# Clinical impact of air pollution on SPECT myocardial perfusion imaging.

O. Bouchot, Stephan Gabet, L. Djaileb, A. Robin, N. Leiris, J. Roux, L. Riou, G. Vanzetto, D. Fagret, C. Ghezzi, et al.

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O. Bouchot, Stephan Gabet, L. Djaileb, A. Robin, N. Leiris, et al. Clinical impact of air pollution on SPECT myocardial perfusion imaging. Environment International, 2025, Environment International, 198, pp.109406. 10.1016/j.envint.2025.109406. hal-05042279

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Clinical impact of air pollution on SPECT myocardial perfusion imaging

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Océane Bouchot<sup>a,1</sup>, Stephan Gabet<sup>b,c,1</sup> ID, Loïc Djaileb<sup>d,f</sup>, Aurélia Robin<sup>d</sup>, Nicolas De Leiris<sup>d,f</sup>, Julie Roux<sup>d,f</sup>, Laurent Riou<sup>d,f</sup>, Gerald Vanzetto<sup>i,g,h</sup>, Daniel Fagret<sup>d,f</sup>, Catherine Ghezzi<sup>d,f</sup>, Rémy Slama<sup>b</sup>, Gilles Barone-Rochette<sup>d,g,h,*</sup> ID

ARTICLE INFO

Associate editor: Adrian Covaci

Keywords:

Air pollution

Single-photon emission computed tomography

Myocardial perfusion imaging

Myocardial ischemia

A B S T R A C T

**Background:** Air pollution contributes to cardiovascular morbimortality. Air pollution effects on cardiovascular function assessed from non-invasive and invasive imaging have been reported but never on myocardial perfusion. This study aimed to characterize relations of air pollution exposure to myocardial perfusion imaging (MPI).

**Methods:** Patients referred to SPECT (single-photon emission computed tomography) MPI were prospectively enrolled between 2017 and 2020. Myocardial ischemia was assessed from the SPECT. Moderate-to-severe ischemia was defined as a > 10 % ischemic myocardium. Exposures to particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) and NO<sub>2</sub> at the home address were characterized *via* a 10-meter resolution air pollutant-dispersion model and air quality monitoring system data. Associations of exposures with scintigraphy parameters were assessed through multivariable regressions, and potential modifying effects by sex and BMI were investigated.

**Results:** A total of 1,585 participants were prospectively included (mean age 67.7 ± 11.6 years, 63.3 % male); 148 (9.3 %) patients presented a moderate-to-severe ischemia. In multivariate analysis, the odds of moderate-to-severe ischemia was increased, with adjusted odds-ratio (ORa) of 1.39 [95 % confidence interval (95 % CI): 1.07–1.80; *p* = 0.013], 1.33 (95 % CI: 1.01–1.75; *p* = 0.042), and 1.22 (95 % CI: 0.96–1.57; *p* = 0.10) for each increase of one Interquartile Range (IQR) in PM<sub>2.5</sub>, PM<sub>10</sub> and NO<sub>2</sub> exposure, respectively (IQR equal to 3.3, 4.3 and 10.0 µg/m<sup>3</sup>, respectively). With further adjustment for cardiometabolic diseases and symptoms, only the association with PM<sub>2.5</sub> remained statistically significant: ORa 1.34 (95 % CI: 1.03–1.75, *p* = 0.031) for each one-IQR increase. Associations with PM<sub>10</sub> and PM<sub>2.5</sub> tended to be stronger in women (interaction *p*-value equal to 0.11 and 0.077, respectively).

**Conclusion:** We provide new insights into a mechanism by which particulate air pollution may influence cardiovascular risk. Exposure to PM was associated with moderate-to-severe ischemia, particularly in women.

1. Introduction

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. An estimated 17.8 million people per year died from CVDs,

representing 32 % of all global deaths (Mensah et al., 2019). In addition to tobacco smoking, arterial hypertension, hypercholesterolemia and diabetes mellitus, air pollution is a well-identified modifiable cardiovascular risk factor (Murray et al., 2020; Visseren et al., 2022). The all-

**Abbreviations:** AQMS, air quality monitoring station; CVDs, Cardiovascular diseases; CZT, Cadmium Zinc Telluride; ECG, electrocardiogram; EVAPERF, Evaluation of Prognostic Value of SPECT-imaging Myocardial Perfusion; IGN, National Institute of Geographic and Forestry Information; IQR, interquartile range; NO<sub>2</sub>, nitrogen dioxide; NO<sub>x</sub>, nitrogen oxides; PM, particulate matter; SD, standard deviation; SPECT, single-photon emission computed tomography; SRS, summed rest score; SSS, summed stress score.

<footnote><sup>a</sup> Department of Cardiology, Annecy Genevois Hospital, France</footnote>

<footnote><sup>b</sup> Univ. Grenoble-Alpes, Inserm, CNRS, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, IAB, 38000 Grenoble, France</footnote>

<footnote><sup>c</sup> Univ. Lille, CHU Lille, Institut Pasteur de Lille, ULR 4483-IMPacts de l'Environnement Chimique sur la Santé (IMPECS), F-59000 Lille, France</footnote>

<footnote><sup>d</sup> Univ. Grenoble Alpes, INSERM, CHU Grenoble Alpes, LRB, 38000 Grenoble, France</footnote>

<footnote><sup>f</sup> Department of Nuclear Medicine, University Hospital, Grenoble Alpes, France</footnote>

<footnote><sup>g</sup> Department of Cardiology, University Hospital, Grenoble Alpes, France</footnote>

<footnote><sup>h</sup> French Alliance Clinical Trial, French Clinical Research Infrastructure Network, 75018 Paris, France</footnote>

<footnote>* Corresponding author at: Clinique Universitaire de Cardiologie / Pôle Thorax et Vx, CHU de Grenoble, 38043 Grenoble cedex 09, France.</footnote>

<footnote>E-mail address: GBarone@chu-grenoble.fr (G. Barone-Rochette).</footnote>

<footnote n="1">¹ OC and SG. contributed equally; co-first authorship.</footnote>
cause mortality attributed to air pollution from indoor and outdoor sources corresponds to a burden of 7 million deaths per year (Murray et al., 2020). Air pollution is a ubiquitous complex mixture of solid and liquid particles and gases, variable in time and space. Particulate matters (e.g., PM₁₀ and PM₂.₅, those with an aerodynamic diameter below 10 and 2.5 µm, respectively) and nitrogen oxides (NOₓ, including nitrogen dioxide (NO₂), are prominent and regulated primary components of atmospheric pollution. PMs come from heating sources, road traffic, industry, agriculture or natural origins; a major source of nitrogen oxides is fossil fuel combustion, mainly from combustion engine vehicles, as well as a stationary power generation. PMs are inhaled and PM₂.₅ can reach pulmonary alveoli which the smallest fraction entering the blood circulation.

