Next Article in Journal
The Utility of Capsicum annuum L. in Internal Medicine and In Dentistry: A Comprehensive Review
Previous Article in Journal
Greencoin as an AI-Based Solution Shaping Climate Awareness
Previous Article in Special Issue
Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 18
10.3390/ijerph191811186
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Short-Term Exposure to Ozone and Heart Rate Variability: A Systematic Review and Meta-Analysis

by
Zhiqiang Zong
1,
Mengyue Zhang
1,
Kexin Xu
1,
Yunquan Zhang
2,* and
Chengyang Hu
3,4,*
1
Department of Clinical Medicine, The Second School of Clinical Medicine, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
2
School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
3
Department of Humanistic Medicine, School of Humanistic Medicine, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
4
Department of Epidemiology and Biostatistics, School of Public health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(18), 11186; https://doi.org/10.3390/ijerph191811186
Submission received: 23 July 2022 / Revised: 29 August 2022 / Accepted: 3 September 2022 / Published: 6 September 2022
(This article belongs to the Special Issue Air Pollution and Cardiorespiratory Health: Population-Based Insights)
Browse Figures
Versions Notes

Abstract

:
At present, ambient air pollution poses a significant threat to patients with cardiovascular disease (CVD). The heart rate variability (HRV) is a marker of the cardiac autonomic nervous system, and it is related to air pollution and cardiovascular disease. There is, however, considerable disagreement in the literature regarding the association between ozone (O3) and HRV. To further investigate the effects of short-term exposure to O3 on HRV, we conducted the first meta-analysis of relevant studies. The percentage change of HRV indicator(s) is the effect estimate extracted for the quantitative analysis in this study. In our meta-analysis, per 10 ppb increase in O3 was significantly associated with decreases in the time-domain measurements, for standard deviation of the normal-to-normal (NN) interval (SDNN) −1.11% (95%CI: −1.35%, −0.87%) and for root mean square of successive differences (RMSSD) −3.26% (95%CI: −5.42%, −1.09%); in the frequency-domain measurements, for high frequency (HF) −3.01% (95%CI: −4.66%, −1.35%) and for low frequency (LF) −2.14% (95%CI: −3.83%, −0.45%). This study showed short-term exposure to O3 was associated with reduced HRV indicators in adults, which suggested that the cardiac autonomic nervous system might be affected after O3 exposure, contributing to the association between O3 exposure and CVD risk.

1. Introduction

The formation of ozone (O3) in the atmosphere is normally caused by the reaction between nitrogen oxides and volatile organic compounds under solar irradiation. At present, it is one of the most important pollutants associated with traffic in urban and industrialized areas and has been linked to a number of health outcomes, including cardiovascular diseases [1,2,3].
Globally, cardiovascular disease (CVD) is one of the leading causes of death, and its incidence is expected to rise steadily in the next decade [4]. Environmental air pollution has been estimated to be a major contributor to cardiovascular mortality worldwide [5], with a recent study reporting that cardiovascular disease was responsible for 18.6 million deaths in 2019 [6]. Nevertheless, there is still uncertainty as to whether short-term O3 exposure is causal and biologically responsible for higher cardiometabolic risks [7,8]. There has been recent research conducted on the impact of exposure to O3 on cardiovascular systems, but a consensus has not been reached due to varied reasons, such as study design, study population, or exposure measurement method [9,10]. In light of this, it is necessary to further investigate the impact of O3 exposure on cardiovascular health.
One method of predicting CVD risk is using surrogate markers, and heart rate variability (HRV) has been shown to be a reliable predictor. Specifically, an increase in HRV indicates that the autonomic nervous system (ANS) is well adapted and functioning efficiently, while a decrease in HRV is often an indication that the ANS has not been sufficiently adapted [11]. All HRV measures are calculated by recording and analyzing the interval between adjacent heartbeats, the inter beat interval (IBI in milliseconds). The most common method of measuring HRV is electrocardiography (ECG). The operationalization of HRV can be classified into two broad categories: time-domain and frequency-domain measures. Time-domain indices are derived directly from the R-R interval series and generally measure the variability contained therein by applying simple statistical computations, such as standard deviation of the normal-to-normal (NN) interval (SDNN) in milliseconds or log-transformed values, or root mean square of successive differences (RMSSD) between adjacent R-R intervals in milliseconds or log-transformed values [12]. Frequency-domain indices have been successfully used to evaluate the cardiac autonomic nervous system, of which high frequency (HF: 0.15–0.40 Hz) spectral power primarily reflects parasympathetic influences, whereas low frequency power (LF: 0.04–0.15 Hz) has been shown to reflect both sympathetic and parasympathetic influences [13,14].
Over the past several years, some studies have assessed the effects of short-term O3 exposure on HRV metrics; however, these results were inconsistent and a more comprehensive study is needed to elucidate the potential relationships [10,15,16]. In the meantime, the heterogeneity of the results across the epidemiologic literature warrants further investigation to better understand the underlying reasons contributing to these disparate findings in order to ultimately determine whether O3 exposure adversely affects the cardiovascular system.
To address this question of surrogate marker of CVD risk related to ozone exposure, we conducted a systematic review and meta-analysis of studies examining associations between short-term O3 exposure (measured on a continuous, rather than categorical, scale) and HRV metrics in the general population. This review uses the Population, Exposure, Comparator, Outcome, Study Design (PECOS) statement shown in Table 1.

2. Materials and Methods

The protocol of this study was not registered in PROSPERO.

2.1. Study Question

The search question was: “Among the general population, what is the effect of a higher exposure to ozone compared to lower level of ozone exposure on HRV indices?”.

2.2. Search Strategy

The PubMed, Embase, and Web of Science databases were searched for eligible studies between inception and 1 June 2022 using the following keywords, which are representative of the exposure and outcomes as described in our PECOS statement: (ozone or O3 or air pollution) AND (heart rate variability or HRV or root mean square of successive heartbeat interval differences or RMSSDs or standard deviation of NN intervals or SDNN) (Supplementary Materials). PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) (Supplementary Materials) were followed in the reporting of this meta-analysis.

2.3. Study Selection

The eligibility criteria for the PECOS are summarized in Table 1. The study population was the general population. A 24-h average of ambient O3 exposure was used to correspond to personal exposure before the HRV protocol. The effect estimates (percentage change (%) and 95% confidence interval (CI)) in the indicators of HRV for an increase in O3 exposure by 10 ppb for a continuous exposure was considered. The outcome was HRV indicators, including RMSSD, SDNN, LF, and HF.
Among the studies included in this review were cohort, case–control, and cross-sectional studies that examined the relationships between O3 and indicators of HRV published in English. We excluded conference papers, reviews, meta-analyses, and commentaries from the analysis. In order to be eligible for the analysis, studies had to be conducted in the general population and they had to contain original data providing effect estimates on at least one of these four HRV indicators: RMSSD, SDNN, LF, and HF. Those studies with overlapping populations and information were excluded from the review. In this case, we retained the publication providing the most complete information AND/OR the most representative population. Figure 1 shows the flow chart of the study selection process.
We selected studies following the screening of titles and abstracts by two investigators (Z.Q.Z. and M.Y.Z.), with any discrepancy being resolved by a third investigator (C.Y.H.); next, the retained potential eligible studies were screened on full-text reading.

