
==== Front
Eur J Neurol
Eur J Neurol
10.1111/(ISSN)1468-1331
ENE
European Journal of Neurology
1351-5101
1468-1331
John Wiley and Sons Inc. Hoboken

39031977
10.1111/ene.16404
ENE16404
EJoN-23-2376.R2
Original Article
Stroke
Ambient air pollution, covert cerebrovascular disease and cognition: results from the ISSYS study
Ambient air pollution, covert cerebrovascular disease and cognition: results from the ISSYS study
Ballvé et al.
Ballvé A. https://orcid.org/0000-0001-7587-2768
1 2 3 alejandro.ballve@vallhebron.com

Pizarro J. 1 2
Maisterra O. 1 2
Riba‐Llena I. 1 4
Pujadas F. 1
Jiménez‐Balado J. 1 5
Palasi A. 1 2
Cirach M. 5 6 7
Turner M. C. 6 7 8
Sunyer J. 6 7 8
Delgado P. 1 2 3
1 Dementia Unit Vall d'Hebron University Hospital Barcelona Spain
2 Neurovascular Research Laboratory Vall Hebron Research Institute (VHIR) Barcelona Spain
3 Institute of Neuroscience, Universitat Autònoma de Barcelona (UAB) Bellaterra Spain
4 Unitat de Trastorns Cognitius, Hospital Universitari Santa Maria Lleida Spain
5 Neurovascular Research Group, Neurology Department Hospital del Mar Barcelona Spain
6 Institut de Salut Global de Barcelona Barcelona Spain
7 Universitat Pompeu Fabra (UPF) Barcelona Spain
8 Consortium for Biomedical Research in Epidemiology and Public Health (CIBER Epidemiología y Salud Pública—CIBERESP) Madrid Spain
* Correspondence
A. Ballvé, Neurology Department, Vall d'Hebron University Hospital, Passeig Vall d'Hebron, no. 119‐129, Barcelona 08035, Spain.
Email: alejandro.ballve@vallhebron.com

19 7 2024
10 2024
31 10 10.1111/ene.v31.10 e1640419 6 2024
20 1 2024
20 6 2024
© 2024 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Abstract

Background and purpose

Although air pollution (AP) has been associated with stroke and dementia, data regarding its relationship with covert cerebrovascular disease (cCVD) and cognition over time are sparse. The aim of this study was to explore these relationships.

Methods

A prospective population‐based study of 976 stroke‐free and non‐demented individuals living in Barcelona, Spain, was conducted during 2010–2016. A land use regression model was used to estimate the exposure of each participant to AP: NO x , NO2, PM2.5, PM10, PMcoarse and PM2.5 absorbance. Cognitive function and cCVD were assessed at baseline (n = 976) and 4 years after (n = 317). Multivariate‐adjusted models were developed.

Results

At baseline, 99 participants (10.1%) had covert brain infarcts and 91 (9.3%) had extensive periventricular white matter hyperintensities (WMHs). Marked subcortical WMH progression was seen in 19.7%; the incidence of other covert cerebrovascular lessons ranged between 5% and 6% each. PM2.5 was related to higher odds of having a covert brain infarct (odds ratio [OR] 2.21; 95% confidence interval [CI] 1.06–4.60). PM2.5 absorbance was related to higher odds of having extensive subcortical WMHs (OR 1.72; 95% CI 1.13–2.60), whereas NO2 was related to higher odds of having extensive subcortical (OR 1.66; 95% CI 1.17–2.35) or periventricular (OR 1.96; 95% CI 1.10–3.50) WMHs and to higher odds of developing marked subcortical WMH progression (OR 1.40; 95% CI 1.05–1.90). NO x was related to incident cerebral microbleeds (OR 1.36; 95% CI 1.04–1.79). There was no association between AP and cognition.

Conclusions

Air pollutant predicts the presence and accumulation of cCVD. Its impact on cognitive impairment remains to be determined.