Exposure to air pollutants (particularly to PMs) has been associated with premature cardiovascular morbimortality (Beelen et al., 2014; Carey et al., 2013; Konduracka and Rostoff, 2022; Pope, 2003; Pope et al., 2015, 2002; Rajagopalan et al., 2018) and their effects on all-cause mortality and cardiovascular mortality are well established. Increased risks of non-fatal stroke (Stafoggia et al., 2014; Wellenius et al., 2012), nonfatal acute coronary syndrome (Cesaroni et al., 2014; Mustafić et al., 2012; Peters et al., 2004), heart failure (Shah et al., 2013), arterial hypertension (Chen et al., 2014), or diabetes mellitus (Pearson et al., 2010) have been identified with varying levels of evidence. Possible effects on arrhythmia (Ljungman et al., 2014) have also been mentioned but require further research.

Multiple pathophysiological mechanisms have been implicated in the relationship between air pollution and coronary artery disease. Air pollutants inhaled into the lungs trigger a local, then systemic pro-inflammatory status. The release of pro-inflammatory and pro-oxidant mediators (increase of circulating levels of leukocytes and various inflammatory cytokines) is deleterious for the cardiovascular system, especially for endothelium integrity (Pope et al., 2016). The PM can also directly affect the vasculature and circulating blood cells. They act by activating a pro-inflammatory response in endothelial cells, up-regulating the expression of adhesion molecules, and generating reactive oxygen species, which are key mediators of accelerated atherosclerosis (Newman et al., 2020). Exposure to PMs can also induce a pro-thrombotic state by enhancing platelet activation and von Willebrand function (Mills et al., 2005). Moreover, exposure to air pollution may induce cardiorespiratory dysautonomia (Pieters et al., 2012). Recent genome-wide association studies showed that exposure to fine particles may increase the risk of cardiovascular events via several single-nucleotide polymorphisms (Cao et al., 2024; Liang et al., 2023).

The pathophysiological mechanisms are therefore complex and probably multiple, as demonstrated by the several studies used imaging which have explored the association between PM exposure and sub-clinical atherosclerosis, studies showing inconsistent data concerning the association between air pollution and accelerated atherosclerosis of the coronary and carotid arteries (Jilani et al., 2020). Numerous invasive and non-invasive methods have been used, such as coronary calcium score (Huynh et al., 2021), coronary computed tomographic angiography (Yang et al., 2019), carotid intima-media thickness (Künzli et al., 2005), and carotid artery plaques (Kilbo Edlund et al., 2022). Another recent study indicated an association between long-term air pollution exposure and vulnerable coronary plaque by optical coherence tomography (Montone et al., 2022), and coronary vasomotor disorders by coronary angiography and intracoronary provocation test (Camilli et al., 2022) in a vulnerable population already affected by clinically significant coronary artery disease. SPECT MPI is one of the most widely used methods for the diagnosis, prognosis and therapeutic management of coronary artery disease by quantification of ischemia (Gulati et al., 2021; Knuuti et al., 2020). However, and to the best of our knowledge, potential effects of air pollution on myocardial perfusion as assessed by SPECT have never been studied.

This study aimed to explore the association between PM₁₀, PM₂.₅, NO₂ air pollution exposure and myocardial perfusion imaging.

# 2. Methods

## 2.1. Study population and ethics

Outpatients referred to SPECT MPI in the nuclear medicine department at Grenoble University Hospital were consecutively and prospectively enrolled between July 2017 and February 2020. Non-inclusion criteria were: inpatient, not being residents of the Rhône-Alpes area in which air pollution exposure could be assessed, pregnancy or breastfeeding, patients under guardianship, and patients with life-threatening comorbidity at six months. For practical reasons, the research nurse dedicated to patients' recruitment could only be present 3 days a week (Monday, Thursday, Friday) for the duration of the study. Patients' medical history, treatment, home address and moving date, and education degree were prospectively recorded by a research nurse dedicated to the study of two randomly selected days a week. Other information was retrieved from medical files and from the review of hospital records. A total of 1,585 patients satisfied the inclusion criteria (Fig. 1) and constituted the final study population. The study was approved by the regional ethics committee. Written informed consent was obtained from each participant. The study protocol was in accordance with the Declaration of Helsinki. The registry number on ClinicalTrials.gov of the present study was NCT02208765.

## 2.2. SPECT imaging protocol and analysis

SPECT MPI was performed and analyzed according to the usual methodology in use at the nuclear cardiology laboratory of Grenoble University Hospital, in accordance with French national guidelines (Furber et al., 2000; Manrique et al., 2003). SPECT MPI was performed as previously described (Barone-Rochette et al., 2019) and as detailed in the Supplementary Appendix. Stress and rest perfusion images were semi-quantitatively scored using a 17-segment model of the left ventricle and a 5-point scale (0 = normal, 1 = equivocal, 2 = moderate, 3 = severely reduced radiotracer uptake, and 4 = no detectable uptake) by one experienced nuclear cardiologist (LD) blind to clinical data and exposure status. Summed Stress Score (SSS) and Summed Rest Score (SRS) were calculated for all 17 segments from stress and rest images, respectively. Abnormal scintigraphy was defined as an SSS ≥ 4 while an SRS ≥ 4 defined scar (Hachamovitch et al., 1998). The extent of ischemia (LV percentage) was determined by dividing the number of ischemic segments (SSS ≥ 2 and SRS ≤ 2) by the total number of LV segments. Mild ischemia was defined by an abnormal scintigraphy (it means SSS ≥ 4) with a threshold < 10 % of myocardium; a threshold of 10 % or more of myocardium has been reported in prognostic series to define moderate-to-severe ischemia (ISCHEMIA Trial Research Group et al., 2018; Shaw et al., 2014). Inter and intranindividual variability was previously analyzed, with intraclass correlation coefficient (ICC) of 0.92 [0.86–0.96] and 0.88 [0.79–0.94] for SSS and of 0.92 [0.86–0.96] and 0.76 [0.6–0.87] for SRS, respectively (Djaileb et al., 2019). More details on the SPECT imaging protocol are given in the Supplementary Appendix (Methods S1).

