# Cross-sectional evaluation of exposure to ozone, nitrogen dioxide, and particulate mass levels on circulating immune markers in women in the California Teachers Study

Emily L. Cauble, Michael Kleeman, Yusheng Zhao, Meredith Franklin, Mandy Yao, Emma Spielfogel, Tarik Benmarhnia, James Lacey, Mitchell S.V. Elkind, Juan Zhao, et al.

► To cite this version:

Emily L. Cauble, Michael Kleeman, Yusheng Zhao, Meredith Franklin, Mandy Yao, et al. Cross-sectional evaluation of exposure to ozone, nitrogen dioxide, and particulate mass levels on circulating immune markers in women in the California Teachers Study. Scientific Reports, 2026, 16 (1), pp.1248. 10.1038/s41598-025-30900-x. hal-05419006v1

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Submitted on 16 Dec 2025 (v1), last revised 19 Jan 2026 (v2)

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# Cross-sectional evaluation of exposure to ozone, nitrogen dioxide, and particulate mass levels on circulating immune markers in women in the California Teachers Study

Received: 8 September 2025

Accepted: 27 November 2025

Published online: 05 December 2025

Cite this article as: Cauble E.L., Kleeman M.J., Zhao Y. et al. Cross-sectional evaluation of exposure to ozone, nitrogen dioxide, and particulate mass levels on circulating immune markers in women in the California Teachers Study. *Sci Rep* (2025). https://doi.org/10.1038/s41598-025-30900-x

Emily L. Cauble, Michael J. Kleeman, Yusheng Zhao, Meredith Franklin, Mandy Yao, Emma S. Spielfogel, Tarik Benmarhnia, James V. Lacey, Mitchell S. V. Elkind, Juan Zhao, Larry Magpantay, Otoniel Martinez-Maza, Sophia S. Wang & Marta Epeldegui

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article.
**Cross-sectional evaluation of exposure to ozone, nitrogen dioxide, and particulate mass levels on circulating immune markers in women in the California Teachers Study**

**Authors:** Emily L. Cauble*<fnref n="1" />, Michael J. Kleeman<fnref n="2" />, Yusheng Zhao<fnref n="2" />, Meredith Franklin<fnref n="3" />, Mandy Yao<fnref n="3" />, Emma S. Spielfogel<fnref n="1" />, Tarik Benmarhnia<fnref n="4" />,<fnref n="5" />, James V. Lacey, Jr. <fnref n="1" />, Mitchell S. V. Elkind<fnref n="6" />, Juan Zhao<fnref n="6" />, Larry Magpantay<fnref n="7" />, Otoniel Martinez-Maza<fnref n="7" />, Sophia S. Wang<fnref n="1" />, Marta Epeldegui<fnref n="8" />⁻<fnref n="10" />

**Affiliations:**

**Correspondence:**

Emily L. Cauble, MA
ecauble@coh.org
Division of Health Analytics
Beckman Research Institute
City of Hope
1218 Fifth Ave.
Monrovia, CA 91016

<footnote n="1">¹Division of Health Analytics, Beckman Research Institute, City of Hope, Duarte, CA, USA</footnote>

<footnote n="2">²Department of Civil and Environmental Engineering, University of California, Davis, Davis, California, USA</footnote>

<footnote n="3">³University of Toronto, Department of Statistical Sciences, Toronto, Canada</footnote>

<footnote n="4">⁴Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, USA</footnote>

<footnote n="5">⁵Irset Institut de Recherche en Santé, Environnement et Travail, UMR-S 1085, Inserm, University of Rennes, EHESP, Rennes, France</footnote>

<footnote n="6">⁶American Heart Association, Dallas, TX, USA</footnote>

<footnote n="7">⁷University of California Los Angeles, Los Angeles, CA, USA</footnote>

<footnote n="8">⁸Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA</footnote>

<footnote n="9">⁹Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA</footnote>

<footnote n="10">¹⁰UCLA AIDS Institute, University of California, Los Angeles, Los Angeles, CA, USA</footnote>
**Abstract**

Exposure to ambient air pollutants, specifically ozone ($O_3$), nitrogen dioxide ($NO_2$), ultrafine, fine or coarse particulate matter ($PM_{0.1}$, $PM_{2.5}$, and $PM_{10}$), has been linked to a number of adverse health outcomes, including cardiovascular disease. Changes in immune response may be a key mechanism underlying these effects. Within the California Teachers Study cohort, we conducted a cross-sectional analysis of 1,898 women to assess the associations between exposure to $O_3$, $NO_2$, $PM_{0.1}$, $PM_{2.5}$, and $PM_{10}$ and 15 immune markers measured from serum samples collected in 2015. Daily residential exposures to $O_3$, $NO_2$, $PM_{0.1}$, $PM_{2.5}$, and $PM_{10}$ were estimated by a validated chemical transport model and averaged over 12-, 3-, and 1-month periods prior to blood draw. Fifteen immune markers (categorized as quartiles) were estimated per interquartile range (IQR) of air pollutant exposures using multivariable ordinal logistic regressions adjusted for age, body mass index, and respective pollutants. Immune markers were also grouped into immune pathways (pro-inflammatory/macrophage activation, B-cell activation, and T-cell activation). After applying Bonferroni correction, elevated exposure to $O_3$ levels at all three exposure windows were associated with elevated circulating levels of IL-1β (interleukin-1 beta), IL-8 (interleukin 8), $sTNFR2$ (soluble tumor necrosis factor receptor 2), and $sgp130$ (soluble glycoprotein 130). Elevated $O_3$ at 3- and 1-month periods were associated with increased levels of $sCD27$ (soluble cluster of differentiation 27) and BAFF (B-cell activating factor). In pathway analyses, $O_3$ was consistently and significantly associated with the pro-inflammatory/macrophage activation pathway (12-month OR=1.49, 3-month OR=1.57, 1-month OR=1.54) and with B-cell activation at all three exposure windows (12-month OR=1.24, 3-month OR=1.53, 1-month OR=1.41). $NO_2$ was positively associated with $TNFα$ at the 3- and 1-month exposure windows. For the PM size fractions, sporadic, mainly inverse, associations with immune markers were observed. Elevated $O_3$ exposure up to one year prior to blood draw was
associated with elevated immune markers related to pro-inflammatory response, macrophage activation, and B cell activation. These findings suggest potential immunologic pathways linking air pollution to adverse health outcomes in women.

