- Review article
- Open Access
Acute effect of ambient fine particulate matter on heart rate variability: an updated systematic review and meta-analysis of panel studies
Environmental Health and Preventive Medicine volume 25, Article number: 77 (2020)
Decreased heart rate variability (HRV) is a predictor of autonomic system dysfunction, and is considered as a potential mechanism of increased risk of cardiovascular disease (CVD) induced by exposure to particulate matter less than 2.5 μm in diameter (PM2.5). Previous studies have suggested that exposure to PM2.5 may lead to decreased HRV levels, but the results remain inconsistent.
An updated systematic review and meta-analysis of panel studies till November 1, 2019 was conducted to evaluate the acute effect of exposure to ambient PM2.5 on HRV. We searched electronic databases (PubMed, Web of Science, and Embase) to identify panel studies reporting the associations between exposure to PM2.5 and the four indicators of HRV (standard deviation of all normal-to-normal intervals (SDNN), root mean square of successive differences in adjacent normal-to-normal intervals (rMSSD), high frequency power (HF), and low frequency power (LF)). Random-effects model was used to calculate the pooled effect estimates.
A total of 33 panel studies were included in our meta-analysis, with 16 studies conducted in North America, 12 studies in Asia, and 5 studies in Europe. The pooled results showed a 10 μg/m3 increase in PM2.5 exposure which was significantly associated with a − 0.92% change in SDNN (95% confidence intervals (95%CI) − 1.26%, − 0.59%), − 1.47% change in rMSSD (95%CI − 2.17%, − 0.77%), − 2.17% change in HF (95%CI − 3.24%, − 1.10%), and − 1.52% change in LF (95%CI − 2.50%, − 0.54%), respectively. Overall, subgroup analysis suggested that short-term exposure to PM2.5 was associated with lower HRV levels in Asians, healthy population, and those aged ≥ 40 years.
Short-term exposure to PM2.5 was associated with decreased HRV levels. Future studies are warranted to clarity the exact mechanism of exposure to PM2.5 on the cardiovascular system through disturbance of autonomic nervous function.
Cardiovascular disease (CVD) is the major cause of mortality worldwide, which contributed to 17.8 million deaths in 2017 . In recent years, epidemiologic studies have shown that exposure to particulate matter less than 2.5 μm in diameter (PM2.5) increases the risk of CVD [2, 3], and even a short-term exposure to PM2.5 may lead to acute cardiovascular events [4,5,6]. One of the potential mechanisms of PM2.5-related acute cardiovascular events is dysfunction of the autonomic nervous system, which is always assessed by the heart rate variability (HRV) levels [7,8,9]. HRV is regulated by parasympathetic autonomic activation including vagus nerve and sympathetic activation , and is usually assessed by time domain indicators (deviation of all normal-to-normal intervals (SDNN), the root mean square of successive differences between normal heartbeats (rMSSD), and frequency domain indicators (high frequency (HF) and low frequency (LF)). The reduction of any of those 4 indicators reflects a dysfunction of the autonomic nervous system [11, 12].
In recent years, many panel-designed studies on the associations between exposure to PM2.5 and HRV have been published, which could provide direct evidence for acute health effects of exposure to PM2.5 and its potential mechanisms [7, 13]. However, the results remain inconsistent. Some studies reported negative associations between exposure to PM2.5 and HRV, whereas others reported no association [5, 14,15,16]. For example, Wu S et al. examined the relationship between PM2.5 exposure and HRV in 11 taxi drivers during the 2008 Olympic Games and found that SDNN and HF change by − 2.2% (95% confidence intervals (95%CI) − 3.8%, − 0.6%) and − 6.2% (95%CI − 10.7%, − 1.5%) with an interquartile range (IQR, 69.5 μg/m3) increase of PM2.5 exposure, respectively . However, Bartell SM et al. evaluated relationship between exposure to PM2.5 and HRV in 50 elderly people with coronary artery disease and did not found significant association in SDNN (percent change = − 0.92%, 95%CI − 3.79%, 1.95%) or rMSSD (percent change = − 0.26%, 95%CI − 4.74%, 4.22%) with an IQR (16.1 μg/m3) increase of PM2.5 exposure . Wheeler A et al. evaluated the effects of exposure to PM2.5 on HRV in 18 chronic obstructive pulmonary disease (COPD) patients and 12 myocardial infarction (MI) patients and observed a significant effect in COPD patients. However, no significant effects were found in MI patients . The inconsistencies in results may be due to different participants, study designs, sample size, PM2.5 exposure measurement, and so on.
Meta-analysis study can deal with inconsistent findings to evaluate a pooled effect estimates. Despite 2 meta-analysis studies on the associations between PM2.5 exposure and HRV have been published [7, 19], most of included studies were conducted in high-income countries. More than 7 panel-designed studies on the associations between PM2.5 exposure and HRV have been published after the latest meta-analysis. Updated studies, especially those conducted in low- and middle-income countries, should be included in the meta-analysis to provide a more comprehensive evidence of effect of PM2.5 exposure on HRV. We therefore conducted an updated systematic review and meta-analysis of panel-designed studies to examine the acute health effects of exposure to PM2.5 on HRV.