The association between exposure to air pollution, calculated using a land use regression model, and the presence and accumulation over time of covert cerebral vascular disease, assessed by cerebral magnetic resonance imaging, was investigated in a cohort of hypertensive, otherwise healthy, outpatient‐based individuals. Exposure to several of the main air pollutants is associated with an increased risk of cerebrovascular disease at baseline and in the follow‐up 4 years after.

air pollution
particulate matter
cerebrovascular disorders
brain infarction
cognitive impairment
Instituto de Salud Carlos III 10.13039/501100004587 PI14/1535 PI19/00217 INT20/00084 CM20/00218 CM22/00226 European Regional Development Fund 10.13039/501100008530 Spanish Research Stroke NetworkRD/16/0019/0021 RICORS‐ICTUS‐Enfermedades Vasculares CerebralesRD21/0006/0007 Ramón y Cajal FellowshipRYC‐2017‐01892 Spanish Ministry of Science, Innovation and UniversitiesEuropean Social Fund 10.13039/501100004895 Spanish Ministry of Science and Innovation 10.13039/501100004837 ‘Centro de Excelencia Severo Ochoa 2019–2023CEX2018‐000806‐S Generalitat de Catalunya 10.13039/501100002809 CERCA Programme source-schema-version-number2.0
cover-dateOctober 2024
details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_JATSPMC version:6.4.8 mode:remove_FC converted:20.09.2024
Ballvé A , Pizarro J , Maisterra O , et al. Ambient air pollution, covert cerebrovascular disease and cognition: results from the ISSYS study. Eur J Neurol. 2024;31 :e16404. doi:10.1111/ene.16404
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pmcBACKGROUND

Cerebrovascular diseases have a broad clinical spectrum, ranging from stroke and transient ischaemic attack to cerebrovascular imaging findings that are not accompanied by acute symptoms, which are then known as covert cerebrovascular disease (cCVD). cCVD is defined by a spectrum of lesions which can be assessed by magnetic resonance imaging (MRI), such as covert brain infarcts (CBIs), white matter hyperintensities (WMHs), cerebral microbleeds (CMBs) and enlarged perivascular spaces (EPSs). Their prevalence rises with age and they increase the risk for future stroke, cognitive impairment and gait problems [1]. cCVD lesions are usually present many years before they are diagnosed, silently progressing over time until they become clinically apparent. Hence, a window of opportunity for prevention exists.

On the other hand, the burden of disease attributable to air pollution (AP) is estimated to be substantial and increasing. Several clinical practice guidelines include AP as a risk factor and point to the need for political interventions to diminish it [5]. Ninety per cent of the world's population is exposed to AP over recommended levels [2]. It has been associated with a range of adverse health outcomes, including neurological conditions such as stroke and cognitive impairment [3]. However, its relationship with cCVD has barely been explored [4].

Nitrogen dioxide (NO2) and, particularly, fine particulate matter (PM2.5) are especially relevant when assessing AP in urban environments for two main reasons. First, road traffic is one of their main sources. Second, they are included in the World Health Organization (WHO) air quality guidelines, reflecting the fact that they are among the AP components that have been more strongly associated with ill‐health outcomes [2]. As an example, PM2.5 has been associated with the Consortium to Establish a Registry for AD (CERAD) score in brain tissue donors. However, its relationship with cCVD has barely been explored [4].

Considering the great impact that cerebrovascular disease and cognitive impairment have, identifying potential modifiable risk factors is essential. Furthermore, the well‐known overlap between cognitive impairment of vascular and neurodegenerative origin could possibly be at least partially explained by common risk factors and pathogenic mechanisms, such as AP [6].

In this study there were two main objectives. First, the aim was to evaluate the association between AP and the prevalence of covert cerebrovascular lesions and their changes over time in a European‐population‐based cohort prone to the development of cCVD. Secondly, the aim was to explore the association between AP and cognitive function and cognitive status or diagnosis, considering cerebrovascular disease and cognition prospectively, in a longitudinal way.