**Keywords (up to 6):**

Air pollution

Immune markers

Human population

Macrophage activation

Pro-inflammatory response

<figure><img src="image_2.png" /></figure>
Introduction

Exposure to ambient air pollutants, including tropospheric ozone (O₃), nitrogen dioxide (NO₂), and particulate matter (PM; PM₀.₁ [ultrafine particles, less than 0.1µm], PM₂.₅ [fine particles, less than 2.5µm], and PM₁₀ [coarse particles, less than 10µm]), has been linked to a number of adverse health outcomes, including cardiovascular and respiratory diseases¹,². NO₂ is an irritant gas that is created from the atmospheric reaction of NO emitted from combustion processes¹,³,⁴.

According to the American Lung Association, the largest contributors of ambient NO₂ in urban areas are transportation-related, including emissions from trucks, buses and cars⁴. NO₂ and NOₓ react in the presence of sunlight in the atmosphere to form secondary pollutants, such as O₃¹,⁴,⁵.

PM is both a primary and secondary pollutant. Primary PM is directly emitted from natural sources, such as wildfires and windblown dust, as well as anthropogenic sources, such as combustion and resuspended road dust³. Secondary PM is formed from precursor gases, including NOₓ and volatile organic compounds (VOCs), through complex chemical reactions.

Although regulatory efforts by the U.S. Environmental Protection Agency have led to declines in NO₂, O₃, and PM₂.₅ over recent decades⁶,⁷, epidemiological studies continue to report adverse health outcomes even at low ambient concentrations¹,³,⁸⁻¹⁰.

The primary route of human exposure to air pollutants is through inhalation¹,³,⁴. Ultrafine particles can penetrate deep into the lung and enter the bloodstream where they may trigger inflammatory response by releasing inflammatory mediators²,⁸,¹⁴. Inflammation can thus occur locally in the lungs and systemically once the pollutant enters the bloodstream. However, prior epidemiological studies of associations between air pollutants and circulating immune biomarkers have been limited in scope to interleukin-6 (IL-6), tumor necrosis factor alpha (TNFα), and interleukin-1 beta (IL-1β); prior studies generally do not include or account for
multiple air pollutants and are often restricted to short-term exposures¹⁵⁻²³. Moreover, studies of PM₀.₁ remain scarce, despite growing concern of their potential to elicit the greatest risk to adverse health outcomes due to their small size and ability to penetrate deep into the lung²⁴⁻²⁷. Of particular concern is the evidence linking air pollutant exposure to increased cardiovascular disease (CVD) risk, particularly among post-menopausal women who face heightened risks of CVD and stroke¹¹,¹². Given that immune system response function also undergoes significant changes during and after menopause, understanding how air pollution influences immune responses in this population during this important time is critical for informing disease prevention efforts¹³. To address these gaps, we evaluated associations between five major air pollutants (O₃, NO₂, PM₀.₁, PM₂.₅ and PM₁₀) and 15 immune markers and across multiple exposure windows in 1,898 women, most of whom are post-menopausal and have a heightened risk of cardiovascular events such as strokes⁹,¹⁴,²⁸ and for whom pollution-induced immune changes may have heightened clinical relevance. We hypothesized that exposures to pollutants would be associated with elevated inflammatory marker levels.
# Methods

## Study population

The California Teachers study (CTS) is a prospective cohort study of 133,477 women who were active or recently retired public-school professionals in 1995 and have been followed since for health outcomes. The CTS cohort has been previously described²⁹ and is approved by the Institutional Review Board of City of Hope. Participants provided informed consent at baseline. All methods were performed in accordance with relevant guidelines and regulations. In all, six questionnaires have been administered to CTS participants, and, in 2013-2016, 14,374 participated in a biobanking study (consent was obtained before collection)³⁰. A subset of 1,900 of these participants were selected for inclusion in the present cross-sectional study if they had blood drawn in 2015; to ensure that a sufficient distribution of characteristics and exposures were reflected in our sample, participants were selected with stratified sampling for: socioeconomic status at baseline (SES; in quartiles determined by occupation, and income), geographical residence at baseline (according to 1990 census block groups), race, and PM₂.₅ estimates. As the goal of this cross-sectional study was to evaluate the associations between air pollutant exposures and immune marker levels, capturing the full range of exposures ensured there would be sufficient statistical power to evaluate all levels of exposure. Two participants were excluded due to missing exposure estimates.

