发展中国家空气污染与2型糖尿病的系统评价和Meta分析

doi:10.24920/004065

摘要/Abstract

摘要：

目的研究指出空气污染是2型糖尿病的危险因素。本文采用Meta分析方法评价发展中国家空气污染与2型糖尿病之间的联系。
方法使用计算机检索PubMed、EMBASE和Web of Science数据库内截至2022年03月31日发表的发展中国家空气污染与2型糖尿病患病率或发病率之间关系的研究论文。使用比值比（OR）来评估疗效,对纳入的研究进行Meta分析。
结果共纳入在发展中国家进行的8项横断面研究和8项队列研究。对8项PM_2.5相关的研究进行的Meta分析显示,暴露于PM_2.5使2型糖尿病患病率增加,相关性为1.12（95% CI 1.07,1.17）（P<0.001）。由于纳入文献数量有限,未能定量评价空气污染与2型糖尿病发病率之间的相关性。
结论在发展中国家,暴露于PM_2.5会导致2型糖尿病患病率增加。糖尿病易感人群应该注意减少空气污染暴露。

关键词: 空气污染, PM_2.5, 2型糖尿病

Abstract:

Objective In recent years, many studies have reported that air pollution is a risk factor for type 2 diabetes mellitus (T2DM). The aim of this systematic review and meta-analysis is to summarize the evidence about the association between exposure to air pollution and T2DM in developing countries.
Methods The databases, including PubMed, EMBASE and Web of Science, were systematically searched for studies published up to 31 March 2022. Studies about the association between air pollution and T2DM prevalence or incidence in developing countries were included. The odds ratio (OR) was used as effect estimate. We synthesized the included studies in the meta-analysis.
Results We included 8 cross-sectional studies and 8 cohort studies, all conducted in developing countries. Meta-analysis of 8 studies on PM_2.5 (particulate matter ≤ 2.5 μm in diameter) showed that T2DM prevalence was significantly associated with PM_2.5 exposure (OR=1.12; 95% CI: 1.07, 1.17; P<0.001). The association between air pollutants and T2DM incidence was not estimated due to the limited relevant studies.
Conclusions The exposure to PM_2.5 would be positively associated with an increased prevalence of T2DM in developing countries. Some effective measures should be taken to reduce air pollutant exposure in people who are vulnerable to diabetes.

Key words: Air pollutants, PM_2.5, type 2 diabetes mellitus

Zihan Chen, Zhou Zhao, Chuiwen Deng, Naishi Li. Association between Air Pollution and Type 2 Diabetes Mellitus in Developing Countries: A Systematic Review and Meta-Analysis[J].Chinese Medical Sciences Journal, 2022, 37(3): 218-227.

