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Abstract

Fine particulate matter <2.5 μm in diameter (PM_2.5) has been validated to associate with cardiovascular diseases (CVD) incidence and mortality. So far, no study has quantitatively evaluated the relationship between the atmospheric PM_2.5 exposure and ischemic heart disease (IHD). We conducted a meta-analysis to illustrate the relationship between PM_2.5 and IHD. Published articles were systematically searched (until June 2022) from PubMed, EMBASE, Cochrane Library. A random-effect model was performed to summarize the total relative risks (RRs) and 95% confidence intervals (CIs). Meta-analysis was performed using Stata 12.0 software. A total of 28 studies among 23 cohorts (23.38 million individuals and 256256 IHD cases) were included. With PM_2.5 increasing 10 μg/m³, the total RRs of IHD incidence and mortality were 1.07 (95% CI: 0.99–1.17), 1.21 (95% CI: 1.15–1.28), respectively. In sub-analyses, our study revealed that the combined RRs of exposure to PM_2.5 on IHD mortality in Asian and European population [1.11 (95% CI: 0.93–1.33); 1.06 (95% CI: 1.02–1.11)] were much lower compared with American and Canadian people [1.27 (95% CI: 1.17–1.37); 1.30 (95% CI: 1.24–1.35)]. Furthermore, study duration, size and some adjustments were related with the total RR. Our findings indicated that exposure of an increase in the concentration of atmospheric PM_2.5 may increase the risk of IHD incidence and mortality. Further evidence is needed to confirmed the association.

Keywords

particulate matter ischemic heart disease systematic review meta-analysis

Introduction

Due to the continuous development of social economy, the lifestyle has been greatly changed, and the number of cardiovascular diseases and deaths has increases by year. As one of the important diseases of cardiovascular diseases, ischemic heart disease (IHD) represents a large burden on individuals and health care resources worldwide.¹ Globally, in 2019, IHD was the world’s leading cause of morbidity and mortality, accounting for 16% (8.8 million) of the global disease deaths.² The high cost of disease prevention presents a growing challenge for health care systems, and cardiovascular diseases contribute substantially to these costs. In America, the estimated direct and indirect cost of heart disease from 2014 to 2015 was $218.7 billion.³

In large prospective trials, several risk factors have been empirically linked to an increased risk of IHD, include physical inactivity, tobacco use, diet, “bad fats” in the blood, hypertension, diabetes mellitus and being overweight.^4–6 There is clear evidence for the contribution of fine particulate (PM_2.5) to cardiovascular outcomes, including IHD. In 2017, IHD attributable to PM_2.5 resulted in 977140 deaths and 21.93 million disability-adjusted life years (DALYs) globally.⁷ Exposure to PM_2.5 may plausibly develop IHD through multiple pathways, such as through aggravating myocardial ischemia/reperfusion (I/R) injury,⁸ triggering inflammatory responses,⁹ and upregulating the endothelin system.¹⁰

The role of PM_2.5 in the development of cardiovascular diseases has been investigated for many years. A growing body of epidemiological literature shows that PM_2.5 is a well-defined risk factor for IHD, earlier studies of long-term PM_2.5 exposure focused more the United States and Europe,^11–13 whereas in the past decade more studies have been published in Asia and Oceania.^14,15 While there was an evidence-based meta-analyses also revealed that long-term exposure of PM_2.5 may be related to a higher risk of cardiovascular disease event.¹⁶ However, the results were still limited and unclear. Therefore, there is an urgent need to summarize and compare the IHD mortality in relation to atmospheric PM_2.5 exposure. To systematically explore the association between PM_2.5 and IHD, we carried out a meta-analysis to draw a more convincing conclusion with respect to PM_2.5 and IHD.

Methods

Search strategy

The present analysis was performed according to the Preferred Reporting Items for Meta-analyses (PRISMA) guideline. We searched the eligible publications in the PubMed, EMBASE, and Cochrane Library up to June 2022. The following keywords were used: “ultrafine fibers” or “airborne particulate matter” or “ambient particulate matter” or “ultrafine particulate matter” or “ultrafine particles” combined with “cohort” and “ischemic heart disease”. Two investigators searched the studies independently. We obtained data by screening titles and abstracts, and examined full text. Reference lists were searched for additional relevant publications. The identified articles should be published in English. According to the flow diagram of literature selection is shown in Figure 1.