## 2.3. Air pollution exposure

Current home address and last moving dates were collected from each patient at recruitment, and home address geographic coordinates were determined to use the regional National Institute of Geographic and Forestry Information (IGN) databases. When the door number or the name of the street was unavailable or not referenced in IGN databases, the patient's home was geolocated at the geographical middle of the street or at the town hall address, respectively. Given that employment status and work address were not available, we considered exposure at home address only.

Ambient air pollutant average concentrations at patient's residential address were assessed using the 'Combine' air pollutant-dispersion
<figure><img src="image_3.png" /><figcaption>Fig. 1. Flow chart of study.</figcaption></figure>

model, a proximity scale model with a 10-meter spatial resolution based on the mesoscale CHIMERE chemistry-transport model (Soulhac et al., 2011). Initial validation of the model has been previously demonstrated (Soulhac et al., 2012) while routine validation of the estimates is conducted with Delta Tool EU's Joint Research Centre, n.d). Air pollution data were obtained for the whole study area in the 2016–2020 period, with a yearly temporal resolution for PM₁₀, PM₂.₅ and NO₂, and also with a daily resolution for PM₁₀ (Fig. 2A–B).

We estimated the PM₂.₅ daily concentrations at the patient’s home address from the PM₁₀ daily concentrations multiplied by a coefficient equal to the PM₂.₅/PM₁₀ ratio between the annual concentrations (i.e., making the assumption that within-year temporal variability was similar to PM₂.₅ and PM₁₀). To estimate NO₂ daily concentrations at the patient’s home address, we relied on daily averages of NO₂ concentrations from air quality monitoring station (AQMS) measurements on which we applied a conversion coefficient equal to the home/AQMS ratio of the NO₂ annual concentrations provided by ‘Combine’, as previously described (Slama et al., 2007b) (Fig. 2B). For each participant, we considered the nearest background AQMS located in an area of the same

urbanization degree as the participant’s home. Urbanization degrees were obtained from the National Institute of Statistics and Economic Studies (Insee, 2021). For more details, see the Supplementary Appendix (Methods S2 and Fig. S1).

Air pollutant daily concentrations of the participants’ home addresses were then averaged over the year preceding the date of SPECT MPI and used as indicators of chronic exposure to ambient air pollution.

## 2.4. Statistical analyses

We described EVAPERF participants’ characteristics and estimated associations with SPECT MPI parameters. Effects of air pollution exposure on myocardial perfusion defects assessed by SPECT MPI were investigated by multivariable logistic regressions. Participants having reported moving during the exposure window were withdrawn from statistical analyses because of potential for increased measurement error [$N = 79 (5.0 \%)$].

Multivariate models were adjusted for participants’ characteristics unlikely to be influenced by clinical status (i.e., sex, age in class,

<figure><img src="image_4.png" /><figcaption>Fig. 2. Geolocation of EVAPERF cohort patients’ home addresses, and (A) daily dispersion model estimates for PM₁₀ or (B) annual dispersion model estimates and location of the Air Quality Monitoring Stations (AQMS) for NO₂.</figcaption></figure>
education degree, body mass index in class, and current active tobacco smoking) and for the season in which SPECT MPI was performed (Model 2). In additional analyses, we further added factors significantly associated with SPECT parameters in univariate analyses. These factors might be on the causal pathway between air pollution exposure and myocardial perfusion and possibly not confounding factors (Model 3). These factors were: chronic renal failure, diabetes mellitus, arterial hypertension, dyslipidemia, history of myocardial infarction, history of coronary artery bypass, and left ventricular ejection fraction. When an association was reported between air pollution exposure and a scintigraphy parameter, we complemented the study of the latter association considering exposure levels by quartiles.

We then investigated the potential modifying effects of some factors that could underlie individual sensitivities, namely sex, body mass index (two qualitative variables tested: <25 or ≥ 25 and < 25, 25–30, or ≥ 30), and smoking (yes or no). We also conducted sensitivity analyses to explore potential misclassification biases induced in case of uncertainties regarding home address geolocation and exposure at work.

All statistical analyses were performed using Stata® 17.0 (Stata Corp, College Station, TX, USA) and R software version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria).

# 3. Results

## 3.1. Study population

In total, 1,585 patients gave their consent to participate in the EVAPERF study. Participants were 67.7 ± 11.6 years old on average [mean ± standard deviation (SD)] and mainly men (63.3 %), school degree graduated (59.7 %), overweight (68.5 %), and non-smokers (85.8 %). The most common indication for SPECT MPI was the evaluation of ischemia for high-risk cardiovascular patients (73.9 %); the most common type of stress was an exercise stress test (51.9 %). Upon SPECT MPI, 43.3 % of patients had a history of at least one cardiovascular event (myocardial infarction, angioplasty, coronary artery bypass, stroke, or peripheral arterial disease, Table 1).