PATIENTS AND METHODS

This study was nested within the ISSYS (Investigating Silent Strokes in hYpertensives Study) cohort [7], which was generated to determine the prevalence and progression of cCVDs and their link with cognitive impairment both at the baseline visit and after 4 years of follow‐up (Figure S1). It included hypertensive patients between 50 and 70 years who attended one of the 14 primary care centres in the northern area of Barcelona, Spain. Enrolment visits were conducted between 2010 and 2012. Sample size was calculated considering the prevalence of CBI in previous studies [8]. The presence of a previous stroke or dementia was ruled out by specific assessment by trained investigators. Participants underwent a cognitive evaluation and a brain MRI at both baseline and follow‐up visits. The complete study protocol has been published elsewhere [7]. Due to budget limitations since the initiation of the cohort, less than half of the cohort was invited to undergo a follow‐up; patients who already had cerebrovascular lesions on the baseline MRI were prioritized (Figure 1).

FIGURE 1 The exposure to the five air pollutants analysed expressed in μg/m3 (PM2.5 absorbance is not included because it is expressed in 10−5/m3) in the four cognitive transitions groups: stable normal ageing (NA) (n = 328), stable mild cognitive impairment (MCI) (n = 29), regression to normal (MCI at baseline and NA at follow‐up) (n = 21) and incident MCI (NA at baseline and MCI at follow‐up) (n = 29). None of the air pollutants was associated with cognitive transitions.

Baseline brain imaging assessment

Covert brain infarcts were defined as lesions ≥3 mm in diameter in their widest dimension which have cerebrospinal‐fluid‐like signal features in all pulse sequences and a hyperintense rim surrouinding them in fluid‐attrenuated inversion recovery (FLAIR) weighted images. WMHs of presumed vascular origin at periventricular (PV WMHs) or subcortical/deep areas (DWMHs) appeared as hyperintense signal lesions in T2‐weighted and FLAIR‐weighted sequences. They were graded according to the Fazekas scale (0–3) [9] at these two locations and they were further categorized as mild (0 or 1) or extensive (2 or 3 for DWMH or 3 for PV WMH). Other brain vascular lesions such as EPSs at the centrum semiovale (CSO) or basal ganglia and lobar and deep CMBs were also evaluated following the STRIVE criteria [10].

Follow‐up brain imaging outcomes

A description of the rating procedures has been published before by our group [11]. Briefly, changes in the previously mentioned cCVD markers between baseline and follow‐up MRI images were evaluated by duplicate readers, who were blinded to the clinical data and the time of MRI acquisition. Discordant cases were agreed on between readers or by consensus with a senior researcher. CBIs and CMBs were rated following the same criteria as at baseline. The progression of WMHs was evaluated by means of the Rotterdam progression scale [12]. Progression were classified as ‘marked’ when the total score in the Rotterdam progression scale was >2.5.

Since the incidence rate of new infarcts or microbleeds was small, all lesions were grouped into a combined progression score, in which radiological progression was determined as the sum (1 point each) of the main four radiological markers of progression of cCVD: incident CBI, incident CMB, incident EPS, marked progression of WMH.

Estimates of ambient air pollution concentrations

Air pollution comprises both gases, such as nitric oxides (NO x ) including nitrogen dioxide (NO2), carbon monoxide or sulfur dioxide, and a heterogeneous group of volatile particles named particulate matter (PM). PM are classified according to their size into coarse particles or PM10 and fine particles or PM2.5, which are terms used to designate particles whose diameter is smaller than 10 microns or 2.5m microns respectively. Whereas PM10 is mainly composed of dust and soil, PM2.5 is mainly generated through combustion of combustible fossils, so industry and road traffic are its main sources. The term PMcoarse was calculated as the difference between PM10 and PM2.5. PM2.5 absorbance is the term used to refer to the approximate quantification of black carbon, one of the most hazardous components of PM2.5 both to environment and health.