图/表 5

Author and year	Study design	Location	Participants	Study period	Pollutants	Average exposure	Effect estimate (95% CI)	Outcomes	Definition of diabetes
Bo Y, et al(2021)^[34]	Cohort	Taiwan, China	146,789	2001 - 2014	PM_2.5	27μg/m³	1.28 (1.19, 1.39)	T2DM incidence	Self-report of physician-diagnosed T2DM
Chilian-Herrera OL, et al (2021)^[35]	Cohort	Municipalities of the State of Mexico and Mexico City	2,297	2006 - 2012	PM_2.5	24.8μg/m³	3.09 (1.17, 8.15)	DM prevalence	Self-report information of “having received a medical diagnosis of diabetes or high blood sugar”
Hassanvand MS, et al(2017)^[36]	Cross-sectional	5 large cities in Iran	2,916	2006 - 2011	PM₁₀	120.15μg/m³(study group), 83.95μg/m³(control group)	1.32 (1.03, 1.69)	T2DM prevalence	Defined as a positive response to either of the following two questions: 1) “Have you ever been told by a doctor or other health worker that you have diabetes?”; 2) “Are you currently taking oral medication or insulin for diabetes prescribed by a doctor or other healthcare professional?”
Jabbari F, et al (2020)^[37]	Cohort	Municipal district 13 of Tehran, Iran	2,428	2009 - 2011	PM₁₀	82.6μg/m³	1.50 (1, 2.32)	T2DM incidence	Defined as the concentration of FBG ≥ 126 mg/dL, non-FBG ≥ 200 mg/dL, and regular use of glucose-lowering medication
Jacob AM, et al (2019)^[38]	Cross-sectional	Chennai, Tamil Nadu, India	410	2017 - 2018	PM_2.5	74.22μg/m³(study group) 27.22μg/m³ (control group)	2.19 (1.40, 2.43)	T2DM prevalence	Meeting either of the following two criteria: 1) RCBG ≥ 140 mg/dl (7.8 mmol/l); 2) RCBG >200 mg/ dl (11.1 mmol/l) and reporting any one of the classic symptoms or weight loss
Liu C, et al(2016)^[39]	Cross-sectional	150 counties or districts from 28 provinces, China	11,847	2011 - 2012	PM_2.5	72.6μg/m³	1.14 (1.08, 1.20)	T2DM prevalence	Self-reported diabetes, and/or FBG ≥7 mmol/L, and/or HbA1c ≥ 6.5%, and/or insulin use according to ADA’s recommendation
Liu F, et al (2019)^[40]	Cohort	Rural areas of Henan province, China	39,259	2015 - 2017	PM₁	57.4μg/m³	1.040 (1.026, 1.054)	T2DM prevalence	Previously diagnosed with T2DM and currently using antidiabetic medicines and/or FBG ≥ 7.0 mmol/L
					PM_2.5	73.4μg/m³	1.068 (1.052, 1.084)
					NO₂	39.9μg/m³	1.050 (1.039, 1.061)
Li CY, et al(2019)^[41]	Cohort	Taiwan, China	505,151	2001 - 2012	PM_2.5	27.28μg/m³	1.08 (1.06, 1.10)	T2DM incidence	Based on ICD-9
Li YL, et al(2021)^[42]	Cohort	Taiwan, China	6,426,802	2005 - 2010	O₃	26.21ppb	1.058 (1.053, 1.064)	T2DM incidence	Based on ICD-9
Li YL, et al(2021)^[42]	Cohort	Taiwan, China	6,426,802	2005 - 2010	SO₂	4.77ppb	1.011 (1.007, 1.015)	T2DM incidence	Based on ICD-9
Qiu H, et al(2018)^[43]	Cohort	Hong Kong, China	61,447	1998 - 2001	PM_2.5	35.8μg/m³	1.06 (1.01, 1.11)	T2DM prevalence	Based on ICD-9
Qiu H, et al(2018)^[43]	Cohort	Hong Kong, China	61,447	1998 - 2001	PM_2.5	35.8μg/m³	1.15 (1.05, 1.25)	T2DM incidence	Based on ICD-9
Shan A, et al (2020)^[44]	Cohort	Four cities in northern China	38,529	1998 - 2009	PM₁₀	143.36μg/m³	1.831 (1.778, 1.886)	T2DM incidence	Self-reported physician diagnosis (from at least a class three hospital), intake of antidiabetic drugs or injection of insulin simultaneously
					SO₂	66.71μg/m³	1.287 (1.256, 1.318)
					NO₂	40.74μg/m³	1.472 (1.419, 1.528)
Suryadhi MAH, et al(2020)^[45]	Cross-sectional	487 regencies/ municipalities in all 33 provinces in Indonesia	647,947	2007 - 2013	PM_2.5	8.3μg/m³	1.09 (1.05, 1.14)	DM prevalence	Using the following question “Have you ever been diagnosed with diabetes mellitus by a doctor?”
Wang M, et al (2020)^[46]	Cross-sectional	Jinchang city, Gansu province, China	19,884	2011 - 2013	PM₁₀	109.43μg/m³	1.17 (1.08, 1.26)	DM incidence	Defined according to the diagnostic criteria recommended by ADA as FBG ≥ 7.0 mmol/L and/or self-report clinical diagnosed of diabetes and/or self-report used of anti-diabetes drugs
Yang BY, et al (2018)^[47]	Cross-sectional	Liaoning province, China	15,477	2009	PM₁	66.0μg/m³	1.13 (1.04, 1.22)	DM prevalence	Defined according to ADA’s recommendations, as FBG ≥ 7.0 mmol/L or 2 h glucose ≥ 11.1mmol/L, or intake of any antidiabetic medication (both insulin and oral antidiabetic drugs), or both
					PM_2.5	82.0μg/m³	1.14 (1.03, 1.25)
					PM₁₀	123.1μg/m³	1.20 (1.12, 1.28)
					SO₂	54.4μg/m³	1.12 (1.04, 1.21)
					NO₂	35.3μg/m³	1.22 (1.12, 1.33)
					O₃	49.4μg/m³	1.14 (1.05, 1.25)
Yang Y, et al (2018)^[48]	Cross-sectional	8 provinces across China	11,504	2007 - 2010	PM_2.5	46.97μg/m³	1.27 (1.12, 1.43)	DM prevalence	Self-reported information using the following three criteria: 1) diagnosed with diabetes mellitus by a health care professional; 2) taking insulin or other medical treatments to reduce blood glucose levels during the last 12 months before the survey; 3) consuming a special diet and physical exercise regimen related to diabetes as recommended by medical professionals
Zhang Q, et al (2021)^[49]	Cross-sectional	150 counties or districts from 28 provinces, China	13,013	2011 - 2012	NO₂	24μg/m³	1.13 (1.01, 1.26)	DM prevalence	Primary definition of diabetes meeting at least one of the following criteria: FBG >126 mg/dl, a non-FBG >200 mg/dl, HbA1c > 6.5%, or using antidiabetic medications; self-reported diabetes defined as answering “Yes” to the question “Have you been diagnosed with diabetes or high blood sugar?”