## 3.2. SPECT MPI

In the entire study population, 148 (9.3 %) participants had moderate-to-severe myocardial ischemia, 190 (11.9 %) participants presented mild ischemia, and 217 (14.1 %) participants presented with myocardial scar. SSS and SRS distributions are shown in the Supplementary Appendix (Fig. S2); SRS could not be obtained for 48

<table><caption>**Table 1**

Characteristics of EVAPERF patients and association with myocardial perfusion (N = 1,585).</caption><thead><tr><th rowspan="2">Characteristics</th><th rowspan="2">Headcount</th><th rowspan="2">Value <sup>a</sup></th><th colspan="2">Association with<br>Summed Stress Score (SSS)</th><th colspan="2">Summed Rest Score (SRS)</th></tr><tr><th>Effect <sup>b</sup></th><th><i>p</i>-value</th><th>Effect <sup>b</sup></th><th><i>p</i>-value</th></tr></thead><tbody><tr><td><strong>Demographics</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>1,003</td><td>63.3 %</td><td>2.1</td><td>&lt;0.001</td><td>1.5</td><td>&lt;0.001</td></tr><tr><td>Age (in classes)</td><td></td><td></td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>Age (years)</td><td>1,585</td><td>67.7 ± 11.6</td><td>0.03<sup>c</sup></td><td>0.029</td><td>0.02<sup>c</sup></td><td>0.040</td></tr><tr><td>Body mass index (in classes)</td><td></td><td></td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>Body mass index (kg/m<sup>2</sup>)</td><td>1,581</td><td>28.2 ± 5.7</td><td>-0.02<sup>d</sup></td><td>0.43</td><td>-0.04<sup>d</sup></td><td>0.078</td></tr><tr><td>Education degree</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>School degree</td><td>927</td><td>59.7 %</td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>High school degree</td><td>249</td><td>16.0 %</td><td>-0.1</td><td>0.75</td><td>-0.2</td><td>0.56</td></tr><tr><td>University degree</td><td>376</td><td>24.2 %</td><td>0.3</td><td>0.42</td><td>0.4</td><td>0.27</td></tr><tr><td><strong>Cardiovascular risk factors</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current smoking, yes</td><td>225</td><td>14.2 %</td><td>-0.03</td><td>0.95</td><td>-0.2</td><td>0.53</td></tr><tr><td>Arterial hypertension, yes</td><td>1,097</td><td>69.3 %</td><td>0.8</td><td>0.016</td><td>0.4</td><td>0.16</td></tr><tr><td>Dyslipidemia, yes</td><td>804</td><td>50.9 %</td><td>1.7</td><td>&lt;0.001</td><td>1.3</td><td>&lt;0.001</td></tr><tr><td>Diabetes mellitus, yes</td><td>635</td><td>40.1 %</td><td>0.5</td><td>0.072</td><td>0.3</td><td>0.24</td></tr><tr><td>Chronic renal failure, yes</td><td>280</td><td>17.7 %</td><td>1.4</td><td>&lt;0.001</td><td>1.1</td><td>&lt;0.001</td></tr><tr><td><strong>Cardiovascular history events</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Myocardial infarction</td><td>295</td><td>18.7 %</td><td>6.6</td><td>&lt;0.001</td><td>5.8</td><td>&lt;0.001</td></tr><tr><td>Angioplasty</td><td>360</td><td>22.8 %</td><td>4.2</td><td>&lt;0.001</td><td>3.5</td><td>&lt;0.001</td></tr><tr><td>Coronary artery bypass</td><td>114</td><td>7.2 %</td><td>5.2</td><td>&lt;0.001</td><td>4.3</td><td>&lt;0.001</td></tr><tr><td>Stroke</td><td>154</td><td>9.7 %</td><td>0.5</td><td>0.35</td><td>0.2</td><td>0.67</td></tr><tr><td>Peripheral arterial disease</td><td>216</td><td>13.7 %</td><td>2.1</td><td>&lt;0.001</td><td>1.6</td><td>&lt;0.001</td></tr><tr><td>Pacemaker-defibrillator</td><td>115</td><td>7.3 %</td><td>5.3</td><td>&lt;0.001</td><td>5.5</td><td>&lt;0.001</td></tr><tr><td><strong>Left ventricular ejection fraction</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt;45 %</td><td>149</td><td>9.5 %</td><td>12.2</td><td>&lt;0.001</td><td>11.1</td><td>&lt;0.001</td></tr><tr><td><strong>Season at scintigraphy date</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>531</td><td>33.5 %</td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>Summer</td><td>263</td><td>16.6 %</td><td>0.2</td><td>0.65</td><td>0.4</td><td>0.34</td></tr><tr><td>Fall</td><td>340</td><td>21.5 %</td><td>0.1</td><td>0.83</td><td>0.1</td><td>0.88</td></tr><tr><td>Winter</td><td>449</td><td>28.4 %</td><td>0.2</td><td>0.67</td><td>0.2</td><td>0.58</td></tr><tr><td><strong>Scintigraphy indication</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Asymptomatic with high risk cardiovascular</td><td>1,171</td><td>73.9 %</td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>Atypical symptoms</td><td>364</td><td>23.0 %</td><td>-0.4</td><td>0.25</td><td>-0.4</td><td>0.22</td></tr><tr><td>Angina</td><td>50</td><td>3.2 %</td><td>0.2</td><td>0.86</td><td>-0.4</td><td>0.58</td></tr><tr><td><strong>Cardiac Stress type</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Exercise</td><td>805</td><td>51.9 %</td><td>Ref</td><td>–</td><td>Ref</td><td>–</td></tr><tr><td>Dipyridamole</td><td>551</td><td>35.5 %</td><td>0.6</td><td>0.069</td><td>0.4</td><td>0.15</td></tr><tr><td>Dobutamine</td><td>39</td><td>2.5 %</td><td>0.4</td><td>0.67</td><td>-0.02</td><td>0.98</td></tr><tr><td>Mix</td><td>156</td><td>10.1 %</td><td>0.6</td><td>0.25</td><td>0.7</td><td>0.15</td></tr></tbody></table>

<footnote><sup>a</sup> Headcount expressed in percent (excluding people with missing data) or mean ± SD.</footnote>

<footnote><sup>b</sup> Univariable linear regression parameter.</footnote>

<footnote><sup>c</sup> Per 1-yr increase.</footnote>

<footnote><sup>d</sup> Per 1-kg/m² increase.</footnote>
participants (3.0 %).

### 3.3. Air pollution exposure

Home addresses were geolocated for 1,583 participants (Fig. 2A–B), 1,488 (93.9 %) with the highest precision, 73 (4.6 %) at the geographical middle of the street, and 22 (1.4 %) at the town hall address. Average exposure to PM₁₀ was 16.7 ± 3.0 µg/m³ (mean ± SD). Corresponding value for PM₂.₅ was 10.7 ± 2.4 µg/m³ and for NO₂ was 16.8 ± 7.4 µg/m³ (Supplementary Appendix, Fig. S3). One interquartile range (IQR) of the exposure levels corresponded to 4.3, 3.3, and 10.0 µg/m³ for PM₁₀, PM₂.₅, and NO₂, respectively.