2.4. Data Extraction and Quality Assessment

From each study, two investigators (Z.Z. and M.Z.) extracted the following data: author name, publication date, study country, study population, sample size, exposure assessment, indicator(s) of HRV, and adjusted covariates. For the purpose of gathering unpublished data, authors were contacted directly when it was considered appropriate. Meta-analysis was performed using the most fully adjusted effect estimate that represents the greatest control over potential confounders.
Cohort, panel, and case–crossover studies were assessed using Newcastle Ottawa Scale (NOS). There are eight items in the NOS, and the items are categorized into three dimensions, including selection, comparability, and outcomes. Studies were evaluated based on an NOS score from 0 to 9, with a score greater than 7 indicating high quality, a score between 5 and 6 indicating moderate quality, and one less than 5 indicating low quality [17].

2.5. Statistical Analysis

Meta-analyses were performed using fixed-effect or random-effects models for the associations of O3 exposure with four common indicators of HRV including two time-domain parameters (RMSSD and SDNN) and two frequency-domain parameters (LF and HF). As most of the included studies reported the HRV measurements on a logarithmic scale, we thus excluded the studies with linear scale models since they were not comparable. The effect estimates of each included study were presented per standard deviation (SD) or interquartile range (IQR) change of the O3 level, and they were converted into per 10 ppb increase in O3. We calculated the percentage change in accordance with the methodology of a previous meta-analysis that assessed the associations of PM2.5 exposure with HRV [18].
In order to determine publication bias, funnel plots and Egger’s regression tests were utilized, and a p-value of Egger’s test less than 0.05 was found to indicate the presence of publication bias. Additionally, the trim-and-fill method was used to evaluate the impact of publication bias when appropriate. Evaluation for presence of heterogeneity was carried out using (1) Cochran’s Q-test with p-value less than 0.05 signifying heterogeneity and (2) I2 statistics, where I2 greater than 50% indicated substantial heterogeneity [19]. The potential source(s) of heterogeneity between the studies was explored using subgroup analysis, based on the characteristics of the original studies and based on the possible influence factors. We performed leave-one-out analyses in order to identify potential outliers and influential studies as sensitivities. All the analyses were performed with Stata version 15.1 (Stata Corp, College Station, TX, USA).

3. Results

3.1. Characteristics of Included Studies

In total, 8339 records were retrieved from three electronic bibliographic databases. The flow chart shows the detailed screening process (Figure 1). A total of 13 studies were eventually included in our systematic review and meta-analysis, of which 10 were panel studies, 2 were case–crossover studies, and 1 was cohort study [20,21,22,23,24,25,26,27,28,29,30,31,32]. There were 8 studies conducted in North America and 5 in East Asia. The quality of all the included studies was assessed as moderate to high. Table 2 provides a more detailed overview of the included studies.

3.2. Association between O3 Exposure and HRV

Thirteen studies have examined the relationships between short-term exposure to O3 and indicators of HRV. Per 10 ppb increase in O3 exposure was associated with a decrease in the indicators of HRV. Specifically, meta-analyses on associations of O3 exposure with RMSSD, HF, and LF showed moderate to high between-study heterogeneity and therefore a random-effects model was used, while for SDNN, a fixed-effects model was used due to the low between-study heterogeneity. As shown in Figure 2, the pooled estimates were −1.11% (95%CI: −1.35% to −0.87%, I2 = 0.0%) for SDNN and −3.26% (95%CI: −5.42% to −1.09%, I2 = 77.7%) for RMSSD, respectively. Similarly, the pooled estimates were −3.01% (95%CI: −4.66% to −1.35%, I2 = 59.7%) for HF and −2.14% (95%CI: −3.83% to −0.45%, I2 = 56.6%) for LF, respectively.

3.3. Subgroup Analysis

We performed the subgroup analyses by age of participants (≤35 year or ≥55 year), study location (North America or East Asia), length of ECG recording (≤30 min or others), O3 exposure (≤24 h or others), exposure assessment (fixed-site exposure or personal exposure), and quality of study (high or moderate) (Table 3). Subgroup analyses indicated that the effects of O3 exposure on the indicators of HRV in North America were more pronounced than in East Asia. For each assessed indicator of HRV, the associations seem inconsistent with each other by some stratified factors.

3.4. Sensitivity Analysis

Sensitivity analyses were performed to assess the stability of the results. Generally, the pooled estimates of O3 exposure on HRV indicators, such as RMSSD, HF, and LF, did not significantly change before and after systematically excluding each study, indicating the robustness of results (Figure 3). The indicator of SDDN, omitting one study at each time, showed that Chuang et al. 2007 was an influential study (Figure 3). When this study was excluded, we observed a non-significant association between O3 exposure and SDNN (−0.54%; 95%CI: −1.41% to 0.33, I2 = 0%).

3.5. Publication Bias

We constructed vertical funnel plots and Egger’s tests to assess the publication bias for each O3 and HRV indicator combination. Vertical funnel plots showed basic symmetry (Figure 4). The p values for Egger’s tests were 0.090 for SDNN, 0.702 for RMSSD, 0.231 for HF, and 0.511 for LF, which indicates that there is no evidence of publication bias (p > 0.05).

