Correlations between Human Alveolar Macrophage Particulate Matter Load, Air Pollution Particulate Matter Levels, and Systemic Inflammation Markers in Mexico City

Correlations between Human Alveolar Macrophage Particulate Matter Load, Air Pollution Particulate Matter Levels, and Systemic Inflammation Markers in Mexico City | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Correlations between Human Alveolar Macrophage Particulate Matter Load, Air Pollution Particulate Matter Levels, and Systemic Inflammation Markers in Mexico City Yeongkwon Son, Claudia Carranza, Sanjana Subramhanya, Carol Gardner, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6430851/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract This study explores potential associations among ambient particulate matter (PM) exposure, PM load in alveolar macrophage (AM), and biomarkers collected from 53 healthy, adult, nonsmoking residents of the Iztapalapa and Iztacalco municipalities in Mexico City. Ambient PM 2.5 concentrations were estimated using an improved Land Use Regression (LUR) model to approximate PM exposure levels. The PM/carbon loading was quantified by the fraction of AM containing PM (%, %AMPM) and the PM area within the AM (µm 2 ) from BAC cytospin microphotography using CellProfiler cell image analysis software. Concentrations of biomarkers were analyzed in bronchoalveolar lavage fluid (BALF), plasma, and urine. Most AM samples contained PM (median = 62.4%, interquartile range [IQR] = 50.0–73.0%). The median PM area in AM was 1.082 µm 2 (IQR = 0.607–1.855 µm 2 ). Participant with low %AMPM (< 33 percentile) showed 8% increase in %AMPM per 10 µg/m 3 increments of six-month averaged, LUR-estimated PM 2.5 concentrations. The %AMPM had a statistically significant, positive association with plasma von Willebrand Factor (vWF) ( p = 0.016) and serum lactase dehydrogenase (LDH) ( p = 0.026). These finding suggest that that ambient PM exposure in Mexico City contributes to PM accumulation in AMs and may trigger systemic inflammation and oxidative stress in healthy young residents. Health sciences/Risk factors Health sciences/Biomarkers Health sciences/Biomarkers/Predictive markers Earth and environmental sciences/Environmental sciences/Environmental impact Particulate matter Alveolar macrophage Land Use Regression Inflammation Oxidative Stress Cardiovascular disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Inhalation exposure to air pollution particulate matter (PM) represents a substantial global public health concern because of its numerous adverse health effects, including respiratory, cardiovascular, nervous system, and cancer morbidity and mortality [ 1 ]. Rapid global urbanization has increased exposure to PM from household solid fuel combustion and ambient sources [ 2 ]. The Mexico City Metropolitan Area (MCMA) is one of the biggest megacities and experiences high levels of air pollution, including from PM [ 3 , 4 ]. As one of the most populated urban environments, the MCMA also deals with tuberculosis (TB) as a considerable public health concern [ 5 ]. PM exposure from household (solid fuel) and ambient combustion sources is the top level-3 risk factor ordered by risk-attributable global disability-adjusted life years (DALYs) [ 6 ]. There is strong epidemiological evidence for positive associations between exposures to air pollution PM from household or ambient sources and TB incidence rates as well as TB-associated mortality [ 7 , 8 ]. Experimental studies have provided biological plausibility of this epidemiological evidence. PM impairs immune system functions and decreases pathogen response gene expression. Experimental studies from our group [ 5 , 9 – 13 ] and others [ 14 – 18 ] have reported PM exposure effects on immune cell responses to Mycobacterium tuberculosis (Mtb) infection. Experimental exposure to urban PM impairs various key immune responses to Mtb infection [ 8 ] in A549 respiratory epithelial cells, human bronchoalveolar cells (BAC) and peripheral blood mononuclear cells (PBMC) [ 5 , 9 – 12 ]. Our findings also suggest that inhalation-acquired PM load in human alveolar macrophages (AM) influences these cells' responsiveness to Mtb [ 5 ]. AM reside in the bronchoalveolar spaces and smaller airways and take up inhaled fine particulate matter (PM 2.5 ; PM with aerodynamic diameter < 2.5 microns). Solid PM cannot be broken down by AM easily [ 19 ]. The AM PM load has been used as a chronic exposure marker [ 20 ], showing associations with various disease conditions (asthma, diabetes) [ 21 ] and biomarkers such as low-density lipoprotein (LDL) [ 22 ]. However, associations between PM load in AM and other health responses are not well understood. The aim of this study was to further explore PM load in AM as a biomarker of PM exposures and associations between this load, estimated ambient exposures, and other biomarkers of oxidative stress and cardiovascular risk. PM load in AM and other biomarker levels were measured from study participants recruited in the MCMA [ 5 , 13 ]. Associations between the biomarker levels and estimated ambient PM 2.5 concentration using land use regression (LUR) were assessed. Methods Research and Ethics Approvals This observational research study was approved by the scientific and bioethics committees of the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” in Mexico City, Mexico (INER, protocol B22-12), and the Institutional Review Board of Rutgers, The State University of New Jersey (protocol 2012001381) in New Brunswick, NJ. All experimental protocols were approved by the respective committees and conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants for the study protocol. Study population, location, and sample collection Candidates were recruited for participation in the U.S. National Institute for Environmental Health Sciences (NIEHS)-funded research project entitled “Air Pollution Particle Effects on Human Antimycobacterial Immunity” at the Universidad Autónoma Metropolitana (UAM) and the Instituto Nacional de Enfermedades Respiratorias (INER), in Mexico City between 2013 and 2018. Study participants (n = 53) were healthy, HIV-seronegative adults (age = 21–60 years), male (n = 25) and female (n = 28) nonsmoking (urine cotinine-negative) students of the UAM and residents (> 6 months) of the Iztapalapa and Iztacalco municipalities in the MCMA (Fig. 1 ). For the study of air pollution particle effects on human antimycobacterial immunity [ 23 ], study participants completed three study visits for consent, a physical exam, and a bronchoalveolar lavage (BAL). At consent, subjects completed questionnaires to obtain demographic information, time-activity information (e.g., commuting in the city, outdoor activities, mode of transportation), household exposures (e.g., housing type, ventilation, cooking fuel, etc.), alcohol use, smoking status, and environmental tobacco smoke exposure. At the physical exam visit, subjects underwent medical history taking, a lung function test, and a chest x-ray. At the third visit, BAL was conducted to collect BAL fluids and bronchoalveolar cells (BAC) and venipuncture to collect peripheral blood mononuclear cells (PBMC). The venipuncture blood sample was used for serum biomarker analysis at the clinic. Spot urine samples were collected at each visit (3 times). BALF, plasma, and urine samples were analyzed for biomarkers, as explained below (2.4. Biomarker Analysis). Particulate Matter Load in Alveolar Macrophages Cytospin preparations from BAC of the study participants (n = 53) were prepared by centrifugation (800 x g) of 0.2 x 10 6 BAC onto a glass slide using a cytocentrifuge (Wescor Cytopro 7620 Cytocentrifuge, Wescor INC Logan, UT, USA). Following modified Wright’s staining with Accustain (Sigma Aldrich, St Louis, MO), the nuclear morphology of BAC and proportions of AM, neutrophils and alveolar lymphocytes were characterized on thin-layer cytospin preparations. Cytospin color photographs (Olympus DP71 digital microscope camera, Tokyo, Japan) were obtained by digital bright field microscopy (1000×, Olympus BX51 digital microscope, Tokyo, Japan). PM load in AM was assessed using the CellProfiler, an open-source cell image analysis tool (CellProfiler 4.0, Broad Institute, Cambridge, MA), which uses modular processing pipelines, allowing users to automate image analysis [ 24 ]. Imported images were separated into three components using hue, saturation, and value channels. The AM object was identified based on size (10 µm diameter) and shape. Form factor (> 0.5, the ratio between the area and the perimeter; a perfectly circular object has a form factor of 1) and eccentricity (< 0.8, the ratio of the distance between the foci of the ellipse and its major axis length, equals 1 for a line segment and 0 for a circle) values were used to select for round-shaped objects. Then, PM objects larger than 0.4 µm were identified within the detected AM objects. The PM object size limit (0.4 µm) was selected based on the maximum resolution of the digital microscope. Finally, estimated variables are the fraction of AM containing PM (%AMPM), absolute PM area within AM (PM area in AM [µm 2 ]), and AM size (µm 2 ). The developed CellProfiler pipeline and example pictures are shown in Supplementary Fig. S1 . Biomarker analysis BALF (n = 53), plasma (n = 27), and urine (n = 53) samples were shipped to Duke University and stored at -20°C or -80°C until laboratory analysis. Based on earlier research by our group [ 25 , 26 ], we assessed concentrations of P-selectin, a marker of platelet activation; C reactive protein (CRP), a marker of inflammation; von Willebrand’s Factor (vWF), an index of endothelial dysfunction or damage; and fibrinogen, a blood clotting agent using commercial ELISA kits following manufacturers’ instructions (Sigma-Aldrich, MO, USA). Malondialdehyde (MDA), a marker of lipid damage and oxidative stress, in BAL and urine samples was analyzed using an HPLC-fluorescence detection method following thiobarbituric acid derivatization, as described previously [ 25 ]. Urinary 8-OHdG concentrations, a marker of oxidative stress, were measured with LC-MS/MS (TSQ Quantum Access Max, Thermo Fisher Scientific, MA, USA) after solid phase extraction by Bond Elut-certify cartridge (500 mg, 6ml, Agilent Technologies, CA, USA), as described previously [ 26 ]. Urinary creatinine concentrations were measured using colorimetric method using commercial kits (Cayman Chemical, MI, USA). Urinary biomarker concentrations were normalized by creatinine concentration. Land Use Regression (LUR) Model To stratify the PM exposure levels of the study participants for PM load in AM and biomarker analysis, we developed a LUR model based on the methodology described earlier by our group [ 27 ]. The least absolute shrinkage and selection operator (LASSO) method was applied to select the best LUR model using R 4.1.3 using the lmmlasso package [ 28 ]. The present study improved the previous LUR model by adding PM 2.5 monitoring data at the participants' homes (n = 21, red stars in Fig. 1 ) in addition to the MCMA compliance monitoring sites (n = 37, black dots in Fig. 1 ). Ambient PM 2.5 concentrations were obtained from the Red Automática de Monitoreo Atmosférico (RAMA) stations over the 2011–2018 period (37 stations across the MCMA) [ 29 ]. In addition, we