### 3.4. Associations between air pollution and scintigraphy parameters

Table 2 and Fig. 3 show relations between air pollutant exposure and myocardial perfusion parameters. Note that sample sizes varied across the statistical models due to the coverage of air pollution models (PM), the availability of AQMS data (NO₂), some missing SRS, and some missing data in the adjusting variables, and because participants with a moderate-to-severe myocardial ischemia were logically excluded from the “mild ischemia” models.

In the most adjusted model (Model 3), the odds-ratio for moderate-to-severe ischemia was 1.34 [95 % confidence interval (95 % CI): 1.03–1.75; *p* = 0.031] for each IQR (3.3-µg/m³) increase in PM₂.₅ exposure. The odds-ratio for moderate-to-severe ischemia tended to increase with PM₁₀ exposure level was similar (ORa, 1.30; 95 % CI: 0.98–1.72; *p* = 0.073 for each 4.3-µg/m³ increase). No statistically significant association was observed between exposure to NO₂ and moderate-to-severe ischemia: OR 1.16 (95 % CI: 0.89–1.50; *p* = 0.25) for each 10.0-µg/m³ increase. Coding exposure levels in quartiles was consistent with an increasing effect estimates for moderate-to-severe ischemia with all air pollutants (Supplementary Appendix, Fig. S4). No association was observed between exposure and mild ischemia and scar for all air pollutants in the most adjusted models.

Associations tended to be stronger in adjusted model 2 than in adjusted model 3 (which included symptoms and diseases), which was in agreement with model 3 possibly corresponding to a model with overadjustment. Thus, ORs for moderate-to-severe ischemia was 1.39

(95 % CI: 1.07–1.80), 1.33 (95 % CI: 1.01–1.75), and 1.22 (95 % CI: 0.96–1.57) for each increase of one IQR in PM₂.₅, PM₁₀ and NO₂ exposure, respectively. Furthermore, additional statistical associations and trends were observed for mild ischemia and scar on model 2. Notably, exposure to NO₂ appeared associated with scar: ORa 1.29 (95 % CI: 1.05–1.59; *p* = 0.016) for each 10.0-µg/m³ increase.

When stratifying on sex, all associations with were consistently stronger in women, with for example ORa of 1.97 (95 % CI: 1.04–3.92) in women, compared to 1.31 (95 % CI: 0.99–1.73) in men (interaction *p*-value = 0.077), between PM₂.₅ exposure and moderate-to-severe ischemia in model 2 (Table 3). No evidence of modifying the effects of body mass index and smoking was observed (data not shown).

Lastly, the odds-ratios reported above did not appear to be reduced toward the null association when restricting analyses to participants geolocated with the highest precision only, or to participants of retirement age only (Supplementary Appendix, Tables S1-S2). On the contrary, most estimates tended to be slightly increased compared to the main analyses, particularly when the analyses focused on participants of retirement age.

### 4. Discussion

This study is the first investigating and reporting an association between air pollution exposure and SPECT MPI. We showed that PM₂.₅ exposure was significantly associated with moderate-to-severe ischemia, independently of other traditional cardiovascular risk factors. The observed associations were stronger in women, and the analyses of gradient of exposure levels support the linearity layout of the observed relationships between air pollution and ischemia.

Particulate matter exposure can alter myocardial perfusion by various mechanisms. The autonomic nervous system imbalance, micro- and macro-circulation impairment (thrombosis, endothelial dysfunction, inflammation, oxidative stress), circulating blood lipid modification and direct cardiovascular toxicity of some particles are the main mechanisms involved in accelerated atherosclerosis and coronary vasomotor disorders. The role of air pollutants on accelerated atherosclerosis has been shown with several non-invasive imaging methodologies such as coronary calcium score (Huynh et al., 2021), coronary computed tomographic angiography (Yang et al., 2019), carotid intima-

<table><caption>**Table 2**
Exposure to air pollution in association with myocardial perfusion imaging.</caption><thead><tr><th rowspan="2">Outcome and exposure</th><th colspan="3">Model 1</th><th colspan="3">Model 2</th><th colspan="3">Model 3</th></tr><tr><th>N</th><th>OR (95 % CI)</th><th>p</th><th>N</th><th>ORa (95 % CI)</th><th>p</th><th>N</th><th>ORa (95 % CI)</th><th>p</th></tr></thead><tbody><tr><td><strong>Moderate-to-severe ischemia</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>1,484</td><td>1.24 (0.97-1.61)</td><td>0.092</td><td>1,449</td><td>1.33 (1.01-1.75)</td><td><strong>0.042</strong></td><td>1,419</td><td>1.30 (0.98-1.72)</td><td>0.073</td></tr><tr><td>PM<sub>2.5</sub></td><td>1,484</td><td>1.29 (1.01-1.65)</td><td><strong>0.038</strong></td><td>1,449</td><td>1.39 (1.07-1.80)</td><td><strong>0.013</strong></td><td>1,419</td><td>1.34 (1.03-1.75)</td><td><strong>0.031</strong></td></tr><tr><td>NO<sub>2</sub></td><td>1,399</td><td>1.13 (0.89-1.44)</td><td>0.31</td><td>1,367</td><td>1.22 (0.96-1.57)</td><td>0.10</td><td>1,336</td><td>1.16 (0.89-1.50)</td><td>0.25</td></tr><tr><td><strong>Mild ischemia</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>1,346</td><td>1.15 (0.91-1.46)</td><td>0.23</td><td>1,312</td><td>1.24 (0.96-1.59)</td><td>0.095</td><td>1,285</td><td>1.11 (0.79-1.56)</td><td>0.57</td></tr><tr><td>PM<sub>2.5</sub></td><td>1,346</td><td>1.14 (0.91-1.42)</td><td>0.26</td><td>1,312</td><td>1.23 (0.98-1.56)</td><td>0.080</td><td>1,285</td><td>1.03 (0.75-1.42)</td><td>0.87</td></tr><tr><td>NO<sub>2</sub></td><td>1,272</td><td>1.18 (0.95-1.46)</td><td>0.14</td><td>1,241</td><td>1.24 (0.99-1.55)</td><td>0.054</td><td>1,213</td><td>1.13 (0.82-1.54)</td><td>0.44</td></tr><tr><td><strong>Scar</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>1,440</td><td>1.16 (0.93-1.44)</td><td>0.18</td><td>1,405</td><td>1.26 (1.00-1.59)</td><td>0.054</td><td>1,377</td><td>1.25 (0.91-1.71)</td><td>0.17</td></tr><tr><td>PM<sub>2.5</sub></td><td>1,440</td><td>1.15 (0.93-1.41)</td><td>0.19</td><td>1,405</td><td>1.25 (1.01-1.60)</td><td><strong>0.045</strong></td><td>1,377</td><td>1.15 (0.86-1.54)</td><td>0.34</td></tr><tr><td>NO<sub>2</sub></td><td>1,361</td><td>1.22 (0.99-1.49)</td><td>0.061</td><td>1,329</td><td>1.29 (1.05-1.59)</td><td><strong>0.016</strong></td><td>1,300</td><td>1.27 (0.95-1.70)</td><td>0.11</td></tr></tbody></table>