Literature was searched in three online databases (PubMed, Embase, and Web of Science), with published date until November 1, 2019. Only panel-designed studies that examined the associations between ambient PM2.5 and HRV levels were included. The search strategy was a combination of exposure and outcome including the following three main domains: (1) ambient PM2.5 exposure; (2) HRV effects; and (3) panel-designed study . The search strategy was shown in Additional file 1: Appendix A.
We first selected articles by reading titles and abstracts, and then read the full texts of the selected articles to determine whether they should be included in the meta-analysis. Reference lists of all the included studies were also manually searched. Literature was reviewed by two authors independently (ZP N and FF L). Conflicts between the two authors during article selection and data extraction were resolved by discussing with an arbitrator (H X).
Inclusion and exclusion criteria
Inclusion criteria for articles were (1) original peer-reviewed human subjects research studies, (2) panel-designed studies, (3) published in English, (4) quantitative assessment of outdoor (ambient) PM2.5 exposure, and (5) reported to the percent change of HRV with per increment in PM2.5 exposure . We excluded studies if they were (1) toxicological studies, summaries or reviews; and (2) focused on indoor or occupational exposure.
Data were extracted from all eligible studies, including (1) study characteristics: first author, published year, study location, and period; (2) study population: sample size, mean age, the number of males and females, health status; (3) outcome assessment: HRV measures; (4) PM2.5 measurement: PM2.5 exposure assessment method, exposure-time-window, lag effects, mean and standard deviations (SD) of PM2.5 concentrations, increment in PM2.5 used in effect estimates; and (5) effect estimates of the association between PM2.5 and HRV: percentage change and 95% CIs per 10 μg/m3, IQR or SD increment in PM2.5 exposure.
Considering that short-term exposure to PM2.5 may disturb the autonomic balance for only 1 day , and the frequently used exposure-time-window among panel designed studies were within 24 h, we therefore selected results by using PM2.5 exposure of the current day and previous one day as the acute HRV effects of exposure to PM2.5. For studies provided multiple effect estimates, we selected the representative result based on the following criteria: (1) if a study reported multiple effect estimates of both fixed-sites and individual monitors, we selected the results of individual monitors since individual monitors might reflect the real exposure to PM2.5 more accurately; (2) if a study used more than one exposure-time-window, the effect estimate with exposure-time-window nearing 24 h was selected; (3) for study that performed multiple subgroup (locations, health status), we chose the combined effect estimates if it was reported. Otherwise, the results of subgroup were treated as several separate studies; (4) for two or more studies from the same population, only the most recent one with the exposure-time-window nearing 24 h was selected ; and (5) for study that performed multiple statistical models, we extracted the results of full-adjusted models.
The Newcastle-Ottawa Scale (NOS) was used for quality assessment of included studies. The NOS designed 8 items to assess the critical appraisal of the potential risk of bias. Total score of NOS ranged 0–9. Studies scored higher than or equal to 6 were regarded as high-quality, while those scored less than 6 as low-quality . Two authors (ZP N and FF L) worked independently and inconsistencies in quality assessment were resolved through discussion.
As studies reported effect estimates change in different increments of PM2.5 (percent change per 10 μg/m3, IQR, or SD increase in PM2.5 exposure), we first converted them into a standardized form (percent change per 10 μg/m3 increase in PM2.5) using the formula as follows:
Standard error (SE) for each effect estimate was calculated by using the formula: (Upper limit – Lower limit)/3.92.
Heterogeneity among different studies was examined using Chi-square-based Cochran Q-statistics test and standard I2. Random-effects model was used to estimate the overall effect. Sources of heterogeneity were explored using subgroup analyses including location (North America, Europe or Asia), exposure assessment method (individual monitor, fixed site, or others), health status of participants (healthy population or patients), and age (< 40 years or ≥ 40 years). The main reason for dividing studies by age of 40 mainly based on the report of World Health Organization (WHO), which defined people aged 40 years or older as high risk of cardiovascular disease . Besides, meta-regression was conducted to explore if heterogeneity was modified by potential modifier (location, PM2.5 measurement, health status, age, and PM2.5 levels). Subgroup analysis and meta-regression were not performed if the number of one subgroup was less than 5.
The publication bias of included studies was assessed by using funnel plot and Begg’s and Egger’s test. In addition, sensitivity analysis was performed by omitting one study at a time to evaluate if the omission of any study would change the significance of the pooled results. All these statistical analyses were conducted in Stata version 15.0 (StataCorp, College Station, TX, USA).
