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. 2024 Oct 31;19(10):e0309912. doi: 10.1371/journal.pone.0309912

Exposure to air pollutants and subclinical carotid atherosclerosis measured by magnetic resonance imaging: A cross-sectional analysis

Sandi M Azab 1,2, Dany Doiron 3, Karleen M Schulze 2,4, Jeffrey R Brook 5, Michael Brauer 6, Eric E Smith 7, Alan R Moody 8, Dipika Desai 4, Matthias G Friedrich 9, Shrikant I Bangdiwala 1,4, Dena Zeraatkar 10, Douglas Lee 11, Trevor J B Dummer 6, Paul Poirier 12, Jean-Claude Tardif 13, Koon K Teo 2,4, Scott Lear 14, Salim Yusuf 4, Sonia S Anand 1,2,4, Russell J de Souza 1,4,*; for the Canadian Alliance of Healthy Hearts and Minds (CAHHM) Study Investigators
Editor: Muhammad Maaz Arif15
PMCID: PMC11527219  PMID: 39480801

Abstract

Objectives

Long-term exposure to air pollution has been associated with higher risk of cardiovascular mortality. Less is known about the association of air pollution with initial development of cardiovascular disease. Herein, the association between low-level exposure to air pollutants and subclinical carotid atherosclerosis in adults without known clinical cardiovascular disease was investigated.

Design

Cross-sectional analysis within a prospective cohort study.

Setting

The Canadian Alliance for Healthy Hearts and Minds Cohort Study; a pan-Canadian cohort of cohorts.

Participants

Canadian adults (n = 6645) recruited between 2014–2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia, were studied, for whom averages of exposures to nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) were estimated for the years 2008–2012.

Main outcome measure

Carotid vessel wall volume (CWV) measured by magnetic resonance imaging (MRI).

Results

In adjusted linear mixed models, PM2.5 was not consistently associated with CWV (per 5 μg/m3 PM2.5; adjusted estimate = -8.4 mm3; 95% Confidence Intervals (CI) -23.3 to 6.48; p = 0.27). A 5 ppb higher NO2 concentration was associated with 11.8 mm3 lower CWV (95% CI -16.2 to -7.31; p<0.0001). A 3 ppb increase in O3 was associated with 9.34 mm3 higher CWV (95% CI 4.75 to 13.92; p<0.0001). However, the coarse/insufficient O3 resolution (10 km) is a limitation.

Conclusions

In a cohort of healthy Canadian adults there was no consistent association between PM2.5 or NO2 and increased CWV as a measure of subclinical atherosclerosis by MRI. The reasons for these inconsistent associations warrant further study.

Introduction

Cardiovascular disease (CVD) is a leading cause of mortality in Canada and worldwide, and traditional risk factors include smoking, obesity, diabetes, hypertension, and dyslipidemia [1]. Growing epidemiological evidence describes the adverse effects of air pollution on cardiovascular health [2–5], however the role of low levels of exposure to air pollution on early subclinical markers of cardiovascular dysfunction, e.g., atherosclerosis, is not well-characterized [6].

Major pollutants include particulate matter (PM) and gaseous air pollutants, such as nitrogen oxides (NOx) and ground level ozone (O3) [7]. Fine particulate matter (PM2.5) is fine inhalable particles ≤2.5 μm in aerodynamic diameter. Nitrogen dioxide (NO2) is mainly associated with road traffic and other forms of fossil combustion and is a precursor to O3, which is formed through chemical reactions between NOx and volatile organic compounds (VOC) in the presence of sunlight [8]. The 2022 special report by the Health Effects Institute (HEI) on long-term exposure to traffic-related air pollution (TRAP), which included 57 studies investigating cardiometabolic effects found that the confidence in the evidence for the association of air pollution with cardiovascular (circulatory and ischemic heart disease) mortality is high, but with cardiovascular morbidity is at best moderate to low [9, 10]. However, the methods of outcome assessment varied substantially and only half of the studies entered a meta-analysis of which one-third rated as high risk of bias for the confounder domain [9, 10].

Imaging of the carotid arteries is a non-invasive biomarker of subclinical atherosclerosis -the progressive buildup of plaques- for early prediction of IHD risk in healthy individuals without clinically significant CVD [11]. In the longitudinal Multiethnic Study of Atherosclerosis (MESA), PM2.5, NO2, and NOX were not associated with carotid intima-media thickness (cIMT) change, while ambient O3 was associated with increased progression of cIMT [2, 8]. Magnetic resonance imaging (MRI) can accurately assess the presence of subclinical cerebrovascular atherosclerosis [11] by measuring carotid vessel wall volume (CWV) i.e. the entire thickness of the wall. MRI-determined CWV, compared to ultrasound-measured cIMT, includes the adventitia, which is the source of vasa vasorum that further proliferates with arterial wall thickening [12]. Thus, CWV is a more sensitive measure of early plaque development [12, 13] and more consistently associated with incident CVD than cIMT [14]. To date, the association of ambient air pollution and MRI-captured CWV has not been studied. In the Canadian Alliance for Healthy Hearts and Minds Cohort (CAHHM) [15] of generally healthy adults, we sought to characterize the associations between low levels of exposure to PM2.5, NO2, and O3, and MRI-measured carotid atherosclerosis as a major CVD pathway.

Methods

Study design and participants

The design and methods of the CAHHM prospective cohort study have been previously described [15]. Participants were recruited from January 1, 2014 to December 31, 2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia in mostly urban locations [15]. Research ethics board approval was obtained from the Hamilton Integrated Research Ethics Board (HiREB # 13–255), and all participants provided written informed consent. All data were deidentified. The cohort includes 8258 adults from across Canada, of whom > 80% were participants in ongoing prospective cohort studies and assessed for CVD traditional risk factors. MRI scans of the brain, heart, carotid artery, and abdomen were performed at enrollment, and 7973 participants completed a standard carotid MRI scan. Adults with known history of CVD (defined as a self-reported history of stroke, coronary artery disease, heart failure, or other heart disease), incomplete data on the non-lab based cardiovascular risk score, or incomplete air pollutants values were excluded for the presented analyses, leaving a final sample size of 6645 participants (S1 Fig).

Assessment of air pollution exposure

The three major air pollutants of interest in this study were PM2.5, NO2, and O3. The development of these exposure datasets has been documented elsewhere [16–18] and they have been used in multiple Canadian epidemiological studies, including the recent Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE) study [19]. Briefly, annual average exposures for the five years prior to the start of CAHHM recruitment (2008–2012), data distributed by the Canadian Urban Environmental Health Research Consortium (CANUE) [20] (www.canue.ca), were linked to CAHHM using the six-character residential postal code of participants at the time of recruitment. The average exposure over the five years prior to recruitment was chosen as it is considered to be representative of long-term air pollution exposure gradients and relevant for investigating subclinical CVD, which manifests over a long period of time [21].

Key emission sources for PM2.5 are industrial emissions, wildfire smoke, space heating, residential wood heating, cooking, agriculture, and vehicle traffic emissions. A significant fraction of PM2.5 is a result of atmospheric chemistry, forming from a range of gaseous precursors such as sulphur dioxide (SO2), NO2, ammonia (NH4), and volatile and semi volatile organic compounds. Yearly averages of PM2.5 concentrations prior to baseline assessment were estimated across a 1x1 kilometer grid covering North America using NASA MODIS, MISR, and SeaWIFS satellite instruments, with aerosol vertical profiles and scattering properties simulated by the GEOS-Chem chemical transport model [16]. To adjust for any residual bias in the satellite-derived PM2.5 estimates, a geographically weighted regression (GWR) incorporating ground-based observations was then applied [16]. Good agreement was found with cross-validated surface observations across North-America (R2 = 0.70). For most residential addresses, postal code areas were considerably smaller than 1x1 km so that the assigned PM2.5 concentration matches the 1x1 km grid square that the postal code is found within. Specifically, assigning PM2.5 to postal codes was performed using the single linkage approach where the PM2.5 grid square selected was the one closest to the x, y coordinate within a postal code polygon that best represents where the majority of the population lived.

NO2 is considered an indicator of TRAP, which is a complex mixture of gases and particles, including ultrafine particles (diameter ≤ 0.1 μm). Annual average NO2 concentrations in parts per billion (ppb) were estimated for each postal code location using a national land use regression (LUR) model for the year 2006 [17] at 100 m resolution and adjusted for prior and subsequent years using long-term air quality monitoring station data. The LUR NO2 model included road length, 2005–2011 satellite NO2 estimates, area of industrial land use within 2 km, and summer rainfall as predictors of regional NO2 variation [17]. Deterministic gradients were used to model local scale variation related to roads (i.e. traffic) [17]. The final NO2 model showed good performance, explaining 73% of the variation in measurements from national air pollution surveillance (NAPS) monitoring data with a root mean square error (RMSE) of 2.9 ppb.

O3 is a photochemically produced oxidant gas that results from the reaction between sunlight and NOx and VOCs emitted from various natural sources and human activities such as fossil fuel combustion and wood combustion. Annual mean concentrations of O3 exposure at 10–15 km resolution were estimated using the GEM-MACH (Global Environmental Multi-scale—Modelling Air Quality and Chemistry) air quality forecast model combined with observations from monitoring networks [18, 22].

Subclinical MRI outcomes

Details of the CAHHM MRI protocol have been previously published [15]. The protocol used validated standard techniques to collect information on morphology, function and tissue characteristics. Briefly, participants underwent a short non-contrast enhanced scan using a 1.5 or 3.0 Tesla magnet. Each of the centres underwent a validated test scan for quality assurance. Carotid artery vessel wall volume (mm3) (left, right, and combined) within a 32-mm vessel length centred on each carotid bifurcation (to include distal common and proximal internal carotid arteries) was measured by subtracting lumen volume from total vessel volume. The lumen was defined semi-automatically from axial bright blood images of the time of flight sequence which were reconstructed at 2 mm intervals. The outer wall of the carotid artery was semi-automatically defined and adjusted as needed by expert readers. The area of the vessel wall in each image was estimated by subtracting the lumen area from the outer wall vessel area. Vessel wall volume per slice was calculated by multiplying by 2 mm per slice. Vessel wall volumes for right and left carotid arteries were estimated by integrating the volume for the total number of slices for each artery. We used the maximum of either the left or right CWV as the measure of atherosclerosis in this study.

The INTERHEART risk score

The non-laboratory-based INTERHEART risk score is a validated tool developed to estimate a person’s myocardial infarction (MI) risk based on a compilation of risk factors [23]. These include age, sex, smoking, second-hand smoke exposure, diabetes, high blood pressure, and family history of MI, waist-to-hip ratio (WHR); home or work social stress, depression, simple dietary questions, and physical activity [1]. The score ranges from 0 to 48 and is categorized into low- (a score ≤ 9), moderate- (a score of 10 to 15), or high- (a score ≥ 16) risk categories and is significantly associated with diagnosed CVD, and also the presence of subclinical cerebrovascular disease without known clinical CVD [11, 23].

Definitions

Individual socioeconomic status: Education was categorized as the highest level of education attainment (High school or less, College or Trade, or University Degree). Employment status was categorized as employed, retired or unemployed. An indicator variable was used to identify individuals who traveled outside of their community of residence for work.

Neighbourhood socioeconomic status: Area-based social deprivation index and material deprivation index from 2011 Canadian census data linked to participants six-character postal code through CANUE databases were used to represent the socioeconomic status of the local community/society to which participants belonged [24]. The indices are the first two components of a principal component analysis (PCA) of the following six variables: the proportion of persons without a high school diploma; the employment population ratio; the average personal income; the proportion of persons living alone; the proportion of individuals separated, divorced or widowed; and the proportion of single-parent families [24].

Neighbourhood walkability: Walkability measures the degree to which a neighbourhood supports walking and was included because, in a nationally representative Canadian study (n = 1.8 million participants) with a 15-year follow-up, neighbourhood walkability was associated with reduced risk of cardiovascular mortality (HR: 0.91 [0.88, 0.95]) [25]. Participants residential postal codes were linked to the Canadian Active Living Environments Index (Can-ALE) categorical variable that characterizes the favourability/friendliness of active living (i.e. walkability) potential of neighbourhoods in census metropolitan areas (CMA) on a scale from 1 (very low) to 5 (very high) based on intersection density, dwelling density, and points of interest measures available for the year 2016. An environment with a very high walkability tends to be densely populated and has very connected street patterns and a variety of walking destinations. Consequently, such environments are more urbanized and tend to experience higher NO2 levels. More information on Can-ALE is available at: http://canue.ca/wp-content/uploads/2018/03/CanALE_UserGuide.pdf.