Figure 1.

Flow diagram of literature selection. IHD, ischemic heart disease; RR, relative risk.

Selection criteria

Studies were eligible if they satisfied the following conditions: (1) cohort studies; (2) exposed from PM_2.5; (3) outcome was IHD incidence or mortality; (4) had available data of relative risk (RR) or odds ratio (OR) or hazard ratio (HR) with corresponding 95% CI. Articles were excluded if they satisfied these criteria: (1) nonhuman experiment; (2) reviews, comments, editorials, letters, interviews, or reports; (3) duplicate studies; (4) incomplete data.

Data extraction

Study basic information and characteristic data included the author, year, design, location, sex, duration, study size, number of cases, source of outcome data, outcome event, relative risk (RR) or hazard ratio (HR) or odds ratio (OR) with 95% confidence intervals (95%CI), and adjusted variables. Two investigators extracted data independently.

Literature quality assessment

We estimated the quality of studies by the Newcastle–Ottawa Quality Assessment Scale with nine questions.¹⁷ One score represented a satisfactory answer, which a maximum point was 9. Only those studies for which the majority of the questions were deemed satisfactory (ie, which a score of 6 or higher) were defined as high methodological quality.

Statistical analysis

All studies which examined the relationship between the exposure to PM_2.5 and IHD with RR or HR or OR and 95% CI were included. All study effect estimates were converted to present a change of 10 μg/m³. Heterogeneity of effect size across the studies was assessed by the Cochran’s Q test and I² statistics. Generally, I² < 30% was defined as no or acceptable heterogeneity, 30–75% as mild heterogeneity, and over 75% as notable heterogeneity. The random-effects model was used only when there existed significant heterogeneity; otherwise, the fixed-effects model was applied for further analysis. Sensitivity analysis was used to determine the stability and reliability of the results by sequentially deleting one study. Subgroup analyses and meta regression analyses were conducted to investigate the heterogeneity sources. The funnel plots, Egger’s linear regression and Begg’s rank correlation test were carried out to assess the publication bias, p < .05 was considered a potential small study bias. All statistical analyses were conducted using STATA statistical package version 12.0.

Results

Studies characteristic for mata-analysis

Detailed process of the relevant study selection was shown in Figure 1. The characteristics of 28 studies among 23 cohorts are presented in Table 1.^12,18–44 The published year of these articles ranged from 2005 to 2022. Among the 28 studies, 11 studies were designed in USA, 6 in Canada, 6 in Asia, 5 in Europe. The sample size ranged from 3239 to 8.5 million.

Table 1.

Characteristics of studies examining the association between long-term PM_2.5 exposure and ischemic heart disease events.