Selection of studies
As shown in Fig. 1, 1168 articles were identified in the initial searches after removing duplicates. By reviewing the abstracts, 147 studies were downloaded for full-text reading. Forty-one studies satisfied the inclusion criteria. However, 5 articles were excluded because they were from the same population. Three articles were excluded because there were no effect estimates. Finally, a total of 33 studies were included in our meta-analysis (Fig. 1).
Table 1 provided the characteristics of 33 studies included in meta-analysis. There were 29 studies involving SDNN [5, 14,15,16,17,18, 21, 24,25,26,27, 29,30,31, 35,36,37,38,39, 41,42,43,44,45,46, 48, 49], 27 studies involving rMSSD [5, 14, 16, 18, 21, 24,25,26, 29,30,31, 35,36,37,38,39, 41,42,43, 45, 46, 49], 24 studies involving HF [5, 14, 16,17,18, 21, 24,25,26, 28, 31, 33, 34, 37,38,39, 42, 43, 49], and 16 studies involving LF [14, 17, 18, 21, 24,25,26, 31, 34, 37, 38, 42, 43, 49]. The sample size of population participated in panel studies ranged from 9 to 619, and most population were healthy people or patients with CVD. Five studies were conducted in Europe, 16 studies in North America, and 12 studies in Asia. Assessment methods of PM2.5 exposure included fixed site (n = 17), individual monitors (n = 14), and van-based mobile laboratory (n = 2) [5, 32]. All included studies adopted linear mixed-effect models to explore associations between exposure to PM2.5 and HRV change, and most of them adjusted for temperature, humidity, age, and body mass index (BMI). As for the study quality assessment, all included studies were considered as “high quality” (Table S2).
Time-domain measures of HRV and PM2.5 exposure
The pooled effected estimates from 29 studies on SDNN showed a 10 μg/m3 increase in exposure to PM2.5 was associated with a − 0.92% change in SDNN (95%CI − 1.26%, − 0.59%) (Fig. 2). Subgroup analysis by location suggested a larger variation of SDNN in Asian (percent change = − 1.38%, 95%CI − 2.13%, − 0.62%) than in European (percent change = − 0.85%, 95%CI − 2.39%, 0.70%) and North American populations (percent change = − 0.62%, 95%CI − 1.05%, − 0.19%) after short-term exposure to PM2.5. Subgroup analysis by health status indicated that a 10 μg/m3 increase in exposure to PM2.5 was associated with a − 0.67% change (95%CI − 1.02%, − 0.32%) in SDNN among healthy population, which was smaller than in patients (percent change = − 1.19%, 95%CI − 2.04%, − 0.34%). In addition, subgroup analysis by age indicated that the decrease level of SDNN caused by PM2.5 exposure among people aged over 40 (percent change = − 0.97%, 95%CI − 1.37%, − 0.58%) was similar with those aged under 40 years (percent change = − 0.87%, 95%CI − 1.77%, 0.03%) (Table 2).
In total, 27 studies investigated the association of short-term exposure to PM2.5 with rMSSD. Meta-analysis showed that a 10 μg/m3 increase in exposure to PM2.5 was associated with a − 1.47% change in rMSSD (95%CI − 2.17%, − 0.77%) (Fig. 3). Subgroup analysis by study location suggested that the adverse effect of PM2.5 on rMSSD was similar among North American populations (percent change = − 1.64%, 95%CI − 2.89%, − 0.38%) and Asian populations (percent change = − 1.58%, 95%CI − 2.43%, − 0.73%). Short-term exposure to PM2.5 was also associated with decreased rMSSD levels in Europeans, although the association was not statistically significant (percent change = − 0.90%, 95%CI − 2.47%, 0.67%). Furthermore, subgroup analyses by health status showed that the decrease level of rMSSD caused by PM2.5 among healthy population (percent change = − 2.43%, 95%CI − 3.40%, − 1.45%) was larger than in patients (percent change = − 0.87%, 95% CI − 1.58%, − 0.77%). Subgroup analysis by age suggested that the decrease level of rMSSD caused by PM2.5 among people aged over 40 (percent change = − 1.64%, 95%CI − 2.39%, − 0.88%) was larger than those aged under 40 (percent change = − 0.20%, 95%CI − 2.64%, 2.23%) (Table 2).
Frequency-domain indices of HRV and PM2.5 exposure
The pooled effect estimates from 24 studies on HF suggested a negative effect. HF changed by − 2.17% (95%CI − 3.24%, − 1.10%) when PM2.5 concentration increased per 10 μg/m3 (Fig. 4). Subgroup analyses by location showed that decrease of HF level in Asia (percent change = − 2,54%, 95%CI − 3.91%, − 1.17%) was larger than in North American populations (percent change = − 1.18%, 95%CI − 3.22%, 0.86%). Subgroup analyses by health status showed that the decrease of HF in patient was − 0.90% (95%CI − 2.34%, 0.54%), which was smaller than decrease of HF in healthy population (percent change = − 3.40%, 95%CI − 4.97%, − 1.83%). Moreover, subgroup analysis by age suggested a greater decreased HF in people aged over 40 years (percent change = − 2.70%, 95%CI − 4.35%, − 1.05%) than those aged under 40 years (percent change = − 1.19%, 95%CI − 2.35%, 0.15%). Subgroup analyses by PM2.5 assessment did not show a remarkable difference (Table 2).