obtained daily ambient PM 2.5 concentrations on the roof sites of the homes of our MexAir study participants (n = 21). For that purpose, the MexAir sampling suitcase containing air quality monitoring instruments ( Supplementary Fig. S2) was placed for a day on the roof of the study participant houses. A Sioutas cascade impactor (SKC, PA, USA) with Teflon filter (0.5 µm, 25 mm, Zefluor supported PTFE, Pall, NY, USA) and SKC Leland Legacy sampling pump (9 LPM, SKC, PA, USA) were used to measure 24-hour PM 2.5 concentrations. For gravimetric analysis of the Teflon filters, filters were weighed before and after PM sampling in a clean, temperature (20–23°C) and humidity (30–40%) controlled weighing facility at the UAM. Other LUR model variables were collected from the RAMA, the meteorology and solar radiation monitoring network (Red de Meteorología y Radiación Solar; REDMET, n = 21) and the atmospheric deposition monitoring network (Red de Depósito Atmosférico, REDDA, n = 16) stations for hourly temperature (T), relative humidity (RH), and wind speed (WS). Google Traffic data was used for typical hourly traffic density information (TD). Land use information and elevation were downloaded from the United States Geological Survey (USGS). Traffic density, land use, and elevation variables within 500 m diameter circular buffer around the PM 2.5 monitoring locations (i.e., 37 RAMA and 21 MexAir participant home sites) were selected. All hourly variables were averaged to daily time resolution to develop LUR model. Statistical analysis Descriptive statistics for participant demographics, PM load in AM markers, PM 2.5 exposures using our LUR model, and biomarker concentrations for BALF, plasma, and urine were estimated. Chi-square and t-tests were used to compare the measurements between low, medium, and high AMPM load groups. Associations between %AMPM, plasma biomarkers, and estimated average PM 2.5 concentrations over different periods (1 day to 6 months) were analyzed using linear regression. Regression analyses were also conducted to study the relationship between short-term PM concentrations (1–7 days before urine sample collection) and urinary oxidative stress markers. Results Study Participants The characteristics of the 53 study participants (25 male and 28 female, age range: 21–60 years) are described in Table 1 . Mean age and body mass index (BMI) were 29.6 years and 26.2 weight [Kg]/height [m 2 ], respectively. Ninety-two percent of the participants had received a college or higher education. Sixty percent of the participants were college students at the time of the study. Twenty-six percent of the participants’ houses had mechanical ventilation systems (e.g., mechanical range hood). Eighteen participants indicated that they had smokers in their households. Table 1 Participant characteristics (n = 53) Characteristics Value Sex, Female (n) 28 Sex, Male (n) 25 Age (years) 26 (23–32)* Body Mass Index (BMI, weight/m 2 ) 24.7 (23.5–28.7)* Educational attainment (n) High school or less 2 College and technical school 47 Graduate degree 2 No response 2 Occupation (n) Don’t work 1 Work indoors 5 Work outdoors 8 Student 26 Student, part-time 13 Residence type (n) Apartment 19 Single Family Home 17 Family Compound 16 Other 1 Environmental condition (Yes, n) House with ventilation system 14 Recent alcohol use 2 Environmental tobacco smoke 18 *median (interquartile range) PM load in AM and biomarker levels PM load in AM was estimated in 53 participant samples (Fig. 2 ) using the automated image analysis method. Overall, a median of 183 (interquartile range [IQR] 134–263) AM cells were identified on each participant’s cytospin image (61–71 images per participant). PM was detected in 62.4% (50.0–73.0%) of AM (%AMPM), and the median and interquartile range of the PM area in AM was 1.082 µm 2 (0.607–1.855 µm 2 ). Table 2 shows PM load in AM results, mean LUR-estimated ambient PM 2.5 concentrations at different periods prior to the BAL date and basic demographics. PM load groups were categorized into tertiles using %AMPM estimates: low (n = 18, 66 percentile). Participants with higher %AMPM had larger PM areas in AM. In the lowest tertile, 46.2% of AM had PM with a median PM area of 0.427 µm 2 , while in the highest tertile 77.7% of AM contained PM with a median area of 2.274 µm 2 (t-test, p = 0.00). PM 2.5 exposure estimated using the LUR model on BAL date (0-day) showed slightly higher concentrations in the AM with high PM load group than in the low PM load group (t-tests, p > 0.33). Age, BMI, and AM size were within similar ranges in the different PM load groups. The medium PM load group included more female participants (70.5% female) than other groups (Chi-Square, p = 0.49). Table 2 . PM load in AM, ambient PM 2.5 concentrations, demographic information (sex, age, BMI) for All, Low, Medium, and High AMPM load groups (median and interquartile range [1Q-3Q], among 53 Mexico City residents, 2013-2018) Parameter All (n=53) Low AMPM load (n=18) Medium AMPM load (n=17) High AMPM load (n=18) %AMPM (%) 62.4 (50.0-73.0) 46.2 (38.6-49.9) a 62.4 (59.2-65.0) b 77.7 (73.2-81.9) ab PM area in AM (µm 2 ) 1.082 (0.607-1.855) 0.427 (0.351-0.604) a 1.158 (1.03-1.279) b 2.274 (1.594-3.399) ab AM size (µm 2 ) 339.6 (264.2-430.8) 338.7 (237.3-433.2) 384.6 (288.0-430.8) 326.3 (289.6-389.8) Estimated ambient PM 2.5 BAL date, 0-day (µg/m 3 ) 26.58 (18.87-34.48) 25.66 (19.84-31.75) 27.07 (16.21-35.53) 29.58 (24.13-35.75) 3-month average (µg/m 3 ) 26.17 (21.62-29.41) 26.54 (23.15-30.29) 26.17 (22.79-28.98) 26.45 (20.56-29.03) 6-month average (µg/m 3 ) 25.68 (23.35-28.48) 26.29 (23.96-29.61) 25.36 (24.10-27.93) 26.31 (22.34-28.51) Sex (male: female) 25:28 8:10 5:12 10:8 Age (years) 26 (23-32) 25 (23-29) 26 (23-33) 26 (23-35) BMI (weight/m 2 ) 24.7 (23.5-28.7) 25.6 (23.7-27.6) 24.0 (23.1-28.4) 25.4 (23.9-29.8) %AMPM: Fraction of AM containing PM; PM area in AM: absolute PM area within AM; Estimated ambient PM 2.5 : ambient PM 2.5 concentrations were estimated for the date of BAL, 3- and 6-month averages prior to BAL date; BMI: Body Mass Index; a, b: statistical significance at α =0.05 level Correlation analysis results between the PM load in AM markers and bronchoalveolar lavage fluid (BALF), plasma, and serum biomarker levels are shown in Fig. 3 . von Willebrand factor (vWF, correlation coefficient [ρ] = 0.42, p = 0.002), Lactate dehydrogenase (LDH, ρ = 0.19, p = 0.045), fibrinogen (ρ = 0.19, p = 0.188), and C-Reactive Protein (CRP, ρ = 0.14, p = 0.336) showed weak to moderate positive correlations with %AMPM (ρ = 0.14–0.42). PM area with AM showed weaker positive correlations with vWF, LDH, fibrinogen, and CRP than %AMPM (ρ = 0.13–0.28). The two biomarkers, vWF and LDH, showed a high correlation with PM load in AM markers were known to indicate inflammation and cardiovascular disease [ 30 , 31 ]. Other markers correlated with PM load in AM markers including fibrinogen, CRP and serum glutamic pyruvic transaminase (TGP) were also [ 32 – 34 ]. Interestingly, AM size were negatively correlation with the inflammation and cardiovascular disease markers (i.e., fibrinogen, vWF, and CRP), but the mechanism is unknown. Associations between ambient PM 2.5 Concentrations and PM Load in AM This study refined the LUR model developed in our previous study [ 27 ]. The current model included additional ambient PM 2.5 monitoring data measured from the rooftops of the homes of 21 MexAir study participants. Compared to the previous study, the LUR model for this study improved its performance from R 2 = 0.49 to R 2 = 0.81 ( Supplemental Figure S3 , Table S1 ). Mean ambient PM 2.5 concentrations were estimated using the refined LUR model to measure its association with PM load in AM. Associations between %AMPM and mean LUR-estimated ambient PM 2.5 concentrations (µg/m 3 ) are shown in Fig. 4 . Considering the lifetime of AM, we estimated the PM 2.5 concentrations on BAL dates (0 days [0D]) and their averages at 7-day (7D), 1-month (1M), 3-months (3M), and up to 6-months (6M) prior to the BAL date [ 35 ]. Medium and high PM loads in AM groups did not show a significant association between %AMPM and estimated ambient PM 2.5 concentrations. Low PM load in AM group with less than 1-month PM 2.5 averaging time did not show significant associations ( p = 0.14–0.88). Averaged PM 2.5 concentrations over longer than a 3-month period, however, showed a significant association with %AMPM ( p < 0.005). Increments of 10 µg/m 3 of 6-month averaged PM 2.5 concentrations in the low PM load group were associated with increased the %AMPM by 8.06% ( p = 0.000, 95% confidence interval = 6.78–9.36%). Associations between PM Load in AM and Biomarkers Figure 5 shows associations between %AMPM and vWF [von Willebrand factor, marker of inflammation-related thrombosis [ 36 ], µg/ml], LDH (U/L), fibrinogen (µg/ml), and CRP (C-Reactive Protein, µg/ml) concentrations from all participants. The three plasma and a serum biomarker showed positive associations with %AMPM. The vWF ( p = 0.016) and LDH ( p = 0.026) concentrations showed a statistically significant relationship with %AMPM. BALF Malondialdehyde and plasma p-selectin showed negative associations with the levels of %AMPM (Table 3 ). Other biomarker analysis results are tabulated in Supplementary Table S2 . Two urinary oxidative stress markers (Malondialdehyde and 8OHdG) didn’t show clear trends with AMPM load levels. However, %AMPM load levels altered the associations between PM2.5 and urinary oxidative stress markers ( Supplementary Table S3 ). Table 3 Bronchoalveolar lavage fluid (BALF, n = 53), plasma (n = 27), serum (n = 53), urine (n = 53), and other (n = 53) biomarker levels for All, Low, Medium, and High AMPM load participant groups (median [1Q-3Q]) Media Parameter All Low AMPM load Medium AMPM load High AMPM load BALF MDAf (nmol/L) 157.7 (75.89-263.14) 172.3 (72.94–261.7) 159.2 (95.47–285.2) 133.5 (79.55–212.2) Plasma P-Selectin (ng/ml) 41.39 (26.67–59.46) 46.80 (30.50-60.15) 42.20 (25.90-65.21) 32.50 (19.80-42.91) CRP (µg/mL) 1.569 (0.644-4.500) 0.763 (0.622–1.448) 1.935 (0.609–5.623) 2.508 (1.673–5.753) vWF (µg/mL) 11.46 (7.892–18.08) 8.501 (7.634–14.73) 11.46 (10.34–17.15) 16.40 (13.79–19.97) Fibrinogen (µg/mL) 2.928 (2.218–4.093) 2.522 (2.231–3.526) 2.892 (1.930–3.860) 3.709 (2.839–5.558) Serum LDH (U/L) 141 (128–161) 135 (123–151) 140 (128–161) 148 (140–164) Urine 8OHdG (ng/mg creatinine) 22.03 (6.25–81.45) 27.99 (5.917–93.77) 19.39 (6.401–63.84) 33.22 (12.62–78.38) MDAf (µM/mM creatinine) 0.091 (0.065–0.117) 0.090 (0.062–0.133) 0.108 (0.065–0.123) 0.086 (0.072–0.112) MDAf: free Malondialdehyde; P-Selectin: type-1 Transmembrane Protein (CD62P); CRP: C-Reactive Protein; vWF: von Willebrand factor; LDH: Lactate dehydrogenase; 8OHdG: 8-Hydroxyguanosine; other biomarker analysis results are tabulated in Supplementary Table S2 Discussion Household and ambient PM exposure is known to cause a multitude of adverse health outcomes [ 1 ]. Identifying biomarkers of PM exposures and their roles within the biological processes of the exposed persons is a topic of great research effort. This study explored correlations between ambient PM exposures and PM load in AM and the consequences of PM accumulation in human AM on various biomarkers. To our knowledge, this study is the first to explore associations between PM load in AM, exposure