最明显(增幅:1.16%(95%CI:0.43%—2.28%));糖尿病人群中,滞后6 d时PM10与IL-6的关联最显著(增幅:1.52%,(95%CI:0.51%—2.53%)),滞后3 d时8-iso-PGF2α升高2.01%(95%CI:0.29%—3.73%);累计滞后0—7 d时PM10与HOMA-β关联最明显(降幅:4.63%(95%CI:−8.00%—−1.13%))。文中结果表明大气PM10短期暴露可导致人群出现不同程度的炎症反应、氧化损伤及胰岛β细胞功能障碍。Abstract: This study explores the effects of short-term exposure to PM10 on related biomarkers of diabetes. Based on the platform of "Jinchang Cohort", a total of 420 patients with type 2 diabetes, pre-diabetes and normal blood glucose are randomly selected. The nearest neighbor model is used to estimate individual exposure levels. IL-6, VCAM-1, 8-iso-PGF2α and INS are detected by ELISA. A multiple linear regression model is conducted to evaluate the effects of PM10 on the biomarkers. For every 10 μg/m3 increase in PM10 concentration, it is found that IL-6 increases by 0.45% (95%CI: 0.19%—0.88%) at lag 6 d, and PM10 was most significantly associated with VCAM-1 at lag 0 d (increase:1.16%, 95%CI: (0.43%—2.28%)) in the prediabetic group. PM10 is most significantly associated with IL-6 (increase:1.52%, (95%CI: 0.51%—2.53%)) at lag 6 d, while 8-iso-PGF2α increases by 2.01% (95%CI: 0.29%—3.73%) at lag 3 d, and the relationship between PM10 and HOMA-β is most significant at lag 0—7 d (decrease:4.63%, (95%CI: −8.00%—−1.13%)) for type 2 diabetes patients. Short-term exposure to PM10 can lead to inflammation, oxidative damage and islet β cell dysfunction.
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Key words:
- PM10 /
- type 2 diabetes /
- Inflammation /
- Oxidative damage /
- Islet β cell function
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表 1 不同糖代谢水平人群的基本特征
Table 1. General characteristics of population with different blood glucose levels
变量 血糖正常(n(%)) 糖尿病前期(n(%)) 糖尿病(n(%)) P 性别 <0.001 男性 90(64.29%) 120(85.71%) 116(82.86%) 女性 50(35.71%) 20(14.29%) 24(17.14%) 年龄(岁) <0.001 <60 124(88.57%) 118(84.29%) 95(67.86%) ≥60 16(11.43%) 22(15.71%) 45(32.14%) BMI(kg/m2) <0.001 <24.0 85(60.72%) 67(47.86%) 48(34.29%) 24.0−27.9 47(33.57%) 52(37.14%) 65(46.43%) ≥28.0 8(5.71%) 21(15.00%) 27(19.28%) 家庭人均月收入(元) 0.974 <2000 79(56.43%) 80(57.15%) 79(56.43%) 2000−4999 60(42.86%) 59(42.14%) 59(42.14%) ≥5000 1(0.71%) 1(0.71%) 2(1.43%) 文化水平 0.015 初中及以下 50(35.71%) 52(37.14%) 73(52.14%) 高中/中专 49(35.00%) 57(40.71%) 36(25.71%) 本科及以上 41(29.29%) 31(22.15%) 31(22.15%) 吸烟 0.392 否 68(48.57%) 60(42.86%) 57(40.71%) 是 72(51.43%) 80(57.14%) 83(59.29%) 饮酒 0.004 否 110(78.57%) 85(60.71%) 92(65.71%) 是 30(21.43%) 55(39.29%) 48(34.29%) 高血压 <0.001 否 107(76.43%) 75(53.57%) 68(48.57%) 是 33(23.57%) 65(46.43%) 72(51.43%) 糖尿病家族史 0.028 否 118(84.29%) 120(85.71%) 104(74.29%) 是 22(15.71%) 20(14.29%) 36(25.71%) 表 2 不同糖代谢状态人群的生物标志物水平
