二氧化氮短期暴露对上海市老年人血管内皮功能的影响

doi:10.3969/j.issn.1674-8115.2025.12.007

摘要/Abstract

摘要：

目的·利用血流介导的血管舒张功能（flow-mediated dilation，FMD）检测技术，探究二氧化氮（nitrogen dioxide，NO₂）暴露时间及浓度对老年人血管内皮功能障碍（vascular endothelial dysfunction，VED）的影响。方法·采用横断面研究，选取2020—2022年间于上海交通大学医学院附属同仁医院接受FMD检测的老年受试者。所有受试者均完成详细问卷调查和临床检查，包括人口学特征、生活方式及疾病史等信息采集。利用中国空气污染追踪（Tracking Air Pollution in China，TAP）平台，将受试者居住地址与污染数据网格匹配，获取个体化的NO₂暴露水平，采用广义线性模型结合分布式滞后非线性模型（distributed lag non-linear model，DLNM）分析NO₂对FMD和VED的影响，同时校正气象因素、个体特征及健康状况等混杂因素。结果·通过严格的纳入排除标准，最终纳入812名受试者。NO₂暴露在滞后1、2、5日与FMD显著下降相关（P=0.003，P=0.034，P=0.022），VED风险在滞后5、6日显著增加。累积效应分析表明，NO₂浓度在35~85 μg/m³范围内与FMD下降显著相关（P<0.05）。亚组分析提示，无血脂异常及无冠状动脉疾病（coronary artery disease，CAD）病史的受试者，其内皮功能对短期NO₂暴露更为敏感。结论·该研究首次揭示了NO₂短期暴露的时间和浓度对老年人血管内皮功能产生的负面影响。NO₂暴露后1~6 d均与血管内皮功能受损相关，其中第5日的影响最为显著。尤为值得注意的是，无高脂血症和CAD病史的老年人对NO₂短期暴露更为敏感。这些发现不仅证实了NO₂在血管内皮损伤中的关键作用，为阐明空气污染物通过影响血管功能导致心血管疾病的机制提供了重要证据，同时也为制定针对老年人群的大气污染防护策略提供了参考。

关键词: 二氧化氮, 内皮功能, 血流介导的血管舒张功能, 血管内皮功能障碍

Abstract:

Objective ·To investigate the effects of nitrogen dioxide (NO₂) exposure timing and concentration on vascular endothelial dysfunction (VED) in elderly individuals using flow-mediated dilation (FMD) technology. Methods ·A cross-sectional study was conducted among elderly participants who underwent FMD testing at Tongren Hospital, Shanghai Jiao Tong University School of Medicine, between 2020 and 2022. All participants completed detailed questionnaires and clinical examinations, including demographic characteristics, lifestyle factors, and medical history. Individual NO₂ exposure levels were obtained by matching residential addresses with pollution data grids from the Tracking Air Pollution in China (TAP) platform. Generalized additive models combined with distributed lag non-linear models (DLNM) were employed to analyze the impact of NO₂ on FMD and VED, with adjustments for meteorological factors, individual characteristics, and health status as potential confounders. Results ·A total of 812 eligible participants were included after rigorous screening. The results demonstrated that NO₂ exposure was significantly associated with decreased FMD at lag days 1, 2, and 5 (P=0.003, P=0.034, P=0.022), while the risk of VED increased significantly at lag days 5 and 6. Cumulative effect analysis revealed a significant dose-response relationship between NO₂ concentrations (35‒85 μg/m³) and FMD reduction (P<0.05). Subgroup analysis indicated that participants without dyslipidemia or a history of coronary artery disease (CAD) were more susceptible to short-term NO₂ exposure. Conclusion ·This study systematically revealed, for the first time, that short-term NO₂ exposure has a negative impact on vascular endothelial function in elderly individuals, exhibiting distinct temporal patterns and concentration thresholds. NO₂ exposure within 1‒6 d significantly impaired endothelial function, with the most pronounced effects observed on day 5. Notably, clinically "healthy" elderly individuals (without hyperlipidemia or CAD) demonstrated higher susceptibility. These findings not only support the critical role of NO₂ in vascular endothelial injury but also provide important evidence for elucidating the mechanisms by which air pollutants contribute to cardiovascular diseases through vascular dysfunction. The study holds significant implications for developing targeted air pollution prevention strategies for the elderly population.