to LUR-estimated ambient PM 2.5 exposure levels, and biomarkers in the BALF, plasma, and urine samples of healthy adult residents of the MCMA. In addition, we obtained daily ambient PM 2.5 concentrations on the roof sites of the homes of our MexAir study participants (n = 21). Being able to add additional PM level data points obtained from the roof sites of our study participant’s residences, we were able to develop a further refined LUR model that dramatically improved compared with its earlier usage in performance [ 27 ]. The model accuracy was improved from R 2 = 0.41 to R 2 = 0.81 (see Supplementary Table S1 ). This study, also for the first time, demonstrates utilization of the CellProfiler image analysis software for automatic assessment of PM load in human AM. PM 2.5 exposure was assessed by averaging PM 2.5 concentrations for 6-month period prior to each study participant’s BAL dates. The 6-month averaged PM 2.5 concentrations were positively correlated with %AMPM in the low PM load group ( p = 0.000). Our findings are supported by a study of type 1 or 2 diabetic patients in Leuven, Belgium, in which a positive correlation was found between modeled 6-month average PM 10 exposure and PM area in AM [ 22 ]. In another study in Leicester, United Kingdom, children (n = 22, 3 months to 16 years) without respiratory symptoms who resided near primary roads with dense traffic had higher %AMPM (10%) than those who lived on quiet residential streets (%AMPM = 3%) [ 37 ]. In other studies, indoor biomass burning increased PM load in AM [ 38 , 39 ]. In a study assessing the longevity of PM load in AM, AMPM clearance half-lives were 54 days on average in persons who had moved from a highly polluted location (mean annual PM 10 108 µg/m 3 ) to Leuven, Belgium (mean annual PM 10 23 µg/m 3 ) [ 40 ]. In that study, persons with high AMPM load (90th percentile) needed a longer time (mean 116 days) to clear PM deposited in AM. Our findings suggest that high ambient PM 2.5 levels in the MCMA underlie the various degrees of PM loads in AM. Interestingly, in the medium and high PM load groups no associations between the estimated ambient PM 2.5 concentrations and %AMPM were observed. Lacking correlation between PM exposure and PM load in the medium and high PM load groups may result from PM exposure saturation effects, or different AM ages and maturity stages. To the best of our knowledge, the processes of PM accumulation in and clearance from AM are still poorly understood. Previous studies have used absolute PM area within the AM to assess PM load in AM [ 22 , 39 – 42 ]. Our findings indicate that the %AMPM is a useful biological marker of long-term PM exposure and likely related to adverse health impacts. The current study showed a stronger correlation between %AMPM and plasma biomarkers than absolute PM size in AM. We observed a significant positive correlation between %AMPM and plasma inflammation biomarker vWF ( p = 0.016) and serum cardiovascular marker LDH ( p = 0.026). %AMPM showed trends toward positive associations with CRP ( p = 0.182) and fibrinogen ( p = 0.405). These observations provide biological plausibility toward a recent EPA report that stated a causal relationship between PM 2.5 exposure and cardiovascular disease [ 1 ]. The %AMPM was negatively correlated with P-selectin, and other markers were positively correlated. This study observed that %AMPM positively correlated with plasma vWF levels ( p = 0.016). In other studies, vWF and fibrinogen levels were associated with short-term air pollution exposures. Short-term PM 2.5 exposure (< 7 days) significantly increased plasma vWF by 0.41% (95%CI: 0.11–0.71) per 10 µg/m 3 increments [ 43 ]. In a murine study, sub-chronic exposure (25–26 days) to tunnel air pollutants increased vWF levels [ 44 ]. Plasma vWF is a predictor of adverse cardiac events and is closely related to systemic inflammation and cardiovascular disease [ 31 , 36 , 45 ]. In addition to vWF, our data also showed a positive relationship between %AMPM and serum LDH (p = 0.026). LDH is known marker of cardiovascular disease [ 30 ]. Plasma LDH levels shown to be higher in diabetes patients than the general public [ 46 ]. A previous study showed positive correlations between PM load in AM and diabetes [ 20 ]. In our study, %AMPM was not correlated with plasma fibrinogen levels ( p = 0.405). In other studies, short-term PM exposures (< 7 days) showed strong positive associations with fibrinogen levels [ 47 , 48 ], while long-term exposures (1 year) to air pollutants did not affect fibrinogen levels [ 47 , 49 , 50 ]. Fibrinogen is a known marker of cardiovascular disease [ 51 ]. This study observed positive correlations between %AMPM and plasma CRP levels ( p = 0.182). Others showed that plasma CRP levels were significantly correlated with long-term PM exposures [ 50 , 52 ]. CRP is related to tissue damage, systemic inflammation, respiratory (e.g., COPD) [ 53 ], and cardiovascular disease [ 54 ]. In contrast to vWF, fibrinogen and CRP, which were positively correlated with %AMPM in this study, were negatively correlated with %AMPM and P-selectin. The impact of air pollution exposures on P-selectin levels is not well understood. P-selectin is a cellular adhesion molecule strongly associated with cardiovascular disease (CVD) [ 55 , 56 ] development and varies by age group [ 56 ]. Further study will be needed to understand better the relationships between air pollution exposure, P-selectin levels, and CVD risk. The findings of this study indicate that %AMPM is more strongly correlated with biomarkers than PM area in AM. This is consistent with our earlier findings in human BAC and PBMC, in which BAC with greater %AMPM more strongly suppressed IL-1β, a key antimycobacterial cytokine, than BAC with lower %AMPM [ 5 ]. Small amounts of PM within AM were shown to alter AM functions such as antigen presentation [ 57 ]. As shown before by our group [ 5 ], the PM load of individual AM can vary widely both within individual BAC samples of study participants and between study participants. Our findings provide new insights into short-term (< 1 week) PM exposure effects in humans. Urinary biomarkers are indicators of shorter-term environmental exposures that include PM [ 25 , 58 ]. Here, we observed increasing significance between PM exposures and urinary oxidative stress markers with higher %AMPM. Even though the relationships were not significant, the results support our earlier findings that %AMPM results in the suppression of key cytokine response to Mtb for the high AMPM load group [ 5 ]. A limitation of the current study is the relatively small participant group and its limited demographic diversity from a single municipality with its specific PM exposure environment. Most study participants were students at the UAM with similar air pollution exposure, behaviors, lifestyles, and mobility radii. The ‘relative homogeneity’ of the study population may have impacted our findings. Interestingly, despite reports of similar behavioral patterns ( Supplemental Table S4 ), AM from study participants often had widely varying PM loads. Further, the cytospin images analyzed here were prepared using standardized processes that may miss PM spots in AM due to the limitations of two-dimensional images. AM and PM size could have been altered during cytospin preparations. Regardless of these limitations, this study observed significant correlations between long-term low level PM 2.5 exposures and %AMPM, and between %AMPM and vWF, and LDH. Future research may further identify the mechanisms by which PM exposure and PM load in AM induce adverse health effects. Such studies may also investigate the role of PM composition differences (source apportionment) on the various cellular responses and health outcomes of interest. Future studies could also consider using three-dimensional (3D) cell image analysis [ 59 ]. The 3D cell imaging techniques with automated cell image analysis processes used in this study provide opportunity for further research studies of PM effects on cellular processes. Studies of dose-response relationships between PM load and AM function, as well as AM phenotypes are needed to follow up on our observations. Conclusion This study explored associations between PM load in AM, PM 2.5 exposure, and biomarkers among residents of MCMA. The results provide novel insights that PM deposited in AM might have altered physiological functions of AM, such as signaling molecule production (e.g., inflammatory cytokines), causing increased systemic inflammation and cardiovascular disease risk. Study results also suggest that even low-level PM exposures lead to uptake of AM by PM, which might increase inflammatory and cardiovascular disease risks. Our findings emphasize the importance of World Health Organization (WHO)’s new air quality recommendation to work towards an annual ambient PM 2.5 concentration of 5 ug/m 3 [ 60 , 61 ] to protect public health. Declarations Competing Interests Statement The author(s) declare no competing interests. Author Contribution Son, Y.: Data curation, Formal analysis, Methodology, Investigation, Validation, Visualization, Writing – original draft, review, & editing; Carranza, C., Subramhanya, S., Torres, M., and Zhang, J.: Data curation, Formal analysis, Methodology, Investigation, Validation, Writing – review & editing; Gardner, C., Jones, L., Meng, Q., Osornio-Vargas, A., and Orman-Strickland, P.: Investigation, Validation, Writing – review & editing, O’Neill, M.: Investigation, Validation; Black, C.: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – review & editing; and Schwander, S.: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original draft, review, & editing. Acknowledgement We thank all study participants for their contribution to this work. 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Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles. J. Expo. Sci. Environ. Epidemiol. 