Statistical analysis

The distribution of continuous variables is presented as means with standard deviation, and categorical variables are presented as counts and percentages. We assessed the association of the continuous measure of air pollutant exposure with CWV, expressed as a 5 μg/m3 increment for PM2.5, a 5 ppb increment for NO2, and a 3 ppb increment for O3, as previously reported for relevant air pollutant concentrations in developed countries [8, 9]. The associations were explored using 5 linear mixed models, each with random intercepts representing the effect of recruitment centre. This random intercept was a proxy for spatial clustering of participants. We considered a parsimonious set of demographic, lifestyle, and environmental characteristics as potential covariates in the association between CWV and air pollution, considering collinearity. Our final variable selection was guided by published literature, previous knowledge, as well as variables that we observed to be effect modifiers of observed associations. Neighbourhood greenness did not meet the threshold for covariate selection and neighbourhood noise could not be tested; however, prior studies with noise adjustment showed stable if not larger effect estimates of association [9, 10]. The following fixed covariates were included in each model: 1) none (“unadjusted model”); 2) participant’s age, sex, and ethnicity (“basic model”); 3) further adjusted for contributing individual-level factors i.e., the INTERHEART risk score, education, and working outside of the lived-in community (“lifestyle model”); 4) further adjusted for community-level factors i.e. walkability, material factor score, and social factor score (our a priori “primary model”) and 5) model further adjusted for co-pollutants within two-pollutant models at a time (“co-pollutant model”). A complete case analysis was employed because missing data on covariates was low (1.6% for education and 0.08% for Can-ALE index). In sensitivity analyses, models 1–5 were i. stratified by sex and ii. repeated after excluding participants based on immigration status for those who had been in Canada for less than ten years (n = 5885), and iii. stratified based on workplace location (residing at home/working in the lived-in community versus working outside the lived-in community) to address possible exposure misclassification due to major time away from residence. To investigate interactions between the pollutants, we modeled the effect of one pollutant on CWV within low, medium, and high levels of a second pollutant with the same fixed covariates of models 1–4 and tested the statistical significance of the interaction term of the two pollutants. A 2-sided p <0.05 was considered nominally significant with no adjustment for multiple testing. All analyses were completed using SAS software, version 9.4 (SAS Institute Inc).

Results

Participant characteristics

Of the 6645 participants enrolled in this study, 3253 (48.9%) were from Ontario, 1575 (23.7%) from Quebec, 744 (11.2%) from British Columbia, 671 (10.1%) from Nova Scotia, and 402 (6.0%) from Alberta. For all the regions, over 92% of the cohort’s postal codes were in urban areas. The mean age of participants at enrolment was 57.6 years (SD = 8.8; range = 32–81 years) and 56.0% of participants were women. The mean CWV of participants was 900.1 mm3 (165.1) at the time of the MRI scan. Demographic, anthropometric, and lifestyle characteristics of the study participants are found in Tables 1 and 2. The mean (SD) 5-year pollutant concentrations immediately preceding enrolment for PM2.5 was 6.9 μg/m3 (2.0), ranging from the lowest [3.2 μg/m3 (0.5)] in parts of the Calgary, Alberta region to the highest [8.6 μg/m3 (1.5)] in London, Ontario; for NO2 was 12.9 ppb (5.9), ranging from lowest [4.1 ppb (1.2)] in Halifax, Nova Scotia to highest [17.0 ppb (3.9)] in Toronto, Ontario; and for O3 was 24.6 ppb (4.0), ranging from lowest [16.9 ppb (3.0)] in Vancouver, British Columbia to highest [30.4 ppb (1.2)] in London, Ontario, as presented in Table 3. Participant characteristics stratified by sex are presented in S1–S3 Tables.

Table 1. Anthropometric characteristics of the study population by region of Canada.

Region of Canada
N Overall BC AB ON QC NS
Number of participants 6645 6645 744 402 3253 1575 671
Women, n (%) 6645 3718 (56.0) 408 (54.8) 197 (49.0) 1948 (59.9) 811 (51.5) 354 (52.8)
Age, y 6645 57.6 (8.8) 56.8 (8.8) 53.3 (8.8) 57.5 (9.0) 58.6 (7.8) 59.0 (9.3)
Weight, kg 6645 76.3 (16.5) 72.5 (15.9) 80.7 (17.5) 75.7 (16.5) 77.4 (16.4) 78.1 (15.4)
Height, cm 6645 168.5 (9.4) 167.5 (9.6) 173.2 (10.1) 168.3 (9.1) 167.6 (9.3) 169.4 (9.2)
Body Mass Index, mean (SD), kg/m 2 6645 26.8 (4.9) 25.7 (4.4) 26.8 (4.9) 26.6 (4.9) 27.5 (5.0) 27.2 (4.8)
 <25 (Normal), n (%) 6645 2657 (40.0) 352 (47.3) 163 (40.5) 1366 (42.0) 535 (34.0) 241 (35.9)
 25–29 (Overweight), n (%) 6645 2536 (38.2) 285 (38.3) 150 (37.3) 1193 (36.7) 647 (41.1) 261 (38.9)
 30+ (Obese), n (%) 6645 1452 (21.9) 107 (14.4) 89 (22.1) 694 (21.3) 393 (25.0) 169 (25.2)
Percent Body Fat, % 6624 30.6 (9.2) 28.9 (8.4) 29.9 (9.1) 30.9 (9.4) 31.3 (8.8) 30.0 (9.6)
Waist, cm 6645 88.6 (13.7) 84.3 (12.8) 91.0 (13.9) 87.5 (13.6) 90.3 (14.0) 93.0 (12.5)
Hip, cm 6645 101.3 (10.7) 99.2 (9.2) 103.6 (10.3) 100.4 (11.5) 102.3 (9.7) 104.7 (9.2)
Waist to hip ratio 6645 0.87 (0.08) 0.85 (0.09) 0.88 (0.08) 0.87 (0.08) 0.88 (0.09) 0.89 (0.08)
Waist circumference obese, n (%) 6645 1924 (29.0) 118 (15.9) 130 (32.3) 917 (28.2) 492 (31.2) 267 (39.8)
Blood Pressure
 Systolic, mmHg 6645 129 (17) 126 (17) 127 (14) 128 (17) 131 (16) 133 (16)
 Diastolic, mmHg 6645 80 (10) 80 (10) 85 (9) 78 (10) 80 (9) 80 (10)
Heart rate, beats/minute 6644 70.4 (11.0) 69.4 (11.0) 70.5 (11.0) 71.1 (11.1) 69.7 (10.7) 69.6 (10.6)
MRI-measured Outcomes
Carotid wall volume, mm3 6645 900.1 (165.1) 897.9 (170.5) 889.9 (170.5) 906.0 (163.2) 893.2 (155.8) 896.2 (184.3)
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Presented data are means (SD) unless otherwise indicated.

Table 2. Demographics & lifestyle characteristics of the study population by region of Canada.

Region of Canada
N Overall BC AB ON QC NS
Number of participants 6645 6645 744 402 3253 1575 671
Women 6645 3718 (56.0) 408 (54.8) 197 (49.0) 1948 (59.9) 811 (51.5) 354 (52.8)
Age, mean (SD), y 6645 57.6 (8.8) 56.8 (8.8) 53.3 (8.8) 57.5 (9.0) 58.6 (7.8) 59.0 (9.3)
Self-reported ethnicity
 East & South East Asian 6645 894 (13.5) 282 (37.9) 14 (3.5) 585 (18.0) 8 (0.5) 5 (0.7)
 South Asian 6645 223 (3.4) 67 (9.0) 3 (0.7) 148 (4.5) 1 (0.1) 4 (0.6)
 White 6645 5387 (81.1) 364 (48.9) 381 (94.8) 2449 (75.3) 1545 (98.1) 648 (96.6)
 Othera 6645 141 (2.1) 31 (4.2) 4 (1.0) 71 (2.2) 21 (1.3) 14 (2.1)
Highest Education Attained
 High school or less 6541 842 (12.9) 100 (13.5) 44 (10.9) 344 (10.6) 290 (18.4) 64 (10.9)
 College or Trade 6541 2107 (32.2) 255 (34.5) 117 (29.1) 891 (27.5) 671 (42.6) 173 (29.4)
 University Degree 6541 3592 (54.9) 385 (52.0) 241 (60.0) 2001 (61.8) 613 (38.9) 352 (59.8)
Smoke status
 Current 6645 352 (5.3) 29 (3.9) 18 (4.5) 156 (4.8) 119 (7.6) 30 (4.5)
 Former 6645 2241 (33.7) 178 (23.9) 132 (32.8) 971 (29.8) 732 (46.5) 228 (34.0)
 Never 6645 4052 (61.0) 537 (72.2) 252 (62.7) 2126 (65.4) 724 (46.0) 413 (61.5)
Living with partner/married 6537 4938 (75.5) 567 (76.6) 314 (78.1) 2431 (75.2) 1141 (72.4) 485 (82.5)
Employment
 Full or part time 6534 4624 (70.8) 554 (74.7) 324 (80.6) 2208 (68.2) 1124 (71.5) 414 (71.4)
 Retired 6534 1436 (22.0) 143 (19.3) 41 (10.2) 727 (22.5) 385 (24.5) 140 (24.1)
 No paid work 6534 474 (7.3) 45 (6.1) 37 (9.2) 302 (9.3) 64 (4.1) 26 (4.5)
Individual Social disadvantage score
Social disadvantage Index, mean (SD) 6121 1.2 (1.3) 1.2 (1.3) 0.8 (1.1) 1.2 (1.3) 1.4 (1.3) 1.1 (1.3)
Low disadvantage 6121 3677 (60.1) 435 (63.0) 283 (73.7) 1758 (59.1) 858 (56.9) 343 (61.0)
Moderate disadvantage 6121 2072 (33.9) 209 (30.3) 92 (24.0) 1031 (34.6) 542 (35.9) 198 (35.2)
High disadvantage 6121 372 (6.1) 46 (6.7) 9 (2.3) 187 (6.3) 109 (7.2) 21 (3.7)
INTERHEART risk score mean (SD) 6645 10.0 (5.7) 9.2 (5.5) 9.6 (5.7) 10.1 (5.7) 10.3 (5.8) 10.2 (5.6)
Low 6645 3392 (51.0) 418 (56.2) 217 (54.0) 1645 (50.6) 785 (49.8) 327 (48.7)
Moderate 6645 2123 (31.9) 223 (30.0) 121 (30.1) 1069 (32.9) 491 (31.2) 219 (32.6)
High 6645 1130 (17.0) 103 (13.8) 64 (15.9) 539 (16.6) 299 (19.0) 125 (18.6)
Usual workplace location
 Outside home community 6605 2301 (34.8) 269 (36.5) 197 (49.0) 1008 (31.1) 675 (43.3) 152 (22.8)
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Presented data are n (%) unless otherwise specified.

a Includes Blacks, Indigenous, Mixed and unknown ethnicity

Table 3. Environmental characteristics of the study population by region of Canada.

Region of Canada
N Overall BC AB ON QC NS
Number of participants 6645 6645 744 402 3253 1575 671
Urban postal code 6645 6390 (96.2) 686 (92.2) 389 (96.8) 3179 (97.7) 1468 (93.2) 668 (99.6)
Neighbourhood Deprivation
Material factor score 6447 -0.017 (0.041) -0.013 (0.039) -0.040 (0.035) -0.016 (0.043) -0.008 (0.039) -0.027 (0.032)
Social factor score 6447 0.001 (0.041) -0.006 (0.036) -0.008 (0.044) -0.005 (0.042) 0.017 (0.036) 0.008 (0.038)
Walkability Measures
ALE Index 6640 1.806 (4.501) 1.535 (3.661) 0.810 (1.854) 2.618 (5.610) 1.172 (2.976) 0.255 (1.764)
ALE Index Class, n (%)
 Class 1: very low 6640 772 (11.6) 81 (10.9) 40 (10.0) 300 (9.2) 232 (14.7) 119 (17.7)
 Class 2 6640 1917 (28.9) 176 (23.7) 193 (48.0) 820 (25.2) 472 (30.0) 256 (38.2)
 Class 3 6640 2014 (30.3) 249 (33.6) 146 (36.3) 1079 (33.2) 326 (20.7) 214 (31.9)
 Class 4 6640 1182 (17.8) 166 (22.4) 12 (3.0) 611 (18.8) 311 (19.8) 82 (12.2)
 Class 5: very high 6640 755 (11.4) 70 (9.4) 11 (2.7) 442 (13.6) 232 (14.7) 0 (0.0)
Linked Air Quality Measures
PM2.5, ug/m3, over 2008–2012 6645 6.9 (2.0) 6.7 (1.4) 3.2 (0.5) 8.2 (1.5) 6.0 (1.5) 5.2 (1.1)
NO2, ppb, over 2008–2012 6645 12.9 (5.9) 15.2 (4.8) 12.8 (3.5) 14.1 (5.3) 13.0 (5.8) 4.1 (1.2)
O3, ppb, over 2008–2012 6645 24.6 (4.0) 17.8 (4.3) 23.4 (2.1) 27.1 (2.6) 23.9 (2.4) 22.3 (1.3)
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Presented data are means (SD) unless otherwise indicated. CMA: census metropolitan area; ALE: active living environment

Long-term pollutant exposure and MRI-measured CWV–Primary analysis

Associations of 5-year pollutant exposures with subclinical atherosclerosis as measured by CWV are presented in Fig 1 and Table 4. The association between PM2.5 and CWV was inconsistent and not statistically significant in the primary model. A 5 μg/m3 higher PM2.5 concentration was not associated with CWV (mean = -8.4 mm3; 95% CI -23.2, 6.48; p = 0.27). A 5 ppb higher NO2 concentration was associated with 11.8 mm3 lower CWV (95% CI -16.2, -7.31; p<0.0001); contradictory to our hypothesis. A 3 ppb increase in O3 was associated with 9.34 mm3 higher CWV (95% CI 4.75, 13.92; p<0.0001). These results remained consistent in the sensitivity analysis in males and females, after excluding immigrants with less than 10 years of residence in Canada, and after excluding participants working away from their lived-in community (Fig 1, Tables 4 and 5). Of note, Pearson correlation between PM2·5 and NO2 was [r = +0·39; p<·0001], between PM2·5 and O3 was [r = +0·16; p<·0001], and between NO2 and O3 was [r = -0·23; p<·0001].