Key words: nitrogen dioxide, endothelial function, flow-mediated dilation, vascular endothelial dysfunction

中图分类号:

R122.7

钟林杉, 丁琳, 姜绮霞. 二氧化氮短期暴露对上海市老年人血管内皮功能的影响[J]. 上海交通大学学报（医学版）, 2025, 45(12): 1620-1628.

ZHONG Linshan, DING Lin, JIANG Qixia. Impact of short-term exposure to nitrogen dioxide on vascular endothelial function in elderly residents of Shanghai[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(12): 1620-1628.

图/表 7

参考文献 21

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Population characteristic	Overall (n=812)	Non-VED (n=178)	VED (n=634)	P value
FMD/%	4.20 (2.80, 5.62)	7.30 (6.53, 8.47)	3.60 (2.40, 4.60)	<0.001
Age/year	70.61±5.43	71.08±5.68	68.94±4.03	<0.001
Gender/n(%)				<0.001
Female	495 (61.0)	131 (73.6)	364 (57.4)
Male	317 (39.0)	47 (26.4)	270 (42.6)
Smoking/n(%)				0.041
No	704 (86.7)	163 (91.6)	541 (85.3)
Yes	108 (13.3)	15 ( 8.4)	93 (14.7)
Drinking/n(%)				0.119
No	714 (87.9)	163 (91.6)	551 (86.9)
Yes	98 (12.1)	15 ( 8.4)	83 (13.1)
Hypertension/n(%)				0.157
No	117 (14.4)	32 (18.0)	85 (13.4)
Yes	695 (85.6)	146 (82.0)	549 (86.6)
Diabetes/n(%)				0.481
No	658 (81.0)	148 (83.1)	510 (80.4)
Yes	154 (19.0)	30 (16.9)	124 (19.6)
Dyslipidemia/n(%)				0.189
No	448 (55.2)	90 (50.6)	358 (56.5)
Yes	364 (44.8)	88 (49.4)	276 (43.5)
CAD/n(%)				0.357
No	736 (90.6)	165 (92.7)	571 (90.1)
Yes	76 ( 9.4)	13 ( 7.3)	63 ( 9.9)
CVD/n(%)				0.490
No	790 (97.3)	175 (98.3)	615 (97.0)
Yes	22 ( 2.7)	3 (1.7)	19 ( 3.0)
CVD family history/n(%)				1.000
No	652 (80.3)	143 (80.3)	509 (80.3)
Yes	160 (19.7)	35 (19.7)	125 (19.7)
Season/n(%)				0.178
Spring	190 (23.4)	41 (23.0)	149 (23.5)
Summer	229 (28.2)	59 (33.1)	170 (26.8)
Autumn	133 (16.4)	21 (11.8)	112 (17.7)
Winter	260 (32.0)	57 (32.0)	203 (32.0)

Population characteristic	Overall (n=812)	Non-VED (n=178)	VED (n=634)	P value
FMD/%	4.20 (2.80, 5.62)	7.30 (6.53, 8.47)	3.60 (2.40, 4.60)	<0.001
Age/year	70.61±5.43	71.08±5.68	68.94±4.03	<0.001
Gender/n(%)				<0.001
Female	495 (61.0)	131 (73.6)	364 (57.4)
Male	317 (39.0)	47 (26.4)	270 (42.6)
Smoking/n(%)				0.041
No	704 (86.7)	163 (91.6)	541 (85.3)
Yes	108 (13.3)	15 ( 8.4)	93 (14.7)
Drinking/n(%)				0.119
No	714 (87.9)	163 (91.6)	551 (86.9)
Yes	98 (12.1)	15 ( 8.4)	83 (13.1)
Hypertension/n(%)				0.157
No	117 (14.4)	32 (18.0)	85 (13.4)
Yes	695 (85.6)	146 (82.0)	549 (86.6)
Diabetes/n(%)				0.481
No	658 (81.0)	148 (83.1)	510 (80.4)
Yes	154 (19.0)	30 (16.9)	124 (19.6)
Dyslipidemia/n(%)				0.189
No	448 (55.2)	90 (50.6)	358 (56.5)
Yes	364 (44.8)	88 (49.4)	276 (43.5)
CAD/n(%)				0.357
No	736 (90.6)	165 (92.7)	571 (90.1)
Yes	76 ( 9.4)	13 ( 7.3)	63 ( 9.9)
CVD/n(%)				0.490
No	790 (97.3)	175 (98.3)	615 (97.0)
Yes	22 ( 2.7)	3 (1.7)	19 ( 3.0)
CVD family history/n(%)				1.000
No	652 (80.3)	143 (80.3)	509 (80.3)
Yes	160 (19.7)	35 (19.7)	125 (19.7)
Season/n(%)				0.178
Spring	190 (23.4)	41 (23.0)	149 (23.5)
Summer	229 (28.2)	59 (33.1)	170 (26.8)
Autumn	133 (16.4)	21 (11.8)	112 (17.7)
Winter	260 (32.0)	57 (32.0)	203 (32.0)