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Study and year	Selection				Comparability	Exposure			Score
Study and year	1	2	3	4	1	1	2	3	Score
Cross-sectional studies
Hassanvand MS, et al. (2017)	*	*	*	*	*		*		6
Jacob AM, et al. (2019)	*		*	*	*		*		5
Liu C, et al. (2016)	*	*	*	*	**		*	*	8
Suryadhi MAH, et al. (2020)	*	*	*	*	**		*	*	7
Wang M, et al. (2020)	*	*	*	*	**	*	*		8
Yang BY, et al. (2018)	*	*	*	*	**	*	*	*	9
Yang Y, et al. (2018)	*	*	*	*	**		*		7
Zhang Q, et al. (2021)	*	*	*	*	**	*	*		8
Cohort Studies
Bo Y, et al. (2021)		*	*	*	**	**	*	*	9
Chilian-Herrera OL, et al. (2021)		*	*	*	**	*		*	7
Jabbari F, et al. (2020)	*	*	*	*	**	*		*	8
Liu F, et al. (2019)	*	*	*	*	**	*		*	8
Li CY, et al. (2019)		*	*	*	**	*	*	*	8
Li YL, et al. (2021)		*	*	*	**	*	*	*	8
Qiu H, et al. (2018)	*	*	*	*	**	**	*	*	10
Shan A, et al. (2020)		*	*	*	**	*	*	*	8

[1]	吴坤荣, 张恕芳, 关紫菀, 李晓黎, 李蕊, 尹影, 李妍. 亚甲基四氢叶酸还原酶基因C677T多态性与中国2型糖尿病患者冠心病风险增加相关[J]. Chinese Medical Sciences Journal, 2021, 36(2): 103-109.
[2]	祖红林,侯骊坤,刘洪伟,詹渊博,何菊. 应用BITOLA系统筛选腹主动脉瘤与2型糖尿病相互作用的候选基因[J]. Chinese Medical Sciences Journal, 2021, 36(1): 50-56.
[3]	尹影, 李蕊, 李晓黎, 吴坤荣, 李玲, 徐曰东, 廖琳, 杨蕊, 李妍. 伴血脂异常2型糖尿病患者亚甲基四氢叶酸还原酶基因多态性与血浆同型半胱氨酸水平的相关性[J]. Chinese Medical Sciences Journal, 2020, 35(1): 85-91.
[4]	陈志晔, 臧秀娟, 刘梦琦, 刘梦雨, 李金锋, 谷昭艳, 马林. 16例新发2型糖尿病患者脑部皮层厚度异常变化的MRI初步研究[J]. Chinese Medical Sciences Journal, 2017, 32(2): 75-82.

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