Fig 1. Associations of 5-year pollutant exposures with subclinical atherosclerosis as measured by carotid wall volume (CWV).

Fig 1

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Presented data are carotid wall volume adjusted estimates (95% CI) per (A) 5 μg/m3 increase for PM2.5, (B) 5 ppb increase for NO2, and (C) 3 ppb increase for O3, from a linear mixed model with centre modelled as a random intercept. The base model has no fixed effects; the adjusted model includes age, (sex), ethnicity, INTERHEART risk score, education, workplace location, walkability categories, material factor score, and social factor score.

Table 4. Association of carotid wall volume (mm3) with pollutants exposure.

Overall (n = 6223) Females (n = 3472) Males (n = 2751) Remove recent immigrants (n = 5885)
Effect (95% CI) p- Effect (95% CI) p- Effect (95% CI) p- Effect (95% CI) p-
PM 2.5
Model 1 -10.1 (-24.7,4.49) 0.1747 -12.3 (-28.3,3.76) 0.1333 0.73 (-20.9,22.35) 0.9469 -12.5 (-27.6,2.71) 0.1075
Model 2 -14.9 (-28.4,-1.42) 0.0303 -17.9 (-33.9,-1.87) 0.0286 -6.46 (-28.2,15.32) 0.5610 -18.2 (-32.1,-4.21) 0.0107
Model 3 -15.0 (-28.5,-1.55) 0.0288 -18.5 (-34.5,-2.46) 0.0238 -6.18 (-27.9,15.58) 0.5777 -18.4 (-32.3,-4.41) 0.0099
Model 4 -8.40 (-23.3,6.48) 0.2685 -9.90 (-27.6,7.79) 0.2728 -1.11 (-24.8,22.54) 0.9269 -12.5 (-27.9,2.96) 0.1130
Model 5:+NO2 2.26 (-13.2,17.70) 0.7747 0.94 (-17.5,19.37) 0.9207 8.84 (-15.6,33.23) 0.4775 -2.32 (-18.3,13.66) 0.7762
Model 5:+O3 0.60 (-14.7,15.91) 0.9391 -1.35 (-19.2,16.46) 0.8818 6.28 (-17.9,30.45) 0.6103 -2.49 (-18.3,13.31) 0.7577
NO 2
Model 1 -10.1 (-14.3,-5.95) <.0001 -11.1 (-15.6,-6.63) <.0001 -11.0 (-17.6,-4.52) 0.0009 -10.2 (-14.5,-5.89) <.0001
Model 2 -11.2 (-15.0,-7.32) <.0001 -11.1 (-15.6,-6.66) <.0001 -10.7 (-17.2,-4.14) 0.0014 -11.2 (-15.2,-7.25) <.0001
Model 3 -11.0 (-14.9,-7.10) <.0001 -11.6 (-16.1,-7.08) <.0001 -9.96 (-16.5,-3.38) 0.0030 -11.0 (-15.0,-7.02) <.0001
Model 4 -11.8 (-16.2,-7.31) <.0001 -11.2 (-16.3,-6.03) <.0001 -12.1 (-19.7,-4.53) 0.0018 -11.9 (-16.5,-7.37) <.0001
Model 5:+O3 -9.66 (-14.8,-4.55) 0.0002 -9.42 (-15.4,-3.47) 0.0019 -9.13 (-17.5,-0.76) 0.0326 -8.85 (-14.1,-3.60) 0.0010
Model 5:+PM2.5 -11.9 (-16.5,-7.32) <.0001 -11.2 (-16.6,-5.88) <.0001 -12.8 (-20.6,-4.96) 0.0014 -11.8 (-16.5,-7.04) <.0001
O 3
Model 1 8.25 (3.84,12.65) 0.0002 9.44 (4.67,14.21) 0.0001 11.16 (4.61,17.71) 0.0008 9.26 (4.76,13.76) <.0001
Model 2 10.45 (6.34,14.55) <.0001 9.86 (5.13,14.59) <.0001 11.01 (4.41,17.60) 0.0011 11.59 (7.41,15.77) <.0001
Model 3 10.36 (6.23,14.48) <.0001 10.27 (5.52,15.02) <.0001 10.44 (3.82,17.05) 0.0020 11.49 (7.29,15.69) <.0001
Model 4 9.34 (4.75,13.92) <.0001 8.77 (3.50,14.03) 0.0011 10.38 (3.09,17.66) 0.0053 10.82 (6.14,15.50) <.0001
Model 5:+NO2 4.27 (-1.06,9.60) 0.1167 3.47 (-2.76,9.69) 0.2746 6.66 (-1.39,14.70) 0.1047 6.25 (0.82,11.68) 0.0241
Model 5:+PM2.5 9.39 (4.62,14.15) 0.0001 8.64 (3.22,14.06) 0.0018 10.81 (3.34,18.28) 0.0046 10.62 (5.77,15.47) <.0001
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Presented data are adjusted estimates (95% CI) per 5 μg/m3 increase for PM2.5, 5 ppb increase for NO2, and 3 ppb increase for O3, from a linear mixed model with centre modelled as a random intercept. Model 1, unadjusted. Model 2, adjusted for age, (sex), and ethnicity. Model 3 further adjusted for the INTERHEART risk score and education and workplace location. Primary model 4 further adjusted for walkability categories, material factor score and social factor score. Model 5 further adjusted for co-pollutants.

Table 5. Association of carotid wall volume (mm3) with pollutants exposure stratified by workplace location.

Overall (n = 6223) Working away (n = 2188) Work in community (n = 4035)
Effect (95% CI) p- Effect (95% CI) p- Effect (95% CI) p-
PM 2.5
Model 1 -10.1 (-24.7,4.49) 0.1747 -1.27 (-24.7,22.21) 0.9157 -13.2 (-31.1,4.67) 0.1476
Model 2 -14.9 (-28.4,-1.42) 0.0303 -5.83 (-27.8,16.16) 0.6034 -16.3 (-32.9,0.25) 0.0536
Model 3 -15.0 (-28.5,-1.55) 0.0288 -5.50 (-27.5,16.44) 0.6229 -16.8 (-33.4,-0.20) 0.0473
Model 4 -8.40 (-23.3,6.48) 0.2685 0.87 (-22.8,24.55) 0.9429 -9.81 (-28.3,8.64) 0.2974
Model 5:+NO2 2.26 (-13.2,17.70) 0.7747 3.77 (-20.8,28.29) 0.7630 5.21 (-14.0,24.40) 0.5944
Model 5:+O3 0.60 (-14.7,15.91) 0.9391 5.53 (-18.2,29.26) 0.6477 0.67 (-18.1,19.40) 0.9441
NO 2
Model 1 -10.1 (-14.3,-5.95) <.0001 -3.62 (-10.6,3.31) 0.3059 -13.1 (-18.2,-7.93) <.0001
Model 2 -11.2 (-15.0,-7.32) <.0001 -4.35 (-10.8,2.06) 0.1833 -14.4 (-19.1,-9.64) <.0001
Model 3 -11.0 (-14.9,-7.10) <.0001 -3.62 (-10.1,2.82) 0.2702 -14.5 (-19.3,-9.69) <.0001
Model 4 -11.8 (-16.2,-7.31) <.0001 -3.40 (-10.9,4.15) 0.3771 -15.5 (-21.0,-10.1) <.0001
Model 5:+O3 -9.66 (-14.8,-4.55) 0.0002 -1.64 (-9.81,6.52) 0.6927 -12.9 (-19.2,-6.53) <.0001
Model 5:+PM2.5 -11.9 (-16.5,-7.32) <.0001 -3.70 (-11.5,4.10) 0.3524 -16.0 (-21.6,-10.3) <.0001
O 3
Model 1 8.25 (3.84,12.65) 0.0002 5.98 (-1.20,13.17) 0.1023 9.52 (4.29,14.75) 0.0004
Model 2 10.45 (6.34,14.55) <.0001 6.65 (-0.07,13.37) 0.0524 12.58 (7.69,17.46) <.0001
Model 3 10.36 (6.23,14.48) <.0001 6.27 (-0.45,12.98) 0.0675 12.64 (7.72,17.56) <.0001
Model 4 9.34 (4.75,13.92) <.0001 4.93 (-2.42,12.27) 0.1884 11.92 (6.42,17.43) <.0001
Model 5:+NO2 4.27 (-1.06,9.60) 0.1167 4.14 (-3.93,12.20) 0.3146 5.16 (-1.33,11.65) 0.1191
Model 5:+PM2.5 9.39 (4.62,14.15) 0.0001 5.34 (-2.15,12.83) 0.1622 11.97 (6.31,17.63) <.0001
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Presented data are adjusted estimates (95% CI) per 5 μg/m3 increase for PM2.5, 5 ppb increase for NO2, and 3 ppb increase for O3, from a linear mixed model with centre modelled as a random intercept. Model 1, unadjusted. Model 2, adjusted for age, sex, and ethnicity. Model 3 further adjusted for the INTERHEART risk score and education. Primary model 4 further adjusted for walkability categories, material factor score and social factor score. Model 5 further adjusted for co-pollutants.

Effect of co-pollutant interactions on CWV

Across the primary models summarized in Fig 2 for testing the effect of one pollutant on CWV within low, medium, and high levels of a second pollutant, there was no evidence of interaction between PM2.5 and O3 or between PM2.5 and NO2. However, the association of NO2 with CWV differed according to the exposure levels of O3 (p<0.0001 for interaction) and the association of O3 with CWV differed according to the NO2 levels (p<0.0001 for interaction). The positive association of O3 with CWV within low and medium levels of NO2 was not observed within high levels of NO2 and the inverse association of NO2 with CWV within medium and high levels of O3 was not observed within low levels of O3. Thus, the gaseous pollutants emerged as mutual effect modifiers (Tables 6–8).

Fig 2. Effect of copollutant interactions on carotid wall volume (CWV).

Fig 2

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Presented data are carotid wall volume adjusted estimates (95% CI) per (A) 5 μg/m3 increase for PM2.5, (B) 5 ppb increase for NO2, and (C) 3 ppb increase for O3, from a linear mixed model with centre modelled as a random intercept and the following fixed effects: age, sex, ethnicity, INTERHEART risk score, education, workplace location, walkability categories, material factor score, and social factor score. Each pollutant is represented within co-pollutant tertiles and a p-value of the interaction term of the two pollutants (p-inter).

Table 6. Association of carotid wall volume (mm3) with O3 within co-pollutant exposure tertiles.

Overall
N = 6223 		Low PM 2.5
(1.78–5.64 μg/m 3 ) N = 1903 		Mid PM 2.5
5.65–8.18 μg/m 3 N = 1898 		High PM 2.5
8.18–11.2 μg/m 3
N = 2422
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 8.25 (3.84,12.65) 0.0002 7.16 (-1.00,15.32) 0.0855 9.56 (2.26,16.85) 0.0103 8.31 (0.24,16.39) 0.0436
Model 2 10.45 (6.34,14.55) <.0001 9.73 (2.20,17.26) 0.0113 7.52 (-0.35,15.40) 0.0612 12.99 (5.55,20.44) 0.0006
Model 3 10.36 (6.23,14.48) <.0001 9.96 (2.39,17.54) 0.0100 7.51 (-0.39,15.41) 0.0625 11.95 (4.49,19.42) 0.0017
Model 4 9.34 (4.75,13.92) <.0001 8.91 (0.08,17.73) 0.0480 6.43 (-1.76,14.62) 0.1240 10.87 (3.21,18.53) 0.0055
Low NO 2
(0.88–8.10 ppb)
N = 1539 		Mid NO 2
(8.12–15.5 ppb)
N = 2202 		High NO 2
(15.5–38.7 ppb)
N = 2482
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 8.25 (3.84,12.65) 0.0002 12.32 (0.91,23.74) 0.0343 0.56 (-7.10,8.21) 0.8869 -5.30 (-16.4,5.78) 0.3483
Model 2 10.45 (6.34,14.55) <.0001 11.99 (0.90,23.08) 0.0341 4.21 (-3.01,11.42) 0.2533 -6.42 (-17.1,4.25) 0.2382
Model 3 10.36 (6.23,14.48) <.0001 12.63 (1.55,23.70) 0.0255 4.28 (-2.93,11.48) 0.2443 -6.81 (-17.5,3.86) 0.2108
Model 4 9.34 (4.75,13.92) <.0001 10.82 (-1.23,22.87) 0.0784 3.82 (-3.37,11.01) 0.2974 -7.66 (-18.4,3.08) 0.1622
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Presented data are adjusted CWV estimates (95% CI) per, 3 ppb increase for O3, from a linear mixed model with centre modelled as a random intercept. Model 1, unadjusted. Model 2, adjusted for age, (sex), and ethnicity. Model 3 further adjusted for the INTERHEART risk score, education and workplace location. Primary model 4 further adjusted for walkability categories, material factor score and social factor score.

Table 8. Association of carotid wall volume (mm3) with NO2 within co-pollutant exposure tertiles.