4. Discussion

The purpose of this meta-analysis was to provide evidence that elevated levels of O3 can increase the risk of cardiovascular diseases in adults. In our meta-analysis, we evaluated the effects of O3 on HRV based on 13 observational studies conducted among the adults. According to the present meta-analysis, short-term exposure to O3 is associated with a decrease in HRV indices. The positive associations indicated that O3 may alter cardiac autonomic function and thus increase the risk of cardiovascular events. To the best of our knowledge, this is the most comprehensive meta-analysis which specifically evaluates the association between O3 exposure and HRV.
In general, the literature on the association between O3 exposure in the short-term and HRV is still scarce. The low number of studies included in the current meta-analysis, as well as the moderate to high heterogeneity observed in the study, may obscure the true association between O3 exposure and cardiovascular disease risk. Previous meta-analyses have shown that short-term exposure to O3 is associated with a variety of adverse health outcomes, including asthma exacerbations [33], pneumonia in children [34], pulmonary embolisms [35], and atrial fibrillation [36]. Despite this, no meta-analysis has been conducted on the connection between O3 exposure and cardiovascular disease. In the present meta-analysis, we emphasized the importance of integrating the results obtained from studies of people with cardiovascular disease with those obtained from studies of healthy individuals. The reason for this is that most previous studies have focused on older individuals with cardiopulmonary disease; there are only a small number of studies that examine associations among healthy and younger individuals. HRV indices have been shown to vary with cardiovascular status and drug mediation, and HRV responses to O3 stimulation are also thought to be affected by health status and drug mediation [37,38,39,40]. To gain a deeper understanding of the exposure–response association, evidence obtained from healthy people is essential.
The mechanisms by which exposure to O3 increases CVD risk have yet to be fully determined. It is a well-established fact that imbalance of ANS, as indicated by a disturbance of HRV, is one of the most important mechanisms by which O3 exposure increases the risk of adverse cardiovascular events [24,41]. An increase in LF/HF ratios and the withdrawal of parasympathetic nerves have been demonstrated as a key pathway in cardiovascular disease morbidity and mortality [42,43,44,45]. Furthermore, neuroendocrine stress responses have also been shown to contribute to cardiometabolic disease development. Hypothalamic–pituitary–adrenal (HPA) and sympathetic adrenal medullary (SAM) are essential components of neuroendocrine systems that maintain homeostasis in response to acute environmental stimuli [29,46]. High levels of O3 exposure may activate the HPA and SAM axis, triggering the release of stress hormones, such as corticotropin-releasing factor (CRF), adrenocorticotropic hormone (ACTH), cortisol, adrenaline, and noradrenaline, which further contribute to cardiovascular and metabolic dysfunction [47,48,49]. In addition, induction of oxidative stress and systemic inflammation are possible pathways through which O3 may affect the cardiovascular system. The initial responses to oxidant injury and inflammation may eventually result in endothelial dysfunction, acute arterial vasoconstriction, procoagulant activity, and atherosclerosis. The stimulation of nociceptive fibers in the airways may result in changes in sympathetic and/or parasympathetic tone, which may result in the onset of cardiac arrhythmias [16]. In general, autonomic dysfunction, neuroendocrine stress response, oxidative stress, and inflammation may be contributing factors to the increased cardiovascular risk associated with exposure to O3.
Moderate to high heterogeneity was detected in the meta-analysis and we further performed subgroup analyses. Several possible categorical variables were identified, such as age of participants, that could explain the heterogeneity among the combinations of O3 exposure with RMSSD and HF. We also observed a positive association between O3-HRV (significant percentage changes of SDNN, HF, and LF) in Asia, where levels of air pollution are much higher than North America [50]. From 2013 onwards, surface O3 levels have increased rapidly in China, during the warm season [51]. In the included studies, two methods of assessing O3 exposure were used, namely fixed-site monitoring and personal monitoring. The majority of studies on O3 exposure and cardiological diseases used measurements collected at centrally located monitoring stations or fixed-site; however, this method may introduce bias and distort epidemiological associations since it does not take into account the temporal variability of all possible sources of contamination and concentration. Moreover, most short-term effects studies of O3 used mean daily maximum 8-h average (MDA8) as exposure measurements and we only selected studies using 24-h averaged O3 to contain more comparable studies in the present study. Thus, the potential risk of exposure misclassification cannot be ruled out. In order to produce more accurate effect estimates, more detailed information on the measurements of various pollutants based on a fine spatiotemporal scale will provide more reliable understanding of the exposure–response associations [52]. Heterogeneity in the groups regarding ECG recording length and O3 exposure also exists. The record of electrocardiograms ranged from 5 min, 7 min, 15 min, 16 min, 30 min, 35 min, and 36 min to 24 h (ambulatory) in our included studies. However, a 5-min recording of electrocardiograms is recommended as longer recordings may be affected by emotions or physical activity [53]. The 24 h mean O3 concentration was the most commonly used; however, there were studies with O3 exposure periods of 1 h, 2 h, 4 h, 12 h, 24 h, 72 h and 120 h. Various mean period of O3 concentrations and ECG recording lengths do not produce the same effect estimates on HRV indicators, which may also explain the heterogeneity observed in the associations of O3 exposure with RMMSD, LF, and HF [18].
Furthermore, sensitivity analyses showed that our effect estimations for short-term O3 exposure and HRV indices were robust, with the exception of the combination of O3 and SDNN (Figure 3). The results for O3 and SDNN combination were not robust upon exclusion of this study (Chuang et al., 2007), suggesting that the mean period concentration of O3 of 72 h might have been the source of heterogeneity. Vertical funnel plots (Figure 4) and Egger’s tests indicated that there was no existence of publication bias among the assessed O3 and indicators of HRV combinations. Therefore, our study findings were reliable. Furthermore, the effects of O3 exposure on HRV indices might be explained by medication-induced modifications. For instance, in the study of Xing et al., air pollution exposure decreased 24-h SDNN by 1.31% (95%CI: 0.54−2.07%) in angiotensin receptor blocker (ARB) nonusers, whereas no obvious changes were observed in ARB users [54]. Peng et al. found that Diltiazem is more effective in treating stable coronary artery disease than ACEI/ARB and β-blockers [55]. Zhong et al. found that flavonoid intake with an increase in IQR was associated with a decrease of 5.09% (95%CI: 0.12−10.06%) in mean TLR2 methylation and prevented the negative effects of air pollution on LF [56]. As a result of the inclusion of different studies that have been adjusted for confounding factors, the pooled results may be heterogeneous. Several of the included studies ignored important confounding factors, including gender, BMI, temperature, humidity, season, and medication, and these factors may influence HRV. In light of this, future studies should take these perspectives into account.
This meta-analysis has several limitations that should be taken into account when interpreting the results. Firstly, there are a limited number of published studies available for each HRV indicator, which limits the statistical power of the analysis. Secondly, the studies included in the present study were observational, so we were unable to determine whether or not there was a causal relationship. Thirdly, the meta-analysis was based on studies conducted in North America and Asia, which limited the generalization of the results to the different geographical regions. Despite the aforementioned limitations, our meta-analysis has several strengths. As far as we are aware, this is the first meta-analysis conducted to examine the relationships between O3 exposure and HRV indices in the general population. We were able to perform multiple subgroup analyses to investigate the source(s) of heterogeneity. Finally, this meta-analysis included older adults with CVD as well as healthy young adults, which contributes to our understanding of the exposure–response relationship between O3 and HRV.

5. Conclusions

In this systematic review and meta-analysis, there is evidence that short-term exposure to O3 is associated with alterations in cardiac autonomic function, as measured by HRV in the general population. Further research is recommended to determine effective interventions for improving air quality and reducing incident CVD, and mechanistic studies are needed to determine the cause of the detrimental effects of ozone on the cardiovascular system.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph191811186/s1, Table S1: Database search term list; Table S2: Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) 2009 Checklist; Table S3: Quality evaluation using Newcastle Ottawa Scale (NOS) for 13 observational studies included in the meta-analysis.