Mild ischemia was defined by a Summed Stress Score (SSS) ≥ 4 on SPECT MPI with a threshold < 10 % of myocardium.

A threshold of 10% or more of myocardium has been reported in prognostic series to define moderate-to-severe ischemia.

Scar was defined by a Summed Rest Score (SRS) ≥ 4 on SPECT MPI.

Exposure defined as the mean over the year before the date of SPECT of the daily air pollutant levels estimated at the participant's home address.

Crude Odds-Ratios (OR), adjusted odds-ratios (ORa), and 95 % confidence intervals (95 %CI) estimated for an increase in exposure level of one Interquartile Range (IQR) of the exposure level; IQR values: 4.3, 3.3, and 10.0 µg/m³ for exposure to PM₁₀, PM₂.₅, and NO₂, respectively.

Model 1: crude ORs.

Model 2: ORs adjusted for sex, age, education degree, body mass index, current active tobacco smoking, and season at the scintigraphy date.

Model 3: ORs additionally adjusted for chronic renal failure, diabetes mellitus, arterial hypertension, dyslipidemia, history of myocardial infarction, history of coronary artery bypass, and left ventricular ejection fraction.

SPECT MPI: single-photon emission computed tomography myocardial perfusion imaging.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Exposure to air pollution in association with ischemia. Mild ischemia was defined by a Summed Stress Score (SSS) ≥ 4 on SPECT MPI with a threshold < 10% of myocardium. A threshold of 10% or more of myocardium has been reported in prognostic series to define moderate-to-severe ischemia. Scar was defined by a Summed Rest Score (SRS) ≥ 4 on SPECT MPI. Exposure defined as the mean over the year before the date of SPECT of the daily air pollutant levels estimated at the participant's home address. Adjusted odds-ratios (ORa) and 95% confidence intervals (95%CI) estimated for an increase in exposure level of one Interquartile Range (IQR) of the exposure level; IQR values: 4.3, 3.3, and 10.0 µg/m³ for exposure to PM₁₀, PM₂.₅, and NO₂, respectively. OR adjusted for sex, age, education degree, body mass index, current active tobacco smoking, season at scintigraphy date, chronic renal failure, diabetes mellitus, arterial hypertension, dyslipidemia, history of myocardial infarction, history of coronary artery bypass, and left ventricular ejection fraction. SPECT MPI: single-photon emission computed tomography myocardial perfusion imaging.</figcaption></figure>

media thickness (Künzli et al., 2005), and carotid artery plaques (Kilbo Edlund et al., 2022). A recently published study revealed the association between PM₂.₅ long-term exposure and coronary plaque vulnerability using invasive imaging by optical coherence tomography (Montone et al., 2022). Another recent study indicated an association between long-term air pollution exposure and coronary vasomotor disorders (Camilli et al., 2022).

Here, we have shown the role of PM₂.₅ in the severity of myocardial ischemia. This is consistent with the literature which shows a strong association between long-term exposure to PM₂.₅ and coronary events. However, our study is the first investigating and demonstrating such an association using SPECT MPI, a widely used methodology worldwide for the routine evaluation of stable ischemic heart disease patients (Hirschfeld et al., 2021). Furthermore, we reported stronger associations in women, supporting a likely vulnerability of women to the cardiovascular effects of air pollution. Indeed, a recent *meta-analysis* focusing on the sex difference in the relation between PM₂.₅ long-term exposure and ischemic heart disease, based on nine cohort studies, had highlighted a significant women-to-men ratio of relative risks: RRR 1.05 (95%CI: 1.02–1.08) per 10-µg/m³ increase (Zhang et al., 2022). Other cohort studies even reported significant associations between long-term exposure to air pollutants and cardiovascular health-related events, such as ischemic heart disease (Katsoulis et al., 2014) or coronary artery calcification (Kaufman et al., 2016), in women only. The present study is the first to report such a sex difference in myocardial perfusion images.

## 4.1. Clinical Implication and Perspectives

The correlation between air pollution and clinical decision thresholds is an important finding of our study and need to be considered in routine practice. Left ventricular myocardial ischemia ≥ 10% is an important clinical decision point in international recommendations since it identifies patients at risk who require an intensification of medical treatment and coronary angiography if symptoms persist despite medical treatment (Gulati et al., 2021; Knuuti et al., 2020). Our data showed the role of long-term exposure to PM in the level of ischemia, particularly at the ≥ 10% threshold, which may change the way patients are managed.

Thus, our results support that air pollution exposure levels should be integrated in cardiovascular risk assessment (Arnett et al., 2019; Visseren et al., 2022). However, this will require longitudinal studies to assess

<caption>**Table 3**</caption>

Associations between exposure to air pollution and myocardial perfusion imaging stratified on sex.