Estimated cumulative exposure to PM and NO x was assigned to the geocoded participants' residence address based on the land use regression (LUR) model. This LUR model was developed for Barcelona as part of the European ESCAPE project, which aims to study the health effects of AP on human populations [13]. Between October 2008 and April 2011, AP data were collected from monitors at urban and regional background locations (20 sites for PM and 40 for NO2) in three 2‐week sampling periods in different seasons. Annual average concentrations were estimated considering temporal trends in data from continuous reference monitors. Following a supervised stepwise regression procedure, models were developed for each pollutant. Models included several variables, like various traffic indicators and proximity to the nearest major roads and other information based on geographical information systems which had a spatial resolution of at least 100 m. It was confirmed that participants had not changed residence by directly asking them in the follow‐up visit. Further, 91% of the patients that were specifically assessed for it (503) reported not having changed their usual residence for 10 years or more.

Cognitive assessment

A screening cognitive test was administered to all patients at the baseline visit. Those who were suspected to be cognitively impaired underwent a further evaluation to determine cognitive status: normal ageing (NA) versus mild cognitive impairment (MCI). The same procedure was repeated at the follow‐up visit. Further details about the cognitive assessment methods are available in Appendix S1.

Statistical analyses

All analyses were conducted with IBM SPSS version 20 (Chicago, IL, USA). Intergroup differences were assessed with the χ 2 test for categorical variables and by the t test, anova, Mann–Whitney U test or Kruskal–Wallis test for continuous variables, depending on the variable distributions. The correlations between continuous variables were determined with Spearman or Pearson coefficients, as appropriate. A p < 0.05 was considered as significant.

Binary logistic regression models were carried out to evaluate the associations between AP and cerebrovascular lesions (presence/absence) and cognitive outcomes. The association between AP and different cardiovascular risk factors and associated conditions such as atrial fibrillation was also evaluated using subgroup analysis. Each individual air pollutant was modelled individually. Further, multipollutant models were constructed and evaluated. Baseline outcomes were the presence of CBIs, extensive PV or subcortical (SC) WMHs, CMBs and extensive EPSs at the basal ganglia or CSO. Outcomes at follow‐up included incident CBIs, incident CMBs and progression (yes/no) of SC or PV WMHs and EPSs. Further, a score combining all incident changes was created to account for the burden of progression. All models used years of education as a proxy for socioeconomic status. Initial models included age and sex as confounders. Additionally, when analysing vascular outcomes, the REGICOR score was included, and when analysing cognitive outcomes, baseline cognition and WMH progression were included instead. The REGICOR score is an adaptation of the Framingham equation of cardiovascular risk for the Spanish population that take into account age, sex, smoking status, diabetes, total cholesterol, high‐density lipoprotein cholesterol and systolic and diastolic blood pressure.

Odds ratios (ORs) and beta values represent estimates of the associations between AP and each outcome for a 10‐unit increase in each pollutant, except for PM2.5 absorbance where estimates are expressed as per‐unit increase. Confidence intervals (CIs) of 95% were used for all the obtained values.

RESULTS

The study flowchart is given in Figure 1. Briefly, there were 976 individuals at the baseline visit and the final sample size for follow‐up included 317 participants with valid imaging and cognitive data at both visits (acceptance rate at baseline 72.4%).

Table 1 shows the median levels of the pollutants analysed. Significant correlations between all air pollutants were found (all p < 0.001). Correlation coefficients between gases (NO2 and NO x ) and particles ranged from 0.14 to 0.6, whereas correlations amongst particles were stronger (between 0.58 and 0.75).

TABLE 1 Air pollution levels and their established limits a .