## Immune marker measurements

From 7/1/2013 to 8/31/2016, under grant award UM1-CA164917, we collected 14,374 blood samples. Blood samples were shipped overnight to Fisher BioServices (FBS) in Rockville, MD, for processing and storage; 96% of samples were processed at FBS within 24 hours of collection (and 99.5% within 27 hours); average time from collection to processing was 18 hours.
Serum samples processed from whole blood collected were evaluated at University of California, Los Angeles for 15 immune markers by multiplexed immunometric assays (two Luminex panels; R&D Systems) and a Bioplex 200 system (Bio-Rad). Panel 1 included the detection of IL-1β, IL-6, IL-8 (interleukin-8), IL-10 (interleukin-10), and TNFα. Panel 2 included the detection of BAFF (B-cell activating factor), CCL2 (chemokine ligand 2), CCL17 (chemokine ligand 17), sCD14 (soluble cluster of differentiation 14), sCD25 (soluble cluster of differentiation 25), sCD27 (soluble cluster of differentiation 27), sCD163 (soluble cluster of differentiation 163), sgp130 (soluble glycoprotein 130), sIL6Rα (soluble interleukin 6 receptor subunit alpha), and sTNFR2 (soluble tumor necrosis factor receptor 2). These immune markers have been identified as contributors in the following immune pathways: 1) pro-inflammatory/macrophage activation: IL-1β, IL-6, sIL-6Rα, IL-8, TNFα, sTNFR2, CCL2, sCD14, sCD163, and sgp130; 2) B-cell activation: IL-10, sIL-6Rα, IL-6, sgp130, CD27, and BAFF; and 3) T-cell activation: CCL17 and sCD25. The cytokine units of measurement are in pg/ml. The laboratory assays included 5% QC replicates and duplicate samples. All assays were conducted blinded and in a single batch using the same reagent production lot to eliminate seasonal and batch variations and were done in the same laboratory (Epeldegui lab, University of California, Los Angeles) to eliminate variation in reagents³¹,³². Additionally, all immune markers were evaluated individually after finding minimal evidence of correlation among them (via Spearman coefficients) (Supplemental Table S1).

## Air pollution exposures

Air pollution exposures were estimated based on geocoded addresses of CTS participants from 2014-2015. Daily air pollution exposures were estimated at 4 km spatial resolution using the University of California at Davis / California Institute of Technology (UCD/CIT) chemical transport model³³. Briefly, the UCD/CIT model was configured to cover regions containing more
than 93% of California's population, including over 100,000 CTS participants⁹. The UCD/CIT model can be configured to use a number of gas-phase chemical mechanisms³⁴. In the current study, the SAPRC11 (Statewide Air Pollution Research Center, 2011) chemical mechanism was used to predict gas-phase pollutant concentrations based on the reliable performance of this mechanism in California³⁵. Model performance statistics met the goals and criteria for Chemical Transport Model (CTM) applications³⁶. Raw CTM predictions are an independent estimate of pollutant concentrations based on fundamental physics and chemistry equations that do not depend on the measured ambient values. The estimates are based on emissions inventories developed through observations of activities that release pollutants, chemical mechanisms developed based on detailed reactions that occur in the atmosphere, transport calculations that account for advection, turbulent diffusion, and vertical mixing, dry- and wet-deposition calculations that remove particles from the atmosphere, nucleation calculations that create condensed phase material from gas-phase precursors, condensation/evaporation calculations that transfer semivolatile compounds between the gas phase and existing liquid/solid phases on particles, thermodynamic calculations that predict the vapor pressure of semivolatile compounds at the surface of each particle, and coagulation calculations that combine particles that collide due to random motion in the atmosphere. Bias in the CTM calculations can occur for various reasons, such as inaccurate wind fields/atmospheric boundary layer height, incomplete emissions estimates, etc. In the current study, we analyzed the bias in the CTM calculations through a comparison to regulatory monitors that measured PM₂.₅ mass and chemical composition. We used a Random Forest Regression (RFR) model to predict the fractional bias (FB) in 24hr average concentrations in grid cells where monitoring data was available. FB is defined as 2(P-O)/(P+O) where P is the prediction and O is the observation in the grid cell of interest. Support
variables in the RFR calculations included predicted meteorological parameters, low-cost sensor
measurements of PM<sub>2.5</sub> mass, satellite observations of aerosol optical depth, and predicted source
activity based on source tracers embedded in the CTM calculations. Raw CTM predictions were
adjusted using the FB predicted by the RFR method according to the correction factor (2-
FB)/(2+FB). UCD/CIT estimates of daily PM mass (PM<sub>0.1</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>; µg/m<sup>3</sup>), O<sub>3</sub> (1 hour
max ppm) and NO<sub>2</sub> (24 hour average ppm) were assigned to the geocoded residential locations
of the CTS participants for the year prior (2014) to the date of their blood draw (2015)<sup>9,37</sup>. From
these daily estimates, different exposure windows were assigned to each participant representing
12-month (long-term), 3-month (short-term), and 1-month (short-term) averages before their
blood draw date. Additionally, Pearson coefficients for the pollutants are presented in
Supplemental Table S2.

Covariates

Covariates considered included those previously associated with immune markers and/or air
pollution<sup>9,12,28,33</sup>. The most parsimonious model was selected and included age and BMI<sup>9,38</sup>. For
multivariable models, in addition to adjusting for age (continuous) and BMI (categorical; <25,
25-29, or 30+ kg/m<sup>2</sup>), O<sub>3</sub> and NO<sub>2</sub> models were each adjusted for all PMs, and the PM models
were similarly each adjusted for O<sub>3</sub> and NO<sub>2</sub>. The addition of temperature (annual averaged
similarly to the pollutants) into the multivariable models did not alter magnitude of risk and was
not included in final models.