34 (3), 1–9 (2023). Chen, J. & Hoek, G. Long-term exposure to PM and all-cause and cause-specific mortality: a systematic review and meta-analysis. Environ. Int. 143 , 105974 (2020). Velasco, R. P. & Jarosińska, D. Update of the WHO global air quality guidelines: Systematic reviews–An introduction. Environ. Int. 170 , 107556 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6430851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452096633,"identity":"2a0aac25-c1bc-4ea4-aef9-29d68616a46b","order_by":0,"name":"Yeongkwon Son","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYNCCAzBGBTMDG5CSIEHLGZK1MLYxg2m8WgyONx97+OXMYTlzBvaHjwvnWefzMTAfvM2DT8uZY+nGMjcOG1s28Bgbz9yWbtnGwJZsjVfLjRwzaYkPtxM3HOBhk+bddtiAjYHHTBqvlvtvYFrYn0nzzgFp4f+GX8sNHjPJDzdAWhjMpHkbwLaw4dUieSYtTZrhzH9jg8NAv/AcSzdgY2YztpyDRwvf8cPHJH8cS5MzON7+8DFPjbWBfHvzwxtv8GhROMDAwAx2BjNMiBmnYgiQbwBG4A8CikbBKBgFo2CEAwBCg0i1boWpkwAAAABJRU5ErkJggg==","orcid":"","institution":"Desert Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Yeongkwon","middleName":"","lastName":"Son","suffix":""},{"id":452096634,"identity":"f441258a-ebb3-495a-b7b7-3a407a388534","order_by":1,"name":"Claudia Carranza","email":"","orcid":"","institution":"Laboratory of Immunobiology of Tuberculosis, National Institute of Respiratory Diseases \"Ismael Cosío Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Carranza","suffix":""},{"id":452096635,"identity":"9556eecb-eb3b-4668-98f7-813fe2d32fc1","order_by":2,"name":"Sanjana Subramhanya","email":"","orcid":"","institution":"Department of Environmental and Occupational Health and Justice, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Sanjana","middleName":"","lastName":"Subramhanya","suffix":""},{"id":452096636,"identity":"e7bd33d0-0a84-4434-ae53-682c078633d1","order_by":3,"name":"Carol Gardner","email":"","orcid":"","institution":"Department of Environmental and Occupational Health and Justice, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"","lastName":"Gardner","suffix":""},{"id":452096637,"identity":"201e739a-38da-4466-ae25-36070d2560a5","order_by":4,"name":"Kathleen Black","email":"","orcid":"","institution":"Department of Environmental and Occupational Health and Justice, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Kathleen","middleName":"","lastName":"Black","suffix":""},{"id":452096641,"identity":"e4559652-758a-4d85-98f9-a3926c7aa72a","order_by":5,"name":"Laura Jones","email":"","orcid":"","institution":"Department of Environmental and Occupational Health and Justice, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Jones","suffix":""},{"id":452096644,"identity":"bf11ce3d-80da-4e41-977c-d73d167b358a","order_by":6,"name":"Qingyu Meng","email":"","orcid":"","institution":"Desert Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Qingyu","middleName":"","lastName":"Meng","suffix":""},{"id":452096647,"identity":"18f2bfb4-a926-45e1-81d2-b533e0e64bb4","order_by":7,"name":"Martha Torres","email":"","orcid":"","institution":"Laboratory of Immunobiology of Tuberculosis, National Institute of Respiratory Diseases \"Ismael Cosío Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"","lastName":"Torres","suffix":""},{"id":452096649,"identity":"13ca6f01-556a-4e8c-9ed1-fb8acda6acc9","order_by":8,"name":"Alvaro Osornio Vargas","email":"","orcid":"","institution":"Department of Pediatrics, University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Alvaro","middleName":"Osornio","lastName":"Vargas","suffix":""},{"id":452096650,"identity":"fb9f92c4-72d6-4b2c-a6af-7172a066465c","order_by":9,"name":"Junfeng (Jim) Zhang","email":"","orcid":"","institution":"Duke Global Health Institute, Duke University","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"(Jim)","lastName":"Zhang","suffix":""},{"id":452096651,"identity":"3bd6d1f3-8163-4c56-889b-d16c930aadda","order_by":10,"name":"Marie S. O'Neill","email":"","orcid":"","institution":"Department of Epidemiology, School of Public Health, University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"S.","lastName":"O'Neill","suffix":""},{"id":452096652,"identity":"c12f6d9f-d779-4bf6-b98a-13a070d5aa3f","order_by":11,"name":"Pamela Ohman Strickland","email":"","orcid":"","institution":"Department of Biostatistics and Epidemiology, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"Ohman","lastName":"Strickland","suffix":""},{"id":452096653,"identity":"61816aea-cda4-4e53-af57-1cc2a2477a29","order_by":12,"name":"Stephan Schwander","email":"","orcid":"","institution":"Department of Environmental and Occupational Health and Justice, School of Public Health, Rutgers University","correspondingAuthor":false,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Schwander","suffix":""}],"badges":[],"createdAt":"2025-04-11 19:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6430851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6430851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82351701,"identity":"7d36cb91-6656-4d42-a0e6-40bf272f289b","added_by":"auto","created_at":"2025-05-09 10:59:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":163249,"visible":true,"origin":"","legend":"\u003cp\u003eThis study area map shows the MéxicoCity compliance monitoring stations (black circles), and the monitoring locations at the homes of the study participants (red stars) within the MCMA\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/76b467f7c54c60aee953719a.png"},{"id":82353251,"identity":"59446cff-6f3c-4847-b5d2-e2cba799876d","added_by":"auto","created_at":"2025-05-09 11:07:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":956265,"visible":true,"origin":"","legend":"\u003cp\u003eBrightfield microscopy (magnification x 1000) of alveolar macrophages (AM) with particulate matter (PM) inclusions. (a) Overview of a typical BAC cytospin preparation with AM. (b) and (c) Magnified AM details from panel (a). Perimeters of AM objects are shown as light blue solid lines. Perimeters of PM objects are shown with red lines\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/9b480e7c45ea9ac71d703e2a.jpeg"},{"id":82350258,"identity":"675f27c1-27bc-49ce-ac5c-0a18e3efbc3d","added_by":"auto","created_at":"2025-05-09 10:51:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":727247,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix between PM load in AM and other biomarkers measured in this study. %AMPM: Fraction of AM containing PM; PM area in AM: absolute PM area within AM; AM size: absolute size of AM; BALF: bronchoalveolar lavage fluid; MDAf: free malondialdehyde level; P-selectin: plasma type-1 Transmembrane Protein (CD62P); CRP: C-Reactive Protein; vWF: von Willebrand factor level; PT: Prothrombin time; PTT: Partial thromboplastin time; TGO: Serum glutamic oxaloacetic transaminase; TGP: Serum glutamic pyruvic transaminase; LDH: Lactase dehydrogenase\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/dddba84cdb1a30b303893b20.jpeg"},{"id":82350263,"identity":"3881da4a-f5c6-44db-bc11-fdd1de296cc8","added_by":"auto","created_at":"2025-05-09 10:51:18","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":363337,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between %AMPM and mean LUR-estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. Fractions of PM-containing AM (%AMPM [%]) per 10 µg/m\u003csup\u003e3\u003c/sup\u003e increment of LUR-estimated mean PM\u003csub\u003e2.5\u003c/sub\u003e concentrations are shown at various lags (0 days to 6 months) before the bronchoalveolar lavage date and by AMPM load level\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/b9626747bb139d38e4d7a8cf.jpeg"},{"id":82350261,"identity":"b643f6b6-7c23-4716-ae39-0d7ef1836fca","added_by":"auto","created_at":"2025-05-09 10:51:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1058470,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between the fraction of PM containing AM (%AMPM [%]) and plasma and serum biomarker levels, (a) plasma vWF (µg/ml), (b) serum LDH (U/L), (c) plasma fibrinogen (µg/ml), and (d) plasma CRP (µg/ml) concentrations. The blue lines and shades indicate the regression lines and confidence intervals.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/2d88219b5856ddf1e19f5ea2.png"},{"id":82355651,"identity":"1866c991-d56b-4d9f-ab92-dbc122cad200","added_by":"auto","created_at":"2025-05-09 11:15:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4118565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/d07f3d3c-adcc-45dc-b790-acb3ed02fab0.pdf"},{"id":82350266,"identity":"7cc76f20-c78c-460f-9190-cf899b75ed3b","added_by":"auto","created_at":"2025-05-09 10:51:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4567995,"visible":true,"origin":"","legend":"","description":"","filename":"MexAirAMPMsupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6430851/v1/3d60068566baacdeddc04a16.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlations between Human Alveolar Macrophage Particulate Matter Load, Air Pollution Particulate Matter Levels, and Systemic Inflammation Markers in Mexico City","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInhalation exposure to air pollution particulate matter (PM) represents a substantial global public health concern because of its numerous adverse health effects, including respiratory, cardiovascular, nervous system, and cancer morbidity and mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Rapid global urbanization has increased exposure to PM from household solid fuel combustion and ambient sources [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Mexico City Metropolitan Area (MCMA) is one of the biggest megacities and experiences high levels of air pollution, including from PM [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As one of the most populated urban environments, the MCMA also deals with tuberculosis (TB) as a considerable public health concern [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePM exposure from household (solid fuel) and ambient combustion sources is the top level-3 risk factor ordered by risk-attributable global disability-adjusted life years (DALYs) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. There is strong epidemiological evidence for positive associations between exposures to air pollution PM from household or ambient sources and TB incidence rates as well as TB-associated mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Experimental studies have provided biological plausibility of this epidemiological evidence.\u003c/p\u003e \u003cp\u003ePM impairs immune system functions and decreases pathogen response gene expression. Experimental studies from our group [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and others [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have reported PM exposure effects on immune cell responses to \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (Mtb) infection. Experimental exposure to urban PM impairs various key immune responses to Mtb infection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] in A549 respiratory epithelial cells, human bronchoalveolar cells (BAC) and peripheral blood mononuclear cells (PBMC) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our findings also suggest that inhalation-acquired PM load in human alveolar macrophages (AM) influences these cells' responsiveness to Mtb [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAM reside in the bronchoalveolar spaces and smaller airways and take up inhaled fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e; PM with aerodynamic diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5 microns). Solid PM cannot be broken down by AM easily [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The AM PM load has been used as a chronic exposure marker [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], showing associations with various disease conditions (asthma, diabetes) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and biomarkers such as low-density lipoprotein (LDL) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, associations between PM load in AM and other health responses are not well understood.\u003c/p\u003e \u003cp\u003eThe aim of this study was to further explore PM load in AM as a biomarker of PM exposures and associations between this load, estimated ambient exposures, and other biomarkers of oxidative stress and cardiovascular risk. PM load in AM and other biomarker levels were measured from study participants recruited in the MCMA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Associations between the biomarker levels and estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentration using land use regression (LUR) were assessed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch and Ethics Approvals\u003c/h2\u003e \u003cp\u003e This observational research study was approved by the scientific and bioethics committees of the Instituto Nacional de Enfermedades Respiratorias \u0026ldquo;Ismael Cos\u0026iacute;o Villegas\u0026rdquo; in Mexico City, Mexico (INER, protocol B22-12), and the Institutional Review Board of Rutgers, The State University of New Jersey (protocol 2012001381) in New Brunswick, NJ. All experimental protocols were approved by the respective committees and conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants for the study protocol.