Overall
N = 6223 		Low O 3
14.2–22.9 ppb
N = 1941 		Mid O 3
23.0–26.3 ppb
N = 2296 		High O 3
26.3–39.1 ppb
N = 1986
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 -10.1 (-14.3,-5.95) <.0001 0.44 (-5.33,6.21) 0.8821 -4.72 (-13.4,3.95) 0.2857 -27.5 (-35.1,-20.0) <.0001
Model 2 -11.2 (-15.0,-7.32) <.0001 6.95 (-1.29,15.18) 0.0981 -6.57 (-14.5,1.38) 0.1054 -27.7 (-34.6,-20.8) <.0001
Model 3 -11.0 (-14.9,-7.10) <.0001 7.06 (-1.19,15.31) 0.0935 -5.80 (-13.7,2.13) 0.1514 -28.0 (-34.9,-21.0) <.0001
Model 4 -11.8 (-16.2,-7.31) <.0001 7.65 (-1.49,16.78) 0.1008 -7.79 (-16.2,0.65) 0.0706 -31.8 (-39.6,-23.9) <.0001
Low PM 2.5
(1.78–5.64 μg/m 3 ) N = 1903 		Mid PM 2.5
5.65–8.18 μg/m 3 N = 1898 		High PM 2.5
8.18–11.2 μg/m 3
N = 2422
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 -10.1 (-14.3,-5.95) <.0001 -5.36 (-14.4,3.68) 0.2450 -13.5 (-23.3,-3.61) 0.0074 -14.8 (-22.3,-7.29) 0.0001
Model 2 -11.2 (-15.0,-7.32) <.0001 -7.41 (-15.8,0.96) 0.0828 -11.1 (-20.7,-1.61) 0.0219 -15.4 (-22.3,-8.59) <.0001
Model 3 -11.0 (-14.9,-7.10) <.0001 -7.68 (-16.1,0.77) 0.0749 -11.1 (-20.6,-1.46) 0.0239 -14.7 (-21.6,-7.73) <.0001
Model 4 -11.8 (-16.2,-7.31) <.0001 -8.00 (-18.0,2.01) 0.1173 -11.8 (-22.2,-1.26) 0.0281 -15.3 (-22.5,-8.18) <.0001
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Presented data are adjusted CWV estimates (95% CI) per 5 ppb increase for NO2 from a linear mixed model with centre modelled as a random intercept. Model 1, unadjusted. Model 2, adjusted for age, (sex), and ethnicity. Model 3 further adjusted for the INTERHEART risk score, education and workplace location. Primary model 4 further adjusted for walkability categories, material factor score and social factor score.

Table 7. Association of carotid wall volume (mm3) with PM2.5 within co-pollutant exposure tertiles.

Overall
N = 6223 		Low O 3
14.2–22.9 ppb
N = 1941 		Mid O 3
23.0–26.3 ppb
N = 2296 		High O 3
26.3–39.1 ppb
N = 1986
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 -10.1 (-24.7,4.49) 0.1747 5.51 (-14.6,25.63) 0.5911 -3.21 (-38.3,31.92) 0.8580 -20.5 (-40.9,-0.16) 0.0482
Model 2 -14.9 (-28.4,-1.42) 0.0303 5.89 (-19.8,31.54) 0.6527 -12.5 (-45.1,20.16) 0.4533 -21.7 (-40.5,-2.89) 0.0238
Model 3 -15.0 (-28.5,-1.55) 0.0288 6.90 (-18.8,32.64) 0.5990 -13.8 (-46.3,18.64) 0.4041 -21.9 (-40.8,-2.98) 0.0233
Model 4 -8.40 (-23.3,6.48) 0.2685 9.42 (-16.8,35.64) 0.4809 -15.2 (-48.8,18.41) 0.3756 -19.1 (-41.7,3.49) 0.0975
Low NO 2
(0.88–8.10 ppb)
N = 1539 		Mid NO 2
(8.12–15.5 ppb)
N = 2202 		High NO 2
(15.5–38.7 ppb)
N = 2482
Effect
(95% CI) 		p- Effect
(95% CI) 		p- Effect
(95% CI) 		p-
Model 1 -10.1 (-24.7,4.49) 0.1747 24.21 (-10.3,58.67) 0.1684 1.51 (-20.3,23.36) 0.8922 31.73 (-4.58,68.04) 0.0867
Model 2 -14.9 (-28.4,-1.42) 0.0303 13.62 (-18.1,45.31) 0.3995 0.59 (-20.2,21.34) 0.9553 32.17 (-2.85,67.20) 0.0718
Model 3 -15.0 (-28.5,-1.55) 0.0288 12.68 (-19.2,44.60) 0.4360 0.18 (-20.6,20.96) 0.9862 33.37 (-1.54,68.27) 0.0610
Model 4 -8.40 (-23.3,6.48) 0.2685 20.92 (-13.6,55.40) 0.2342 -6.50 (-28.8,15.82) 0.5678 35.57 (0.35,70.80) 0.0478
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Presented data are adjusted CWV estimates (95% CI) per 5 μg/m3 increase for PM2.5 from a linear mixed model with centre modelled as a random intercept. Model 1, unadjusted. Model 2, adjusted for age, (sex), and ethnicity. Model 3 further adjusted for the INTERHEART risk score, education and workplace location. Primary model 4 further adjusted for walkability categories, material factor score and social factor score.

Discussion

In this cohort study among 6645 Canadian healthy adults using spatially resolved pollutant concentrations and comprehensive covariate adjustments, long-term exposure to air pollution was inconsistently associated with CWV as measured by MRI. PM2.5 associations with CWV were inconsistent while NO2 was associated with decreased CWV; a finding that was contradictory to our expectation. Exposure to ground-level O3 was associated with increased CWV (noting the limitation that O3 resolution is 10 km). These findings were consistent between men and women and remained robust even after exclusion of recent immigrants. Lastly, NO2 and O3 mutually modified the associations of these pollutants with CWV.

There is limited evidence that direct biological measures of early CVD are influenced by exposure to air pollution at low levels. However, the MAPLE study and MAPLE phase 2 report associations between nonaccidental mortality and cardiovascular-related mortality and long-term exposure to ambient PM2.5 levels, including at concentrations below national air quality standards [19, 26]. The evidence is adequate for overall TRAP association with CVD mortality, but remains inconclusive for CVD morbidity i.e., the effect of air pollution on traditional cardiovascular risk factors [9, 27]. The 2022 HEI report concludes that additional studies are needed for cardiometabolic outcomes and subclinical biomarkers [10].

Ultrasound measurement of cIMT is widely used because it is inexpensive, non-invasive, and does not involve exposure to a magnetic field, but it has limited clinical usefulness beyond traditional CVD risk factors [20]. Carotid wall thickness, determined by MRI, generates more reproducible measurements than ultrasound and is a superior measure of early plaque development [12, 13]—ideal for the investigation of healthy middle-aged populations. It is also more consistently associated with incident CVD than ultrasound-measured cIMT [14]. Compared to cIMT, CWV includes the adventitia, the source of vasa vasorum [12]. In terms of sensitivity of MRI-measured CWV, studies have suggested adventitial thickening to be an early sign of atherosclerosis, whereas a dense network of adventitial vasa vasorum can signify progression of atherosclerosis to symptomatic disease [12]. We have previously shown in CAHHM that simple cardiovascular risk scores were significantly associated with CWV, where mean (SD) CWV for low, medium, and high INTERHEART risk score categories were 881.5 (163.1), 915.4 (166.6), and 940.9 (172.9) mm3, respectively [11]. Therefore, the overall mean in the current analysis set (900 mm3) corresponds to someone with a low-moderate INTERHEART risk score. The inconsistent association between PM2.5 and a state-of-the-art surrogate marker for subclinical atherosclerosis we observed across Canada parallels previous findings of the Multicultural Community Health Assessment Trial (M-CHAT) study between multiple particle and gaseous measures of TRAP (e.g., NO2, PM2.5, black carbon) and progression of carotid artery atherosclerosis in Vancouver, BC.6 This suggests that PM2.5 is not affecting CV risk through early atherosclerosis formation. Therefore, if there is an increased risk with exposure to PM2.5, it may operate through other intermediary pathways such as release of proinflammatory mediators, autonomic nervous system perturbations, and translocation of particle constituents into the blood, acting independently from the promotion of plaque build-up [28]. We therefore recommend employing CWV over cIMT, especially in relatively low exposure settings such as Canada where a more precise measurement of subclinical atherosclerosis may be of higher yield. Of note, walkability was shown to modulate the effects of PM2.5 in the main and secondary analyses; a neighbourhood characteristic that has been scarcely captured in previous literature.

The weak inverse association of NO2 (an indicator of TRAP and/or combustion pollution in general) with CWV in our study is unexpected and needs careful evaluation. There is insufficient collective evidence in the literature linking NO2 or TRAP with subclinical atherosclerosis in healthy adults specifically. The evidence of an association between TRAP and cardiovascular morbidity is low [9]. Three of 5 studies (n = 144,787) included in a meta-analysis of NO2 and ischemic heart disease (IHD) [10] incidence showed a positive association (a prospective cohort conducted across 11 European cohorts [the European Study of Cohorts for Air Pollution Effects: ESCAPE] [29]; Athens, Greece [30]; and Oakland, California [31]), while two studies (n = 663,751) reported a negative association (London, UK [32]; Vancouver, BC [6]). In fact, the meta-analytical summary estimate, relative risk of IHD events per 10 μg/m3 NO2, was 0.99 (0.94;1.05). MESA-Air did not find a relationship between NO2 or other pollutant exposures and cIMT change, instead exposure was positively associated with coronary artery calcification progression [2]. In four European cohort, ESCAPE findings were inconsistent for an association between NO2 and cIMT, in fact, all four cohorts and their meta-analytical estimate, showed an inverse association, similar to our observation in CAHHM [33]. However, in a study of a high cardiovascular risk population (2227 patients mean age of 62.9 years) in London, Ontario, NO2 exposure was associated with cumulative plaque burden as captured by carotid total plaque area (TPA) using two-dimensional ultrasound [34]. Collectively within the existing body of literature, it is plausible that NO2 is probably not involved in early carotid thickening but perhaps in more advanced morbid stages.

Our finding on the effect of chronic ambient O3 exposure on subclinical atherosclerosis is congruent with what the MESA study had previously reported on progression of IMT of the common carotid artery and new carotid plaque formation with outdoor O3 exposure in six U.S. city regions [8]. One underlying biochemical pathway might be through the formation of reactive oxygen species that further give rise to increased oxidative stress and persistent chronic systemic inflammation [8]. Yet, given the inverse association between O3 and NO2 generally in urban settings, this “effect” of O3 may simply reflect the inverse of the NO2 effect, and because the O3 spatial resolution is 10 km, more caution is needed. The Multicenter Ozone Study in oldEr Subjects (MOSES) found that controlled exposure to low-levels of O3 did not affect selected blood biomarkers of systemic inflammation and prothrombotic state (C-reactive protein, monocyte-platelet conjugates, and microparticle-associated tissue factor activity) [35].

Next, given the observed interaction between O3 and NO2, our study emphasizes the need for further investigation of different exposures in combination. It also emphasizes the value of adjusting for novel neighbourhood characteristics such as active living environment, to examine effect modification and help further investigate regional variability. Neighbourhood factors that might modify the association between air pollution and CWV include poverty/affluence, overcrowding, living in apartment buildings, commuting, and proximity to roads.

Strengths and limitations

Our study has obtained unique health measurements of subclinical cardiovascular markers using MRI on nearly 6600 Canadians along with individual-level information on environmental factors and lifestyle known to influence cardiometabolic outcomes. While IMT has been criticized as an accurate marker of atherosclerosis with sensitivity limitations of the ultrasound methodology [34], our study uses MRI-characterized CWV to assess atherosclerosis. Moreover, compared to MESA which reported on cIMT progression in 3392 participants with low-exposure levels, our study is well-powered. Next, the cohort’s diverse geographic coverage across Canada offers an exposure gradient in ambient PM2.5, NO2, and O3 that parallels what has previously been explored by the national Canadian Census Health and Environment Cohort (CanCHEC) [19]. Finally, exposure to air pollution values represented a time frame prior to knowledge of the outcome for each participant, i.e., the air pollution data collected for the 5-year period prior to the MRI. Several limitations are important to mention. First, individual-level exposures were estimated based on residence address. While this is common in epidemiological air pollution studies, exposure misclassification is inevitable because in this study participants’ time away from residence and residential history were not taken into account in estimating long-term exposure to air pollutants. Second, the 5-year pollutant exposure period was fixed for all participants (2008–2012) regardless of when an individual’s enrolment occurred in the 2014–2018 window. Thus, the exposure window was not consistently 5-years prior to enrolment for all study participants (i.e., depending on the date of participant MRI scan, the 5-year window may lag behind the MRI by ~2 years if it was done in 2014, but by up to ~6 years if it was done in 2018), which further increases risk for exposure misclassification. However, studies have demonstrated temporal stability in the spatial patterns of air pollutants over 10 years, thus temporal variability in the exposure window relative to the enrollment date is not a significant source of uncertainty [36]. Because the causally relevant window for air pollution exposures remains unknown [37], future investigations are needed to examine varying exposure time-windows and lag periods. Moreover, the spatial resolution of O3 is 10 km, which may not be fine enough to capture exposure variability, may have led to a spurious association between O3 and CWV, or may have been confounded by suburban living. Third, because CAHHM is a prospective pan-Canadian cohort of cohorts across five provinces and participants were selected from existing cohorts, the sample is not a random sample of the Canadian population distribution, thereby limiting the generalizability of these findings. When compared to a cohort of adults who responded to the 2015 Canadian Community Health Survey, CAHHM participants were older, of higher socioeconomic status, but had a similar mean INTERHEART risk score [38]. This does not affect the exposure-to-outcome reliability of our results within CAHHM, but generalizability to younger populations and Canadians living outside major Canadian cities should be done with caution. Fourth, because of the small number of events in our cohort (n = 156 events (2.35%)), we were not powered to look at intraplaque hemorrhage. Lastly, as with any observational study, the risk of residual confounding (from factors such as diet, lifestyle factors, or pre-existing health conditions) cannot be excluded.