A total of 16 studies investigated the associations between exposure to PM2.5 and LF. Meta-analysis suggested that LF would change by − 1.52% (95%CI − 2.50%, − 0.54%) with a 10 μg/m3 increment of PM2.5 (Fig. 5). Subgroup analyses by location suggested a − 1.64% (95%CI − 2.94%, − 0.32%) change of LF level in Asia, while a weak but not statistically significant association in North American populations (percent change = − 1.39%, 95%CI − 2.99%, 0.20%). Subgroup analyses by health status revealed a greater effect in healthy population (percent change = − 1.91%, 95%CI − 3.25%, − 0.57%) than patients (percent change = − 1.03%, 95%CI − 2.73%, 0.68%). Besides, subgroup analyses by age showed a higher effect estimate in people aged over 40 years (percent change = − 2.23%, 95%CI − 4.00%, − 0.46%) than people aged under 40 years (percent change = − 0.71%, 95%CI − 1.32%, − 0.11%) (Table 2).
Heterogeneity, meta-regression analysis, publication bias, and sensitivity analysis
Heterogeneity existed in all four measures of HRV (I2 > 50%, P < 0.001). The meta-regression analysis identified that health status could explain the heterogeneity for rMSSD change associated with exposure of PM2.5 (Table S4, Figure S1, Figure S2). Funnel plots of PM2.5 and rMSSD, HF and LF showed a slight asymmetry, but the P values of Begg's test and Egger’s test were all greater than 0.05, demonstrating that publication bias were acceptable (Table S3, Figure S3). However, publication bias may exist among studies on SDNN, since the P value of Egger’s test was less than 0.05. In sensitivity analyses, we found that the average percent changes in time (SDNN, rMSSD) and frequency domains (LF, HF) of HRV were all in the combined confidence interval, suggesting that the results of the meta-analysis were reliable and stable (Figure S4).
Our meta-analysis demonstrated that exposure to ambient PM2.5 was significantly associated with decreased HRV levels, suggesting that PM2.5 may increase the risk of CVD through automatic nervous system dysfunction. A 10 μg/m3 increase in PM2.5 exposure was associated with a − 0.92% change in SDNN, − 1.47% change in rMSSD, − 2.17% change in HF, and − 1.52% change in LF, respectively.
Previous meta-analyses indicated that increased exposure to PM2.5 was negatively associated with HRV levels [7, 19]. For example, Pieters N et al. reported that a 10 μg/m3 increase in PM2.5 was associated with a − 0.12% change in SDNN (95%CI − 0.22%, − 0.03%), − 2.18% change in rMSSD (95%CI − 3.33%, 1.03%), and − 2.44% change in HF (95% CI − 3.76%, − 1.12%), respectively . Buteau S et al. reported that a 10 μg/m3 increase in PM2.5 was associated with a − 2.11% change in SDNN (95%CI − 4.00%, − 0.23%), − 3.29% change in rMSSD (95%CI − 6.32% , − 0.25%), − 4.76% change in LF (95%CI − 12.10%, 2.58%), and − 1.74% change in HF (95%CI − 7.79%, 4.31%), respectively . The pooled effects of Buteau’ study was much higher than our study, especially for rMSSD and LF, possibly because Buteau’ study only conducted the meta-analysis among the older participants.
The results of subgroup analysis showed that percent changes of HRV for a 10 μg/m3 PM2.5 increase among Asians were larger than in North American populations and European populations, which may be attributed to the serious environmental pollution and different composition of particulate matters [50, 51]. Previous studies have reported the air pollution levels in many Asian countries, such as China, were 10 times higher than that in Europe and North America . Combined with the results of our study that the effect estimates of exposure to PM2.5 on HRV in Asia were higher than that in Europe and Northern America, it may partly explain why the risk of PM2.5 on cardiovascular diseases/mortality is higher in Asian countries [53, 54].
We found that the decreased levels of rMSSD, HF, and LF were greater in healthy population than in patients, since patients may take anti-autonomic nerve dysfunction medication, such as benazepril, nimodipine, and thus weaken the effect of PM2.5 on HRV response. In addition, subgroup analysis by age in our study showed a higher effect estimates in people aged over 40 compared with those aged under 40. Previous studies also reported that elders were more vulnerable to the PM2.5-related risk of decreased HRV levels and adverse cardiovascular events [55,56,57]. For example, the European Study of Cohorts for Air Pollution Effects (ESCAPE) project reported that participants aged over 60 years were more sensitive to PM2.5 exposure than the younger participants .