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population, location, and sample collection\u003c/h3\u003e\n\u003cp\u003e Candidates were recruited for participation in the U.S. National Institute for Environmental Health Sciences (NIEHS)-funded research project entitled \u0026ldquo;Air Pollution Particle Effects on Human Antimycobacterial Immunity\u0026rdquo; at the Universidad Aut\u0026oacute;noma Metropolitana (UAM) and the Instituto Nacional de Enfermedades Respiratorias (INER), in Mexico City between 2013 and 2018. Study participants (n\u0026thinsp;=\u0026thinsp;53) were healthy, HIV-seronegative adults (age\u0026thinsp;=\u0026thinsp;21\u0026ndash;60 years), male (n\u0026thinsp;=\u0026thinsp;25) and female (n\u0026thinsp;=\u0026thinsp;28) nonsmoking (urine cotinine-negative) students of the UAM and residents (\u0026gt;\u0026thinsp;6 months) of the Iztapalapa and Iztacalco municipalities in the MCMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the study of air pollution particle effects on human antimycobacterial immunity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], study participants completed three study visits for consent, a physical exam, and a bronchoalveolar lavage (BAL). At consent, subjects completed questionnaires to obtain demographic information, time-activity information (e.g., commuting in the city, outdoor activities, mode of transportation), household exposures (e.g., housing type, ventilation, cooking fuel, etc.), alcohol use, smoking status, and environmental tobacco smoke exposure. At the physical exam visit, subjects underwent medical history taking, a lung function test, and a chest x-ray. At the third visit, BAL was conducted to collect BAL fluids and bronchoalveolar cells (BAC) and venipuncture to collect peripheral blood mononuclear cells (PBMC). The venipuncture blood sample was used for serum biomarker analysis at the clinic. Spot urine samples were collected at each visit (3 times). BALF, plasma, and urine samples were analyzed for biomarkers, as explained below (2.4. Biomarker Analysis).\u003c/p\u003e\n\u003ch3\u003eParticulate Matter Load in Alveolar Macrophages\u003c/h3\u003e\n\u003cp\u003eCytospin preparations from BAC of the study participants (n\u0026thinsp;=\u0026thinsp;53) were prepared by centrifugation (800 x g) of 0.2 x 10\u003csup\u003e6\u003c/sup\u003e BAC onto a glass slide using a cytocentrifuge (Wescor Cytopro 7620 Cytocentrifuge, Wescor INC Logan, UT, USA). Following modified Wright\u0026rsquo;s staining with Accustain (Sigma Aldrich, St Louis, MO), the nuclear morphology of BAC and proportions of AM, neutrophils and alveolar lymphocytes were characterized on thin-layer cytospin preparations. Cytospin color photographs (Olympus DP71 digital microscope camera, Tokyo, Japan) were obtained by digital bright field microscopy (1000\u0026times;, Olympus BX51 digital microscope, Tokyo, Japan).\u003c/p\u003e \u003cp\u003ePM load in AM was assessed using the CellProfiler, an open-source cell image analysis tool (CellProfiler 4.0, Broad Institute, Cambridge, MA), which uses modular processing pipelines, allowing users to automate image analysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Imported images were separated into three components using hue, saturation, and value channels. The AM object was identified based on size (10 \u0026micro;m diameter) and shape. Form factor (\u0026gt;\u0026thinsp;0.5, the ratio between the area and the perimeter; a perfectly circular object has a form factor of 1) and eccentricity (\u0026lt;\u0026thinsp;0.8, the ratio of the distance between the foci of the ellipse and its major axis length, equals 1 for a line segment and 0 for a circle) values were used to select for round-shaped objects. Then, PM objects larger than 0.4 \u0026micro;m were identified within the detected AM objects. The PM object size limit (0.4 \u0026micro;m) was selected based on the maximum resolution of the digital microscope. Finally, estimated variables are the fraction of AM containing PM (%AMPM), absolute PM area within AM (PM area in AM [\u0026micro;m\u003csup\u003e2\u003c/sup\u003e]), and AM size (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e). The developed CellProfiler pipeline and example pictures are shown in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eBiomarker analysis\u003c/h3\u003e\n\u003cp\u003eBALF (n\u0026thinsp;=\u0026thinsp;53), plasma (n\u0026thinsp;=\u0026thinsp;27), and urine (n\u0026thinsp;=\u0026thinsp;53) samples were shipped to Duke University and stored at -20\u0026deg;C or -80\u0026deg;C until laboratory analysis. Based on earlier research by our group [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we assessed concentrations of P-selectin, a marker of platelet activation; C reactive protein (CRP), a marker of inflammation; von Willebrand\u0026rsquo;s Factor (vWF), an index of endothelial dysfunction or damage; and fibrinogen, a blood clotting agent using commercial ELISA kits following manufacturers\u0026rsquo; instructions (Sigma-Aldrich, MO, USA). Malondialdehyde (MDA), a marker of lipid damage and oxidative stress, in BAL and urine samples was analyzed using an HPLC-fluorescence detection method following thiobarbituric acid derivatization, as described previously [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Urinary 8-OHdG concentrations, a marker of oxidative stress, were measured with LC-MS/MS (TSQ Quantum Access Max, Thermo Fisher Scientific, MA, USA) after solid phase extraction by Bond Elut-certify cartridge (500 mg, 6ml, Agilent Technologies, CA, USA), as described previously [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Urinary creatinine concentrations were measured using colorimetric method using commercial kits (Cayman Chemical, MI, USA). Urinary biomarker concentrations were normalized by creatinine concentration.\u003c/p\u003e\n\u003ch3\u003eLand Use Regression (LUR) Model\u003c/h3\u003e\n\u003cp\u003eTo stratify the PM exposure levels of the study participants for PM load in AM and biomarker analysis, we developed a LUR model based on the methodology described earlier by our group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The least absolute shrinkage and selection operator (LASSO) method was applied to select the best LUR model using R 4.1.3 using the lmmlasso package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The present study improved the previous LUR model by adding PM\u003csub\u003e2.5\u003c/sub\u003e monitoring data at the participants' homes (n\u0026thinsp;=\u0026thinsp;21, red stars in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in addition to the MCMA compliance monitoring sites (n\u0026thinsp;=\u0026thinsp;37, black dots in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmbient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were obtained from the Red Autom\u0026aacute;tica de Monitoreo Atmosf\u0026eacute;rico (RAMA) stations over the 2011\u0026ndash;2018 period (37 stations across the MCMA) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, we obtained daily ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations on the roof sites of the homes of our MexAir study participants (n\u0026thinsp;=\u0026thinsp;21). For that purpose, the MexAir sampling suitcase containing air quality monitoring instruments (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Fig. S2)\u003c/span\u003e was placed for a day on the roof of the study participant houses. A Sioutas cascade impactor (SKC, PA, USA) with Teflon filter (0.5 \u0026micro;m, 25 mm, Zefluor supported PTFE, Pall, NY, USA) and SKC Leland Legacy sampling pump (9 LPM, SKC, PA, USA) were used to measure 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. For gravimetric analysis of the Teflon filters, filters were weighed before and after PM sampling in a clean, temperature (20\u0026ndash;23\u0026deg;C) and humidity (30\u0026ndash;40%) controlled weighing facility at the UAM.\u003c/p\u003e \u003cp\u003eOther LUR model variables were collected from the RAMA, the meteorology and solar radiation monitoring network (Red de Meteorolog\u0026iacute;a y Radiaci\u0026oacute;n Solar; REDMET, n\u0026thinsp;=\u0026thinsp;21) and the atmospheric deposition monitoring network (Red de Dep\u0026oacute;sito Atmosf\u0026eacute;rico, REDDA, n\u0026thinsp;=\u0026thinsp;16) stations for hourly temperature (T), relative humidity (RH), and wind speed (WS). Google Traffic data was used for typical hourly traffic density information (TD). Land use information and elevation were downloaded from the United States Geological Survey (USGS). Traffic density, land use, and elevation variables within 500 m diameter circular buffer around the PM\u003csub\u003e2.5\u003c/sub\u003e monitoring locations (i.e., 37 RAMA and 21 MexAir participant home sites) were selected. All hourly variables were averaged to daily time resolution to develop LUR model.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics for participant demographics, PM load in AM markers, PM\u003csub\u003e2.5\u003c/sub\u003e exposures using our LUR model, and biomarker concentrations for BALF, plasma, and urine were estimated. Chi-square and t-tests were used to compare the measurements between low, medium, and high AMPM load groups. Associations between %AMPM, plasma biomarkers, and estimated average PM\u003csub\u003e2.5\u003c/sub\u003e concentrations over different periods (1 day to 6 months) were analyzed using linear regression. Regression analyses were also conducted to study the relationship between short-term PM concentrations (1\u0026ndash;7 days before urine sample collection) and urinary oxidative stress markers.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThe characteristics of the 53 study participants (25 male and 28 female, age range: 21\u0026ndash;60 years) are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mean age and body mass index (BMI) were 29.6 years and 26.2 weight [Kg]/height [m\u003csup\u003e2\u003c/sup\u003e], respectively. Ninety-two percent of the participants had received a college or higher education. Sixty percent of the participants were college students at the time of the study. Twenty-six percent of the participants\u0026rsquo; houses had mechanical ventilation systems (e.g., mechanical range hood). Eighteen participants indicated that they had smokers in their households.