Conclusion

In healthy adults living in clean or only mildly polluted environments, we found no consistent association between air pollution and atherosclerosis. Exposure to NO2 was negatively associated, and O3 was positively associated with CWV, a sensitive measure of subclinical atherosclerosis, while PM2.5 was not associated with CWV. These inconsistent results raise questions as to whether previous reports linking low-level exposure to air pollution and CVD morbidity may have suffered from uncontrolled confounding. The role of NO2 in atherosclerosis is complex and requires further investigation, as do combinations of exposure to air pollutants.

Supporting information

S1 Fig. Flow chart for Air pollution and MRI markers in CAHHM.

(PDF)

pone.0309912.s001.pdf (74.2KB, pdf)
S2 Fig. Scatterplot of air pollutants by carotid wall volume measurements with regression lines stratified by sex.

(PDF)

pone.0309912.s002.pdf (264.8KB, pdf)
S1 Table. Anthropometric characteristics of the study population by sex.

(PDF)

pone.0309912.s003.pdf (105.4KB, pdf)
S2 Table. Demographics & lifestyle characteristics of the study population by sex.

(PDF)

pone.0309912.s004.pdf (119KB, pdf)
S3 Table. Environmental characteristics of the study population by sex.

(PDF)

pone.0309912.s005.pdf (105.3KB, pdf)

Acknowledgments

Steering Committee of Canadian Alliance of Healthy Hearts and Minds: S.S. Anand (Chair)*, M.G. Friedrich (Co-Chair), Douglas S. Lee (Co-Chair), P Awadalla (Ontario Health Study), T. Dummer (BC Generations Project), J. Vena (Alberta’s Tomorrow Project), P. Broët (CARTaGENE), J. Hicks (Atlantic PATH), J-C. Tardif (MHI Biobank), K. Teo, S. Yusuf (PURE-Central), B-M. Knoppers (Ethics, Legal and Social Issues). Project Office Staff at Population Health Research Institute (PHRI): D. Desai, S. Zafar. Statistics/Biometrics Programmers Team at PHRI: K. Schulze, S. Bangdiwala. Cohort Operations Research Personnel: K McDonald (Ontario Health Study), N. Noisel (CARTaGENE), J. Chu (BC Generations Project), J. Hicks (Atlantic PATH), H. Whelan (Alberta’s Tomorrow Project), S. Rangarajan (PURE), D. Busseuil (MHI Biobank). Site Investigators and Staff: J. Leipsic, S. Lear, V. de Jong; M. Noseworthy, K. Teo, E. Ramezani, N. Konyer; P. Poirier, A-S. Bourlaud, E Larose, K. Bibeau; J. Leipsic, S. Lear, V. de Jong; E. Smith, R. Frayne, A. Charlton, R Sekhon; A. Moody, V. Thayalasuthan; A.Kripalani, G Leung; M. Noseworthy, S. Anand, R. de Souza, N. Konyer, S. Zafar; G. Paraga,L. Reid; A.J. Dick, F. Ahmad; D. Kelton, H. Shah; F. Marcotte, H. Poiffaut; M.G. Friedrich, J. Lebel; E. Larose, K. Bibeau; R. Miller, L. Parker, D. Thompson, J. Hicks; J-C. Tardif, H.Poiffaut; J. Tu, K. Chan, A. Moody, V. Thayalasuthan. MRI Working Group and Core Lab Investigators/Staff: Chair: M.G. Friedrich; Brain Core Lab: E.E. Smith; Carotid Core Lab: A. Moody, V. Thayalasuthan; Abdomen: E. Larose, K. Bibeau, Cardiac: F. Marcotte, F. Henriques. Contextual Working Group: R. de Souza, S. Anand, G. Booth, J. Brook, D. Corsi, L. Gauvin, S. Lear, F. Razak, S.V. Subramanian, J. Tu. CAHHM Founding Advisory Group: Jean Rouleau, Pierre Boyle, Caroline Wong, Eldon Smith.

The Canadian Alliance of Healthy Hearts and Minds (CAHHM) investigators include the following individuals: Sonia S. Anand, MD, PhD, Department of Medicine, Department of Health Research Methods, Evidence, and Impact, McMaster University, Population Health Research Institute, Hamilton Health Sciences, Hamilton, Canada; Philip Awadalla, PhD, Department of Molecular Genetics, Ontario Institute for Cancer Research, University of Toronto, Toronto, Canada; Sandra E. Black, MA, MD, OC, Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada; Broët Philippe, MD, PhD, Department of Preventive and Social Medicine, École de santé publique, Université de Montréal, and Research Centre, CHU Sainte Justine, Montréal, Canada; Alexander Dick, MD, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Canada; Trevor Dummer, PhD, MSc, Department of Epidemiology, Biostatistics, and Public HealthPractice, School of Population and Public Health, University of British Columbia, and BC Cancer Agency, Vancouver, Canada; Matthias G. Friedrich, MD, Department of Cardiology, McGill University, Montréal, Canada; Jason Hicks, MSc, Atlantic Partnership for Tomorrow’s Health, Dalhousie University, Halifax, Canada; David Kelton, MD, RPVI, Department of Medicine, William Osler Health System; Anish Kirpalani, MD, MASc, Department of Medical Imaging, St. Michael’s Hospital, University of Toronto, Toronto, Canada; Maria Bartha Knoppers, PhD, Centre of Genomics and Policy, McGill University, Montréal, Canada; Scott A. Lear, PhD, Department of Pathology, Simon Fraser University, Burnaby, Canada; Eric Larose, DVM, MD, Department of Medicine, University of Laval, Quebec City, Canada; Russell J. de Souza, RD, ScD, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada; Douglas S. Lee, MD, PhD, ICES Central, Cardiovascular Research Program, Institute for Clinical Evaluative Sciences, Peter Munk Cardiac Centre University Health Network, Department of Medicine, University of Toronto, Toronto, Canada; Jonathan Leipsic, MD, Department of Radiology, University of British Columbia, Vancouver, Canada; Francois Marcotte, MD, Department of Cardiology, Montreal Heart Institute, University of Montreal, Montréal, Canada; Alan R. Moody, MBBS, Department of Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Canada; Michael D. Noseworthy, PhD, PEng, Department of Electrical and Computer Engineering, McMaster University, St. Joseph’s Health Care, Hamilton, Canada; Grace Parraga, PhD, Department of Medical Biophysics, Robarts Research Institute, Western University, London, Canada; Louise Parker, PhD, Atlantic Partnership for Tomorrow’s Health, Dalhousie University, Halifax, Canada; Paul Poirier, MD, PhD, Institut de cardiologie et de pneumologie de Quebec, Université of Laval, Quebec City, Canada; Eric E. Smith, MD, MPH, Hotchkiss Brain Institute, Department of Clinical Neurosciences, University of Calgary, Calgary, Canada; Jean-Claude Tardif, MD, Department of Cardiology, Montreal Heart Institute, University of Montreal, Montréal, Canada; Koon K. Teo, MBBCh, PhD, Department of Medicine, Department of Health Research Methods, Evidence, and Impact, McMaster University, Population Health Research Institute, Hamilton Health Sciences, Hamilton, Canada; Jack V. Tu, MD, PhD, MSc, Department of Medicine, University of Toronto, Institute for Clinical Evaluative Sciences, Sunnybrook Schulich Heart Centre, Toronto, Canada (deceased); Jennifer Vena, PhD, Cancer Research and Analytics, Cancer Control Alberta, Alberta Health Services, Edmonton, Canada.

Data Availability

CAHHM data cannot be deposited publicly as these collaborative data originate from multiple Canadian cohorts with different legal frameworks. CAHHM represents a "cohort of cohorts", and at the time participants were enrolled into each respective cohort, data sharing was not part of the consent process. Thus, the appropriate legal frameworks to allow for data to be deposited publicly (identified or de-identified) is not in place, nor was such sharing anticipated or run by the respective REB at the time of cohort invitations. Therefore, any requests for data may be made to CAHHM (Alliance@phri.ca), with requests for data sharing to be considered on a case-by-case basis. A detailed statistical analysis plan and the code used in the analysis are also available on reasonable request.

Funding Statement

CAHHM was funded by the Canadian Partnership Against Cancer (CPAC), Heart and Stroke Foundation of Canada (HSF-Canada), and the Canadian Institutes of Health Research (CIHR). Financial contributions were also received from the Population Health Research Institute and CIHR Foundation Grant no. FDN-143255 to S.S.A.; FDN-143313 to J.V.T.; and FDN 154317 to E.E.S. In-kind contributions from A.R.M. and S.E.B. from Sunnybrook Hospital, Toronto for MRI reading costs, and Bayer AG for provision of IV contrast. The Canadian Partnership for Tomorrow’s Health is funded by the Canadian Partnership Against Cancer and Health Canada, BC Cancer, Genome Quebec, Centre Hospitalier Universitaire (CHU) Sainte-Justine, Dalhousie University, Ontario Institute for Cancer Research, Alberta Health, Alberta Cancer Foundation, and Alberta Health Services. The PURE Study was funded by multiple sources. The Montreal Heart Institute Biobank is funded by Mr André Desmarais and Mrs France Chrétien-Desmarais and the Montreal Heart Institute Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Sabeena Jalal

22 Aug 2023

PONE-D-23-23654Ambient Air Pollution and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort Study

PLOS ONE

Dear Dr. de Souza,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Dear Authors,

I have reviewed your manuscript detailing the association between air pollution and subclinical atherosclerosis in a Canadian cohort. Below, I outline both the strengths of your work and areas that require attention and revision.

Strengths:

Subject Matter: The topic of your study is highly relevant and timely, addressing an important public health concern.

Methodology: The use of MRI-characterized CWV to assess atherosclerosis is innovative and provides a more precise measurement.

Please address reviewer comments made by the two reviewers.

Furthermore, Areas for Revision:

Exposure Misclassification: Please provide a more detailed explanation of how individual-level exposures were estimated based on residence address. Consideration of participants' time away from residence and residential history could enhance the accuracy of the exposure assessment.

Inconsistent Exposure Window: The fixed 5-year pollutant exposure period for all participants may introduce bias. Please discuss this limitation and consider conducting sensitivity analyses to assess its impact.

Unexpected Findings: The negative association of NO2 with CWV and the lack of association with PM2.5 were unexpected. A more comprehensive discussion of these findings and potential underlying factors is needed.

Resolution of O3 Data: The spatial resolution of O3 data (10 km) may not be fine enough to capture local variations. Please discuss this limitation and its potential impact on the findings related to O3.

Lack of Consideration for Intraplaque Hemorrhage: Please discuss the limitation of not being able to look at intraplaque hemorrhage and its potential significance in understanding the relationship between air pollution and atherosclerosis.

Additional Limitations: Consider addressing other potential limitations such as selection bias, lack of longitudinal data, potential confounding variables, measurement error in pollution data, limited geographic scope, and potential interaction effects.

Your study has the potential to make a significant contribution to the field. However, the above concerns need to be addressed to enhance the robustness and credibility of the findings. I recommend a minor revision that includes a more detailed analysis of the exposure assessment, consideration of additional confounding variables, and a more comprehensive discussion of the unexpected findings and other limitations.

I look forward to seeing the revised manuscript.