Potential pathophysiological mechanisms included autonomic imbalance, increased oxidative stress, and inflammation, through which PM2.5 may accelerate the development of CVD . The dysfunction of the autonomic nervous system has been found as the major pathway that result in PM2.5-related adverse cardiovascular outcomes [7,8,9]. Both toxicology experiments and epidemiological studies have provided substantial evidence that PM2.5 exposure would decrease HRV level, and then lead to autonomic nervous dysfunction, which subsequently increase the risk of CVD [21, 24,25,26, 60]. For example, Chiarella SE et al. found that the levels of alveolar lavage fluid and plasma catecholamine in mice increased after inhaling PM2.5, with the activation of sympathetic nervous system . Chen SY et al. examined the effects of short-term exposue to urban air pollution among 61 high-risk CVD subjects and found that PM2.5 caused an immediate autonomic nervous dysfunction as well as long-term inflammatory and thrombotic responses .
Some limitations of our meta-analysis should be noticed. Firstly, subgroup analysis and meta-regression by location, PM2.5 measurement, and age group did not explain the observed heterogeneity. Meta-regression suggested that heterogeneity among exposure to PM2.5 and rMSSD changes may be explained by health status. Secondly, we could not assess the effect of lag more than 24 h of PM2.5 exposure, because there were large differences on the reported lag effects of PM2.5 exposure among different studies and many studies were not represented in the percent change of lag effects. Finally, we failed to explore gender disparities in associations between PM2.5 exposure and HRV levels because most of the panel studies including our meta-analysis only reported the combined effects estimates both male and female participants and subgroup effects estimates were not represented.
Our study demonstrated that exposure to PM2.5 was associated with decreased levels of HRV, suggesting that exposure to ambient PM2.5 may increase CVD risk through the activation of autonomic nervous system. Further studies should be conducted to clarity the specific mechanism of exposure to PM2.5 on health effects.
Availability of data and materials
All the data generated or analyzed during this study are included in this published article.
James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392:1789–858. https://doi.org/10.1016/s0140-6736(18)32279-7.
Li N, Chen G, Liu F, Mao S, Liu Y, Hou Y, et al. Associations of long-term exposure to ambient PM1 with hypertension and blood pressure in rural Chinese population: The Henan rural cohort study. Environ Int. 2019;128:95–102. https://doi.org/10.1016/j.envint.2019.04.037.
Yang BY, Qian Z, Howard SW, Vaughn MG, Fan SJ, Liu KK, et al. Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis. Environ Pollut. 2018;235:576–88. https://doi.org/10.1016/j.envpol.2018.01.001.
Mustafic H, Jabre P, Caussin C, Murad MH, Escolano S, Tafflet M, et al. Main air pollutants and myocardial infarction: a systematic review and meta-analysis. Jama. 2012;307:713–21. https://doi.org/10.1001/jama.2012.126.
Bartell SM, Longhurst J, Tjoa T, Sioutas C, Delfino RJ. Particulate air pollution, ambulatory heart rate variability, and cardiac arrhythmia in retirement community residents with coronary artery disease. Environ Health Perspect. 2013;121:1135–41. https://doi.org/10.1289/ehp.1205914.
Buteau S, Goldberg MS. Methodological issues related to pooling results from panel studies of heart rate variability and its association with ambient air pollution. Environ Res. 2015;140:462–5. https://doi.org/10.1016/j.envres.2015.05.002.
Buteau S, Goldberg MS. A structured review of panel studies used to investigate associations between ambient air pollution and heart rate variability. Environ Res. 2016;148:207–47. https://doi.org/10.1016/j.envres.2016.03.013.
Franchini M, Mannucci PM. Thrombogenicity and cardiovascular effects of ambient air pollution. Blood. 2011;118:2405–12. https://doi.org/10.1182/blood-2011-04-343111.
Nelin TD, Joseph AM, Gorr MW, Wold LE. Direct and indirect effects of particulate matter on the cardiovascular system. Toxicol Lett. 2012;208:293–9. https://doi.org/10.1016/j.toxlet.2011.11.008.
Stein PK, Pu Y. Heart rate variability, sleep and sleep disorders. Sleep medicine reviews. 2012;16:47–66. https://doi.org/10.1016/j.smrv.2011.02.005.
Koch C, Wilhelm M, Salzmann S, Rief W, Euteneuer F. A meta-analysis of heart rate variability in major depression. Psychol Med. 2019;49:1948–57. https://doi.org/10.1017/S0033291719001351.
Shaffer F, McCraty R, Zerr CL. A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Front Psychol. 2014;5:1040. https://doi.org/10.3389/fpsyg.2014.01040.
Schneider A, Neas LM, Graff DW, Herbst MC, Cascio WE, Schmitt MT, et al. Association of cardiac and vascular changes with ambient PM2.5 in diabetic individuals. Part Fibre Toxicol. 2010;7:14. https://doi.org/10.1186/1743-8977-7-14.