Study [References]	Design	Location	Sex	Duration	Case/participants	Source of outcome data	Outcome event	Adjusted RR (95% CI)	Adjusted variables
Weichenthal 2014¹⁸	Agricultural health study (AHS) cohort	USA	Both	1993–2009	213/83378	Mortality: National database	IHD mortality	2.68 (1.05–6.87)	Age, sex, calendar time, BMI, smoking, alcohol, diet, marital status, education, vegetable
Beelen 2014¹⁹	European study of cohorts for air pollution Effects(ESCAPE)	Europe	Both	1985–2004	4992/367383	Mortality: National database	IHD mortality	0.96 (0.55–1.69)	Age, sex, calendar time, BMI, marital smoking, alcohol, diet, other covariates status, SES^a
Cai 2018²⁰	HUNT, EPIC-Oxford and UK Biobank cohorts	UK	Both	1993–2010	3515/355732	Mortality: National database	IHD incidence	1.005 (0.912–1.109)	Employment, education level, smoking status, weighted 24-h road traffic noise
Cakmak 2018²¹	Canadian census health and environment cohort (CanCHEC)	Canada	Both	1991–2011	Not listed/2,291,250	Mortality: National database	IHD mortality	1.25 (1.21–1.29)	Age, sex, race, marital status
Crouse 2012²²	Canadian national-level cohort study	Canada	Both	1991–2001	43400/2.1 million	Mortality: National database	IHD mortality	1.31 (1.27–1.35)	Age, sex, race, marital status,other covariates
Hales 2021²³	Retrospective cohort study	New Zealand	Both	2013–2016	13314/2,347,467	Mortality data collection and the national minimum dataset of publicly-funded hospital discharges	IHD mortality	1.082 (0.992–1.179)	Age, sex, ethnicity, income, education, smoking,temperature
Jalali 2021²⁴	Isfahan cohort study (ICS)	Iran	Both	2001–2016	349/6504	Isfahan cardiovascular research center	IHD incidence	1.028 (1.017–1.039)	Age, sex, smoking status, physical activity, dietary behaviors, obesity, SES, hypertension status, diabetes, cholesterol, triglycerides, LDL to HDL ratio, history of heart disease in the family, urban/rural status, and cluster random efect
Jerrett 2017²⁵	American cancer Society CPS-II cohort	USA	Both	1982–2004	45 624/668,629	Mortality: National database after 1989 and ‖ before 1989 other so	IHD mortality	1.20 (1.16–1.24)	Age, sex, race, BMI, smoking, marital status, alcohol, diet, other covariates SE
Liang 2022²⁶	Prospective cohort study	China	Both	2010–2017	557/90672	Mortality: National database	IHD mortality	1.05 (1.01–1.09)	Age group (every 5 years) and gender education, BMI, marital status, smoking status, years of smoking for current smokers, cigarettes per day for current smokers, passive smoking, alcohol drinking status, consumption of alcohol (g/day), consumption of fresh vegetables and fruits (separately, g/day) and household solid fuel use
Lim 2019²⁷	NIH-AARP diet and health study cohort	USA	Both	1995–2011	22,329/548,845	Mortality: National database	IHD mortality	1.16 (1.10–1.23)	Sex and location as strata; and age (time-varying), race, BMI, education, smoking, diet (aMED), and marriage at individual-level; and median income and % with high school education at census-tract level
Miller 2007²⁸	WHI cohort study	USA	Female	1994–2003	139/65 893	Mortality: National database and other sources	IHD mortality	2.21 (1.17–4.16)	Age, race or ethnic group, educational level, household income, smoking status, systolic blood pressure, BMI, and presence or absence of diabetes, hypertension, or hypercholesterolemia, myocardial infarction, coronary revascularization, and death from coronary heart disease
Motesaddi Zarandi 2021²⁹	Prospective cohort study	Iran	Both	2014–2017	3881/8.5 million	Mortality: National database	IHD mortality	0.99 (0.79–1.24)	PM₁₀, PM_2.5, SO₂, NO₂, O₃, CO, relative humidity, temperature, barometric pressure, wind speed, and total cardiovascular hospital admissions and mortality
Ostro 2010³⁰	California teachers study (cohort)	USA	Female	2002–2007	474/44 847	Mortality: National database	IHD mortality	1.91 (1.65–2.21)	Age, race, family history, BMI, physical smoking, alcohol, diet, marital activity, medications, other covariates status
Ostro 2015³¹	California teachers study (cohort)	USA	Female	2001–2007	1085/101 884	Mortality: National database	IHD mortality	1.19 (1.08–1.31)	Age, race, family history, BMI, physical smoking, alcohol, diet, marital activity, medications, other covariates status
Pinault 2017³²	Canadian census health and environment cohort (cohort)	Canada	Both	2001–2011	40400/2.4 million	Mortality: National database	IHD mortality	1.36 (1.28–1.44)	Age, sex, race, marital status, other covariates
To 2015³³	Longitudinal cohort	Canada	Female	1980–2013	12169/29549	National database	IHD incidence	1.22 (1.14, 1.30)	Age at baseline, education, occupation, marital status, smoking, BMI and four contextual variables derived from census area measures (mean income, proportion with high school education, percentage of low income households, and unemployment rate)
Wong 2015³⁴	Elderly health centre of the department of health cohort study	China	Both	1998–2011	1810/66 820	Mortality: National database	IHD mortality	1.42 (1.16, 1.73)	Age, sex, BMI, smoking, other physical activity, SES^a, covariates
Lo 2022³⁵	Taiwan national HealthInterview survey (NHIS) (cohort study)	Taiwan	Both	2001–2016	1035/62 694	NHIS database	IHD incidence	1.052 (0.971, 1.140)	Age, sex, marital status, education level and household income, BMI, smoking status, drinking status, physical activity and vegetable and fruit intake and urbanization level, medical resources and median household income at township level
Badaloni 2017³⁶	Rome longitudinal study (cohort study)	Italy	Both	2001–2010	22234/1.2 million	Mortality: National database	IHD mortality	1.06 (1.01–1.11)	Age, sex, calendar time, marital status, SES^a, other covariates
Carey 2013³⁷	Canadian census health and environment cohort (CANCHEC)	England	Both	2003–2007	8168/835607	Mortality: National database	IHD mortality	1.05 (0.86–1.29)	Age, sex, BMI, smoking, SES^a
Chen 2005³⁸	Adventist health study on the health effects of smog (AHSMOG) (cohort study)	USA	Both	1977–1998	250/3239	Mortality: National database and other sources	IHD mortality	1.14 (0.73–1.78)	Age, sex, calendar time, BMI, smoking, diet, SES^a
Chen 2010³⁹	Enhanced feedback for effective cardiac treatment (EFFECT) (cohort study)	Canada	Both	1999–2011	1650/8873	Mortality: National database	IHD mortality	1.43 (1.12–1.83)	Age, sex, family history, comorbidities, smoking, CVD history, medications, marital status, SES^a, other covariates
Gan 2011⁴⁰	Residents in vancouver cohort study	Canada	Both	1999–2002	3104/452735	Mortality: National database	IHD mortality	1.07 (0.83–1.36)	Age, sex, comorbidities, SES^a
Hart 2011⁴¹	Trucking industry men (cohort study)	USA	Male	1985–2000	1109/53814	Mortality: National database	IHD mortality	1.18 (0.91–1.54)	Age, race, calendar time, other covariates
Hayes 2020⁴²	National institutes of health NIH-AARP diet and health study (cohort study)	USA	Both	1995–2011	23328/565477	Mortality: National database	IHD mortality	1.16 (1.10–1.22)	Age, sex, race, BMI, smoking, alcohol, marital status, SES^a, other covariates
Lipsett 2011⁴³	California teachers study (cohort)	USA	Both	1999–2005	773/73489	Mortality: National database	IHD mortality	1.20 (1.02–1.41)	Age, race, family history, BMI, smoking, alcohol, diet, physical activity, medications, marital status, SES^a, other covariates
Loop 2018⁴⁴	REasons for geographic and racial differences in stroke (REGARDS) cohort	USA	Both	2003–2012	215/17126	Mortality: National database and other sources	IHD mortality	1.57 (0.71–3.48)	Age, sex, race, calendar time, comorbidities, BMI, smoking, alcohol, physical activity, medications, SES^a, other covariates
Tseng 2015 ⁴⁵	Civil servants (cohort study)	Taiwan	Both	1992–2008	139/43227	Mortality: National database	IHD mortality	0.76 (0.31–1.84)	Age, sex, BMI, smoking, alcohol, marital status, SES^a