<table><thead><tr><th rowspan="2">Exposure</th><th colspan="3">Women</th><th colspan="3">Men</th><th rowspan="2">p-interaction</th></tr><tr><th>N</th><th>OR (95% CI)</th><th>N</th><th>ORa (95% CI)</th><th>N</th><th>OR (95% CI)</th><th>N</th><th>ORa (95% CI)</th></tr></thead><tbody><tr><td><strong>Moderate-to-severe ischemia</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>554</td><td>2.03 (1.07-3.94)</td><td>547</td><td>1.88 (0.95-3.95)</td><td>930</td><td>1.15 (0.87-1.51)</td><td>902</td><td>1.26 (0.94-1.71)</td><td>0.11</td></tr><tr><td>PM<sub>2.5</sub></td><td>554</td><td>2.18 (1.18-4.12)</td><td>547</td><td>1.97 (1.04-3.92)</td><td>930</td><td>1.19 (0.92-1.55)</td><td>902</td><td>1.31 (0.99-1.73)</td><td>0.077</td></tr><tr><td>NO<sub>2</sub></td><td>521</td><td>1.74 (0.94-3.18)</td><td>514</td><td>1.93 (0.98-3.80)</td><td>878</td><td>1.07 (0.82-1.38)</td><td>853</td><td>1.17 (0.88-1.52)</td><td>0.16</td></tr><tr><td><strong>Mild ischemia</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>531</td><td>2.06 (1.20-3.62)</td><td>524</td><td>1.98 (1.11-3.64)</td><td>815</td><td>1.02 (0.78-1.32)</td><td>788</td><td>1.10 (0.83-1.45)</td><td>0.021</td></tr><tr><td>PM<sub>2.5</sub></td><td>531</td><td>1.97 (1.18-3.37)</td><td>524</td><td>1.92 (1.12-3.39)</td><td>815</td><td>1.02 (0.79-1.30)</td><td>788</td><td>1.10 (0.84-1.43)</td><td>0.023</td></tr><tr><td>NO<sub>2</sub></td><td>500</td><td>1.39 (0.84-2.26)</td><td>493</td><td>1.41 (0.83-2.36)</td><td>772</td><td>1.15 (0.90-1.46)</td><td>748</td><td>1.21 (0.94-1.54)</td><td>0.51</td></tr><tr><td><strong>Scar</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PM<sub>10</sub></td><td>542</td><td>1.84 (1.09-3.17)</td><td>535</td><td>1.81 (1.02-3.30)</td><td>898</td><td>1.07 (0.84-1.36)</td><td>870</td><td>1.17 (0.90-1.52)</td><td>0.065</td></tr><tr><td>PM<sub>2.5</sub></td><td>542</td><td>1.78 (1.08-2.98)</td><td>535</td><td>1.75 (1.02-3.09)</td><td>898</td><td>1.07 (0.85-1.34)</td><td>870</td><td>1.17 (0.92-1.49)</td><td>0.069</td></tr><tr><td>NO<sub>2</sub></td><td>511</td><td>1.52 (0.93-2.47)</td><td>504</td><td>1.53 (0.91-2.58)</td><td>850</td><td>1.18 (0.94-1.48)</td><td>825</td><td>1.26 (1.00-1.59)</td><td>0.36</td></tr></tbody></table>

Mild ischemia was defined by a Summed Stress Score (SSS) ≥ 4 on SPECT MPI with a threshold < 10% of myocardium.

A threshold of 10% or more of myocardium has been reported in prognostic series to define moderate-to-severe ischemia.

Scar was defined by a Summed Rest Score (SRS) ≥ 4 on SPECT MPI.

Exposure defined as the mean over the year before the date of SPECT of the daily air pollutant levels estimated at the participant's home address.

Crude Odds-Ratios (OR), adjusted odds-ratios (ORa), and 95% confidence intervals (95%CI) estimated for an increase in exposure level of one Interquartile Range (IQR) of the exposure level; IQR values: 4.3, 3.3, and 10.0 µg/m³ for exposure to PM₁₀, PM₂.₅, and NO₂, respectively.

ORs adjusted for age, education degree, body mass index, current active tobacco smoking, and season at the scintigraphy date (Model 2).

p-interaction means the p-value issued from the Chi-square test for the variable corresponding to the interaction term between sex and the exposure.

SPECT MPI: single-photon emission computed tomography myocardial perfusion imaging.
the number of individuals who would be reclassified, or the improvement in risk classification by including air pollution in these risk algorithms. Moreover, developing collective and individual actions to reduce air pollution within lower maximum tolerable exposure limit ("Directive - EU - 2024/2881 - EN - EUR-Lex," n.d.) must now be fully integrated into cardiovascular prevention actions (Newman et al., 2020).

## 4.2. Limits and strengths

This is an observational, prospective and monocentric epidemiological study with all potential confounders of a non-randomized design. A large majority of our patients were asymptomatic with high-risk cardiovascular, and all study participants had to have undergone clinically indicated SPECT MPI; this should be kept in mind in interpreting results, which may not be generalizable to low-risk populations. The limits of our study include the lack of control invasive evaluation of coronary physiology for mechanistic purposes. Our study shows a link between air pollution and ischemia. But the causes of ischemia are multiple and can be due to the presence of epicardial stenosis, microcirculatory endothelial dysfunction or coronary spasm (Del Buono et al., 2021). The population with perfusion defects but without any obstructive coronary artery disease and the absence of symptoms is a well-known group, known as Ischemia with Non-Obstructive Coronary Artery disease (INOCA). This population continues to have a poor prognosis, particularly for patients with impaired coronary microcirculation (Odanović et al., 2024). In all cases, the extent of ischemia retains its prognostic value, whatever the mechanism (Wang et al., 2023). Coronary microvascular dysfunction could play an important role and explain why studies so far have shown an inconsistent relationship between PM exposure and subclinical atherosclerosis (Jilani et al., 2020). In case of coronary microvascular dysfunction, myocardial ischemia can be caused by abnormalities in coronary microvasculature which can be explained by structural and functional abnormalities, which can occur independently or concomitantly. The possible causes of structural microvascular disease are luminal narrowing of microvessels, luminal obstruction caused by thromboembolism, capillary rarefaction, extrinsic vascular compression, and vascular wall infiltration. Functional mechanisms responsible for coronary microvascular dysfunction may be related to the presence of an impaired dilation (vasodilator abnormalities) and/or an increased constriction of coronary microvessels (microvascular spasm). In these cases, epicardial atherosclerosis is not at the center of the pathology, but endothelial dysfunction has been attributed to a central pathogenetic role (Del Buono et al., 2021).