Statistical analyses

Multivariable ordinal logistic regression was used to estimate associations between air pollutant exposures and immune markers (in quartiles). Quartiles were used in order to inform whether the resultant associations were dose-dependent (e.g., increasing exposure with increasing levels of
immune markers) or conferred a threshold effect (e.g., increasing exposure associated after
certain level or only the highest level of circulating immune markers). Because each pollutant
had different units (e.g., O₃ is measured as 1-hour max ppm whereas NO₂ is measured as 24-hour
average ppm; PMs were measured as 24-hour average µg/m³), we scaled the estimates by the
interquartile range (IQR) to facilitate comparisons among the exposures. Results are reported as
odds ratios (OR) and 95% confidence intervals (95% CI) scaled by the IQR of the pollutant. To
account for multiple comparisons, a Bonferroni correction was applied. Sensitivity analyses were
conducted by first removing immune marker outliers (defined as ±1.5*IQR) and rerunning the
models, followed by excluding extreme pollutant values. Although the purpose of evaluating
immune markers by quartiles was to assess a dose-dependent response, additional analyses were
performed for continuous immune markers (per pg/ml) using linear models as well as applying
inverse probability weighting to assess robustness of the results.

Immune markers were further categorized into immune pathways as: 1) pro-
inflammatory/macrophage activation: elevated levels of IL-1β, IL-6, sIL-6Rα, IL-8, TNFα,
sTNFR2, CCL2, sCD14, sCD163, and sgp130; 2) B-cell activation: elevated levels of IL-10, sIL-
6Rα, IL-6, sgp130, CD27, and BAFF; and 3) T-cell activation: elevated levels of CCL17 and
sCD25. As previously conducted, pathways were defined by first identifying those with
individual immune marker levels above the respective median; of those individuals, participants
with the number of elevated immune markers that exceeded the median number of total immune
markers in the pathway were defined as expressing the specific immune pathway³⁹. For example,
if a participant had elevated levels (dichotomized as above the median) of >3 immune markers in
the B-cell activation pathway, then they were designated in the B-cell activation pathway. To be
considered expressing a pro-inflammatory pathway, at least 5 of the immune markers designated
in that pathway would need to be elevated (above the median) for each participant. Associations
between the exposures and immune pathways (dichotomized as either expressing or not
expressing the specific pathway) were also assessed via ORs and 95% CIs and adjusted by age,
BMI, and other pollutants. All statistical analyses were performed using SAS 9.4 (SAS Institute
Inc., Cary, NC), and data analyses were conducted via the CTS Researcher Platform⁴⁰.

<figure><img src="image_3.png" /></figure>
# Results

## Study population characteristics

Of the 1,898 participants, 83% were aged 50 years or older, 42% had normal BMI (<25kg/m²), half reported NSAID use of >1/week, most were non-diabetic (86%), and most did not use statins (71%) at blood draw (Supplemental Table S3). As described in the methods section, compared to the general CTS population, our study population derived from the biobanking project reflected the oversampling for more racially, geographically, and economically diverse population. Briefly, 23.3% were non-White participants and the analytic population contained a more diverse group of women; our study population SES Quartile 4 (the most advantageous group) was 28.7% compared to the full CTS cohort SES Quartile 4 of 47.2%. (Supplemental Table S3).

## Air pollutant and immune marker measurements

Descriptive statistics for the five air pollutant exposures measured in our study are shown in Figure 1. For visualization and consistency with other studies, estimates for O₃ and NO₂ were converted to ppb; however, the units of the models remain as estimated (ppm). Overall, there was relatively high consistency between exposures at the 1-month, 3-month and 12-month time periods before blood draw, particularly for O₃ and NO₂ where the medians were the same for the 3 exposure periods examined (O₃ ~ 51 ppb, 1 hr max; NO₂ ~ 12 ppb, 24 hr avg). PM exposures over the 12-months before blood draw had higher medians than 1- and 3-month exposures (PM₀.₁: 0.80µg/m³, PM₂.₅: 9.49µg/m³; PM₁₀: 10.50µg/m³). Mean levels were largely consistent with the median, although slightly higher for PMs. Exposures averaged over the 1-month window were more variable, as reflected by their wider ranges.
<figure><img src="image_4.png" /><figcaption>Figure 1. Distribution of long-term (12-month) and short-term (3-month and 1-month) averages of Ozone (O₃; 1 hour maximum parts per billion[ppb]), Nitrogen dioxide (NO₂; 24 hour maximum ppb), PM₀.₁ (µg/m³), PM₂.₅ (µg/m³), and PM₁₀ (µg/m³) prior to the respective blood draw date for 1,898 women in the California Teachers Study.</figcaption></figure>

Quantiles and ranges of the immune markers are shown in Table 1. Most immune markers were present at detectable (>LOD) levels; all markers were detected at the 10th percentile.