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, Female (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, Male (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (23\u0026ndash;32)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index (BMI, weight/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7 (23.5\u0026ndash;28.7)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and technical school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDon\u0026rsquo;t work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork indoors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork outdoors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent, part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence type (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle Family Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily Compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental condition (Yes, n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse with ventilation system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent alcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental tobacco smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e*median (interquartile range) \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePM load in AM and biomarker levels\u003c/h2\u003e \u003cp\u003ePM load in AM was estimated in 53 participant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) using the automated image analysis method. Overall, a median of 183 (interquartile range [IQR] 134\u0026ndash;263) AM cells were identified on each participant\u0026rsquo;s cytospin image (61\u0026ndash;71 images per participant). PM was detected in 62.4% (50.0\u0026ndash;73.0%) of AM (%AMPM), and the median and interquartile range of the PM area in AM was 1.082 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e (0.607\u0026ndash;1.855 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows PM load in AM results, mean LUR-estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations at different periods prior to the BAL date and basic demographics. PM load groups were categorized into tertiles using %AMPM estimates: low (n\u0026thinsp;=\u0026thinsp;18, \u0026lt;\u0026thinsp;33 percentile), medium (n\u0026thinsp;=\u0026thinsp;17, 33\u0026ndash;66 percentile), and high (n\u0026thinsp;=\u0026thinsp;18, \u0026gt;\u0026thinsp;66 percentile). Participants with higher %AMPM had larger PM areas in AM. In the lowest tertile, 46.2% of AM had PM with a median PM area of 0.427 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e, while in the highest tertile 77.7% of AM contained PM with a median area of 2.274 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e (t-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00). PM\u003csub\u003e2.5\u003c/sub\u003e exposure estimated using the LUR model on BAL date (0-day) showed slightly higher concentrations in the AM with high PM load group than in the low PM load group (t-tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.33). Age, BMI, and AM size were within similar ranges in the different PM load groups. The medium PM load group included more female participants (70.5% female) than other groups (Chi-Square, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. PM load in AM, ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, demographic information (sex, age, BMI) for All, Low, Medium, and High AMPM load groups (median and interquartile range [1Q-3Q], among 53 Mexico City residents, 2013-2018)\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eAll (n=53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eLow AMPM load (n=18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMedium AMPM load (n=17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eHigh AMPM load (n=18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e%AMPM (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e62.4 (50.0-73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e46.2 (38.6-49.9)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e62.4 (59.2-65.0)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e77.7 (73.2-81.9)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003ePM area in AM\u0026nbsp;(\u0026micro;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e1.082 (0.607-1.855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.427 (0.351-0.604)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.158 (1.03-1.279)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e2.274 (1.594-3.399)\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAM size\u0026nbsp;(\u0026micro;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e339.6 (264.2-430.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e338.7 (237.3-433.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e384.6 (288.0-430.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e326.3 (289.6-389.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eEstimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eBAL date, 0-day (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26.58 (18.87-34.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e25.66 (19.84-31.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e27.07 (16.21-35.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e29.58 (24.13-35.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3-month average (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26.17 (21.62-29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26.54 (23.15-30.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e26.17 (22.79-28.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26.45 (20.56-29.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6-month average (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e25.68 (23.35-28.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26.29 (23.96-29.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e25.36 (24.10-27.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26.31 (22.34-28.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eSex (male: female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e25:28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e8:10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5:12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e10:8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e26 (23-32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e25 (23-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e26 (23-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e26 (23-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eBMI (weight/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e24.7 (23.5-28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e25.6 (23.7-27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e24.0 (23.1-28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e25.4 (23.9-29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e%AMPM: Fraction of AM containing PM; PM area in AM: absolute PM area within AM; Estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e: ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated for the date of BAL, 3- and 6-month averages prior to BAL date; BMI: Body Mass Index; a, b: statistical significance at \u003cem\u003e\u0026alpha;\u003c/em\u003e=0.05 level\u003c/p\u003e \u003cp\u003eCorrelation analysis results between the PM load in AM markers and bronchoalveolar lavage fluid (BALF), plasma, and serum biomarker levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. von Willebrand factor (vWF, correlation coefficient [ρ]\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), Lactate dehydrogenase (LDH, ρ\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), fibrinogen (ρ\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.188), and C-Reactive Protein (CRP, ρ\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.336) showed weak to moderate positive correlations with %AMPM (ρ\u0026thinsp;=\u0026thinsp;0.14\u0026ndash;0.42). PM area with AM showed weaker positive correlations with vWF, LDH, fibrinogen, and CRP than %AMPM (ρ\u0026thinsp;=\u0026thinsp;0.13\u0026ndash;0.28). The two biomarkers, vWF and LDH, showed a high correlation with PM load in AM markers were known to indicate inflammation and cardiovascular disease [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Other markers correlated with PM load in AM markers including fibrinogen, CRP and serum glutamic pyruvic transaminase (TGP) were also [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Interestingly, AM size were negatively correlation with the inflammation and cardiovascular disease markers (i.e., fibrinogen, vWF, and CRP), but the mechanism is unknown.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between ambient PM\u003csub\u003e2.5\u003c/sub\u003e Concentrations and PM Load in AM\u003c/h2\u003e \u003cp\u003eThis study refined the LUR model developed in our previous study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The current model included additional ambient PM\u003csub\u003e2.5\u003c/sub\u003e monitoring data measured from the rooftops of the homes of 21 MexAir study participants. Compared to the previous study, the LUR model for this study improved its performance from R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.49 to R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.81 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplemental Figure S3\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/span\u003e). Mean ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated using the refined LUR model to measure its association with PM load in AM.\u003c/p\u003e \u003cp\u003eAssociations between %AMPM and mean LUR-estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Considering the lifetime of AM, we estimated the PM\u003csub\u003e2.5\u003c/sub\u003e concentrations on BAL dates (0 days [0D]) and their averages at 7-day (7D), 1-month (1M), 3-months (3M), and up to 6-months (6M) prior to the BAL date [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Medium and high PM loads in AM groups did not show a significant association between %AMPM and estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. Low PM load in AM group with less than 1-month PM\u003csub\u003e2.5\u003c/sub\u003e averaging time did not show significant associations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14\u0026ndash;0.88). Averaged PM\u003csub\u003e2.5\u003c/sub\u003e concentrations over longer than a 3-month period, however, showed a significant association with %AMPM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Increments of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e of 6-month averaged PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in the low PM load group were associated with increased the %AMPM by 8.06% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000, 95% confidence interval\u0026thinsp;=\u0026thinsp;6.78\u0026ndash;9.36%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between PM Load in AM and Biomarkers\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows associations between %AMPM and vWF [von Willebrand factor, marker of inflammation-related thrombosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], \u0026micro;g/ml], LDH (U/L), fibrinogen (\u0026micro;g/ml), and CRP (C-Reactive Protein, \u0026micro;g/ml) concentrations from all participants. The three plasma and a serum biomarker showed positive associations with %AMPM. The vWF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) concentrations showed a statistically significant relationship with %AMPM. BALF Malondialdehyde and plasma p-selectin showed negative associations with the levels of %AMPM (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other biomarker analysis results are tabulated in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table S2\u003c/span\u003e. Two urinary oxidative stress markers (Malondialdehyde and 8OHdG) didn\u0026rsquo;t show clear trends with AMPM load levels. However, %AMPM load levels altered the associations between PM2.5 and urinary oxidative stress markers (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table S3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBronchoalveolar lavage fluid (BALF, n\u0026thinsp;=\u0026thinsp;53), plasma (n\u0026thinsp;=\u0026thinsp;27), serum (n\u0026thinsp;=\u0026thinsp;53), urine (n\u0026thinsp;=\u0026thinsp;53), and other (n\u0026thinsp;=\u0026thinsp;53) biomarker levels for All, Low, Medium, and High AMPM load participant groups (median [1Q-3Q])\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow AMPM load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium AMPM load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh AMPM load\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBALF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDAf (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.7 (75.89-263.