Author Contributions

Conceptualization, Y.Z. and C.H.; methodology, Z.Z.; software, Z.Z.; validation, M.Z. and K.X.; formal analysis, Z.Z.; writing—original draft preparation, Z.Z.; writing—review and editing, C.H.; visualization, Z.Z.; supervision, C.H.; project administration, C.H.; funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Projects of Natural Science Research of Anhui Provincial Department of Education (grant number 2020KJA0163) and the Open Fund Project of Hubei Province Key Laboratory of Occupational Hazard Identification and Control (grant number OHIC2020Y01).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, J.J.; Wei, Y.; Fang, Z. Ozone Pollution: A Major Health Hazard Worldwide. Front. Immunol. 2019, 10, 2518. [Google Scholar] [CrossRef]
  2. Jin, Y.; Andersson, H.; Zhang, S. Air Pollution Control Policies in China: A Retrospective and Prospects. Int. J. Environ. Res. Public Health 2016, 13, 1219. [Google Scholar] [CrossRef]
  3. García-Serna, A.M.; Hernández-Caselles, T.; Jiménez-Guerrero, P.; Martín-Orozco, E.; Pérez-Fernández, V.; Cantero-Cano, E.; Muñoz-García, M.; Ballesteros-Meseguer, C.; Pérez de Los Cobos, I.; García-Marcos, L.; et al. Air pollution from traffic during pregnancy impairs newborn′s cord blood immune cells: The NELA cohort. Environ. Res. 2021, 198, 110468. [Google Scholar] [CrossRef] [PubMed]
  4. Muhammad, K.; Ayoub, M.A.; Iratni, R. Vascular Inflammation in Cardiovascular Disease: Is Immune System Protective or Bystander? Curr. Pharm. Des. 2021, 27, 2141–2150. [Google Scholar] [CrossRef] [PubMed]
  5. Al-Kindi, S.G.; Brook, R.D.; Biswal, S.; Rajagopalan, S. Environmental determinants of cardiovascular disease: Lessons learned from air pollution. Nat. Rev. Cardiol. 2020, 17, 656–672. [Google Scholar] [CrossRef] [PubMed]
  6. Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. J. Am. Coll. Cardiol. 2020, 76, 2982–3021. [Google Scholar] [CrossRef]
  7. Thomas, L.; Meredith, L.; Rebecca, D.; Lance, A.; Michelle, B. Integrated Science Assessment (ISA) for Ozone and Related Photochemical Oxidants; U.S. Environmental Protection Agency: Washington, DC, USA, 2020. [Google Scholar]
  8. Barath, S.; Langrish, J.P.; Lundbäck, M.; Bosson, J.A.; Goudie, C.; Newby, D.E.; Sandström, T.; Mills, N.L.; Blomberg, A. Short-term exposure to ozone does not impair vascular function or affect heart rate variability in healthy young men. Toxicol. Sci. 2013, 135, 292–299. [Google Scholar] [CrossRef]
  9. Balmes, J.R.; Arjomandi, M.; Bromberg, P.A.; Costantini, M.G.; Dagincourt, N.; Hazucha, M.J.; Hollenbeck-Pringle, D.; Rich, D.Q.; Stark, P.; Frampton, M.W. Ozone effects on blood biomarkers of systemic inflammation, oxidative stress, endothelial function, and thrombosis: The Multicenter Ozone Study in oldEr Subjects (MOSES). PLoS ONE 2019, 14, e0222601. [Google Scholar] [CrossRef] [PubMed]
  10. Devlin, R.B.; Duncan, K.E.; Jardim, M.; Schmitt, M.T.; Rappold, A.G.; Diaz-Sanchez, D. Controlled exposure of healthy young volunteers to ozone causes cardiovascular effects. Circulation 2012, 126, 104–111. [Google Scholar] [CrossRef]
  11. Thayer, J.F.; Yamamoto, S.S.; Brosschot, J.F. The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Int. J. Cardiol. 2010, 141, 122–131. [Google Scholar] [CrossRef]
  12. Koenig, J.; Thayer, J.F. Sex differences in healthy human heart rate variability: A meta-analysis. Neurosci. Biobehav. Rev. 2016, 64, 288–310. [Google Scholar] [CrossRef] [PubMed]
  13. Hayano, J.; Yuda, E. Assessment of autonomic function by long-term heart rate variability: Beyond the classical framework of LF and HF measurements. J. Physiol. Anthropol. 2021, 40, 21. [Google Scholar] [CrossRef] [PubMed]
  14. Martín-Montero, A.; Gutiérrez-Tobal, G.C.; Kheirandish-Gozal, L.; Jiménez-García, J.; Álvarez, D.; Del Campo, F.; Gozal, D.; Hornero, R. Heart rate variability spectrum characteristics in children with sleep apnea. Pediatr. Res. 2021, 89, 1771–1779. [Google Scholar] [CrossRef] [PubMed]
  15. McCraty, R.; Shaffer, F. Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health risk. Glob. Adv. Health Med. 2015, 4, 46–61. [Google Scholar] [CrossRef]
  16. Rich, D.Q.; Balmes, J.R.; Frampton, M.W.; Zareba, W.; Stark, P.; Arjomandi, M.; Hazucha, M.J.; Costantini, M.G.; Ganz, P.; Hollenbeck-Pringle, D.; et al. Cardiovascular function and ozone exposure: The Multicenter Ozone Study in oldEr Subjects (MOSES). Environ. Int. 2018, 119, 193–202. [Google Scholar] [CrossRef]
  17. Correll, C.U.; Solmi, M.; Croatto, G.; Schneider, L.K.; Rohani-Montez, S.C.; Fairley, L.; Smith, N.; Bitter, I.; Gorwood, P.; Taipale, H.; et al. Mortality in people with schizophrenia: A systematic review and meta-analysis of relative risk and aggravating or attenuating factors. World Psychiatry 2022, 21, 248–271. [Google Scholar] [CrossRef]
  18. Wang, F.; Liang, Q.; Sun, M.; Ma, Y.; Lin, L.; Li, T.; Duan, J.; Sun, Z. The relationship between exposure to PM2.5 and heart rate variability in older adults: A systematic review and meta-analysis. Chemosphere 2020, 261, 127635. [Google Scholar] [CrossRef]
  19. Luo, J.; Wang, X.; Yuan, L.; Guo, L. Association of thyroid disorders with gestational diabetes mellitus: A meta-analysis. Endocrine 2021, 73, 550–560. [Google Scholar] [CrossRef]
  20. Chuang, K.J.; Chan, C.C.; Su, T.C.; Lee, C.T.; Tang, C.S. 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, 370–376. [Google Scholar] [CrossRef]
  21. Gold, D.R.; Litonjua, A.; Schwartz, J.; Lovett, E.; Larson, A.; Nearing, B.; Allen, G.; Verrier, M.; Cherry, R.; Verrier, R. Ambient pollution and heart rate variability. Circulation 2000, 101, 1267–1273. [Google Scholar] [CrossRef]