Item	Model 1	P value	Model 2	P value	Model 3	P value
FMD
Lag0/%	-18.00 (-32.56, -3.44)	0.015	-20.99 (-38.71, -3.27)	0.020	-21.40 (-39.13, -3.67)	0.018
Lag1/%	-19.49 (-34.03, -4.95)	0.009	-26.40 (-43.83, -8.96)	0.003	-26.87 (-44.34, -9.40)	0.003
Lag2/%	-14.86 (-29.48, -0.24)	0.046	-18.78 (-36.85, -0.71)	0.042	-19.53 (-37.62, -1.44)	0.034
Lag3/%	-12.30 (-26.99, 2.39)	0.101	-17.20 (-35.56, 1.16)	0.066	-18.14 (-36.52, 0.24)	0.053
Lag4/%	-8.86 (-23.81, 6.08)	0.245	-14.18 (-32.17, 3.81)	0.122	-15.05 (-33.10, 3.00)	0.102
Lag5/%	-15.87 (-30.63, -1.12)	0.035	-18.49 (-35.28, -1.69)	0.031	-19.64 (-36.48, -2.81)	0.022
Lag6/%	-12.94 (-27.78, 1.90)	0.087	-14.82 (-32.36, 2.72)	0.098	-15.93 (-33.49, 1.63)	0.075
Lag7/%	-9.43 (-24.09, 5.24)	0.208	-10.54 (-27.70, 6.62)	0.229	-11.96 (-29.14, 5.23)	0.173
Lag8/%	-11.50 (-26.28, 3.27)	0.127	-12.08 (-28.82, 4.66)	0.157	-13.48 (-30.21, 3.25)	0.114
Lag9/%	-8.13 (-22.77, 6.50)	0.276	-6.73 (-23.45, 9.99)	0.430	-8.07 (-24.82, 8.67)	0.345
Lag10/%	-6.19 (-20.77, 8.39)	0.405	-6.53 (-23.36, 10.30)	0.447	-7.55 (-24.44, 9.34)	0.381
VED
Lag0	1.21 (1.04, 1.41)	0.014	1.26 (1.04, 1.53)	0.019	1.27 (1.05, 1.55)	0.016
Lag1	1.13 (0.97, 1.31)	0.111	1.17 (0.97, 1.42)	0.106	1.18 (0.98, 1.44)	0.092
Lag2	1.11 (0.96, 1.29)	0.166	1.14 (0.94, 1.39)	0.189	1.15 (0.94, 1.40)	0.169
Lag3	1.12 (0.97, 1.31)	0.139	1.17 (0.96, 1.43)	0.122	1.18 (0.97, 1.45)	0.107
Lag4	1.06 (0.91, 1.23)	0.448	1.10 (0.90, 1.33)	0.339	1.11 (0.91, 1.35)	0.300
Lag5	1.20 (1.03, 1.40)	0.020	1.26 (1.04, 1.51)	0.015	1.27 (1.05, 1.53)	0.013
Lag6	1.16 (0.99, 1.35)	0.061	1.20 (0.99, 1.46)	0.066	1.22 (1.01, 1.49)	0.045
Lag7	1.04 (0.90, 1.21)	0.603	1.03 (0.85, 1.24)	0.759	1.04 (0.86, 1.26)	0.687
Lag8	1.14 (0.98, 1.33)	0.093	1.18 (0.98, 1.41)	0.075	1.19 (0.99, 1.43)	0.064
Lag9	1.07 (0.92, 1.24)	0.374	1.05 (0.88, 1.26)	0.594	1.06 (0.88, 1.27)	0.534
Lag10	1.03 (0.88, 1.19)	0.701	1.02 (0.85, 1.22)	0.830	1.03 (0.85, 1.24)	0.759