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172.3 (72.94\u0026ndash;261.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159.2 (95.47\u0026ndash;285.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133.5 (79.55\u0026ndash;212.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-Selectin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.39 (26.67\u0026ndash;59.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.80 (30.50-60.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.20 (25.90-65.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.50 (19.80-42.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRP (\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.569 (0.644-4.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.763 (0.622\u0026ndash;1.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.935 (0.609\u0026ndash;5.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.508 (1.673\u0026ndash;5.753)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evWF (\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.46 (7.892\u0026ndash;18.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.501 (7.634\u0026ndash;14.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.46 (10.34\u0026ndash;17.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.40 (13.79\u0026ndash;19.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFibrinogen (\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.928 (2.218\u0026ndash;4.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.522 (2.231\u0026ndash;3.526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.892 (1.930\u0026ndash;3.860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.709 (2.839\u0026ndash;5.558)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (128\u0026ndash;161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (123\u0026ndash;151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140 (128\u0026ndash;161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148 (140\u0026ndash;164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8OHdG (ng/mg creatinine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.03 (6.25\u0026ndash;81.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.99 (5.917\u0026ndash;93.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.39 (6.401\u0026ndash;63.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.22 (12.62\u0026ndash;78.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDAf (\u0026micro;M/mM creatinine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.091 (0.065\u0026ndash;0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.090 (0.062\u0026ndash;0.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.108 (0.065\u0026ndash;0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.086 (0.072\u0026ndash;0.112)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMDAf: free Malondialdehyde; P-Selectin: type-1 Transmembrane Protein (CD62P); CRP: C-Reactive Protein; vWF: von Willebrand factor; LDH: Lactate dehydrogenase; 8OHdG: 8-Hydroxyguanosine; other biomarker analysis results are tabulated in Supplementary Table S2\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHousehold and ambient PM exposure is known to cause a multitude of adverse health outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Identifying biomarkers of PM exposures and their roles within the biological processes of the exposed persons is a topic of great research effort. This study explored correlations between ambient PM exposures and PM load in AM and the consequences of PM accumulation in human AM on various biomarkers. To our knowledge, this study is the first to explore associations between PM load in AM, exposure to LUR-estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e exposure levels, and biomarkers in the BALF, plasma, and urine samples of healthy adult residents of the MCMA.\u003c/p\u003e \u003cp\u003eIn addition, we obtained daily ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations on the roof sites of the homes of our MexAir study participants (n\u0026thinsp;=\u0026thinsp;21). Being able to add additional PM level data points obtained from the roof sites of our study participant\u0026rsquo;s residences, we were able to develop a further refined LUR model that dramatically improved compared with its earlier usage in performance [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The model accuracy was improved from R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41 to R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.81 (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This study, also for the first time, demonstrates utilization of the CellProfiler image analysis software for automatic assessment of PM load in human AM.\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e exposure was assessed by averaging PM\u003csub\u003e2.5\u003c/sub\u003e concentrations for 6-month period prior to each study participant\u0026rsquo;s BAL dates. The 6-month averaged PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were positively correlated with %AMPM in the low PM load group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). Our findings are supported by a study of type 1 or 2 diabetic patients in Leuven, Belgium, in which a positive correlation was found between modeled 6-month average PM\u003csub\u003e10\u003c/sub\u003e exposure and PM area in AM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In another study in Leicester, United Kingdom, children (n\u0026thinsp;=\u0026thinsp;22, 3 months to 16 years) without respiratory symptoms who resided near primary roads with dense traffic had higher %AMPM (10%) than those who lived on quiet residential streets (%AMPM\u0026thinsp;=\u0026thinsp;3%) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In other studies, indoor biomass burning increased PM load in AM [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In a study assessing the longevity of PM load in AM, AMPM clearance half-lives were 54 days on average in persons who had moved from a highly polluted location (mean annual PM\u003csub\u003e10\u003c/sub\u003e 108 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) to Leuven, Belgium (mean annual PM\u003csub\u003e10\u003c/sub\u003e 23 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In that study, persons with high AMPM load (90th percentile) needed a longer time (mean 116 days) to clear PM deposited in AM.\u003c/p\u003e \u003cp\u003eOur findings suggest that high ambient PM\u003csub\u003e2.5\u003c/sub\u003e levels in the MCMA underlie the various degrees of PM loads in AM. Interestingly, in the medium and high PM load groups no associations between the estimated ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations and %AMPM were observed. Lacking correlation between PM exposure and PM load in the medium and high PM load groups may result from PM exposure saturation effects, or different AM ages and maturity stages. To the best of our knowledge, the processes of PM accumulation in and clearance from AM are still poorly understood.\u003c/p\u003e \u003cp\u003ePrevious studies have used absolute PM area within the AM to assess PM load in AM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our findings indicate that the %AMPM is a useful biological marker of long-term PM exposure and likely related to adverse health impacts. The current study showed a stronger correlation between %AMPM and plasma biomarkers than absolute PM size in AM. We observed a significant positive correlation between %AMPM and plasma inflammation biomarker vWF (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and serum cardiovascular marker LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). %AMPM showed trends toward positive associations with CRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.182) and fibrinogen (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.405). These observations provide biological plausibility toward a recent EPA report that stated a causal relationship between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and cardiovascular disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The %AMPM was negatively correlated with P-selectin, and other markers were positively correlated.\u003c/p\u003e \u003cp\u003eThis study observed that %AMPM positively correlated with plasma vWF levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). In other studies, vWF and fibrinogen levels were associated with short-term air pollution exposures. Short-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure (\u0026lt;\u0026thinsp;7 days) significantly increased plasma vWF by 0.41% (95%CI: 0.11\u0026ndash;0.71) per 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increments [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In a murine study, sub-chronic exposure (25\u0026ndash;26 days) to tunnel air pollutants increased vWF levels [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Plasma vWF is a predictor of adverse cardiac events and is closely related to systemic inflammation and cardiovascular disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition to vWF, our data also showed a positive relationship between %AMPM and serum LDH (p\u0026thinsp;=\u0026thinsp;0.026). LDH is known marker of cardiovascular disease [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Plasma LDH levels shown to be higher in diabetes patients than the general public [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A previous study showed positive correlations between PM load in AM and diabetes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, %AMPM was not correlated with plasma fibrinogen levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.405). In other studies, short-term PM exposures (\u0026lt;\u0026thinsp;7 days) showed strong positive associations with fibrinogen levels [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], while long-term exposures (1 year) to air pollutants did not affect fibrinogen levels [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Fibrinogen is a known marker of cardiovascular disease [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This study observed positive correlations between %AMPM and plasma CRP levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.182). Others showed that plasma CRP levels were significantly correlated with long-term PM exposures [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. CRP is related to tissue damage, systemic inflammation, respiratory (e.g., COPD) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and cardiovascular disease [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to vWF, fibrinogen and CRP, which were positively correlated with %AMPM in this study, were negatively correlated with %AMPM and P-selectin. The impact of air pollution exposures on P-selectin levels is not well understood. P-selectin is a cellular adhesion molecule strongly associated with cardiovascular disease (CVD) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] development and varies by age group [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Further study will be needed to understand better the relationships between air pollution exposure, P-selectin levels, and CVD risk.