^aSocioeconomic status (SES) may include education, income, occupation/employment, medical eligibility or other indicators. These variables were mesured on individual or neighborhood levels depending on data availability in studies.

RR, relative risks; IHD, ischemic heart disease; CVD, cardiovascular diseases.

Among the 28 studies, the mean of the quality scores was 7.86, and all articles were deemed as high methodological quality. The quality scores of 15 studies were presented in Table 2.

Table 2.

Quality evaluation of all included studies.

Studies	Selection	Comparability	Outcome	Score
Weichenthal 2014¹⁸	**	**	***	7
Beelen 2014¹⁹	***	**	***	8
Cai 2018²⁰	***	**	***	8
Cakmak 2018²¹	***	**	***	8
Crouse 2012²²	**	**	***	7
Hales 2021²³	****	**	***	9
Jalali 2021²⁴	***	**	***	8
Jerrett 2017²⁵	**	**	***	7
Liang 2022²⁶	***	**	***	8
Lim 2019²⁷	****	**	***	9
Miller 2007²⁸	***	**	***	8
Motesaddi Zarandi 2021²⁹	****	**	***	9
Ostro 2010³⁰	**	**	***	7
Ostro 2015³¹	***	**	***	8
Pinault 2017³²	***	**	***	8
To 2015³³	***	**	***	8
Wong 2015³⁴	**	**	***	7
Lo 2022³⁵	**	**	***	7
Badaloni 2017³⁶	***	**	***	8
Carey 2013³⁷	***	**	***	8
Chen 2005³⁸	***	**	***	8
Chen 2010³⁹	***	**	***	8
Gan 2011⁴⁰	****	**	***	9
Hart 2011⁴¹	**	**	***	7
Hayes 2020⁴²	**	**	***	7
Lipsett 2011⁴³	***	**	***	8
Loop 2018⁴⁴	****	**	***	9
Tseng 2015⁴⁵	**	**	***	7