  22. Holguín, F.; Téllez-Rojo, M.M.; Hernández, M.; Cortez, M.; Chow, J.C.; Watson, J.G.; Mannino, D.; Romieu, I. Air pollution and heart rate variability among the elderly in Mexico City. Epidemiology 2003, 14, 521–527. [Google Scholar] [CrossRef]
  23. Huang, W.; Zhu, T.; Pan, X.; Hu, M.; Lu, S.E.; Lin, Y.; Wang, T.; Zhang, Y.; Tang, X. Air pollution and autonomic and vascular dysfunction in patients with cardiovascular disease: Interactions of systemic inflammation, overweight, and gender. Am. J. Epidemiol. 2012, 176, 117–126. [Google Scholar] [CrossRef]
  24. Jia, X.; Song, X.; Shima, M.; Tamura, K.; Deng, F.; Guo, X. Acute effect of ambient ozone on heart rate variability in healthy elderly subjects. J. Expo. Sci. Environ. Epidemiol. 2011, 21, 541–547. [Google Scholar] [CrossRef]
  25. Park, S.K.; O’Neill, M.S.; Vokonas, P.S.; Sparrow, D.; Schwartz, J. Effects of air pollution on heart rate variability: The VA normative aging study. Environ. Health Perspect. 2005, 113, 304–309. [Google Scholar] [CrossRef]
  26. Schwartz, J.; Litonjua, A.; Suh, H.; Verrier, M.; Zanobetti, A.; Syring, M.; Nearing, B.; Verrier, R.; Stone, P.; MacCallum, G.; et al. Traffic related pollution and heart rate variability in a panel of elderly subjects. Thorax 2005, 60, 455–461. [Google Scholar] [CrossRef] [PubMed]
  27. Shutt, R.H.; Kauri, L.M.; Weichenthal, S.; Kumarathasan, P.; Vincent, R.; Thomson, E.M.; Liu, L.; Mahmud, M.; Cakmak, S.; Dales, R. Exposure to air pollution near a steel plant is associated with reduced heart rate variability: A randomised crossover study. Environ. Health 2017, 16, 4. [Google Scholar] [CrossRef]
  28. Suh, H.H.; Zanobetti, A. Exposure error masks the relationship between traffic-related air pollution and heart rate variability. J. Occup. Environ. Med. 2010, 52, 685–692. [Google Scholar] [CrossRef]
  29. Wang, C.; Lin, J.; Niu, Y.; Wang, W.; Wen, J.; Lv, L.; Liu, C.; Du, X.; Zhang, Q.; Chen, B.; et al. Impact of ozone exposure on heart rate variability and stress hormones: A randomized-crossover study. J. Hazard. Mater. 2022, 421, 126750. [Google Scholar] [CrossRef] [PubMed]
  30. Wheeler, A.; Zanobetti, A.; Gold, D.R.; Schwartz, J.; Stone, P.; Suh, H.H. The relationship between ambient air pollution and heart rate variability differs for individuals with heart and pulmonary disease. Environ. Health Perspect. 2006, 114, 560–566. [Google Scholar] [CrossRef]
  31. Wu, C.F.; Kuo, I.C.; Su, T.C.; Li, Y.R.; Lin, L.Y.; Chan, C.C.; Hsu, S.C. Effects of Personal Exposure to Particulate Matter and Ozone on Arterial Stiffness and Heart Rate Variability in Healthy Adults. Am. J. Epidemiol. 2010, 171, 1299–1309. [Google Scholar] [CrossRef] [Green Version]
  32. Zanobetti, A.; Gold, D.R.; Stone, P.H.; Suh, H.H.; Schwartz, J.; Coull, B.A.; Speizer, F.E. Reduction in heart rate variability with traffic and air pollution in patients with coronary artery disease. Environ. Health Perspect. 2010, 118, 324–330. [Google Scholar] [CrossRef] [PubMed]
  33. Zheng, X.Y.; Orellano, P.; Lin, H.L.; Jiang, M.; Guan, W.J. Short-term exposure to ozone, nitrogen dioxide, and sulphur dioxide and emergency department visits and hospital admissions due to asthma: A systematic review and meta-analysis. Environ. Int. 2021, 150, 106435. [Google Scholar] [CrossRef]
  34. Nhung, N.T.T.; Amini, H.; Schindler, C.; Kutlar Joss, M.; Dien, T.M.; Probst-Hensch, N.; Perez, L.; Künzli, N. Short-term association between ambient air pollution and pneumonia in children: A systematic review and meta-analysis of time-series and case-crossover studies. Environ. Pollut. 2017, 230, 1000–1008. [Google Scholar] [CrossRef] [PubMed]
  35. Miao, H.; Li, X.; Wang, X.; Nie, S. Air pollution increases the risk of pulmonary embolism: A meta-analysis. Rev. Environ. Health 2022, 37, 259–266. [Google Scholar] [CrossRef]
  36. Shao, Q.; Liu, T.; Korantzopoulos, P.; Zhang, Z.; Zhao, J.; Li, G. Association between air pollution and development of atrial fibrillation: A meta-analysis of observational studies. Heart Lung 2016, 45, 557–562. [Google Scholar] [CrossRef]
  37. Halaris, A. Comorbidity between depression and cardiovascular disease. Int. Angiol. 2009, 28, 92–99. [Google Scholar]
  38. Sangaleti, C.T.; Katayama, K.Y.; De Angelis, K.; Lemos de Moraes, T.; Araújo, A.A.; Lopes, H.F.; Camacho, C.; Bortolotto, L.A.; Michelini, L.C.; Irigoyen, M.C.; et al. The Cholinergic Drug Galantamine Alleviates Oxidative Stress Alongside Anti-inflammatory and Cardio-Metabolic Effects in Subjects With the Metabolic Syndrome in a Randomized Trial. Front. Immunol. 2021, 12, 613979. [Google Scholar] [CrossRef]
  39. Cheng, Y.C.; Huang, Y.C.; Huang, W.L. Heart rate variability in patients with dementia or neurocognitive disorders: A systematic review and meta-analysis. Aust. N. Z. J. Psychiatry 2022, 56, 16–27. [Google Scholar] [CrossRef]
  40. Rajendra Acharya, U.; Paul Joseph, K.; Kannathal, N.; Lim, C.M.; Suri, J.S. Heart rate variability: A review. Med. Biol. Eng. Comput. 2006, 44, 1031–1051. [Google Scholar] [CrossRef]
  41. Kleiger, R.E.; Stein, P.K.; Bigger, J.T., Jr. Heart rate variability: Measurement and clinical utility. Ann. Noninvasive Electrocardiol. 2005, 10, 88–101. [Google Scholar] [CrossRef] [PubMed]
  42. Tsuji, H.; Larson, M.G.; Venditti FJJr Manders, E.S.; Evans, J.C.; Feldman, C.L.; Levy, D. Impact of reduced heart rate variability on risk for cardiac events. Fram. Heart Study. Circ. 1996, 94, 2850–2855. [Google Scholar] [CrossRef] [Green Version]
  43. Kop, W.J.; Verdino, R.J.; Gottdiener, J.S.; O’Leary, S.T.; Bairey Merz, C.N.; Krantz, D.S. Changes in heart rate and heart rate variability before ambulatory ischemic events. J. Am. Coll. Cardiol. 2001, 38, 742–749. [Google Scholar] [CrossRef]