==============================

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Kind regards,

Sabeena Jalal, MBBS, MSc, MSc, SM

Academic Editor

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RJ de Souza has served as an external resource person to the World Health Organization’s Nutrition Guidelines Advisory Group on trans fats, saturated fats, and polyunsaturated fats.  The WHO paid for his travel and accommodation to attend meetings from 2012-2017 to present and discuss this work.  He has presented updates of this work to the WHO in 2022. He has also done contract research for the Canadian Institutes of Health Research’s Institute of Nutrition, Metabolism, and Diabetes, Health Canada, and the World Health Organization for which he received remuneration.  He has received speaker’s fees from the University of Toronto, and McMaster Children’s Hospital. He has held grants from the Canadian Institutes of Health Research, Canadian Foundation for Dietetic Research, Population Health Research Institute, and Hamilton Health Sciences Corporation as a principal investigator, and is a co-investigator on several funded team grants from the Canadian Institutes of Health Research. He has served as an independent director of the Helderleigh Foundation (Canada). He serves as a member of the Nutrition Science Advisory Committee to Health Canada (Government of Canada), and a co-opted member of the Scientific Advisory Committee on Nutrition (SACN) Subgroup on the Framework for the Evaluation of Evidence (Public Health England). Dr Anand reported receiving grants from Canadian Partnership Against Cancer, Heart and Stroke Foundation of Canada, and Canadian Institutes of Health Research, and a Canadian Institutes of Health Research Foundation grant during the conduct of the study and serving as the Tier 1 Canada Research Chair Ethnicity and Cardiovascular Disease and as the Michael G Degroote Heart and Stroke Foundation Chair in Population Health Research, and receiving grants from Heart and Stroke Foundation of Canada and Canadian Institutes of Health Research, and receiving personal fees from Bayer outside the submitted work. Dr Friedrich reported receiving personal fees from Circle CVI Inc for serving as a board member and adviser and being a shareholder outside the submitted work. Dr Dummer reported receiving grants from Canadian Partnership Against Cancer during the conduct of the study. Dr Lear reported receiving grants from the Canadian Institutes of Health Research and grants from Michael Smith Foundation for Health Research during the conduct of the study and personal fees from Curatio Inc outside the submitted work. Dr Tardif reported receiving grants from Amarin, Ceapro, Esperion, Ionis, Novartis, Pfizer, RegenXBio, Sanofi, AstraZeneca, and DalCor Pharmaceuticals, receiving personal fees from AstraZeneca, HLS Pharmaceuticals, Pendopharm, and DalCor Pharmaceuticals, and having a minor equity interest in DalCor Pharmaceuticals Minor outside the submitted work. In addition, Dr Tardif had a patent for Pharmacogenomics-Guided CETP Inhibition issued by DalCor Pharmaceuticals, a patent for Use of Colchicine After Myocardial Infarction pending, and a patent for Genetic Determinants of Response to Colchicine pending. No other disclosures were reported. Dr Brauer served on the WHO Guideline Development Group (no remuneration was provided but travel costs to meetings were covered). "

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Additional Editor Comments:

Dear Authors,

I have reviewed your manuscript detailing the association between air pollution and subclinical atherosclerosis in a Canadian cohort. Below, I outline both the strengths of your work and areas that require attention and revision.

Strengths:

Subject Matter: The topic of your study is highly relevant and timely, addressing an important public health concern.

Methodology: The use of MRI-characterized CWV to assess atherosclerosis is innovative and provides a more precise measurement.

Areas for Revision:

Exposure Misclassification: Please provide a more detailed explanation of how individual-level exposures were estimated based on residence address. Consideration of participants' time away from residence and residential history could enhance the accuracy of the exposure assessment.

Inconsistent Exposure Window: The fixed 5-year pollutant exposure period for all participants may introduce bias. Please discuss this limitation and consider conducting sensitivity analyses to assess its impact.

Unexpected Findings: The negative association of NO2 with CWV and the lack of association with PM2.5 were unexpected. A more comprehensive discussion of these findings and potential underlying factors is needed.

Resolution of O3 Data: The spatial resolution of O3 data (10 km) may not be fine enough to capture local variations. Please discuss this limitation and its potential impact on the findings related to O3.

Lack of Consideration for Intraplaque Hemorrhage: Please discuss the limitation of not being able to look at intraplaque hemorrhage and its potential significance in understanding the relationship between air pollution and atherosclerosis.

Additional Limitations: Consider addressing other potential limitations such as selection bias, lack of longitudinal data, potential confounding variables, measurement error in pollution data, limited geographic scope, and potential interaction effects.

Conclusion:

Your study has the potential to make a significant contribution to the field. However, the above concerns need to be addressed to enhance the robustness and credibility of the findings. I recommend a minor revision that includes a more detailed analysis of the exposure assessment, consideration of additional confounding variables, and a more comprehensive discussion of the unexpected findings and other limitations.

I look forward to seeing the revised manuscript.

Thank you.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Hello,

The study presents valuable insights into the relationship between air pollution and subclinical atherosclerosis, utilizing a large sample size and advanced MRI techniques. The geographical diversity and comprehensive analysis add to the study's strengths.

However, there are areas that require clarification and improvement:

Exposure Assessment: The authors should provide a more detailed explanation of the exposure misclassification risk and how the fixed 5-year pollutant exposure period might have affected the results. Clarifying these aspects would enhance the paper's transparency and allow readers to better assess the findings' validity.

Unexpected Findings: The unexpected negative association with NO2 and the lack of association with PM2.5 should be discussed more thoroughly. The authors should explore potential reasons for these findings and compare them more extensively with existing literature.

Resolution of O3 Data: The authors should discuss the limitations of the O3 spatial resolution and how it might have influenced the results.

Additional Analyses (Optional): If possible, further analyses could be conducted to address some of the limitations, such as considering participants' time away from residence or exploring the relationship with intraplaque hemorrhage.

The paper's contributions are significant, and the limitations do not undermine the overall value of the research. However, addressing these minor revisions would enhance the paper's clarity, coherence, and impact. Therefore, acceptance with minor revisions seems the most appropriate recommendation.

Thank you.

Reviewer #2: Mention the limitations

Selection Bias: If the study's participants were not randomly selected or if there was a lack of diversity in the sample (e.g., age, gender, geographic location), this could introduce selection bias, limiting the generalizability of the findings.

Lack of Longitudinal Data: If the study was cross-sectional in nature, the lack of longitudinal data might hinder the ability to establish causal relationships between air pollution and atherosclerosis.

Potential Confounding Variables: If not all relevant confounding variables were controlled for, such as diet, lifestyle factors, or pre-existing health conditions, this could affect the validity of the associations found.

Measurement Error in Pollution Data: If there were inaccuracies in the measurement of pollution levels, such as reliance on satellite data or modeling without sufficient ground-truthing, this could lead to misestimation of exposure levels.

Lack of Sensitivity Analysis: If sensitivity analyses were not conducted to assess the robustness of the findings to different modeling assumptions or potential outliers, this could raise questions about the stability of the results.

Concerns Regarding Exposure Assessment: The methodology used to gauge individual-level exposures, relying solely on residence addresses, might lead to inaccuracies in exposure classification. The failure to account for variations in participants' locations and their residential histories could compromise the integrity of the exposure evaluation.

Inconsistency in Exposure Time Frame: The application of a uniform 5-year window for pollutant exposure across all participants, irrespective of their specific enrollment dates, could introduce an element of bias, potentially skewing the assessment of exposure.

Unanticipated Results: The findings that NO2 was inversely associated with CWV and that there was no discernible connection with PM2.5 were surprising and at odds with certain existing studies. These outcomes prompt questions about possible unaccounted confounding variables or other hidden factors that might have shaped the results.

Limitations in O3 Data Resolution: The 10 km spatial resolution used for O3 data might be insufficient to detect localized fluctuations, which could have an impact on the precision of the conclusions drawn regarding O3.

Omission of Intraplaque Hemorrhage Analysis: The study's inability to examine intraplaque hemorrhage, owing to its limited scope, might overlook a crucial aspect in unraveling the complex relationship between air pollution and atherosclerosis. This limitation could be significant in the overall interpretation of the findings.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2024 Oct 31;19(10):e0309912. doi: 10.1371/journal.pone.0309912.r002

Author response to Decision Letter 0

17 Oct 2023

3 October, 2023

Sabeena Jalal, MBBS, MSc, MSc, SM

Academic Editor

PLOS ONE

Response to initial response: [PONE-D-23-23654] ‘Ambient Air Pollution and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort Study’

Dear Dr. Jalal,

We are pleased to resubmit our manuscript to PLOS ONE following the 2nd review. We have edited the manuscript and provide a point-by-point response to your comments. We feel based on your comments, and those from reviewers, that the manuscript quality has been strengthened and we look forward to your second review and response.

Please note that the editorial and reviewers’ comments are in blue italics, while our responses follow in normal font. We have tracked all changes made to the revised manuscript based on the reviewers’ comments in one copy of the manuscript.

Sincerely,

Russell de Souza, Sc.D., R.D.

Associate Professor, Population Genomics Program

Department of Health Research Methods, Evidence, and Impact

1200 Main St. West

MDCL, Room 3210

Hamilton, Ontario, Canada

L8N 3Z5

Phone (905) 525-9140 x. 22109

desouzrj@mcmaster.ca

Editor:

Dear Authors,

I have reviewed your manuscript detailing the association between air pollution and subclinical atherosclerosis in a Canadian cohort. Below, I outline both the strengths of your work and areas that require attention and revision.

Strengths:

Subject Matter: The topic of your study is highly relevant and timely, addressing an important public health concern.

Methodology: The use of MRI-characterized CWV to assess atherosclerosis is innovative and provides a more precise measurement.

Please address reviewer comments made by the two reviewers.

Furthermore, Areas for Revision:

Exposure Misclassification: Please provide a more detailed explanation of how individual-level exposures were estimated based on residence address. Consideration of participants' time away from residence and residential history could enhance the accuracy of the exposure assessment.

Inconsistent Exposure Window: The fixed 5-year pollutant exposure period for all participants may introduce bias. Please discuss this limitation and consider conducting sensitivity analyses to assess its impact.

Unexpected Findings: The negative association of NO2 with CWV and the lack of association with PM2.5 were unexpected. A more comprehensive discussion of these findings and potential underlying factors is needed.

Resolution of O3 Data: The spatial resolution of O3 data (10 km) may not be fine enough to capture local variations. Please discuss this limitation and its potential impact on the findings related to O3.

Lack of Consideration for Intraplaque Hemorrhage: Please discuss the limitation of not being able to look at intraplaque hemorrhage and its potential significance in understanding the relationship between air pollution and atherosclerosis.

Additional Limitations: Consider addressing other potential limitations such as selection bias, lack of longitudinal data, potential confounding variables, measurement error in pollution data, limited geographic scope, and potential interaction effects.

Your study has the potential to make a significant contribution to the field. However, the above concerns need to be addressed to enhance the robustness and credibility of the findings. I recommend a minor revision that includes a more detailed analysis of the exposure assessment, consideration of additional confounding variables, and a more comprehensive discussion of the unexpected findings and other limitations.

We thank the Editor for recognizing the value of our work in cardiovascular disease and for sharing the reviewers’ viewpoints on our manuscript. We are confident that the raised points are adequately addressed in the revisions we have made as well as our detailed point-by-point responses below.

Reviewer #1:

Hello,

The study presents valuable insights into the relationship between air pollution and subclinical atherosclerosis, utilizing a large sample size and advanced MRI techniques. The geographical diversity and comprehensive analysis add to the study's strengths. However, there are areas that require clarification and improvement:

We thank Reviewer 1 for taking the time to evaluate our manuscript, for the favourable response, and for the constructive feedback.

1. Exposure Assessment: The authors should provide a more detailed explanation of the exposure misclassification risk and how the fixed 5-year pollutant exposure period might have affected the results. Clarifying these aspects would enhance the paper's transparency and allow readers to better assess the findings' validity.

We thank Reviewer 1 for pointing out the need for further clarity, a point also raised by Reviewer 2, that we shall address collectively here. We have now further elaborated on this limitation (Discussion, page 19) adding that “the exposure window was not consistently 5-years prior to enrolment for all study participants (i.e., depending on the date of participant MRI scan, the 5 year window may lag behind the MRI by ~2 years if it was done in 2014, but by up to ~6 years if it was done in 2018), which further increases risk for exposure misclassification” and that “future investigations are needed to examine varying exposure time-windows and lag periods.”

2. Unexpected Findings: The unexpected negative association with NO2 and the lack of association with PM2.5 should be discussed more thoroughly. The authors should explore potential reasons for these findings and compare them more extensively with existing literature.

We agree with both Reviewer 1 and 2 that the negative association was surprising and unexpected. It is quite plausible that air pollution would not adversely affect all biological systems. Other MRI-measured outcome, including ectopic adipose tissue, silent vascular brain injury and brain volume, carotid atherosclerosis, left ventricular volumes, function and mass and silent myocardial infarction (MI) may not be associated with air pollution. We are currently examining some of these additional outcomes in work in progress. We have further discussed this point: “Collectively within the existing body of literature, it is plausible that NO2 is probably not involved in early carotid thickening but perhaps in more advanced morbid stages.”

We have also compared our NO2 results more extensively with existing literature (Discussion, page 18): “MESA-Air did not find a relationship between NO2 or other pollutant exposures and IMT change, instead exposure was positively associated with coronary artery calcification progression .2 In four European cohort, ESCAPE findings were inconsistent for an association between NO2 and cIMT, in fact, all four cohorts and their meta-analytical estimate, showed an inverse association, similar to our observation in CAHHM.” Please find below ESCAPE results summarized in Figure 1 of the paper.

Figure 1. Forest plot of the percent difference in CIMT (geometric mean with 95% CIs) for model M3 for (A) ESCAPE air pollutants per standard contrast of exposure as indicated in the figure. Source: Environmental Health Perspectives • volume 123 | number 6 | June 2015

3. Resolution of O3 Data: The authors should discuss the limitations of the O3 spatial resolution and how it might have influenced the results.

We agree with Reviewer 1 that the resolution of O3 data is a limitation of our study. We have stated this limitation under methods and twice in the discussion. However, the same O3 data has been used in the MAPLE study (the major, recent air pollution and mortality work in Canada), where O3 was seen to be impacting mortality. Nevertheless, we have now added this limitation in the abstract and again in the last paragraph of the discussion (page 20), acknowledging the potential for a spurious association due to the difference in spatial resolution of this marker. More-resolved exposure estimates of O3 are very much needed for future work to better understand this, but this is complex because of the strong inverse correlation between O3 and NO2 as you go to finer and finer resolutions.