PM_2.5 and IHD risk

We identified 28 studies of PM_2.5 exposure and IHD risk with study populations restricted to patients without a previous IHD. As shown in Figure 2, the multivariable-adjusted RRs from 28 articles were extracted. A random-effect model was used to calculate the combined RR due to the high heterogeneity (I² = 94.8%). The total RR was determined to 1.18 (95% CI: 1.12–1.25) per 10 μg/m³ increase in PM_2.5 by combining the IHD mortality and incidence. The RR of the IHD mortality in the random effects meta-analysis was 1.21 (95% CI: 1.15–1.128) and the RR of an incident IHD was 1.07 (95% CI: 0.99–1.17).

Figure 2.

The total RRs of IHD incidence and mortality. IHD, ischemic heart disease; RR, relative risk.

Subgroup and meta-regression analysis

Subgroup analysis were performed according to some characteristics. We stratified the studies by location, duration, study size and adjustment for sex, smoking, BMI, alcohol, physical activity, and marital status, shown in Table 3. There were some positive associations in some strata. The RRs were 1.27 (95% CI: 1.17–1.37) in USA, 1.30 (95% CI:1.24–1.35) in Canada, 1.06 (95% CI: 1.02–1.11) in Europe, 1.25 (95% CI: 1.19–1.30) for duration ≥ 10y, 1.18 (95% CI: 1.12–1.24) for study size ≥ 1000 and 1.37 (95% CI: 1.13–1.66) for study size < 1000. There are also positive associations in adjustment for potential confounding factors, adjustment for sex, smoking, BMI, alcohol, physical activity, and marital status.

Table 3.

Subgroup analysis for the association between PM_2.5 and the mortality of ischemic heart disease.

Subgroup		N	RR (95% CI)	Heterogeneity
Subgroup		N	RR (95% CI)	I², %	p value
All studies		24	1.21 (1.15–1.28)	88.2	<.001
Location	USA	11	1.27 (1.17–1.37)	79.5	<.001
	Canada	5	1.30 (1.24–1.35)	62.5	.031
	Asia	4	1.11 (0.93–1.33)	67.9	.025
	Europe	4	1.06 (1.02–1.11)	0.0	.957
Duration	≥10y	13	1.25 (1.19–1.30)	73.4	<.001
Duration	< 10y	11	0.65 (0.37–1.15)	86.2	<.001
Size	≥1000	13	1.18 (1.12–1.24)	87.1	<.001
Size	< 1000	11	1.37 (1.13–1.66)	87.7	<.001
Adjustment for potential confounding factors
Sex	Yes	18	1.19 (1.12–1.25)	88.6	<.001
Sex	No	6	1.32 (1.07–1.64)	87.4	<.001
Smoking	Yes	17	1.22 (1.14–1.30)	83.0	<.001
Smoking	No	7	1.20 (1.11–1.30)	91.4	<.001
BMI	Yes	15	1.23 (1.14–1.32)	84.3	<.001
BMI	No	9	1.20 (1.11–1.29)	90.1	<.001
Alcohol	Yes	10	1.24 (1.12–1.36)	88.8	<.001
Alcohol	No	14	1.20 (1.13–1.28)	86.1	<.001
Physical activity	Yes	5	1.41 (1.13–1.76)	86.9	<.001
Physical activity	No	19	1.17 (1.11–1.24)	88.2	<.001
Marital status	Yes	13	1.24 (1.17–1.33)	93.1	<.001
Marital status	No	11	1.14 (1.06–1.22)	28.8	.171

Meta-regression analysis was used to further identify sources of heterogeneity (Table 4). The p values from meta-regression analysis with the covariate of study location, duration, size, and adjustment of sex, smoking, BMI, alcohol, physical activity and marital status were 0.986, 0.911, 0.738, 0.894, 0.971, 0.981, 0.910, 0.886, 0.927, respectively. These results verify that no covariate significantly contributed to the between-study heterogeneity.