<table><caption>Table 1. Distribution of measured cytokines (pg/ml) in the serum of 1,898 women in the California Teachers Study (collected in 2015).</caption><thead><tr><th>Variable</th><th>Full variable name</th><th>Limit of Detection</th><th>Min<sup>a</sup></th><th>25th<sup>b</sup></th><th>Median</th><th>75th<sup>b</sup></th><th>Max<sup>c</sup></th></tr></thead><tbody><tr><td>IL-1β</td><td>Interleukin-1 beta</td><td>0.42</td><td>0</td><td>1.14</td><td>1.94</td><td>3.18</td><td>26,830.31</td></tr><tr><td>IL-6</td><td>Interleukin-6</td><td>0.85</td><td>0</td><td>1.98</td><td>3.09</td><td>4.54</td><td>35,605.13</td></tr><tr><td>IL-8</td><td>Interleukin-8</td><td>0.82</td><td>0.26</td><td>14</td><td>18.99</td><td>27.08</td><td>69,406.81</td></tr><tr><td>IL-10</td><td>Interleukin-10</td><td>0.51</td><td>0.03</td><td>0.95</td><td>1.55</td><td>2.54</td><td>502.16</td></tr><tr><td>TNFα</td><td>Tumor necrosis factor alpha</td><td>0.78</td><td>1.31</td><td>14.51</td><td>19.33</td><td>24.8</td><td>1,414.93</td></tr><tr><td>BAFF</td><td>B-cell activating factor</td><td>5.14</td><td>350.87</td><td>638.86</td><td>723.93</td><td>825.63</td><td>2,747.91</td></tr><tr><td>CCL2</td><td>C-C motif chemokine ligand 2</td><td>11.71</td><td>0</td><td>359.69</td><td>416.08</td><td>486.11</td><td>14,372.14</td></tr><tr><td>CCL17</td><td>C-C motif chemokine ligand 17</td><td>32.47</td><td>0</td><td>761.72</td><td>898.32</td><td>1,101.71</td><td>20,197.32</td></tr><tr><td>sCD14</td><td>Soluble cluster of differentiation 14</td><td>94.81</td><td>226,516.77</td><td>855,261.99</td><td>941,781.34</td><td>1,030,400.00</td><td>2,222,100.00</td></tr><tr><td>sCD25</td><td>Soluble cluster of differentiation 25</td><td>9.53</td><td>209.99</td><td>453.59</td><td>551.86</td><td>680.01</td><td>2,751.44</td></tr><tr><td>sCD27</td><td>Soluble cluster of differentiation 27</td><td>30.30</td><td>2,649.49</td><td>5,350.39</td><td>6,305.89</td><td>7,677.47</td><td>68,915.68</td></tr><tr><td>sCD163</td><td>Soluble cluster of differentiation 163</td><td>1,997.97</td><td>0</td><td>423,469.16</td><td>535,244.81</td><td>677,261.24</td><td>3,129,200.00</td></tr><tr><td>sgp130</td><td>Soluble glycoprotein 130</td><td>143.24</td><td>45,813.76</td><td>150,910.60</td><td>169,374.84</td><td>188,790.38</td><td>400,817.39</td></tr><tr><td>sIL-6Rα</td><td>Soluble interleukin-6 receptor subunit alpha</td><td>38.11</td><td>25,274.42</td><td>46,918.78</td><td>56,027.03</td><td>65,089.76</td><td>148,465.14</td></tr><tr><td>sTNFR2</td><td>Soluble tumor necrosis factor receptor 2</td><td>3.24</td><td>529.69</td><td>1,673.25</td><td>2,028.20</td><td>2,526.87</td><td>278,416.25</td></tr></tbody></table>

<footnote><sup>a</sup>Min=minimum. <sup>b</sup>10th, 25th, 75th and 90th respective percentiles. <sup>c</sup>Max=maximum.</footnote>
## Air pollutant exposure associations with immune markers

The multivariable ordinal logistic regressions (scaled by IQR) showed consistent associations between $O_3$ exposure and immune markers (Figure 2, Supplemental Tables S4-8). Across all exposure windows, higher $O_3$ was statistically significantly associated with increased odds of having higher circulating levels of IL-1β, IL-8, sTNFR2, and sgp130, with additional associations for sCD27 and BAFF at shorter exposure periods. Associations were highest for 12-month exposure for IL-1β (OR<sub>Quartile 4</sub>=1.99, 95% CI=1.55-2.56, p-trend <0.0001) and IL-8 (OR<sub>Quartile 4</sub>=2.92, 95% CI=2.30-3.71, p-trend <0.0001), whereas sTNFR2, sgp130, sCD27, and BAFF showed highest associations at 3- and 1-month exposures (Figure 2 and Supplemental Table S4).

Figure 2. Visual of the associations for exposures (scaled by IQR for 12-month, 3-month, and 1-month prior to date of blood draw) with immune markers (quartiles). Significant associations (ORs) are denoted by color: 1) positive associations = red and pink; 2) inverse associations = dark blue and light blue; 3) p-trend <0.05 = lighter shades; 4) p-trend < Bonferroni correction p value = darker shades. $O_3$ and $NO_2$ models were adjusted for age, BMI, and all other exposures (Bonferroni correction p value=0.001). PM models were adjusted for $O_3$ and $NO_2$ (Bonferroni correction p value = 0.002).