4. Additional Analyses (Optional): If possible, further analyses could be conducted to address some of the limitations, such as considering participants' time away from residence or exploring the relationship with intraplaque hemorrhage.

We thank Reviewer 1 for this suggestion. We would like to point out that we have adjusted in our models for residing at home/working in the lived-in community versus working outside the lived-in community. We have now added to our sensitivity analyses, the stratified analysis based on this variable as a proxy for time away from residential address. These sensitivity analyses showed results consistent with our main analysis for all three pollutants for those working in the community (supplemental Table S5). As for intraplaque hemorrhage, we acknowledge that it is an important clinical/atherosclerosis outcome to investigate, however, in this cohort, prevalence of IPH (n=156 IPH events (2.35%)) is too low to be able to estimate this association reliably.

5. The paper's contributions are significant, and the limitations do not undermine the overall value of the research. However, addressing these minor revisions would enhance the paper's clarity, coherence, and impact. Therefore, acceptance with minor revisions seems the most appropriate recommendation. Thank you.

We would like to thank Reviewer 1 for recognising the value of our work within the field. We feel based on your comments that the manuscript quality has been strengthened and we look forward to your second review and response.

Reviewer #2:

Mention the limitations.

1. Selection Bias: If the study's participants were not randomly selected or if there was a lack of diversity in the sample (e.g., age, gender, geographic location), this could introduce selection bias, limiting the generalizability of the findings.

We thank Reviewer 2 for this comment. We have now added this limitation to the final paragraph of the discussion: “Third, because CAHHM is a prospective pan-Canadian cohort of cohorts across five provinces and participants were selected from existing cohorts, the sample is not a random sample of the Canadian population distribution, thereby limiting the generalizability of these findings. When compared to a cohort of adults who responded to the 2015 Canadian Community Health Survey, CAHHM participants were older, of higher socioeconomic status, but had a similar mean INTERHEART risk score.38 This does not affect the exposure-to-outcome reliability of our results within CAHHM, but generalizability to younger populations and Canadians living outside major Canadian cities should be done with caution.”

2. Lack of Longitudinal Data: If the study was cross-sectional in nature, the lack of longitudinal data might hinder the ability to establish causal relationships between air pollution and atherosclerosis.

We have elaborated on this limitation based on the Bradford Hill criteria for causality. “Exposure to air pollution values represented a time frame prior to knowledge of the outcome for each participant, i.e., the air pollution data collected for the 5-year period prior to the MRI. We are therefore comfortable describing this as a prospective association, despite the lack of longitudinal follow-up.”

3. Potential Confounding Variables: If not all relevant confounding variables were controlled for, such as diet, lifestyle factors, or pre-existing health conditions, this could affect the validity of the associations found.

We thank Reviewer 2 for this point of consideration. We argue that a major strength of our study is the detailed and well-characterized phenotyping of our cohort. We have considered a parsimonious yet comprehensive set of confounders based on subject matter expertise and prior literature. These include the INTERHEART risk score that captures some dietary components, physical exercise, lifestyle and comorbidities summarizing individual cardiovascular risk. Nevertheless, we have now added to the limitations (Discussion, page 20) that “as with any observational study, the risk of residual confounding (from factors such as diet, lifestyle factors, or pre-existing health conditions) cannot be excluded.”

4. Measurement Error in Pollution Data: If there were inaccuracies in the measurement of pollution levels, such as reliance on satellite data or modeling without sufficient ground-truthing, this could lead to misestimation of exposure levels.

We thank Reviewer 2 for this comment, however, as we mention under (Methods, page 7), “To adjust for any residual bias in the satellite-derived PM2.5 estimates, a geographically weighted regression (GWR) incorporating ground-based observations was then applied. Good agreement was found with cross-validated surface observations across North-America (R2 = 0.70).” This geographically-weighted regression uses the surface (ground-truth) PM2.5 to correct for bias in the satellite estimates resulting in an improved R2 so that the exposures (at least at the scale of North America as a whole) are not biased and the magnitude of the gradient (from lowest to highest exposure) is representative of what ground measurements show. Inevitably, there are limited numbers of ground stations, thus, there is still room for errors but we are using the best available estimates at this time.

5. Lack of Sensitivity Analysis: If sensitivity analyses were not conducted to assess the robustness of the findings to different modeling assumptions or potential outliers, this could raise questions about the stability of the results.

We thank Reviewer 2 for this comment. Sensitivity analyses were conducted as outlined under statistical analysis in methods (results in supplemental table S4). In sensitivity analyses, models 1-5 were i. stratified by sex and ii. repeated after excluding participants based on immigration status for those who had been in Canada for less than ten years (n=5885). Moreover, as mentioned in our response to Reviewer 1, we have now added to our sensitivity analyses, iii. a stratified analysis based on workplace location (working outside the community) to address possible exposure misclassification.

6. Concerns Regarding Exposure Assessment: The methodology used to gauge individual-level exposures, relying solely on residence addresses, might lead to inaccuracies in exposure classification. The failure to account for variations in participants' locations and their residential histories could compromise the integrity of the exposure evaluation.

We have addressed this concern in our response to Reviewer 1; please see above under point #1.

7. Inconsistency in Exposure Time Frame: The application of a uniform 5-year window for pollutant exposure across all participants, irrespective of their specific enrollment dates, could introduce an element of bias, potentially skewing the assessment of exposure.

Thank you. We have addressed this limitation above under point #1 and strived to make it clearer and more transparent in the manuscript.

8. Unanticipated Results: The findings that NO2 was inversely associated with CWV and that there was no discernible connection with PM2.5 were surprising and at odds with certain existing studies. These outcomes prompt questions about possible unaccounted confounding variables or other hidden factors that might have shaped the results.

We have addressed this concern in our response to Reviewer 1; please see above under point #2.

9. Limitations in O3 Data Resolution: The 10 km spatial resolution used for O3 data might be insufficient to detect localized fluctuations, which could have an impact on the precision of the conclusions drawn regarding O3.

We have addressed this concern in our response to Reviewer 1; please see above under point #3.

10. Omission of Intraplaque Hemorrhage Analysis: The study's inability to examine intraplaque hemorrhage, owing to its limited scope, might overlook a crucial aspect in unraveling the complex relationship between air pollution and atherosclerosis. This limitation could be significant in the overall interpretation of the findings.

We have addressed this concern in our response to Reviewer 1; please see

Attachment

Submitted filename: Response to Reviewers.docx

pone.0309912.s006.docx (1.5MB, docx)

Decision Letter 1

Muhammad Maaz Arif

8 Mar 2024

PONE-D-23-23654R1Ambient Air Pollution and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort StudyPLOS ONE

Dear Dr. de Souza,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: 

1. Although "air pollution" is cited in the study title, "air pollutants exposure" is explicitly addressed in the findings. Therefore, the study should use more precise language to avoid drawing readers' attention away from the discrepancy between the title and the findings. Suggested Title: Exposure to Air Pollutants and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort Study

2. Tables S4 through S8 appear to include the study's most significant findings. Given that these are the most significant findings, why are these tables listed in the supplementary materials? These should be mentioned in the main manuscript.

3. Please take into account the feedback from the reviewers and thoroughly proofread the study for errors and grammatical flaws.

4. The manuscript may be accepted after minor revisions.

==============================

Please submit your revised manuscript by Apr 22 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Muhammad Maaz Arif, M.B.B.S, M.Phil

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

ACADEMIC EDITOR Comments:

Following changes are the top-most priority considering the reviewers comments:

1. Although "air pollution" is cited in the study title, "air pollutants exposure" is explicitly addressed in the findings. Therefore, the study should use more precise language to avoid drawing readers' attention away from the discrepancy between the title and the findings. Suggested Title: Exposure to Air Pollutants and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort Study.

2. Tables S4 through S8 appear to include the study's most significant findings. Given that these are the most significant findings, why are these tables listed in the supplementary materials? These should be mentioned in the main manuscript.

3. Please take into account the feedback from the reviewers and thoroughly proofread the study for errors and grammatical flaws.

4. The manuscript may be accepted after minor revisions.

Reviewer's Comments:

The study aimed to investigate the effect of long-term exposure to air pollution on cardiovascular mortality. Therefore, the authors used data from a cohort study of 6,645 individuals recruited from previous Canadian cohort studies. The strength of this study is the well assessed outcome of carotid vessel wall volume (CWV) by MRI, the four-year average duration of air pollution exposure, and the extensive adjustment for confounders including walkability and neighborhood socioeconomic status.

Interestingly, PM2.5 and NO2 were not or negatively associated with CVW, which requires further investigation.

The reviewers' comments were understandable, and the authors responded to and addressed all comments thoroughly. Furthermore, the feedback has been extensively implemented, including additional sensitivity analyses, resulting in a substantial improvement of the manuscript, which adequately discusses the present limitations. However, there are still a few minor points that need to be addressed:

## Minor

- Abstract

o for the effect of NO2, a “minus” is missing in the 95%-CI, it must be -7.32

- Methods

o More description is needed on the exclusion criterion of "known CVD history". Is this validated or self-reported? And what diseases were included in the CVD history?

o For clarification: add a sentence describing whether residential addresses were only available on a postcode grid. If so, then PM2.5 available on a 1km*1km grid was probably averaged over the postcode area? This is not clearly described in the methods section.

o As an additional analysis, the ratio of wall volume to total vessel volume (Normalized wall index) should be used instead of using maximum wall volume [e.g. PMID: 33183741]. This would account for individual variation in overall vessel size.

- Results:

o Information in the text do not match the information in the table, e.g., in the Results - participant characteristics section it says "54.8% of participants were women", but in Table 1 it says 56%. Same section: "92% of cohort postcodes were in urban areas", but in Table 3 it is 96.2%. It seems that information in the text was mixed up with the “British Columbia” column. Authors should double-check tables and text to correct any discrepancies.

- Discussion

o Any clinically relevant cut-off values for CWV? Is the mean value high (900 mm³)? Could be more addressed in the discussion and may help to contextualize the results and health of study participants to previous studies.

o I agree with Reviewer 2 that the study was rather cross-sectional in nature than longitudinal. The authors argue “Exposure to air pollution values represented a time frame prior to knowledge of the outcome for each participant, i.e., the air pollution data collected for the 5-year period prior to the MRI. We are therefore comfortable describing this as a prospective association, despite the lack of longitudinal follow-up.” However, since no information exists on the CVW thickness before the exposure window, one cannot argue that the thickness was not present at this exposure window or even before. Therefore, I suggest revising this sentence and to name it a cross-sectional association.

- Optional: instead of A, B, C, in the plots, naming the air pollutants would improve the readability of the plots.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: The study aimed to investigate the effect of long-term exposure to air pollution on cardiovascular mortality. Therefore, the authors used data from a cohort study of 6,645 individuals recruited from previous Canadian cohort studies. The strength of this study is the well assessed outcome of carotid vessel wall volume (CWV) by MRI, the four-year average duration of air pollution exposure, and the extensive adjustment for confounders including walkability and neighborhood socioeconomic status.

Interestingly, PM2.5 and NO2 were not or negatively associated with CVW, which requires further investigation.

The reviewers' comments were understandable, and the authors responded to and addressed all comments thoroughly. Furthermore, the feedback has been extensively implemented, including additional sensitivity analyses, resulting in a substantial improvement of the manuscript, which adequately discusses the present limitations. However, there are still a few minor points that need to be addressed:

## Minor

- Abstract

o for the effect of NO2, a “minus” is missing in the 95%-CI, it must be -7.32

- Methods

o More description is needed on the exclusion criterion of "known CVD history". Is this validated or self-reported? And what diseases were included in the CVD history?

o For clarification: add a sentence describing whether residential addresses were only available on a postcode grid. If so, then PM2.5 available on a 1km*1km grid was probably averaged over the postcode area? This is not clearly described in the methods section.

o As an additional analysis, the ratio of wall volume to total vessel volume (Normalized wall index) should be used instead of using maximum wall volume [e.g. PMID: 33183741]. This would account for individual variation in overall vessel size.

- Results:

o Information in the text do not match the information in the table, e.g., in the Results - participant characteristics section it says "54.8% of participants were women", but in Table 1 it says 56%. Same section: "92% of cohort postcodes were in urban areas", but in Table 3 it is 96.2%. It seems that information in the text was mixed up with the “British Columbia” column. Authors should double-check tables and text to correct any discrepancies.

- Discussion

o Any clinically relevant cut-off values for CWV? Is the mean value high (900 mm³)? Could be more addressed in the discussion and may help to contextualize the results and health of study participants to previous studies.

o I agree with Reviewer 2 that the study was rather cross-sectional in nature than longitudinal. The authors argue “Exposure to air pollution values represented a time frame prior to knowledge of the outcome for each participant, i.e., the air pollution data collected for the 5-year period prior to the MRI. We are therefore comfortable describing this as a prospective association, despite the lack of longitudinal follow-up.” However, since no information exists on the CVW thickness before the exposure window, one cannot argue that the thickness was not present at this exposure window or even before. Therefore, I suggest revising this sentence and to name it a cross-sectional association.

- Optional: instead of A, B, C, in the plots, naming the air pollutants would improve the readability of the plots.