Item	Model 1	P value	Model 2	P value	Model 3	P value
FMD
Lag0/%	-18.00 (-32.56, -3.44)	0.015	-20.99 (-38.71, -3.27)	0.020	-21.40 (-39.13, -3.67)	0.018
Lag1/%	-19.49 (-34.03, -4.95)	0.009	-26.40 (-43.83, -8.96)	0.003	-26.87 (-44.34, -9.40)	0.003
Lag2/%	-14.86 (-29.48, -0.24)	0.046	-18.78 (-36.85, -0.71)	0.042	-19.53 (-37.62, -1.44)	0.034
Lag3/%	-12.30 (-26.99, 2.39)	0.101	-17.20 (-35.56, 1.16)	0.066	-18.14 (-36.52, 0.24)	0.053
Lag4/%	-8.86 (-23.81, 6.08)	0.245	-14.18 (-32.17, 3.81)	0.122	-15.05 (-33.10, 3.00)	0.102
Lag5/%	-15.87 (-30.63, -1.12)	0.035	-18.49 (-35.28, -1.69)	0.031	-19.64 (-36.48, -2.81)	0.022
Lag6/%	-12.94 (-27.78, 1.90)	0.087	-14.82 (-32.36, 2.72)	0.098	-15.93 (-33.49, 1.63)	0.075
Lag7/%	-9.43 (-24.09, 5.24)	0.208	-10.54 (-27.70, 6.62)	0.229	-11.96 (-29.14, 5.23)	0.173
Lag8/%	-11.50 (-26.28, 3.27)	0.127	-12.08 (-28.82, 4.66)	0.157	-13.48 (-30.21, 3.25)	0.114
Lag9/%	-8.13 (-22.77, 6.50)	0.276	-6.73 (-23.45, 9.99)	0.430	-8.07 (-24.82, 8.67)	0.345
Lag10/%	-6.19 (-20.77, 8.39)	0.405	-6.53 (-23.36, 10.30)	0.447	-7.55 (-24.44, 9.34)	0.381
VED
Lag0	1.21 (1.04, 1.41)	0.014	1.26 (1.04, 1.53)	0.019	1.27 (1.05, 1.55)	0.016
Lag1	1.13 (0.97, 1.31)	0.111	1.17 (0.97, 1.42)	0.106	1.18 (0.98, 1.44)	0.092
Lag2	1.11 (0.96, 1.29)	0.166	1.14 (0.94, 1.39)	0.189	1.15 (0.94, 1.40)	0.169
Lag3	1.12 (0.97, 1.31)	0.139	1.17 (0.96, 1.43)	0.122	1.18 (0.97, 1.45)	0.107
Lag4	1.06 (0.91, 1.23)	0.448	1.10 (0.90, 1.33)	0.339	1.11 (0.91, 1.35)	0.300
Lag5	1.20 (1.03, 1.40)	0.020	1.26 (1.04, 1.51)	0.015	1.27 (1.05, 1.53)	0.013
Lag6	1.16 (0.99, 1.35)	0.061	1.20 (0.99, 1.46)	0.066	1.22 (1.01, 1.49)	0.045
Lag7	1.04 (0.90, 1.21)	0.603	1.03 (0.85, 1.24)	0.759	1.04 (0.86, 1.26)	0.687
Lag8	1.14 (0.98, 1.33)	0.093	1.18 (0.98, 1.41)	0.075	1.19 (0.99, 1.43)	0.064
Lag9	1.07 (0.92, 1.24)	0.374	1.05 (0.88, 1.26)	0.594	1.06 (0.88, 1.27)	0.534
Lag10	1.03 (0.88, 1.19)	0.701	1.02 (0.85, 1.22)	0.830	1.03 (0.85, 1.24)	0.759