<table><thead><tr><th>Exposure</th><th>Exposure window</th><th>IL-1β</th><th>IL-8</th><th>TNFα</th><th>sTNFR2</th><th>CCL2</th><th>sCD14</th><th>sCD163</th><th>IL-6</th><th>sIL-6Rα</th><th>sgp130</th><th>IL-10</th><th>BAFF</th><th>sCD27</th><th>CCL17</th><th>sCD25</th></tr></thead><tbody><tr><td rowspan="3">O<sub>3</sub></td><td>12-month</td><td>1.99</td><td>2.92</td><td>1.24</td><td>2.00</td><td>1.33</td><td>1.14</td><td>1.25</td><td>1.07</td><td>0.98</td><td>1.43</td><td>1.08</td><td>1.11</td><td>1.14</td><td>1.07</td><td>1.31</td></tr><tr><td>3-month</td><td>1.54</td><td>2.37</td><td>1.09</td><td>2.08</td><td>1.29</td><td>1.39</td><td>0.90</td><td>0.94</td><td>1.07</td><td>3.14</td><td>1.03</td><td>1.37</td><td>1.81</td><td>1.09</td><td>1.27</td></tr><tr><td>1-month</td><td>1.51</td><td>2.29</td><td>1.12</td><td>2.01</td><td>1.46</td><td>1.38</td><td>0.92</td><td>0.99</td><td>1.09</td><td>2.22</td><td>1.03</td><td>1.35</td><td>1.70</td><td>0.97</td><td>1.22</td></tr><tr><td rowspan="3">NO<sub>2</sub></td><td>12-month</td><td>1.59</td><td>1.40</td><td>1.26</td><td>1.35</td><td>0.87</td><td>0.82</td><td>1.36</td><td>1.52</td><td>0.98</td><td>0.98</td><td>0.89</td><td>1.12</td><td>1.59</td><td>0.95</td><td>1.22</td></tr><tr><td>3-month</td><td>1.31</td><td>1.41</td><td>1.63</td><td>1.13</td><td>0.90</td><td>0.93</td><td>1.34</td><td>1.28</td><td>0.99</td><td>0.86</td><td>1.00</td><td>1.03</td><td>1.11</td><td>1.20</td><td>1.26</td></tr><tr><td>1-month</td><td>1.30</td><td>1.05</td><td>1.40</td><td>1.30</td><td>0.98</td><td>0.93</td><td>1.32</td><td>1.32</td><td>1.18</td><td>0.94</td><td>1.16</td><td>1.16</td><td>1.27</td><td>1.09</td><td>1.26</td></tr><tr><td rowspan="3">PM<sub>0.1</sub></td><td>12-month</td><td>0.70</td><td>0.57</td><td>0.98</td><td>0.69</td><td>1.10</td><td>0.79</td><td>0.90</td><td>0.98</td><td>0.93</td><td>0.76</td><td>1.01</td><td>0.94</td><td>0.72</td><td>1.00</td><td>0.82</td></tr><tr><td>3-month</td><td>0.64</td><td>0.70</td><td>0.87</td><td>0.83</td><td>1.30</td><td>0.89</td><td>1.02</td><td>1.02</td><td>0.95</td><td>0.83</td><td>0.93</td><td>1.05</td><td>0.85</td><td>0.91</td><td>0.91</td></tr><tr><td>1-month</td><td>0.66</td><td>0.80</td><td>0.83</td><td>0.73</td><td>1.09</td><td>0.85</td><td>1.00</td><td>0.90</td><td>0.80</td><td>0.79</td><td>0.83</td><td>0.94</td><td>0.76</td><td>1.12</td><td>0.91</td></tr><tr><td rowspan="3">PM<sub>2.5</sub></td><td>12-month</td><td>0.63</td><td>0.85</td><td>0.97</td><td>0.83</td><td>1.45</td><td>1.19</td><td>0.78</td><td>0.81</td><td>0.88</td><td>0.99</td><td>1.18</td><td>0.93</td><td>0.78</td><td>1.19</td><td>0.90</td></tr><tr><td>3-month</td><td>0.90</td><td>0.78</td><td>0.70</td><td>0.98</td><td>1.23</td><td>0.93</td><td>0.88</td><td>0.92</td><td>0.92</td><td>1.08</td><td>1.05</td><td>1.02</td><td>0.92</td><td>0.97</td><td>0.85</td></tr><tr><td>1-month</td><td>0.76</td><td>0.83</td><td>0.77</td><td>0.80</td><td>1.11</td><td>0.91</td><td>0.88</td><td>0.86</td><td>0.75</td><td>0.97</td><td>0.95</td><td>0.88</td><td>0.79</td><td>1.05</td><td>0.82</td></tr><tr><td rowspan="3">PM<sub>10</sub></td><td>12-month</td><td>0.56</td><td>0.49</td><td>0.83</td><td>0.87</td><td>1.24</td><td>1.29</td><td>0.85</td><td>0.74</td><td>1.06</td><td>1.30</td><td>1.03</td><td>1.01</td><td>0.93</td><td>1.10</td><td>0.96</td></tr><tr><td>3-month</td><td>0.90</td><td>0.64</td><td>0.76</td><td>0.95</td><td>1.15</td><td>0.98</td><td>0.94</td><td>0.87</td><td>0.98</td><td>1.10</td><td>1.01</td><td>1.00</td><td>0.92</td><td>0.98</td><td>0.96</td></tr><tr><td>1-month</td><td>0.82</td><td>0.70</td><td>0.79</td><td>0.86</td><td>1.07</td><td>1.01</td><td>0.92</td><td>0.83</td><td>0.86</td><td>1.09</td><td>0.93</td><td>0.93</td><td>0.86</td><td>1.02</td><td>0.93</td></tr></tbody></table>

P for trend < Bonferroni

Positive P for trend <0.05

Inverse P for trend <0.05

P for trend < Bonferroni

By immune pathway (Table 2), $O_3$ was consistently associated with the pro-inflammatory/macrophage activation pathway across all exposure timepoints (12-month
OR=1.49, 95% CI=1.26-1.75; 3-month OR=1.57, 95%CI=1.34-1.85; 1-month OR=1.54, 95% CI=1.33-1.80) and for the B-cell activation pathway (12-month OR=1.24, 95% CI=1.05-1.47; 3-month OR=1.53, 95%CI=1.30-1.81; 1-month OR=1.41, 95% CI=1.21-1.65). Associations with the T cell activation pathway were significant at the shorter time periods (3-month OR=1.19, 95%CI=1.00-1.42; 1-month OR=1.17, 95%CI=1.00-1.38).