**********

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Reviewer #3: No

**********

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PLoS One. 2024 Oct 31;19(10):e0309912. doi: 10.1371/journal.pone.0309912.r004

Author response to Decision Letter 1

4 Apr 2024

ACADEMIC EDITOR Comments:

Following changes are the top-most priority considering the reviewers comments:

1. Although "air pollution" is cited in the study title, "air pollutants exposure" is explicitly addressed in the findings. Therefore, the study should use more precise language to avoid drawing readers' attention away from the discrepancy between the title and the findings. Suggested Title: Exposure to Air Pollutants and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A Prospective Cohort Study.

We thank the Academic Editor for this suggestion; we have changed the title accordingly.

2. Tables S4 through S8 appear to include the study's most significant findings. Given that these are the most significant findings, why are these tables listed in the supplementary materials? These should be mentioned in the main manuscript.

Table S4 is summarized in Figure 1 in the main manuscript; Table S5 is additional (secondary) sensitivity analysis while tables S6-S8 are all succinctly summarized in Figure 2. Thus, we chose to keep these tables as supplemental to avoid redundancy, while presenting the main messages of these analyses through the figures in the main manuscript. The additional detail is available for detailed review, should a reader wish, but we felt the current presentation to be easier to understand. However, if you feel very strongly, we can move these to the main text.

3. Please take into account the feedback from the reviewers and thoroughly proofread the study for errors and grammatical flaws.

We thank the Academic Editor for this comment and have now proofread and revised the article accordingly.

4. The manuscript may be accepted after minor revisions.

We thank the Academic Editor for re-reviewing our revised manuscript and for all the valuable insights to strengthen our manuscript.

Reviewer's Comments:

The study aimed to investigate the effect of long-term exposure to air pollution on cardiovascular mortality. Therefore, the authors used data from a cohort study of 6,645 individuals recruited from previous Canadian cohort studies. The strength of this study is the well assessed outcome of carotid vessel wall volume (CWV) by MRI, the four-year average duration of air pollution exposure, and the extensive adjustment for confounders including walkability and neighborhood socioeconomic status.

We thank the Reviewer for echoing the strengths and comprehensive analyses conducted within the Canadian Alliance for Healthy Hearts and Minds (CAHHM).

Interestingly, PM2.5 and NO2 were not or negatively associated with CVW, which requires further investigation.

The reviewers' comments were understandable, and the authors responded to and addressed all comments thoroughly. Furthermore, the feedback has been extensively implemented, including additional sensitivity analyses, resulting in a substantial improvement of the manuscript, which adequately discusses the present limitations. However, there are still a few minor points that need to be addressed:

We thank the Reviewer for taking the time to evaluate our revised manuscript and for the favourable response.

## Minor

-Abstract

for the effect of NO2, a “minus” is missing in the 95%-CI, it must be -7.32

Thank you for noting this error; this is now corrected.

- Methods

More description is needed on the exclusion criterion of "known CVD history". Is this validated or self-reported? And what diseases were included in the CVD history?

We have now added how CVD history was defined as following: (defined as a self-reported history of stroke, coronary artery disease, heart failure, or other heart disease).

o For clarification: add a sentence describing whether residential addresses were only available on a postcode grid. If so, then PM2.5 available on a 1km*1km grid was probably averaged over the postcode area? This is not clearly described in the methods section.

The following paragraph has now been added to the methods section:

“For most residential addresses, postal code areas were considerably smaller than 1x1 km so that the assigned PM2.5 concentration matches the 1x1 km grid square that the postal code is found within. Specifically, assigning PM2.5 to postal codes was performed using the single linkage approach where the PM2.5 grid square selected was the one closest to the x, y coordinate within a postal code polygon that best represents where the majority of the population lived.”

o As an additional analysis, the ratio of wall volume to total vessel volume (Normalized wall index) should be used instead of using maximum wall volume [e.g. PMID: 33183741]. This would account for individual variation in overall vessel size.

We thank the Reviewer for this suggestion and reference. However, we prefer to use CWV as our outcome because we have previously validated it in CAHHM where we’ve published CWV ranges related to cardiovascular risk; which is useful clinically [Anand SS, Tu JV, Desai D, et al. Cardiovascular risk scoring and magnetic resonance imaging detected subclinical cerebrovascular disease. Eur Heart J - Cardiovasc Imaging. 2020;21(6):692-700]. Of note, in the sensitivity analysis stratified by sex to account for sex variation, all results were consistent between men and women.

We have now added this sentence to our Discussion (this also addresses the Reviewer’s comment below on clinically relevant cut-off values for CWV):

“We have previously shown in CAHHM how simple cardiovascular risk scores were significantly associated with CWV, where mean (SD) CWV for low, medium, and high INTERHEART risk score categories were 881.5 (163.1), 915.4 (166.6), and 940.9 (172.9) mm3, respectively.”

If the Academic Editor still would like to have this additional analysis, we can conduct it.

-Results:

o Information in the text do not match the information in the table, e.g., in the Results - participant characteristics section it says "54.8% of participants were women", but in Table 1 it says 56%. Same section: "92% of cohort postcodes were in urban areas", but in Table 3 it is 96.2%. It seems that information in the text was mixed up with the “British Columbia” column. Authors should double-check tables and text to correct any discrepancies.

We thank the Reviewer for noting this error. This change has been made for % women. As for urbanicity, we wrote that: “For all the regions, over 92% of the cohort’s postal codes were in urban areas.” i.e. that the minimum % for any of the provinces was 92%.

-Discussion

Any clinically relevant cut-off values for CWV? Is the mean value high (900 mm³)? Could be more addressed in the discussion and may help to contextualize the results and health of study participants to previous studies.

We thank the Reviewer for this valuable suggestion which also reinforces our choice of using CWV as our outcome measure. As noted above we have added the following sentence to the discussion: “We have previously shown in CAHHM that simple cardiovascular risk scores were significantly associated with CWV, where mean (SD) CWV for low, medium, and high INTERHEART risk score categories were 881.5 (163.1), 915.4 (166.6), and 940.9 (172.9) mm3, respectively. Therefore, the overall mean in the current analysis set (900 mm3) corresponds to someone with a low-moderate IHRS.”

I agree with Reviewer 2 that the study was rather cross-sectional in nature than longitudinal. The authors argue “Exposure to air pollution values represented a time frame prior to knowledge of the outcome for each participant, i.e., the air pollution data collected for the 5-year period prior to the MRI. We are therefore comfortable describing this as a prospective association, despite the lack of longitudinal follow-up.” However, since no information exists on the CVW thickness before the exposure window, one cannot argue that the thickness was not present at this exposure window or even before. Therefore, I suggest revising this sentence and to name it a cross-sectional association.

We acknowledge that the study design is not straight-forward because CAHHM itself is a prospective cohort study and the air pollution exposure measures are in the years prior to the outcome measurement but recognize that these air pollution estimates are rather stable over time in the years to follow as well (as noted in the discussion). Thus, to adopt a more conservative approach and because of the lack of longitudinal follow-up, we have now changed the study design to cross-sectional analysis in the title and abstract and have removed this sentence from the discussion.

Optional: instead of A, B, C, in the plots, naming the air pollutants would improve the readability of the plots.

We thank the Reviewer for this optional comment. We prefer to leave as A, B, C, to avoid confusion in case of naming of the air pollutants in Figure 2 where we are testing the interactions for each pollutant within the other 2 pollutant tertiles.

We thank the Academic Editor and external Reviewer for their feedback and hope that our revised manuscript is now deemed acceptable for publication in PLOS One.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0309912.s007.docx (368.1KB, docx)

Decision Letter 2

Muhammad Maaz Arif

30 May 2024

PONE-D-23-23654R2Exposure to Air Pollutants and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A cross-sectional analysisPLOS ONE

Dear Dr. de Souza,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: The authors have addressed many changes as advised and have improved the article. The article may be accepted after these last few recommendations. Following issue still needs to be addressed:May be accepted after minor revisions:Editor's comment last time: "Tables S4 through S8 appear to include the study's most significant findings. Given that these are the most significant findings, why are these tables listed in the supplementary materials? These should be mentioned in the main manuscript."   The author responded that the tables are summarized in a few figures. I would strongly advise you to include all of those tables (S4 through S8) in the manuscript. These tables are closely related to the manuscript's actual objective.    Please also consider the reviewer's remarks and final checks for proofreading issues.

==============================

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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Kind regards,

Muhammad Maaz Arif, M.B.B.S, M.Phil

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

May be accepted after minor revisions.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #4: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #4: This article was well performed and suggested to be published soon, while there were some additional information should be disclosed:

1. the sensitive of Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging.How is it varied by time? it is an important basis for the observation period.

2.the difinition of Carotid artery vessel wall volume should be described more minutely.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #4: Yes: Minjin Peng, Taihe Hospital, Hubei University of Medicine, https://orcid.org/0000-0002-1350-4780

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Oct 31;19(10):e0309912. doi: 10.1371/journal.pone.0309912.r006

Author response to Decision Letter 2

4 Jun 2024

ACADEMIC EDITOR Comments:

The authors have addressed many changes as advised and have improved the article. The article may be accepted after these last few recommendations.

We thank the Academic Editor for this comment and hope the article is deemed to be accepted for publications now that we have addressed the last few recommendations.

Following issue still needs to be addressed:

May be accepted after minor revisions:

Editor's comment last time: "Tables S4 through S8 appear to include the study's most significant findings. Given that these are the most significant findings, why are these tables listed in the supplementary materials? These should be mentioned in the main manuscript." The author responded that the tables are summarized in a few figures. I would strongly advise you to include all of those tables (S4 through S8) in the manuscript. These tables are closely related to the manuscript's actual objective.

We thank the Academic Editor for this suggestion, and we have now accordingly moved tables S4-S8 to the main manuscript.

Please also consider the reviewer's remarks and final checks for proofreading issues.

We thank the Academic Editor for this comment and have now proofread and revised the article accordingly.

4. The manuscript may be accepted after minor revisions.

We thank the Academic Editor for this comment and hope the article is deemed to be accepted for publications now that we have addressed the last few recommendations.

Reviewer #4 Comments:

This article was well performed and suggested to be published soon, while there were some additional information should be disclosed:

We thank the Reviewer for echoing the strengths and comprehensive analyses conducted within our study.

1. the sensitive of Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging.

How is it varied by time? it is an important basis for the observation period.

We have now added the following section to the discussion [page 23, line 1]:

“Compared to cIMT, CWV includes the adventitia, the source of vasa vasorum.12 In terms of sensitivity of MRI-measured CWV, studies have suggested adventitial thickening to be an early sign of atherosclerosis, whereas a dense network of adventitial vasa vasorum can signify progression of atherosclerosis to symptomatic disease.12 “

2.the difinition of Carotid artery vessel wall volume should be described more minutely.

We have now added a detailed description under methods [page 8, under subclinical MRI outcome]:

“The lumen was defined semi-automatically from axial bright blood images of the time of flight sequence which were reconstructed at 2 mm intervals. The outer wall of the carotid artery was semi-automatically defined and adjusted as needed by expert readers. The area of the vessel wall in each image was estimated by subtracting the lumen area from the outer wall vessel area. Vessel wall volume per slice was calculated by multiplying by 2 mm per slice. Vessel wall volumes for right and left carotid arteries were estimated by integrating the volume for the total number of slices for each artery.”

We thank the Academic Editor and external Reviewer for their feedback and hope that our revised manuscript is now deemed acceptable for publication in PLOS One.

Decision Letter 3

Muhammad Maaz Arif

21 Aug 2024

Exposure to Air Pollutants and Subclinical Carotid Atherosclerosis Measured by Magnetic Resonance Imaging: A cross-sectional analysis

PONE-D-23-23654R3

Dear Dr. de Souza,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Muhammad Maaz Arif, M.B.B.S, M.Phil

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Accept. Manuscript should be thoroughly checked for proofreading errors before the final galley proof.

Acceptance letter

Muhammad Maaz Arif

2 Sep 2024

PONE-D-23-23654R3

PLOS ONE

Dear Dr. de Souza,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Flow chart for Air pollution and MRI markers in CAHHM.

    (PDF)

    pone.0309912.s001.pdf (74.2KB, pdf)
    S2 Fig. Scatterplot of air pollutants by carotid wall volume measurements with regression lines stratified by sex.

    (PDF)

    pone.0309912.s002.pdf (264.8KB, pdf)
    S1 Table. Anthropometric characteristics of the study population by sex.

    (PDF)

    pone.0309912.s003.pdf (105.4KB, pdf)
    S2 Table. Demographics & lifestyle characteristics of the study population by sex.

    (PDF)

    pone.0309912.s004.pdf (119KB, pdf)
    S3 Table. Environmental characteristics of the study population by sex.

    (PDF)

    pone.0309912.s005.pdf (105.3KB, pdf)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0309912.s006.docx (1.5MB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0309912.s007.docx (368.1KB, docx)

    Data Availability Statement

    CAHHM data cannot be deposited publicly as these collaborative data originate from multiple Canadian cohorts with different legal frameworks. CAHHM represents a "cohort of cohorts", and at the time participants were enrolled into each respective cohort, data sharing was not part of the consent process. Thus, the appropriate legal frameworks to allow for data to be deposited publicly (identified or de-identified) is not in place, nor was such sharing anticipated or run by the respective REB at the time of cohort invitations. Therefore, any requests for data may be made to CAHHM (Alliance@phri.ca), with requests for data sharing to be considered on a case-by-case basis. A detailed statistical analysis plan and the code used in the analysis are also available on reasonable request.

    Articles from PLOS ONE are provided here courtesy of PLOS

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