Table 4.

Meta-regression analysis.

Variable	Coefficient	Standard error	Tau (τ)	p value	95% CI
Location	−0.011	0.622	−0.020	.986	−1.345 to 1.323
Duration	0.123	1.083	0.110	.911	−2.200 to 2.445
Size	0.226	0.662	0.340	.738	−1.193 to 1.645
Adjustment of sex	−0.127	0.932	−0.140	.894	−2.127 to 1.873
Adjustment of smoking	0.076	2.083	0.040	.971	−4.391 to 4.543
Adjustment of BMI	−0.052	2.144	−0.020	.981	−4.650 to 4.567
Adjustment of alcohol	−0.140	1.214	−0.110	.910	−2.743 to 2.464
Adjustment of physical activity	0.159	1.087	0.150	.886	−2.173 to 2.491
Adjustment of marital status	0.091	0.979	0.090	.927	−2.009 to 2.191

Publication bias and sensitivity analysis

There was no indication of publication bias in the reporting of results on PM_2.5 and risk of skin cancer, from either visualization of Begg’s funnel plot (p = .395) or the Egger’s test (p = .395). In sensitivity analysis, each study was sequentially deleted at a time, and the remaining data were re-calculated, which estimated showed that the result was statistically robust.

Discussion

In our research, we reviewed 28 published articles among 23 cohorts regarding the effect of exposure to PM_2.5 on IHD incidence and mortality, covering 256256 IHD cases. Our findings indicated that IHD incidence and IHD mortality had obvious increases in risk. In our meta-analysis, the results showed that a 10 μg/m³ increase in PM_2.5 exposure was related to a 21% increased risk of IHD mortality and 7% increased risk of IHD incidence. Our results are consistent with data from other meta-analyses.¹⁶ Additionally, we found that the combined RRs of exposure to PM_2.5 on IHD mortality in Asian and European population [1.11 (95% CI: 0.93–1.33); 1.06 (95% CI: 1.02–1.11)] were much lower compared with American and Canadian people [1.27 (95% CI: 1.17–1.37); 1.30 (95% CI: 1.24–1.35)]. Furthermore, study duration, size and some adjustments were related with the total RR.

Atmospheric particulate matter (PM) is segmented into four categories based on aerodynamic diameter: total suspended particulate (TSP ≤ 100 μm), particulate matter (≤ 10 μm), fine particulate matter (≤ 2.5 μm) and ultrafine particles (≤ 0.1 μm). Due to the small particle size, light quality and a relatively large specific surface area, PM_2.5 are considered as ambient air quality management worldwide based on some known cardiorespiratory health effects.⁴⁵ Several potential mechanisms might exit to account for the health effect. First, PM_2.5 enters the respiratory system causing systemic inflammation, oxidative stress and reactive oxygen species. A vivo model experiment revealed that PM_2.5 exacerbated inflammation, vascular remodeling and right ventricular hypertrophy.⁴⁶ PM_2.5 exposure induced systemic inflammation, cerebrovascular oxidative injury, and intracranial atherosclerosis in rat models.⁴⁷ In numerous vitro studies have showed that exposure to PM_2.5 induces apoptosis and oxidative stress in human cells.^48,49 Second, PM_2.5 has the potential to reduce cell viability, induce oxidative DNA damage and induce global DNA methylation.⁵⁰ Extractable organic matters from PM_2.5 significantly enhanced 8-OHdG and γH2AX signals and caused DNA damage.^51,52 Recent studies have demonstrated that PM_2.5 causes cardiovascular pathological damage and functional changes, involving DNA methylation, non-coding RNA, histone modification and chromosome remodeling.^53–55 Next, PM_2.5 may affect the coagulation system, change autonomic nerve function, injure the vascular endothelium and affect vasomotor function. A recent study has demonstrated that PM_2.5 exposure induced more serious inflammation and oxidative stress in the circulation system and promoted a hypercoagulable state by JNK/P53 pathway.⁵⁶ PM_2.5 enters the human body through various pathways to affect central nervous system (CNS) diseases. PM_2.5 activates the microglia in the central nervous system, which can lead to inflammatory and neurological damage.⁵⁷ Moreover, exposure to ambient levels of PM_2.5 were related to chronic reductions in brachial endothelial function. In a mouse model, exposure to low concentration of PM_2.5 altered vasomotor tone, induced vascular inflammation and potentiated atherosclerosis.⁵⁸ PM_2.5 causes other reaction through entering into the circulatory system via the digestive tract. A potential mechanism was found that PM_2.5 was associated with increase serum levels of hormones, PM_2.5 exposure may affect the HPA axis through the gastrointestinal tract microbiota pathway (GBA mechanism).⁵⁹ Additionally, clinical studies have shown that exposure to PM_2.5 can increase insulin resistance and blood pressure. Diabetes and hypertension resulted in higher magnitude associations between PM_2.5 and CVD mortality.^60,61