Wu CF, Kuo IC, Su TC, Li YR, Lin LY, Chan CC, et al. Effects of personal exposure to particulate matter and ozone on arterial stiffness and heart rate variability in healthy adults. Am J Epidemiol. 2010;171:1299–309. https://doi.org/10.1093/aje/kwq060.
Lee MS, Eum KD, Fang SC, Rodrigues EG, Modest GA, Christiani DC. Oxidative stress and systemic inflammation as modifiers of cardiac autonomic responses to particulate air pollution. Int J Cardiol. 2014;176:166–70. https://doi.org/10.1016/j.ijcard.2014.07.012.
Suh HH, Zanobetti A. Exposure error masks the relationship between traffic-related air pollution and heart rate variability. J Occup Environ Med. 2010;52:685–92. https://doi.org/10.1097/JOM.0b013e3181e8071f.
Wu S, Deng F, Niu J, Huang Q, Liu Y, Guo X. Association of heart rate variability in taxi drivers with marked changes in particulate air pollution in Beijing in 2008. Environ Health Perspect. 2010;118:87–91. https://doi.org/10.1289/ehp.0900818.
Wheeler A, Zanobetti A, Gold DR, Schwartz J, Stone P, Suh HH. The relationship between ambient air pollution and heart rate variability differs for individuals with heart and pulmonary disease. Environ Health Perspect. 2006;114:560–6. https://doi.org/10.1289/ehp.8337.
Pieters N, Plusquin M, Cox B, Kicinski M, Vangronsveld J, Nawrot TS. An epidemiological appraisal of the association between heart rate variability and particulate air pollution: a meta-analysis. Heart. 2012;98:1127–35. https://doi.org/10.1136/heartjnl-2011-301505.
Luben TJ, Nichols JL, Dutton SJ, Kirrane E, Owens EO, Datko-Williams L, et al. A systematic review of cardiovascular emergency department visits, hospital admissions and mortality associated with ambient black carbon. Environ Int. 2017;107:154–62. https://doi.org/10.1016/j.envint.2017.07.005.
Chen SY, Chan CC, Su TC. Particulate and gaseous pollutants on inflammation, thrombosis, and autonomic imbalance in subjects at risk for cardiovascular disease. Environ Pollut. 2017;223:403–8. https://doi.org/10.1016/j.envpol.2017.01.037.
Liu F, Chen G, Huo W, Wang C, Liu S, Li N, et al. Associations between long-term exposure to ambient air pollution and risk of type 2 diabetes mellitus: A systematic review and meta-analysis. Environ Pollut. 2019;252:1235–45. https://doi.org/10.1016/j.envpol.2019.06.033.
Kaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, et al. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7:e1332–e45. https://doi.org/10.1016/s2214-109x(19)30318-3.
Pan L, Dong W, Li H, Miller MR, Chen Y, Loh M, et al. Association patterns for size-fractioned indoor particulate matter and black carbon and autonomic function differ between patients with chronic obstructive pulmonary disease and their healthy spouses. Environ Pollut. 2018;236:40–8. https://doi.org/10.1016/j.envpol.2018.01.064.
Shutt RH, Kauri LM, Weichenthal S, Kumarathasan P, Vincent R, Thomson EM, et al. Exposure to air pollution near a steel plant is associated with reduced heart rate variability: a randomised crossover study. Environ Health. 2017;16:4. https://doi.org/10.1186/s12940-016-0206-0.
Lim YH, Bae HJ, Yi SM, Park E, Lee BE, Hong YC. Vascular and cardiac autonomic function and PM2.5 constituents among the elderly: A longitudinal study. Sci Total Environ. 2017;607-608:847–54. https://doi.org/10.1016/j.scitotenv.2017.07.077.
Lee MS, Eum KD, Rodrigues EG, Magari SR, Fang SC, Modest GA, et al. Effects of personal exposure to ambient fine particulate matter on acute change in nocturnal heart rate variability in subjects without overt heart disease. Am J Cardiol. 2016;117:151–6. https://doi.org/10.1016/j.amjcard.2015.10.015.
Xie Y, Bo L, Jiang S, Tian Z, Kan H, Li Y, et al. Individual PM2.5 exposure is associated with the impairment of cardiac autonomic modulation in general residents. Environ Sci Pollut Res Int. 2016;23:10255–61. https://doi.org/10.1007/s11356-015-5933-1.
Peters A, Hampel R, Cyrys J, Breitner S, Geruschkat U, Kraus U, et al. Elevated particle number concentrations induce immediate changes in heart rate variability: a panel study in individuals with impaired glucose metabolism or diabetes. Part Fibre Toxicol. 2015;12:7. https://doi.org/10.1186/s12989-015-0083-7.