<table><caption>Table 2. Associations between immune marker pathways and ozone (O₃) exposures 12-months, 3-months and 1-month before blood draw.</caption><thead><tr><th>Immune Pathway</th><th>Exposure Time<sup>d</sup></th><th>Odds ratio (OR)</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="3">Pro-inflammatory/macrophage activation<sup>a</sup></td><td>12-month prior</td><td>1.49</td><td>1.26-1.75</td></tr><tr><td>3-month prior</td><td>1.57</td><td>1.34-1.85</td></tr><tr><td>1-month prior</td><td>1.54</td><td>1.33-1.80</td></tr><tr><td rowspan="3">B cell activation<sup>b</sup></td><td>12-month prior</td><td>1.24</td><td>1.05-1.47</td></tr><tr><td>3-month prior</td><td>1.53</td><td>1.30-1.81</td></tr><tr><td>1-month prior</td><td>1.41</td><td>1.21-1.65</td></tr><tr><td rowspan="3">T cell activation<sup>c</sup></td><td>12-month prior</td><td>1.02</td><td>0.85-1.21</td></tr><tr><td>3-month prior</td><td>1.19</td><td>1.00-1.42</td></tr><tr><td>1-month prior</td><td>1.17</td><td>1.00-1.38</td></tr></tbody></table>

Fewer associations were observed with NO₂, though a notable positive association was observed with higher levels of TNFα across short term exposure windows (3-month OR<sub>Quartile 4</sub>=1.63, 95%CI=1.22-2.17; 1-month OR<sub>Quartile 4</sub>=1.40, 95% CI=1.07-1.83) (Figure 2 and Supplemental Table S5).

PMs yielded largely consistent patterns: 1) decreased IL-1β levels at all 3 time points, except a lack of significance at the 3-month period for PM₂.₅ and PM₁₀, 2) decreased IL-8 levels at all 3 exposure windows for PM₀.₁ and PM₁₀ but only the 3-month exposure for PM₂.₅, and 3) decreased TNFα levels for shorter exposure periods for PM₂.₅ and PM₁₀ size fractions (Figure 2 and Supplemental Tables S6-8). P-trends for all associations are presented in Supplemental Table

<footnote><sup>a</sup>Pro-inflammatory/macrophage activation: elevated levels of IL-1β, IL-6, sIL-6Rα, IL-8, TNFα, sTNFR2, CCL2, sCD14, sCD163, and sgp130</footnote>

<footnote><sup>b</sup>B-cell activation: : elevated levels of IL-10, sIL-6Rα, IL-6, sgp130, CD27, and BAFF</footnote>

<footnote><sup>c</sup>T-cell activation: elevated levels of CCL17 and sCD25</footnote>

<footnote><sup>d</sup>IQR values: 12-month=0.0095, 3-month=0.0169, 1-month=0.0259</footnote>

<footnote>All models adjusted for age, BMI, and odds ratios are scaled by the IQR</footnote>
S9. In sensitivity analyses that excluded air pollutant outliers, results were consistent with the original models (Supplemental Tables S10-14). Analyses of immune markers as continuous outcomes were also consistent with reported immune marker outcomes as quartiles (Supplemental Table S15).

<figure><img src="image_5.png" /></figure>
Discussion

In this cross-sectional analysis within the California Teachers Study, we examined the association between exposures to five air pollutants (O₃, NO₂, PM₀.₁, PM₂.₅ and PM₁₀), estimated using a state-of-the-science chemical transport model at the participants' residences with 15 circulating immune markers assessed from the blood samples of 1,898 participants collected in 2015. We observed two main findings: 1) higher O₃ levels across all exposure windows (1-month to 1-year averages prior to blood draw) were associated with increased levels of several circulating immune markers linked to macrophage activation, pro-inflammatory response and B cell activation, including IL-1β, IL-8, sTNFR2, sgp130, sCD27, and BAFF; and 2) higher NO₂ exposure was associated with increased levels of TNFα. In contrast, we note inconsistent, inverse associations between PMs and immune markers, specifically IL-1β, IL-8, and TNFα.

Overall, O₃ emerged as the most robust exposure eliciting immune responses, with positive associations across multiple immune markers. These findings align with the small number of epidemiologic studies that have linked O₃ exposure with higher IL-1β and IL-8 levels, despite differing methods of exposure estimates and immune marker measurements⁴¹⁻⁴³. Our study results are also consistent with prior *in vitro* and *in vivo* studies showing that O₃ exposure increases levels of IL-8 expression⁴⁴⁻⁴⁷. The observed association between O₃ exposure and increased sTNFR2 is consistent with a prior report of increased TNFR2 levels in relation to short-term (1-7 day) O₃ exposure⁴⁸. To our knowledge, the associations of O₃ with sgp130, sCD27, and BAFF have not been previously reported in epidemiological studies.