In addition, some data are lacking in our survey: specifically, the education degree was only available whereas many studies reported a relationship between ethnicity and socioeconomic status with ambient air pollution exposure. Moreover, employment status and work address were missing which could lead to a misestimation of air pollution exposure for active people. The lack of taking into account work-related exposures is a usual limitation of epidemiological studies basing the assessment of exposure exclusively on the address of the accommodation. Nonetheless, all associations observed in the main analyses were retained in sensitivity analyses focusing on working-age participants. This is consistent given the feature of our study population, which was quite elderly with most participants of retirement age; indeed, the mean age in our study population was 67.7 ± 11.6 years old for a legal retirement age in France of 62 years (at the time of the study). Thus, the risk of exposure misclassification appeared limited in our study.

The strengths of our study include the fact that enrolled patients were from a large geographical area, which was allowed to encompass a broad range of exposure levels. Moreover, the dispersion model used for air pollutant exposure assessment had a 10-meter spatial resolution, which is especially fine and rare in the literature. However, access to daily data only for PM₁₀ was a constraint as it required us to preliminary extrapolate PM₂.₅ and NO₂ daily data from the annual model estimates. Daily PM₂.₅ data were extrapolated from PM₁₀ data, in the sense that the

temporal (daily) variations in PM₂.₅ were borrowed from those observed with PM₁₀, by applying a conversion coefficient equal to the PM₂.₅/PM₁₀ ratio in each air pollutant model grid. Thus, applied conversion coefficients were specific to the study area, and even to each model grid in our study, as recommended by the French Agency for Food, Environmental and Occupational Health & Safety Avis complété relatif à l'élaboration de VTR par voie respiratoire pour les particules de l'air ambiant extérieur (PM₂.₅ et PM₁₀) et le carbone suie de l'air ambiant extérieur et rapport relatif aux VTR par voie respiratoire pour le carbone suie de l'air ambiant extérieur [WWW Document], 2024). It is more refined than if we had simply applied the same generic coefficient on all PM₁₀ data (about 0.65 for Europe), which is an approach commonly encountered in health risk assessment studies Health risks of air pollution in Europe – HRAPIE project. Recommendations for concentration-response functions for cost-benefit analysis of particulate matter, ozone and nitrogen dioxide, 2013). These both approaches make the assumption that within-year temporal variability was similar to PM₂.₅ and PM₁₀, and so, PM₂.₅/PM₁₀ ratios constant throughout the year. In our case, this was reasonable given that the latter ratio has been shown of low variability in our study area (Boudet et al., 2000). Furthermore, daily PM₂.₅ data were therefore extrapolated from both the PM₁₀ and PM₂.₅ data, and not the PM₁₀ only, which explains the possible variations in the effect estimates. Concerning NO₂ level extrapolation, we preferred to rely on daily averages of AQMS's NO₂ measurements with regard to their excellent temporal resolution. For overcoming the low spatial resolution of the latter, we have localized them to each participant's home address relying on the annual modeling data at the 10-meter resolution, applying a method previously described (Slama et al., 2007a).

Lastly, to limit exposure misclassification bias, we chose to withdraw participants who reported moving during the one-year exposure window, assuming a risk for statistical power reduction. Indeed, the address of the current accommodation and the date of moving into this accommodation were collected, while no information on the previously occupied accommodation was collected. We nevertheless included in the analyses all geolocated participants, even those less accurately (i.e., in the middle of the street or even at the town hall address) in order to preserve our study sample. We did this due to the part of participants concerned (6 %) and because the least precise addresses are in general rural, it means where the exposure level contrasts are the lowest. The sensitivity analyses carried out showed the low impact of this methodological choice.

## 5. Conclusion

Our study is the first reporting association between air pollution exposure and moderate-to-severe ischemia as assessed by SPECT MPI. Consequently, efforts to reduce air pollutant emissions by lowering maximum tolerable exposure limits must continue.

### CRediT authorship contribution statement

**Océane Bouchot:** Writing – review & editing, Writing – original draft, Investigation, Formal analysis. **Stephan Gabet:** Writing – review & editing, Writing – original draft, Formal analysis, Data curation. **Loïc Djaileb:** Writing – review & editing, Investigation. **Aurélia Robin:** Writing – review & editing. **Nicolas De Leiris:** Writing – review & editing, Investigation. **Julie Roux:** Writing – review & editing, Investigation. **Laurent Riou:** Writing – review & editing. **Gerald Vanzetto:** Writing – review & editing. **Daniel Fagret:** Writing – review & editing. **Catherine Ghezzi:** Writing – review & editing. **Rémy Slama:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization. **Gilles Barone-Rochette:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.
Declaration of competing interest

The authors declare the following financial interests/personal re-
lationships which may be considered as potential competing interests:
[Gilles Baorne-Rochette reports financial support was provided by
Fondation coeur et recherche. GBR received research grants from the
company MSD, Pfizer, Abbott vascular, and consulting fees from the
companies Bayer, Abbott Vascular, NovoNordisk, Sanofi, General Elec-
tric, Medis imaging, and Amgen. If there are other authors, they declare
that they have no known competing financial interests or personal re-
lationships that could have appeared to influence the work reported in
this paper].

Acknowledgments

We thank Maïlys Barbagallo, Clémence Charlon, Julie Hildt, Sarah
Limon, and Delphine Pollet for their helpful involvement in the coor-
dination of the EVAPERF study. We also thank Atmo Auvergne-Rhône-
Alpes (Atmo AuRA), and in particular Marie-Blanche Personnaz, Flor-
ence Troude, Cécile Fontagné, Charles-Elie Petitjean, and Harry Dupont
for providing ‘Combine’ air pollutant-dispersion model data and meth-
odological references. We are grateful to all women and men who
participated in this study for their continuous involvement in the
EVAPERF cohort.

Sources of Funding.

The present study was funded by "La Fondation Cœur & Recherche".
Disclosures.

GBR received research grants from the company MSD, Pfizer, Abbott vascular, and consulting fees from the companies Bayer, Abbott Vascular, NovoNordisk, Sanofi, General Electric, Medis imaging, and Amgen. The other authors have no conflict of interest.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envint.2025.109406.

Data availability

The data that has been used is confidential.

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