  44. La Rovere, M.T.; Pinna, G.D.; Maestri, R.; Mortara, A.; Capomolla, S.; Febo, O.; Ferrari, R.; Franchini, M.; Gnemmi, M.; Opasich, C.; et al. Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation 2003, 107, 565–570. [Google Scholar] [CrossRef]
  45. Thayer, J.F.; Lane, R.D. The role of vagal function in the risk for cardiovascular disease and mortality. Biol. Psychol. 2007, 74, 224–242. [Google Scholar] [CrossRef]
  46. Ulrich-Lai, Y.M.; Herman, J.P. Neural regulation of endocrine and autonomic stress responses. Nat. Rev. Neurosci. 2009, 10, 397–409. [Google Scholar] [CrossRef]
  47. Chrousos, G.P.; Kino, T. Glucocorticoid signaling in the cell. Expanding clinical implications to complex human behavioral and somatic disorders. Ann. N. Y. Acad. Sci. 2009, 1179, 153–166. [Google Scholar] [CrossRef]
  48. Snow, S.J.; Henriquez, A.R.; Costa, D.L.; Kodavanti, U.P. Neuroendocrine Regulation of Air Pollution Health Effects: Emerging Insights. Toxicol. Sci. 2018, 164, 9–20. [Google Scholar] [CrossRef]
  49. Godoy, L.D.; Rossignoli, M.T.; Delfino-Pereira, P.; Garcia-Cairasco, N.; de Lima Umeoka, E.H. A Comprehensive Overview on Stress Neurobiology: Basic Concepts and Clinical Implications. Front. Behav. Neurosci. 2018, 12, 127. [Google Scholar] [CrossRef]
  50. Fowler, D.; Brimblecombe, P.; Burrows, J.; Heal, M.R.; Grennfelt, P.; Stevenson, D.S.; Jowett, A.; Nemitz, E.; Coyle, M.; Lui, X.; et al. A chronology of global air quality. Philos. Trans. A Math. Phys. Eng. Sci. 2020, 378, 20190314. [Google Scholar] [CrossRef]
  51. Lu, X.; Zhang, L.; Wang, L.; Gao, M.; Li, K.; Zhang, Y.; Yue, X. Rapid Increases in Warm-Season Surface Ozone and Resulting Health Impact in China Since 2013. Environ. Sci. Technol. Lett. 2020, 7, 240–247. [Google Scholar] [CrossRef]
  52. Evangelopoulos, D.; Chatzidiakou, L.; Walton, H.; Katsouyanni, K.; Kelly, F.J.; Quint, J.K.; Jones, R.L.; Barratt, B. Personal exposure to air pollution and respiratory health of COPD patients in London. Eur. Respir. J. 2021, 58, 2003432. [Google Scholar] [CrossRef] [PubMed]
  53. Vural, C.; Dinleyici, E.C.; Kosger, P.; Bolluk, O.; Kilic, Z.; Ucar, B. Evaluation of cardiac autonomic function using heart rate variability in children with acute carbon monoxide poisoning. Cardiol. Young 2017, 27, 1662–1669. [Google Scholar] [CrossRef] [PubMed]
  54. Xing, X.; Liu, F.; Yang, X.; Liu, Q.; Wang, X.; Lin, Z.; Huang, K.; Cao, J.; Li, J.; Fan, M.; et al. Declines in heart rate variability associated with short-term PM2.5 exposure were modified by blood pressure control and treatment: A multi-city panel study in China. Environ. Pollut. 2021, 287, 117572. [Google Scholar] [CrossRef] [PubMed]
  55. Peng, X.; Peng, D.F.; Tang, S.Y. Effect of diltiazem on exercise tolerance in patients with stable coronary artery disease and hypertension. J. Physiol. Pharmacol. 2021, 72. [Google Scholar] [CrossRef]
  56. Zhong, J.; Colicino, E.; Lin, X.; Mehta, A.; Kloog, I.; Zanobetti, A.; Byun, H.M.; Bind, M.A.; Cantone, L.; Prada, D.; et al. Cardiac autonomic dysfunction: Particulate air pollution effects are modulated by epigenetic immunoregulation of Toll-like receptor 2 and dietary flavonoid intake. J. Am. Heart Assoc. 2015, 4, e001423. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ijerph 19 11186 g001 550
Figure 1. Flow chart of study selection.
Figure 1. Flow chart of study selection.
Ijerph 19 11186 g001
Ijerph 19 11186 g002 550
Figure 2. Forest plot of the meta-analysis: per 10 ppb increase in O3 exposure was associated with pooled percentage changes (%) in HRV indicators: (a) SDNN, (b) RMSSD, (c) HF, and (d) LF. MI: myocardial infarction; COPD: chronic obstructive pulmonary disease [20,21,22,23,24,25,26,27,28,29,30,31,32].
Figure 2. Forest plot of the meta-analysis: per 10 ppb increase in O3 exposure was associated with pooled percentage changes (%) in HRV indicators: (a) SDNN, (b) RMSSD, (c) HF, and (d) LF. MI: myocardial infarction; COPD: chronic obstructive pulmonary disease [20,21,22,23,24,25,26,27,28,29,30,31,32].
Ijerph 19 11186 g002
Ijerph 19 11186 g003 550
Figure 3. Sensitivity analysis of the association between short-term O3 exposure and HRV indicators: (a) SDNN, (b) RMSSD, (c) HF, and (d) LF [20,21,22,23,24,25,26,27,28,29,30,31,32].
Figure 3. Sensitivity analysis of the association between short-term O3 exposure and HRV indicators: (a) SDNN, (b) RMSSD, (c) HF, and (d) LF [20,21,22,23,24,25,26,27,28,29,30,31,32].
Ijerph 19 11186 g003
Ijerph 19 11186 g004 550
Figure 4. Funnel plot of the effects of short-term O3 exposure and HRV indicators. (a) SDNN (b) RMSSD (c) HF (d) LF. The ordinate axis in funnel plot represents standard error (SE) of percentage change (%).
Figure 4. Funnel plot of the effects of short-term O3 exposure and HRV indicators. (a) SDNN (b) RMSSD (c) HF (d) LF. The ordinate axis in funnel plot represents standard error (SE) of percentage change (%).
Ijerph 19 11186 g004
Table
Table 1. PECOS for epidemiology study identification.
Table 1. PECOS for epidemiology study identification.
PECOS ElementEvidence
PopulationGeneral population, of all ages, developed and developing areas, both urban and rural. No geographical restrictions.
ExposureExposure to ambient O3 pollution. Exposure was expressed in continuous.
ComparatorA comparation population exposed to lower levels of O3 pollution.
OutcomesHeart rate variability including four common indicators (RMSSD, SDNN, LF, and HF).
Study designCohort, nested or not nested case–control, case–cohort, or cross-sectional study designs, were considered.
Table
Table 2. Basic characteristics of the studies included in the meta-analysis.
Table 2. Basic characteristics of the studies included in the meta-analysis.
Author and Year of PublicationStudy Location, Period and DesignStudy PopulationOutcome AssessmentOzone Exposure TimeMonitoring TypeAdjusted CovariatesHeart Rate Variability Indicators and Percentage Change (%)NOS Score