Some previous studies have synthesized the overall evidence of IHD and PM_2.5 in the past decade. The data from the Global Burden of Disease Study (GBD) in 2017 had revealed that IHD attributable to PM_2.5 resulted in big burdens, especially in Asia, Oceania and sub-Saharan Africa.⁷ While there have been two meta-analyses of long-term PM_2.5 on the risks of IHD mortality. Results from the first meta-analysis of PM_2.5 and incident AMI and IHD mortality, which showed a 23% increased risk of IHD mortality per 10 μg/m³ increase in long-term average PM_2.5 exposure.¹⁶ Another meta-analysis found that acute effect of PM2.5 on IHD mortality was less than the chronic effect, and the combined effect values were much lower in Asian population.⁶² We observed an obvious difference in the risk of IHD mortality in Asian and European population (11%, 6% increased risk, respectively) compared with American and Canadian people (27%, 30% increased risk, respectively). Furthermore, study duration, size and some adjustments were related with the total RR. There are some reasonable explanations. Different regions have different demographics, and the compositions of atmospheric particulate matter are diverse from each other. In addition, the concentration of particulate matter in the air in Asian countries is general higher than in Europe and the USA, so the expose-reaction relationship curve tends to be flat at high concentration. In sensitivity analysis, we found the combined RRs was robust. Considering that the positive results are often easier to publish than negative results, therefore, there is still a certain publication bias in analyzing the effect of PM_2.5 on IHD mortality. In addition, there was a high heterogeneity in the literature included in this study, which may be caused by different assessment methods of exposure to PM_2.5, population susceptibility and adjustment methods of confounding factors in different studies.

In our meta-analysis, there is several shortcomings. Primarily, other air pollutants such as sulfur dioxide (SO₂₎, nitrogen dioxide (NO₂), carbon monoxide (CO) and ozone (O₃) may also contribute to IHD death.^63,64 However, only the effect estimates of the single pollutant model were included, and the potential joint effect and collinearity between multiple pollutants were not considered. Second, high heterogeneity exists in the including literatures, which may be caused by the different exposure levels and different measurement methods, and the differences in susceptibility of populations in different regions. Moreover, we identified characteristics of 28 studies among 23 cohorts. Results from different study designs should be expected to differ systematically, resulting in increased heterogeneity. Therefore, we excluded the randomized controlled trials (RCTs). In addition, studies on PM_2.5 contribution to IHD deaths are still insufficient, thus further evidence will be obtained from well-designed and large-scale prospective studies.

Conclusions

In summary, the current systematic review and meta-analysis demonstrated that exposure of PM_2.5 may increase the risk of IHD incidence and mortality. Given to the limited number of included studies and the potential biases in the present study, the result is still uncertain. Hence, high-quality prospective cohort studies and double-blinded randomized-controlled trials were supposed to investigate the influence of PM_2.5 on IHD. In addition, it’s necessary that more accurate evaluation of PM_2.5 exposure was moved forward in time.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jingyan Cao

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Atmospheric PM 2.5 exposure and risk of ischemic heart disease: A systematic review and meta-analysis of observational studies

Abstract

Keywords

Introduction

Methods

Search strategy

Selection criteria

Data extraction

Literature quality assessment

Statistical analysis

Results

Studies characteristic for mata-analysis

PM2.5 and IHD risk

Subgroup and meta-regression analysis

Publication bias and sensitivity analysis

Discussion

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

PM_2.5 and IHD risk