Table 2. Biomarker levels of population with different blood glucose states
生物标志物 血糖正常($ \bar{x} $±s) 糖尿病前期($ \bar{x} $±s) 糖尿病($ \bar{x} $±s) P IL-6(ng/L) 43.57±15.55 52.18±13.88 43.18±13.42 <0.001 VCAM-1(μg/L) 821.21±527.99 1058.44±410.16 720.89±403.09 <0.001 8-iso-PGF2α(pg/L) 4667.89±1496.67 4608.17±1872.76 3131.12±1965.84 <0.001 INS(mIU/L) 32.53±14.56 40.64±12.07 25.79±13.27 <0.001 HOMA-IR 7.08±3.20 10.76±3.34 10.43±6.67 <0.001 表 3 2009—2013年金昌市主要空气污染物与气象要素分布特征
Table 3. Distribution characteristics of air pollutants and meteorological factors in Jinchang city from 2009 to 2013
变 量 $ \bar{x} $ Min 25%分位数 50%分位数 75%分位数 Max IQR PM10(μg/m3) 92.29 17.00 59.00 76.00 100.00 1102.00 41.00 SO2(μg/m3) 65.55 3.00 40.00 57.00 81.00 290.00 41.00 NO2(μg/m3) 24.41 4.00 18.00 24.00 29.00 70.00 11.00 平均气温(℃) 9.54 −19.00 −0.70 10.95 19.70 32.00 20.40 相对湿度(%) 40.87 7.00 27.00 38.00 52.00 98.00 25.00 注:IQR:四分位数间距。 表 4 PM10浓度每升高10 μg/m3与生物标志物变化的关系
Table 4. Association between per 10 μg/m3 increment in PM10 concentration and biomarkers
滞后期 IL-6 [%(95%CI)] VCAM-1 [%(95%CI)] 血糖正常 糖尿病前期 糖尿病 血糖正常 糖尿病前期 糖尿病 Lag0 0.46(−0.51—1.43) 0.72(−0.31—1.48) 0.02(−0.97—1.01) 0.23(−1.38—1.84) 1.16(0.43—2.28)* 1.03(−2.27—4.32) Lag1 0.06(−0.54—0.65) 0.03(−0.80—0.86) −0.71(−1.88—0.46) −0.02(0.85—0.96) 1.05(−0.14—2.25) 1.86(−2.15—5.88) Lag2 −0.58(−1.67—0.51) −0.03(−0.67—0.61) −0.16(−1.58—1.26) −0.16(−1.97—1.65) 0.37(−0.55—1.30) 2.14(−2.38—6.67) Lag3 −0.11(−1.33—1.12) 0.34(−0.36—1.05) 1.68(−0.46—3.82) 1.15(−0.82—3.13) 0.81(−0.19—1.82) 3.45(−3.55—10.46) Lag4 0.08(−0.45—0.61) 0.09(−0.33—0.52) −0.88(−2.43—0.66) 0.47(−0.42—1.36) 0.25(−0.35—0.84) 3.38(−1.65—8.40) Lag5 0.20(−0.92—1.32) 0.27(−0.07—0.62) 1.35(0.31—2.38)* 0.55(−1.32—2.43) 0.54(0.05—1.03)* 0.14(−3.13—3.41) Lag6 0.36(−0.32—1.04) 0.45(0.19—0.88)* 1.52(0.51—2.53)* −0.26(−1.40—0.88) 0.55(−0.09—1.18) 0.82(2.64—4.28) Lag7 −0.14(−0.83—0.54) −0.19(−0.67—0.30) −0.26(−1.15—0.64) 0.61(−0.49—1.71) −0.01(−0.75—0.73) 2.26(−0.70—5.21) Lag07 −0.49(−2.35—1.41) 0.63(−0.29—1.55) −2.53(−5.14—0.16) 0.21(−2.93—3.44) 1.32(−0.01—2.65) 6.28(−2.66—16.04) -
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