\u003c/p\u003e \u003cp\u003eThe findings of this study indicate that %AMPM is more strongly correlated with biomarkers than PM area in AM. This is consistent with our earlier findings in human BAC and PBMC, in which BAC with greater %AMPM more strongly suppressed IL-1β, a key antimycobacterial cytokine, than BAC with lower %AMPM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Small amounts of PM within AM were shown to alter AM functions such as antigen presentation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. As shown before by our group [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the PM load of individual AM can vary widely both within individual BAC samples of study participants and between study participants.\u003c/p\u003e \u003cp\u003eOur findings provide new insights into short-term (\u0026lt;\u0026thinsp;1 week) PM exposure effects in humans. Urinary biomarkers are indicators of shorter-term environmental exposures that include PM [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Here, we observed increasing significance between PM exposures and urinary oxidative stress markers with higher %AMPM. Even though the relationships were not significant, the results support our earlier findings that %AMPM results in the suppression of key cytokine response to Mtb for the high AMPM load group [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA limitation of the current study is the relatively small participant group and its limited demographic diversity from a single municipality with its specific PM exposure environment. Most study participants were students at the UAM with similar air pollution exposure, behaviors, lifestyles, and mobility radii. The \u0026lsquo;relative homogeneity\u0026rsquo; of the study population may have impacted our findings. Interestingly, despite reports of similar behavioral patterns (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplemental Table S4\u003c/span\u003e), AM from study participants often had widely varying PM loads. Further, the cytospin images analyzed here were prepared using standardized processes that may miss PM spots in AM due to the limitations of two-dimensional images. AM and PM size could have been altered during cytospin preparations.\u003c/p\u003e \u003cp\u003eRegardless of these limitations, this study observed significant correlations between long-term low level PM\u003csub\u003e2.5\u003c/sub\u003e exposures and %AMPM, and between %AMPM and vWF, and LDH. Future research may further identify the mechanisms by which PM exposure and PM load in AM induce adverse health effects. Such studies may also investigate the role of PM composition differences (source apportionment) on the various cellular responses and health outcomes of interest. Future studies could also consider using three-dimensional (3D) cell image analysis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The 3D cell imaging techniques with automated cell image analysis processes used in this study provide opportunity for further research studies of PM effects on cellular processes. Studies of dose-response relationships between PM load and AM function, as well as AM phenotypes are needed to follow up on our observations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study explored associations between PM load in AM, PM\u003csub\u003e2.5\u003c/sub\u003e exposure, and biomarkers among residents of MCMA. The results provide novel insights that PM deposited in AM might have altered physiological functions of AM, such as signaling molecule production (e.g., inflammatory cytokines), causing increased systemic inflammation and cardiovascular disease risk. Study results also suggest that even low-level PM exposures lead to uptake of AM by PM, which might increase inflammatory and cardiovascular disease risks. Our findings emphasize the importance of World Health Organization (WHO)\u0026rsquo;s new air quality recommendation to work towards an annual ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentration of 5 ug/m\u003csup\u003e3\u003c/sup\u003e [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] to protect public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e \u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSon, Y.: Data curation, Formal analysis, Methodology, Investigation, Validation, Visualization, Writing \u0026ndash; original draft, review, \u0026amp; editing; Carranza, C., Subramhanya, S., Torres, M., and Zhang, J.: Data curation, Formal analysis, Methodology, Investigation, Validation, Writing \u0026ndash; review \u0026amp; editing; Gardner, C., Jones, L., Meng, Q., Osornio-Vargas, A., and Orman-Strickland, P.: Investigation, Validation, Writing \u0026ndash; review \u0026amp; editing, O\u0026rsquo;Neill, M.: Investigation, Validation; Black, C.: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing \u0026ndash; review \u0026amp; editing; and Schwander, S.: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing \u0026ndash; original draft, review, \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all study participants for their contribution to this work. We would also like to thank the leadership of the Universidad Aut\u0026oacute;noma Metropolitana (UAM) and of the Instituto Nacional de Enfermedades Respiratorias (INER) for their support of research work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUS EPA., Supplement to the 2019 Integrated Science Assessment for Particulate Matter (Final Report, 2022). U.S. Environmental Protection Agency, Washington, DC. EPA/635/R-22/028. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee, A. \u0026amp; Agrawal, M. World air particulate matter: sources, distribution and health effects. \u003cem\u003eEnviron. Chem. 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Long-term exposure to PM and all-cause and cause-specific mortality: a systematic review and meta-analysis. \u003cem\u003eEnviron. Int.\u003c/em\u003e \u003cb\u003e143\u003c/b\u003e, 105974 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelasco, R. P. \u0026amp; Jarosińska, D. Update of the WHO global air quality guidelines: Systematic reviews\u0026ndash;An introduction. \u003cem\u003eEnviron. Int.\u003c/em\u003e \u003cb\u003e170\u003c/b\u003e, 107556 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Particulate matter, Alveolar macrophage, Land Use Regression, Inflammation, Oxidative Stress, Cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-6430851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6430851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores potential associations among ambient particulate matter (PM) exposure, PM load in alveolar macrophage (AM), and biomarkers collected from 53 healthy, adult, nonsmoking residents of the Iztapalapa and Iztacalco municipalities in Mexico City. Ambient PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were estimated using an improved Land Use Regression (LUR) model to approximate PM exposure levels. The PM/carbon loading was quantified by the fraction of AM containing PM (%, %AMPM) and the PM area within the AM (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e) from BAC cytospin microphotography using CellProfiler cell image analysis software. Concentrations of biomarkers were analyzed in bronchoalveolar lavage fluid (BALF), plasma, and urine. Most AM samples contained PM (median\u0026thinsp;=\u0026thinsp;62.4%, interquartile range [IQR]\u0026thinsp;=\u0026thinsp;50.0\u0026ndash;73.0%). The median PM area in AM was 1.082 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e (IQR\u0026thinsp;=\u0026thinsp;0.607\u0026ndash;1.855 \u0026micro;m\u003csup\u003e2\u003c/sup\u003e). Participant with low %AMPM (\u0026lt;\u0026thinsp;33 percentile) showed 8% increase in %AMPM per 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increments of six-month averaged, LUR-estimated PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. The %AMPM had a statistically significant, positive association with plasma von Willebrand Factor (vWF) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and serum lactase dehydrogenase (LDH) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). These finding suggest that that ambient PM exposure in Mexico City contributes to PM accumulation in AMs and may trigger systemic inflammation and oxidative stress in healthy young residents.\u003c/p\u003e","manuscriptTitle":"Correlations between Human Alveolar Macrophage Particulate Matter Load, Air Pollution Particulate Matter Levels, and Systemic Inflammation Markers in Mexico City","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 10:51:13","doi":"10.21203/rs.3.rs-6430851/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T06:12:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T11:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276403523012614343946679602149256519152","date":"2025-05-27T10:53:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65256497060735207240621531133978854467","date":"2025-05-26T14:08:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T05:09:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T19:26:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302520760340715238391051260109508218155","date":"2025-05-03T18:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295302242900633916387449480941905144921","date":"2025-05-03T14:37:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252707901890232315833818923847739355775","date":"2025-05-03T06:05:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T12:42:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T11:44:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-22T11:58:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T05:13:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-11T19:41:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbaf44f3-6a33-480d-870c-95849f6f97cd","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":48080718,"name":"Health sciences/Risk factors"},{"id":48080719,"name":"Health sciences/Biomarkers"},{"id":48080720,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":48080721,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-08-11T12:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 10:51:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6430851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6430851","identity":"rs-6430851","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}