Liu WT, Ma CM, Liu IJ, Han BC, Chuang HC, Chuang KJ. Effects of commuting mode on air pollution exposure and cardiovascular health among young adults in Taipei, Taiwan. Int J Hyg Environ Health. 2015;218:319–23. https://doi.org/10.1016/j.ijheh.2015.01.003.
Xu MM, Jia YP, Li GX, Liu LQ, Mo YZ, Jin XB, et al. Relationship between ambient fine particles and ventricular repolarization changes and heart rate variability of elderly people with heart disease in Beijing, China. Biomed Environ Sci. 2013;26:629–37. https://doi.org/10.3967/0895-3988.2013.08.001.
Shields KN, Cavallari JM, Hunt MJ, Lazo M, Molina M, Molina L, et al. Traffic-related air pollution exposures and changes in heart rate variability in Mexico City: a panel study. Environ Health. 2013;12:7. https://doi.org/10.1186/1476-069x-12-7.
Huang J, Deng F, Wu S, Lu H, Hao Y, Guo X. The impacts of short-term exposure to noise and traffic-related air pollution on heart rate variability in young healthy adults. J Expo Sci Environ Epidemiol. 2013;23:559–64. https://doi.org/10.1038/jes.2013.21.
Jia X, Song X, Shima M, Tamura K, Deng F, Guo X. Effects of fine particulate on heart rate variability in Beijing: a panel study of healthy elderly subjects. Int Arch Occup Environ Health. 2012;85:97–107. https://doi.org/10.1007/s00420-011-0646-3.
Rich DQ, Zareba W, Beckett W, Hopke PK, Oakes D, Frampton MW, et al. Are ambient ultrafine, accumulation mode, and fine particles associated with adverse cardiac responses in patients undergoing cardiac rehabilitation? Environ Health Perspect. 2012;120:1162–9. https://doi.org/10.1289/ehp.1104262.
Hampel R, Breitner S, Schneider A, Zareba W, Kraus U, Cyrys J, et al. Acute air pollution effects on heart rate variability are modified by SNPs involved in cardiac rhythm in individuals with diabetes or impaired glucose tolerance. Environ Res. 2012;112:177–85. https://doi.org/10.1016/j.envres.2011.10.007.
Huang W, Zhu T, Pan X, Hu M, Lu SE, Lin Y, et al. Air pollution and autonomic and vascular dysfunction in patients with cardiovascular disease: interactions of systemic inflammation, overweight, and gender. Am J Epidemiol. 2012;176:117–26. https://doi.org/10.1093/aje/kwr511.
Chuang KJ, Chan CC, Su TC, Lee CT, Tang CS. The effect of urban air pollution on inflammation, oxidative stress, coagulation, and autonomic dysfunction in young adults. Am J Respir Crit Care Med. 2007;176:370–6. https://doi.org/10.1164/rccm.200611-1627OC.
Zanobetti A, Gold DR, Stone PH, Suh HH, Schwartz J, Coull BA, et al. Reduction in heart rate variability with traffic and air pollution in patients with coronary artery disease. Environ Health Perspect. 2010;118:324–30. https://doi.org/10.1289/ehp.0901003.
Schneider A, Hampel R, Ibald-Mulli A, Zareba W, Schmidt G, Schneider R, et al. Changes in deceleration capacity of heart rate and heart rate variability induced by ambient air pollution in individuals with coronary artery disease. Part Fibre Toxicol. 2010;7:29. https://doi.org/10.1186/1743-8977-7-29.
Folino AF, Scapellato ML, Canova C, Maestrelli P, Bertorelli G, Simonato L, et al. Individual exposure to particulate matter and the short-term arrhythmic and autonomic profiles in patients with myocardial infarction. Eur Heart J. 2009;30:1614–20. https://doi.org/10.1093/eurheartj/ehp136.
Yeatts K, Svendsen E, Creason J, Alexis N, Herbst M, Scott J, et al. Coarse particulate matter (PM2.5-10) affects heart rate variability, blood lipids, and circulating eosinophils in adults with asthma. Environ Health Perspect. 2007;115:709–14. https://doi.org/10.1289/ehp.9499.
Adar SD, Gold DR, Coull BA, Schwartz J, Stone PH, Suh H. Focused exposures to airborne traffic particles and heart rate variability in the elderly. Epidemiology. 2007;18:95–103. https://doi.org/10.1097/01.ede.0000249409.81050.46.
Timonen KL, Vanninen E, de Hartog J, Ibald-Mulli A, Brunekreef B, Gold DR, et al. Effects of ultrafine and fine particulate and gaseous air pollution on cardiac autonomic control in subjects with coronary artery disease: the ULTRA study. J Expo Sci Environ Epidemiol. 2006;16:332–41. https://doi.org/10.1038/sj.jea.7500460.
Riediker M, Cascio WE, Griggs TR, Herbst MC, Bromberg PA, Neas L, et al. Particulate matter exposure in cars is associated with cardiovascular effects in healthy young men. Am J Respir Crit Care Med. 2004;169:934–40. https://doi.org/10.1164/rccm.200310-1463OC.
Schwartz J, Litonjua A, Suh H, Verrier M, Zanobetti A, Syring M, et al. Traffic related pollution and heart rate variability in a panel of elderly subjects. Thorax. 2005;60:455–61. https://doi.org/10.1136/thx.2004.024836.
Magari SR, Schwartz J, Williams PL, Hauser R, Smith TJ, Christiani DC. The association between personal measurements of environmental exposure to particulates and heart rate variability. Epidemiology. 2002;13:305–10. https://doi.org/10.1097/00001648-200205000-00011.
Brauer M, Ebelt ST, Fisher TV, Brumm J, Petkau AJ, Vedal S. Exposure of chronic obstructive pulmonary disease patients to particles: respiratory and cardiovascular health effects. J Expo Anal Environ Epidemiol. 2001;11:490–500. https://doi.org/10.1038/sj.jea.7500195.
Luttmann-Gibson H, Suh HH, Coull BA, Dockery DW, Sarnat SE, Schwartz J, et al. Short-term effects of air pollution on heart rate variability in senior adults in Steubenville. Ohio. J Occup Environ Med. 2006;48:780–8. https://doi.org/10.1097/01.jom.0000229781.27181.7d.
Song X, Liu Y, Hu Y, Zhao X, Tian J, Ding G, et al. Short-term exposure to air pollution and cardiac arrhythmia: a meta-analysis and systematic review. Int J Environ Res Public Health. 2016;13. https://doi.org/10.3390/ijerph13070642.
Tibuakuu M, Michos ED, Navas-Acien A, Jones MR. Air pollution and cardiovascular disease: a focus on vulnerable populations worldwide. Curr Epidemiol Rep. 2018;5:370–8. https://doi.org/10.1007/s40471-018-0166-8.
Lu F, Xu D, Cheng Y, Dong S, Guo C, Jiang X, et al. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environ Res. 2015;136:196–204. https://doi.org/10.1016/j.envres.2014.06.029.
Shah ASV, Langrish JP, Nair H, McAllister DA, Hunter AL, Donaldson K, et al. Global association of air pollution and heart failure: a systematic review and meta-analysis. Lancet. 2013;382:1039–48. https://doi.org/10.1016/s0140-6736(13)60898-3.
Maji KJ, Arora M, Dikshit AK. Burden of disease attributed to ambient PM2.5 and PM10 exposure in 190 cities in China. Environ Sci Pollut Res Int. 2017;24:11559–72. https://doi.org/10.1007/s11356-017-8575-7.
Pun VC, Kazemiparkouhi F, Manjourides J, Suh HH. Long-term PM2.5 exposure and respiratory, cancer, and cardiovascular mortality in older US adults. Am J Epidemiol. 2017;186:961–9. https://doi.org/10.1093/aje/kwx166.
Goldberg MS, Burnett RT, Stieb DM, Brophy JM, Daskalopoulou SS, Valois MF, et al. Associations between ambient air pollution and daily mortality among elderly persons in Montreal, Quebec. Sci Total Environ. 2013;463-464:931–42. https://doi.org/10.1016/j.scitotenv.2013.06.095.
Zhong P, Huang S, Zhang X, Wu S, Zhu Y, Li Y, et al. Individual-level modifiers of the acute effects of air pollution on mortality in Wuhan, China. Glob Health Res Policy. 2018;3:27. https://doi.org/10.1186/s41256-018-0080-0.
Cesaroni G, Forastiere F, Stafoggia M, Andersen ZJ, Badaloni C, Beelen R, et al. Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. BMJ. 2014;348:f7412. https://doi.org/10.1136/bmj.f7412.
Liu Q, Gu X, Deng F, Mu L, Baccarelli AA, Guo X, et al. Ambient particulate air pollution and circulating C-reactive protein level: A systematic review and meta-analysis. Int J Hyg Environ Health. 2019;222:756–64. https://doi.org/10.1016/j.ijheh.2019.05.005.
Chiarella SE, Soberanes S, Urich D, Morales-Nebreda L, Nigdelioglu R, Green D, et al. beta(2)-Adrenergic agonists augment air pollution-induced IL-6 release and thrombosis. J Clin Invest. 2014;124:2935–46. https://doi.org/10.1172/JCI75157.
The authors acknowledge all the participants and administrators of this study.
This work was supported by the Bill & Melinda Gates Foundation (Grant No. OOP1148464), National Natural Science Funding of China (Grant No. 81903279) and the Natural Science Fund of Hubei Province (Granter number: 2018CFB634).
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Niu, Z., Liu, F., Li, B. et al. Acute effect of ambient fine particulate matter on heart rate variability: an updated systematic review and meta-analysis of panel studies. Environ Health Prev Med 25, 77 (2020). https://doi.org/10.1186/s12199-020-00912-2
- Fine particulate matter
- Heart rate variability
- Panel study