Suh and Zanobetti, 2010 [28]Atlanta (USA), Fall 1999 and Spring 2000
Panel study		30 subjects: 12 with a recent myocardial infarction and 18 with chronic obstructive pulmonary disease Mean age: 65 year, 57% malemin ECG daily on seven consecutive days in one or both seasons. The ECG protocol involved 5 min of rest, 5 min of standing, 5 min of exercise out- doors, 5 min of recovery, and 20 cycles of slow breathing24 hFixed-site;
Personal exposure		Body mass index (BMI), temperature, relative humidity, sex, age, season, hour of day, day of week, medications use (beta-blockers, calcium channel blockers, angiotensin converting enzyme (ACE) inhibitors, and bronchodilators)Per 16.02 ppb increase:
SDNN: −0.03 (−8.40, 9.10)
RMSSD: 10.83 (−12.63, 40.58)
HF: 20.84 (−13.47, 68.76)		8
Huang et al., 2011 [23]Beijing (China), during summer 2007 and summer 2008
Panel study		40 nonsmoking CVD patients (mean age = 65.6 years (standard deviation, 5.8) recruited through the on-campus clinic of Peking University Health Science Center (PKUHSC. A subset of 23 patients participated in 24-h ambulatory blood pressure monitoringConsecutive 5-min measurements of heart rate and various measures of HRV were calculated for each monitoring session of each subject using personal computer-based software12 hFixed-siteAge, BMI, gender, time of day, day of the week, visit, temperature, and relative humidityPer 27.7 ppb increase
SDNN: 0.8 (−1.8, 3.5)
RMSSD: −3.0 (−7.6, 1.9)
HF: −8.7 (−16.4, −0.2)
LF: −6.6 (−12.8, −0.01)		8
Zanobetti et al., 2010 [32]Boston (USA), 1999–2003
Panel study		46 patients with coronary artery disease, mean age: 57 year, 80% male, non-smoking24 h ambulatory ECG, up to four with approximately 3-month intervals between visits120 hFixed-siteDay of the week, traffic, average heart rate, hour of the day, date, mean temperaturePer 19 ppb increase
RMSSD: −3.4 (−5.2, −1.5) 		8
Wheeler et al., 2006 [30]Atlanta (USA), Fall 1999 and Spring 2000
Panel study		30 subjects: 12 with a recent myocardial infarction and 18 with chronic obstructive pulmonary disease Mean age: 65 year, 57% malemin ECG daily on seven consecutive days in one or both seasons
The ECG protocol involved 5 min of rest, 5 min of standing, 5 min of exercise out- doors, 5 min of recovery, and 20 cycles of slow breathing		4 hFixed-siteBMI, temperature, relative
humidity, sex, age, season, hour of day, day of week, medications use (beta-blockers, calcium channel blockers, angiotensin converting enzyme (ACE) inhibitors, and bronchodilators)		Total (per 9.61 ppb increase)
SDNN: 0.75 (−3.6, 5.3)
With MI (per 8.08 ppb increase)
SDNN: 0.13 (−6.5, 7.2)
With COPD (per 10.66 increase)
SDNN: 2.45 (−3.4, 8.7)		7
Schwartz et al., 2005 [26]Boston (USA), Summer 1999
Panel study		28 subjects living near the exposure and health monitoring site, 61–89 year, 25% male myocardial infarction (n = 3), congestive heart failure (n = 2), chronic pulmonary disease (n = 2)30-min ECG weekly over 12 weeks The ECG protocol involved 5 min of rest, 5 min of standing, 5 min of exercise outdoors, 5 min of recovery, and 3 min and 20 s of slow breathing24 hFixed-siteTemperature, day of the week, hour of the day, medication use, time trendPer 26 ppb increase
SDNN: −1.5 (−5.7, 2.9)
RMSSD: −2.3 (−11.6, 7.9)		6
Holguin et al., 2003 [22]Mexico City (Mexico), 8 February–30 April 2000
Panel study		34 elderly residents of a nursing home, hypertension (n = 13), diabetes mellitus (n = 6), Parkinson’s disease (n = 4), chronic bronchitis (n = 4), 60–96 year, 44% male5-min resting ECG in supine position, every other day be- tween 8:00 a.m. and 1:00 p.m. for three months1 hFixed-siteAge, heart ratePer 10 ppb increase
Total
HF: −0.1 (−0.016, 0.013)
LF: −0.5 (−0.019, 0.009)
With hypertension
HF: −1.4 (−4.0, 1.2)
LF: −2.1 (−0.045, 0.003)
Without hypertension
HF: 0.007 (−0.010, 0.024)
LF: 0.005 (−0.011, 0.022) 		6
Jia et al., 2011 [24]Beijing (China), Summer 2008 and Winter 2009
Panel study		20 healthy elderlies, mean age 58.7 year, living near busy road, 25% male, non-smokingTwo 24 h ambulatory ECGs: one in summer 2008; one in winter 20092 hFixed-sitePM2.5, NOx, temperature, relative humidity, gender, age, BMI, survey number, activityPer 10 ppb increase
HF: −4.87 (−8.62, −0.97)
LF: −2.84 (−6.03, 0.46)		7
Chuang et al., 2007 [20]Taipei (China), April–June of 2004 or 2005
Panel study		76 healthy college students, no history of cardiovascular disease and of smoking, mean age: 21 year, 60% maleOne monthly 16 min resting ECG in the sitting position, during daytime (8 a.m. to 2 p.m.),
for three months (~30 days between measurements)		72 hFixed-siteSex, age, BMI, weekday, temperature of day before, relative humidityPer 12.0 ppb increase
SDNN: −8.3 (−10.1, −6.5)
RMSSD: −8.5 (−13.6, −3.3)
HF: −6.6 (−11.8, −1.4)
LF: −5.6 (−8.2, −3.0)		6
Wu et al., 2010
[31]		Taipei (China), February–March 2007
Panel study		17 healthy mail carriers, 32.4 year, 100% male, non-smokingAmbulatory electrocardiographic data were collected continuously during their working periods, starting and ending 30 min before and after the mail delivery periods24 hPersonal exposureAge, BMI, second-hand smoke exposure, temperature during the working periodPer 17.6 ppb increase
SDNN: 1.97 (−10.06, 15.62)
RMSSD: −0.19 (−10.40, 11.19)
HF: 5.41 (−7.60, 20.25)
LF: 3.82 (−8.76, 18.13)		6
Shutt et al., 2017 [27]Ottawa (Canada),
Summer 2010
Case–crossover study		60 healthy adults, 24.2 ± 5.8 year, 46 male, 14 femaleHRV analysis was undertaken on a segment of the ambulatory ECG recording during a 15 min rest period, near the end of the 8-h on-site day120 hFixed-siteAge, heart rate, sex, BMI, temperature and relative humidityPer 8.7 ppb increase