Briefly, IL-1β is released by cells among the innate immune system to drive inflammatory processes⁴⁹ while IL-8 is released by a variety of immune cells to help activate neutrophils and promote inflammation⁵⁰. sTNFR2 is a protein that promotes T cell activity to drive pro-
inflammatory processes while also suppressing immune activity by preventing TNF-induced cell death⁵¹. sgp130 is involved in pro-inflammatory processes by altering T cell differentiation⁵².

sCD27 is a member of the TNF family and is a marker for B cell activation, specifically that of memory B cells⁵³,⁵⁴. Finally, and similarly to sCD27, BAFF belongs to the TNF family and plays a role in B cell maturation and survival⁵⁵.

Our *a priori* delineation of immune pathways supported these individual marker results. Higher O₃ levels at all 3 exposure windows was associated with pro-inflammatory/macrophage activation pathway, consistent with experimental *in vivo* and *in vitro* studies showing that O₃ induces pro-inflammatory gene expression and the subsequent release of inflammatory markers⁴⁴,⁴⁵,⁵⁶,⁵⁷. Notably this pathway encompasses immune markers regulated by the NFkB pathway, a pro-central mechanism linking air pollution to cardiovascular disease risk through increased thrombosis and atherosclerosis⁵⁸⁻⁶¹. Our results also suggested a link between O₃ exposure and the B-cell activation pathway, driven by sgp130, sCD27, and BAFF. These novel findings are not yet supported in the current literature and warrant replication in other studies.

The significant associations with IL-1β in our findings are noteworthy due to its established role as an inflammatory mediator in atherosclerotic cardiovascular disease (ASCVD), including stroke and CVD. In a large, randomized, double-blind trial, an IL-1β antagonist drug, canakinumab, was reported to reduce recurrent cardiovascular events. These findings in conjunction with our reported findings present the possibility that those who are exposed to higher levels of ambient air pollutants, particularly O₃, may benefit from pharmaceutical interventions targeting IL-1β to reduce risk of ASCVD⁶².
We also report associations between increasing NO₂ exposure across all 3 exposure windows with higher TNFα levels. These results complement current evidence of *in vivo* and *in vitro* studies that show a relationship between elevated TNFα and NO₂ exposure⁶³.

Our results for the PMs were contrary to what was expected. Across PMs, there were decreased risk of elevated IL-1β and IL-8 levels at the different exposure windows, and short-term exposure to PM₂.₅ and PM₁₀ were associated with decreased TNFα. These findings contradict prior work that have shown that exposure to PM is associated with elevated levels of IL-1β and TNFα¹⁶,¹⁷. It is possible, however, that the discrepant results may be due to the population subset evaluated. Prior reports have suggested associations between PM₁₀ with elevated levels of IL-1β in men, but not women¹⁶,¹⁷; moreover, associations with IL-1β, IL-6 and TNF-α were demonstrated in younger populations, versus our population of largely post-menopausal women where baseline inflammation levels may already be higher¹⁶,¹⁷. It is also possible that PM-mediated damage may be more localized to the lung and/or to the lung macrophages, resulting in a decrease of systemic immune marker production. It has been shown that PM exposure can lead to oxidative stress and impaired immune cell function, specifically of lung macrophages, which can lead to decreased immune marker production⁶⁴. Additionally, PMs have been shown to accumulate in macrophages within the lung, thus having a local, direct effect on immune responses and cell function, while O₃ and NO₂ elicit more chronic, systemic immune responses, especially with long-term exposures⁶⁵⁻⁶⁷.

Major strengths of our study include the use of multiplex immune marker assays and the use of a state-of-the-science chemical transport model for exposure assessment. Limitations include the cross-sectional design, which precludes establishing temporal relationships, and reliance on residential address-based exposures, which may not capture exposures from work and/or travel.
Our study was also nested in an established cohort (CTS) that was originally designed to investigate breast cancer, restricting our evaluation to women. Although this design was intentional for interrogating a population subset and age range that encompasses postmenopausal women, we recognize our results may not be necessarily generalizable to males and other populations⁶⁸,⁶⁹.

In conclusion, we found consistent evidence that $O_3$ exposure is associated with elevated levels of immune markers in both the pro-inflammatory/macrophage activation and B cell activation pathways, with exposures averaged from 1-month to one-year prior to blood draw conferring risk. For certain immune markers, such as IL-1β, TNFα and IL-8, however, associations varied by exposure window, suggesting potential critical periods of susceptibility. Replication in diverse populations and study designs will be essential to clarify the temporal and biological dynamics underlying these associations, and to better understand their role in mediating air pollution-related health outcomes.
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**Acknowledgements**

The authors would like to thank the women of the California Teachers Study who have contributed their invaluable time and information. We would like to thank the California Teachers Study Steering Committee for the formation and maintenance of the Study within which this research was conducted. A full list of California Teachers Study team members is available at https://www.calteachersstudy.org/team.

**Funding**

The California Teachers Study and the research reported in this publication were supported by the National Institute of Environmental Health Sciences and the National Cancer Institute of the National Institutes of Health under the award numbers R01-ES033413; U01-CA199277; P30-CA033572; P30-CA023100; UM1-CA164917; and R01-CA077398. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Environmental Health Sciences, the National Cancer Institute or the National Institutes of Health.

**Author contributions**

ME, MK, MF, SW, and OM designed the study. ES, EC, MK, YZ, TB, and LM prepared data for analyses and contributed to methodology. EC performed data analyses. EC, ME, and SW drafted the manuscript. All authors contributed to the editing and review of the final manuscript.

**Competing interests**

The author(s) declare no competing interests.

**Data availability statement (mandatory)**

The data used in the current study are available for research use. The California Teachers Study welcomes all inquiries. Please visit https://www.calteachersstudy.org/for-researchers.