NO2 (μg/m3)	57.1 (53.4–59.9) b	10 c	40 d	
NO x (μg/m3)	92.5 (92.2–95.9) b			
PM2.5 (fine) (μg/m3)	16.0 (14.4–17.0) b	5 c	25 d	
PM10 (μg/m3)	33.7 (31.4–35.9) b	15 c	40 d	
PM2.5 absorbance (10−5/m3)	2.77 (2.74–2.96) b			
PMcoarse (μg/m3)	19.1 (17.6–20.2) b			
a All values (including World Health Organization and European Union guidelines) are for long‐term exposure (annual).

b Median and interquartile range (IQR) of the values estimated for all the cohort at baseline (n = 966).

c Limits established by the World Health Organization in Global Air Quality Guidelines 2021.

d Limits established by the European Union in ambient air quality and clean air for Europe standards in 2008.

General characteristics of the cohort

The median age of participants was 64 (54, 67) years. A total of 49.4% were men. Participants had a median of 8 (7, 12) years of education.

Median systolic blood pressure was 142 (132–155) mmHg and median diastolic blood pressure (DBP) was 78 (71–85) mmHg, indicating that participants were reasonably well controlled despite being hypertensive. Patients had had an arterial hypertension diagnosis for a mean of 10.5 (SD 7.8) years and many (55%) were treated with two or more blood pressure lowering drugs. Abdominal obesity was the most prevalent vascular risk factor (69%) following hypertension. The mean low‐density lipoprotein level was 128.9 (±34.0). 225 had dyslipidaemia (28.2%), 229 (23.5%) were diabetic and 148 (15.2%) were active smokers. A total of 38 patients (3.9%) had an atrial fibrillation.

As summarized in Table S1, people with dyslipidaemia and atrial fibrillation were exposed to higher median levels of PM2.5. A small positive correlation between DBP and PMcoarse levels was found, as well as a small negative correlation between DBP and NO2, NO x and PM2.5 absorbance. No other associations between exposure to AP and vascular risk factors at baseline were found, nor to established cardiovascular or renal disease or atrial fibrillation.

Air pollution and prevalence of covert cerebrovascular disease

At the baseline visit, 99 patients (10.1%) had CBIs, 65 (6.7%) had CMBs, 91 (9.3%) had extensive PV WMHs and 47 (4.8%) had extensive SC or deep WMHs.

As shown in Table 2, exposure to PM2.5 was related to higher odds of having CBIs (OR 2.21, 95% CI 1.06–4.60; p = 0.03, n = 976), whereas exposure to NO2 was associated with extensive (OR 1.66, 95% CI 1.17–2.35; p < 0.01) and PV (OR 1.96, 95% CI 1.10–3.50; p = 0.02, n = 976) WMHs. PM2.5 absorbance was associated with higher odds (OR 1.72, 95% CI 1.13–2.60; p = 0.01, n = 976) of having extensive SC WMHs. No associations were found between any of the pollutants and the presence of CMBs or extensive EPSs at basal ganglia or CSO at baseline (data not shown).

TABLE 2 Association between ambient air pollutants and brain imaging results at baseline.

	CBIs (99/976)	Extensive a SC WMHs (47/976)	Extensive a PV WMHs (15/976)	
OR	p value	OR	p value	OR	p value	
NO2	1.13 (0.90–1.40)	0.28	1.66 (1.17–2.35)	<0.01	1.96 (1.10–3.50)	0.02	
NO x	1.04 (0.92–1.17)	0.56	1.17 (0.99–1.38)	0.05	1.24 (0.96–1.60)	0.09	
PM2.5	2.21 (1.06–4.60)	0.03	1.59 (0.56–4.45)	0.38	1.98 (0.34–11.6)	0.45	
PM2.5 absorbance	1.16 (0.82–1.64)	0.39	1.72 (1.13–2.60)	0.01	1.01 (0.70–1.45)	0.27	
PM10	1.35 (0.74–2.46)	0.32	2.11 (0.99–4.49)	0.05	2.41 (0.69–8.39)	0.17	
PMcoarse	2.48 (0.80–7.67)	0.11	1.43 (0.31–6.58)	0.64	1.52 (0.09–25.2)	0.77	
Note: Values shown are the odds ratio obtained in the multivariate models, followed by their p value. Covariates included age, sex, years of education and REGICOR score. Missing values in covariates were as follows: air pollutants, n = 10; REGICOR score, n = 15; PV VWHs, n = 18; SC WMHs, n = 18; years of education, n = 65. Bold values denote statistical significance at the p < 0.05 level.

Abbreviations: CBIs, covert brain infarcts; OR, odds ratio; PV WMHs, periventricular white matter hyperintensities; SC WMHs, subcortical white matter hyperintensities.

a Extensive SC WMHs included 2 and 3 Fazekas score, whereas extensive PV WMHs included score 3.

In multipollutant models, increases in 10 μg/m3 PM2.5 exposure doubled the risk of CBIs independently of cardiovascular risk factors (VRF), education and all other pollutants, whereas extensive WMHs (both in PV and SC areas) were only related to NO2 exposure.

Air pollution and incidence of covert cerebrovascular disease (changes or progression)

As published before, the most frequent vascular change was marked SC WMH progression, which was seen in 71 patients (19.7%) [11]. The incidence of other lesions (incident CBIs, incident CMBs and marked WMH PVH progression) was from 5 to 6%.

As shown in Table 3, NO2. was related to higher odds of developing marked SC WHM progression (OR 1.4, 95% CI 1.05–19; p = 0.02, n = 317) and so was NO x with incident CMBs (OR 1.36, 95% CI 1.04–1.79; p = 0.02, n = 317).

TABLE 3 Association between ambient air pollutants and brain imaging results at follow‐up.

Pollutant	Odds ratio	95% confidence interval	p value	
NO2	
Incident infarcts	1.38	0.79–2.39	0.38	
Marked a PV WMH progression	1.04	0.62–1.73	0.87	
Marked a SC WMH progression	1.4	1.05–1.9	0.02	
Incident CMBs	1.43	0.78–2.63	0.24	
Progression total score	1.32	1.0–1.67	0.02	
NO x	
Incident infarcts	1.25	0.96–1.63	0.10	
Marked a PV WMH progression	0.95	0.73–1.23	0.68	
Marked a SC WMH progression	1.13	1.04–1.79	0.02	
Incident CMBs	1.36	1.04–1.79	0.02	
Progression total score	1.18	1.03–1.34	0.01	
PM2.5	
Incident infarcts	3.11	0.79–12.28	0.11	
Marked a PV WMH progression	0.45	0.06–3.02	0.41	
Marked a SC WMH progression	0.92	0.37–2.30	0.86	
Incident CMBs	1.01	0.19–5.44	0.99	
Progression total score	1.00	0.46–2.15	0.99	
PM2.5 absorbance	
Incident infarcts	1.16	0.53–2.56	0.71	
Marked a PV WMH progression	0.96	0.40–2.31	0.93	
Marked a SC WMH progression	1.4	0.93–2.12	0.11	
Incident CMBs	1.30	0.57–2.97	0.54	
Progression total score	1.28	0.89–1.82	0.17	
PM10	
Incident infarcts	1.91	0.24–3.55	0.89	
Marked a PV WMH progression	0.36	0.07–1.92	0.23	
Marked a SC WMH progression	0.95	0.45–1.98	0.89	
Incident CMBs	1.01	0.23–4.32	0.98	
Progression total score	0.83	0.44–1.54	0.56	
PMcoarse	
Incident infarcts	1.23	0.12–12.68	0.86	
Marked a PV WMH progression	0.17	0.01–2.26	0.18	
Marked a SC WMH progression	0.48	0.13–1.79	0.27	
Incident CMBs	1.21	0.09–15.80	0.89	
Progression total score	0.49	0.16–1.47	0.20	
Note: Values shown are the odds ratio obtained in the multivariate models, followed by their p value. Covariates were age, sex, years of education and REGICOR score. Valid sample size was 346 for incident infarcts, 342 for PV or SC marked WMH progression, 343 for CMBs and 342 for the progression score.

Abbreviations: CMBs, cerebral microbleeds; PV WMHs, periventricular white matter hyperintensities; SC WMHs, subcortical white matter hyperintensities.

a Score >2.5 in Rotterdam progression score.

When the combination of new lesions was considered as a burden score, a radiological progression was found in about 35% of the patients at follow‐up (63.9% no progression; 26.2% one lesion and 9.9% two or more lesions). With this approach, NO2 (OR 1.33, 95% CI 1.04–1.68; p = 0.019, n = 317) and NOx (OR 1.18, 95% CI 1.03–1.35; p = 0.015, n = 317) were the only AP constituents that related to higher odds of progression. More specifically, it was found that each increase in 10 units of NO2 or NOx was equivalent to 10 years of ageing in this cohort.

Cognition

No association between AP and cognitive function (global, memory or executive) or status (MCI vs. NA) at baseline or follow‐up (Tables S2 and S3) was found, nor with cognitive transitions over the follow‐up (Figure S2). Further details are provided in Appendix S1.

DISCUSSION

Our study shows that several AP components relate to the presence and progression of cCVD in the mid‐term in individuals without a personal history of stroke. In this sense, our results agree with previous studies, including meta‐analyses, that have reported an association between short‐ and long‐term exposure to AP and the risk of stroke [3] and its mortality [14], as well as with some of its aetiologies, like atrial fibrillation and vascular risk factors, including hypertension and diabetes [15]. However, the studies are heterogeneous both in results and in methodological aspects. The last Integrated Science Assessment on PM2.5 from the US Environmental Protection Agency in 2019 [16] concluded that, overall, the association between exposure to PM2.5 and stroke remains inconsistent. Regarding cCVD, AP has been associated with an increased risk of having brain infarcts on almost one and a half million MRIs performed in health screening centres in China [17]. However, to the best of our knowledge, studies exploring the effect of AP on cCVD over time are lacking. In this sense, our study provides new evidence that could be explored further.

Deeper understanding of the mechanisms linking AP to CVD is needed [18]. So far, however, several mechanisms that might underlie this relationship have been described, including—but not limited to—prothrombotic [19] and proinflammatory effects and increased oxidative stress [18], autonomic dysregulation and endothelial dysfunction that might lead to altered blood pressure and cerebral blood flow [20, 21], and neurovascular unit impairment [22], whose proper functioning is fundamental for maintaining brain health.

In our study, a higher exposure to PM2.5 was associated with dyslipidaemia and with atrial fibrillation, whilst NO x showed negative correlations, which might be due to the vasodilator properties of these gases. However, considering that only two out of 36 associations between air pollutants and risk factors were significant with the chosen criterion for significance (p < 0.05), these associations might have been casual.

On the other hand, in our study no association between AP and cognition was found. However, AP has been associated with lower cognitive functions both globally and by domains [22], cognitive impairment throughout all life [22, 23, 24], neuropsychiatric symptoms in cognitive impairment [26], clinical syndromes like MCI and dementia [27] and biomarkers of neurodegeneration and Alzheimer's disease [6, 28]. A recent systematic review concludes that the association between AP and lower cognition throughout life is robust [25].

The analysed AP is above the recommended maximum levels established by WHO and the European Union (Table 1). Interestingly, NO2 and PM2.5 absorbance, which are related to traffic, are amongst the pollutants which show the strongest associations with cCVD in our study. Traffic regulations remain an important issue to be dealt with. A Chinese study reported similar levels of AP (PM2.5, 15.61 μg/m3; PM10, 30.08 μg/m3; NO2, 35.98 μg/m3), whilst in a European study their mean levels varied significantly between the 11 cohorts included (PM2.5, 8–19; PM10, 14–46; NO2, 15–60) [17, 29]. It is worth pointing out that, although AP levels in low‐ to middle‐income countries are higher, most of these types of studies have been performed in high‐income countries [25].

To the best of our knowledge, this is one of the few studies assessing the relationship between AP and cCVD, and the first study to assess the relationship of AP and CVD progression over time. Furthermore, the study was performed in a population which is at a higher risk of CVD, so the results are especially relevant from a clinical point of view. The most important AP contributors were analysed and a well‐established method to estimate the exposure of participants to them was used. The cognitive outcomes were adjusted by cCVD presence and progression, which are not usually considered.

However, the study of this subject is complex. Although the aetiological mechanisms underlying cCVD and AP have not been clearly elucidated yet, a complex multifactorial model is the most likely. The degree to which AP contributes to them might be discrete. Exposure to AP is difficult to estimate on an individual level, and there is an important heterogeneity in the methods used for it. Heterogeneity is present in other methodological aspects of the studies regarding this topic, such as the main outcomes or the included variables, thus contributing to inconclusive or conflicting results in the literature.

As limitations, there is a risk of selection bias because the participants who already had cerebrovascular lesions on the baseline MRI were prioritized for the follow‐up. The study may lack statistical power for some of the explored relationships (i.e., AP and cognitive impairment). Acknowledging that exposure to AP might have lag effects on health, usual residence locations prior to the beginning of the study were not assessed in all the participants. A longer follow‐up would have been needed for cognitive outcomes, since the prevalence of cognitive impairment amongst 50–70‐year‐old healthy individuals is not higher than 10%–20% [30]. The progression of cCVD was only measured qualitatively. It is also important, outlining the effect that the multiple analysis performed may have had in the results. Although it has been ascertained that several European cities have similar AP levels [31], it is acknowledged that the study has been performed in a specific population in a particular location, which might limit the generalizability of our findings. In this sense, future studies with a wider and more diverse cohort would be interesting.

In our study, exposure to AP and its effects between different MCI groups (i.e. vascular vs. neurodegenerative, amnestic vs. non‐amnestic) was not compared, due to the limited number of participants with MCI at baseline, but it might be something interesting to explore.

AUTHOR CONTRIBUTIONS

Alejandro Ballve: Writing – original draft; writing – review and editing. J. Pizarro: Investigation; project administration. O. Maisterra: Investigation. Iolanda Riba‐Llena: Investigation. F. Pujadas: Supervision. J. Jiménez‐Balado: Formal analysis. A. Palasi: Investigation. M. Cirach: Conceptualization; project administration; resources. M. C. Turner: Conceptualization; project administration; resources. J. Sunyer: Project administration; conceptualization; resources. P. Delgado: Conceptualization; project administration; methodology; supervision; resources.

FUNDING INFORMATION

This work was supported by the Instituto de Salud Carlos III (grant numbers PI14/1535, PI19/00217, INT20/00084, CM20/00218 and CM22/00226) co‐financed by the European Regional Development Fund. The Neurovascular Research Laboratory receives funds from the Spanish Research Stroke Network (RD/16/0019/0021 and RICORS‐ICTUS‐Enfermedades Vasculares Cerebrales, RD21/0006/0007). MCT is funded by a Ramón y Cajal Fellowship (RYC‐2017‐01892) from the Spanish Ministry of Science, Innovation and Universities and co‐funded by the European Social Fund. ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the ‘Centro de Excelencia Severo Ochoa 2019–2023’ Programme (CEX2018‐000806‐S) and support from the Generalitat de Catalunya through the CERCA Programme.

CONFLICT OF INTEREST STATEMENT

None.

INFORMED CONSENT

All the included patients gave their informed consent to the treatment of their data.

Supporting information

Appendix S1.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.
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