SDNN: −5.59 (−10.01, 1.18)
RMSSD: −6.11 (−10.87, 1.36)
HF: −2.50 (−4.67, −0.33)
LF: −2.24 (−17.32, 12.84)		7
Wang et al., 2022
[29]		Shanghai (China)
October to November 2018
Case–crossover study		22 young participants (10 males and 12 females, 18–30 year) with complete data for final analyses24-h ECG monitoring was performed using a 3-lead electrographic Holter monitor (Seer Light, GE Medical Systems) with a sampling rate of 128 Hz2 hFixed-siteAge, sex, BMI, the collinearity between ozone and relative humidity in chamberPer 10 ppb increase
SDNN: 4.34 (−1.15, 10.14)
RMSSD: −3.25 (−7.66, 1.38)
HF: −5.99 (−10.44, −1.33)
LF: 1.7 (−3.71, 7.40)		8
Gold et al., 2000
[21]		Boston (USA)
May to July 1997
Panel study		21 volunteers, 73.3 year,
10 males and 11 females		25 min per week of continuous ECG monitoring, including 5 min of rest, 5 min of standing, 5 min of exercise outdoors, and 5 min of recovery1 hFixed-siteAge, BMI, sex, smoking status, race, medication use, hypertension, coronary artery disease (history of angina or heart attack), history of congestive heart failurePer 23.0 ppb increase
RMSSD: −17.9 (−7.66, 1.38)		6
Park et al., 2005
[25]		Boston (USA)
14 November 2000–30 October 2003
Cohort study		497 elderly men, 72.7 ± 6.6After the participants had rested for 5 min, the ECG was recorded for approximately 7 min with the subject seated. The best 4-consecutive-minute interval was used for the HRV calculations4 hFixed-siteAge, BMI, mean arterial blood pressure (MAP), fasting blood glucose (FBG), cigarette smoking, use of beta-blocker, calcium-channel blocker, and/or ACE inhibitor, room temperature, season, and cubic smoothing splines (3 df) for moving averages of apparent temperature corresponding for the predictorPer 13.0 ppb increase
With hypertension
SDNN: −5.5 (−15.7, 0.3)
HF: −17.0 (−31.6, 0.7)
LF: −12.6 (−25.0, 1.9)
Without hypertension
SDNN: 1.8 (−7.4, 11.8)
HF: 8.8 (−14.7, 38.7)
LF: −5.4 (−21.6, 14.1)		7
Table
Table 3. Subgroup analysis of percentage change in indicators of HRV in association with each 10 ppb increase in short-term O3 exposure.
Table 3. Subgroup analysis of percentage change in indicators of HRV in association with each 10 ppb increase in short-term O3 exposure.
HRV IndicesSubgroupSubgroup CriteriaPooled Percentage
Changes (%) with 95%CI		No. of Effect EstimatesNo. of StudiesHeterogeneity
I2 (%)p Value for Heterogeneity
SDNNAge of participants≤35 year−0.15 (−3.09, 2.79)4436.80.191
≥55 year−0.65 (−1.54, 0.24)850.00.710
Study locationNorth America−0.91 (−1.89, 0.08)850.00.733
East Asia−1.12 (−1.37, −0.87)4448.80.119
ECG recording lengthLength of ECG ≤ 30 min−0.89 (−1.88, 0.09)530.00.541
Others−1.12 (−1.37, −0.87)6419.00.290
O3 exposure timeO3 exposure < 24 h−0.90 (−0.90, 2.70)430.00.563
Others−1.14 (−1.39, −0.90)860.00.863
Exposure assessmentFixed-site exposure−1.12 (−1.36, −0.87)1086.20.385
Personal exposure−0.16 (−2.70, 3.01)220.00.778
Quality of studyHigh−0.23 (−1.09, 1.55)960.00.650
Medium−1.15 (−1.40, −0.91)330.00.828
RMSSDAge of participants≤35 year−4.36 (−7.13, −1.59)4419.90.290
≥55 year−2.67 (−5.55, 0.21)6585.4<0.001
Study locationNorth America−3.43 (−7.02, 0.16)6584.3<0.001
East Asia−2.81 (−5.78, 0.17)4458.00.067
ECG recording lengthLength of ECG ≤ 30 min−3.78 (−8.20, 0.67)4488.9<0.001
Others−2.52 (−4.50, −0.54)6531.30.201
O3 exposure timeO3 exposure < 24 h−4.08 (−9.01, 0.85)3392.1<0.001
Others−2.55 (−4.56, −0.54)7632.10.183
Exposure assessmentFixed-site exposure−3.69 (−5.98, −1.39)8881.8<0.001
Personal exposure−0.72 (−5.04, 6.47)220.00.446
Quality of studyHigh−1.74 (−2.56, −0.92)650.00.586
Medium−4.38 (−8.42, −0.33)4478.70.003
HFAge of participants≤35 year−3.56 (−5.61, −1.51)4420.90.285
≥55 year−2.54 (−4.90, −0.17)8562.10.014
Study locationNorth America−1.75 (−3.89, 0.39)7456.40.032
East Asia−4.11 (−6.20, −2.62)550.00.802
ECG recording lengthLength of ECG ≤ 30 min−2.10 (−3.88, −0.32)7557.40.029
Others−5.22 (−7.58, −2.86)540.00.716
O3 exposure timeO3 exposure < 24 h−2.92 (−5.23, −0.62)5475.10.003
Others−3.28 (−5.75, −0.81)7514.60.318
Exposure assessmentFixed-site exposure−3.10 (−4.83, −1.37)10860.90.006
Personal exposure0.08 (−12.44, 12.60)2228.20.238
Quality of studyHigh−3.42 (−5.15, −1.68)8614.30.318
Medium−2.43 (−5.20, 0.34)4374.90.007
LFAge of participants≤35 year−1.33 (−5.70, 3.03)4454.80.084
≥55 year−2.02 (−3.80, −0.25)6451.90.065
Study locationNorth America−1.86 (−4.51, 0.78)5550.10.091
East Asia−2.50 (−4.52, −0.49)5543.40.133
ECG recording lengthLength of ECG ≤ 30 min−1.62 (−3.43, 0.19)7540.80.119
Others−2.79 (−5.77, 0.19)3356.80.099
O3 exposure timeO3 exposure < 24 h−1.49 (−3.14, 0.16)5453.80.070
Others−4.29 (−6.37, −2.20)540.80.402
Exposure assessmentFixed-site exposure−2.33 (−4.07, −0.58)9759.40.011
Personal exposure----------
Quality of studyHigh−2.34 (−4.07, −0.62)6581.20.001
Medium−1.94 (−4.76, 0.87)440.00.530
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zong, Z.; Zhang, M.; Xu, K.; Zhang, Y.; Hu, C. Association between Short-Term Exposure to Ozone and Heart Rate Variability: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 11186. https://doi.org/10.3390/ijerph191811186

AMA Style

Zong Z, Zhang M, Xu K, Zhang Y, Hu C. Association between Short-Term Exposure to Ozone and Heart Rate Variability: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2022; 19(18):11186. https://doi.org/10.3390/ijerph191811186

Chicago/Turabian Style

Zong, Zhiqiang, Mengyue Zhang, Kexin Xu, Yunquan Zhang, and Chengyang Hu. 2022. "Association between Short-Term Exposure to Ozone and Heart Rate Variability: A Systematic Review and Meta-Analysis" International Journal of Environmental Research and Public Health 19, no. 18: 11186. https://doi.org/10.3390/ijerph191811186

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop