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Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome
Environmental Research 161 (2018) 485–491
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Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome
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Arvind Dabassa, Evelyn O. Talbotta, , Judith R. Ragera, Gary M. Marshb, Arvind Venkatc, Fernando Holguind, Ravi K. Sharmae a
University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA, USA University of Pittsburgh, Graduate School of Public Health, Department of Biostatistics, Pittsburgh, PA, USA Allegheny Health Network, Department of Emergency Medicine, Pittsburgh, PA, USA d University of Colorado, School of Medicine, Pulmonary Disease and Critical Care Medicine, Aurora, CO, USA e University of Pittsburgh, Graduate School of Public Health, Department of Behavioral and Community Health Sciences, Pittsburgh, PA, USA b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Air pollution Cardiovascular disease Metabolic syndrome PM2.5 Environmental public health tracking
Background: There has been no investigation to date of adults with metabolic syndrome examining the association of short and long-term exposure to fine particulate matter (PM2.5) air pollution with cardiovasculardisease related inflammatory marker (WBC and CRP) levels in a nationally representative sample. The goal of this study is to assess the susceptibility of adults with metabolic syndrome to PM2.5 exposure as suggested by increased cardiovascular-disease related inflammatory marker levels. Methods: A cross sectional analysis of adult National Health and Nutrition Examination Survey (NHANES) participants (2000–2008) was carried out with linkage of CDC WONDER meteorological data and downscaler modeled USEPA air pollution data for census tracts in the continental United States. Participants were non-pregnant NHANES adults (2000–2008) with complete data for evaluating presence of metabolic syndrome and laboratory data on WBC and CRP. Exposures studied included short (lags 0–3 days and their averages), long-term (30 and 60 day moving and annual averages) PM2.5 exposure levels at the census tract level in the continental United States. The main outcomes included CRP and WBC levels the day of NHANES study visit analyzed using multiple linear regression, adjusting for age, gender, race, education, smoking status, history of any cardiovascular disease, maximum apparent temperature and ozone level, for participants with and without metabolic syndrome. Results: A total of 7134 NHANES participants (35% with metabolic syndrome) met the inclusion criteria. After adjusting for confounders, we observed a significant effect of PM2.5 acutely at lag day 0 on CRP level; a 10 µg/m3 rise in lag day 0 PM2.5 level was associated with a 10.1% increase (95% CI: 2.2–18.6%) in CRP levels for participants with metabolic syndrome. For those without metabolic syndrome, the change in CRP was −1.3% (95% CI −8.8%, 6.8%). There were no significant associations for WBC count. In this first national study of the effect of PM2.5 air pollution on levels of cardiovascular-disease related inflammatory markers in adults with metabolic syndrome, CRP levels were found to be significantly increased in those with this condition with increased fine particulate matter levels at lag day 0. With one third of US adults with metabolic syndrome, the health impact of PM2.5 in this sensitive population may be significant.
1. Introduction Metabolic syndrome (MetS), defined by a combination of individual cardiovascular risk factors (hypertension, dyslipidemia (elevated triglycerides and lowered high-density lipoprotein cholesterol), raised fasting glucose, and central obesity), increases the likelihood of
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cardiovascular disease (CVD) (Grundy, 2008; Wilson et al., 1999; Suzuki et al., 2008) and is associated with systemic inflammation (Alberti et al., 2009). MetS increases the risk of CVD to an extent greater than that conferred by any of its individual components (Wilson et al., 1999; Arnlov et al., 2010; Noda et al., 2009). Individuals with MetS have greater susceptibility to autonomic dysfunction (e.g. heart
Correspondence to: A526 Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA 15261, USA. E-mail address: [email protected] (E.O. Talbott).
https://doi.org/10.1016/j.envres.2017.11.042 Received 19 July 2017; Received in revised form 31 October 2017; Accepted 23 November 2017 0013-9351/ © 2017 Elsevier Inc. All rights reserved.
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In addition to these biomarkers, we also obtained data on demographic variables (including age, gender, race/ethnicity, and education) and potential risk factors for cardiovascular disease, such as smoking status and history of any cardiovascular diseases (congestive heart failure, coronary heart diseases, angina/angina pectoris, heart attack, or stroke). Smoking status was defined as follows: current-presently smoking cigarettes or serum cotinine levels were greater than or equal to 10 ng/mL; former-have smoked 100 cigarettes in life but currently not smoking; never-had not smoked at least 100 cigarettes in life. We also obtained data from NHANES on infections within the last 30 days (cold, gastrointestinal illness, flu/pneumonia/ear infection), chronic obstructive pulmonary disease (COPD) (includes emphysema and chronic bronchitis), household smoker presence, and rheumatoid arthritis for conducting sensitivity analyses.
rate variability) in response to PM2.5 exposure (Park et al., 2010). Previous studies have shown that the association between particulate matter (PM) exposure and systemic inflammation may be stronger among participants with diabetes, obesity, and hypertension (Dubowsky et al., 2006; Ostro et al., 2014; Emmerechts et al., 2012; Zeka et al., 2006; Chen and Schwartz, 2008). Dubowsky et al. studied 44 people with hypertension, diabetes or obesity and examined the association of exposure to ambient PM2.5 during the previous week on WBC and CRP. They noted associations between PM2.5 and WBC, with a 5.5% [95% CI, 0.10–11%)] increase per interquartile increase (5.4 μg/ m3) of PM2.5 averaged over the previous week. This was based on a very small sample, however. Chen and Schwartz (2008) studied the effect of longer term exposure to PM10 on WBC among NHANES survey 1988–1994 participants with and without MetS, linking the residence of those in urban areas to an air monitor. They noted increased WBC levels related to increased one-year average PM10 exposure among individuals with MetS. No study to date, however, has considered the association of both short-term and longer term exposure to PM2.5 on the inflammatory markers WBC and CRP with all adult participants of a nationally representative (NHANES) population. Our objective is to use data from the National Health and Nutrition Examination Survey (NHANES) for 2001–2008, a US based national sample, to investigate the potential susceptibility of participants with MetS to acute and longer term exposure to PM2.5 air pollution for markers of systemic inflammation, specifically C-reactive protein (CRP) and white blood cell (WBC) count, compared to participants without MetS. Our hypothesis is that acute and/or longer term exposure to PM2.5 particulate air pollution will result in a greater inflammatory response among those with MS than among those without the condition. Unlike previous investigations, all individuals in this large sample will be assigned an exposure level based on nationwide modeled USEPA PM2.5 data with the national sample population weighted to represent the continental US as a whole.
2.3. Definition of metabolic syndrome MetS was defined as per the Joint Scientific Statement Harmonizing the Metabolic Syndrome (Alberti et al., 2009). According to these guidelines, MetS is defined as the presence of three or more of the following five conditions: high blood pressure, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), elevated fasting glucose, and abdominal obesity. High blood pressure was defined as systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg. For this analysis, high blood pressure could also be defined by a self-report of current use of antihypertensive medication. Hypertriglyceridemia was identified based on triglycerides ≥150 mg/ dL. Low HDL-C was a level of < 40 mg/dL in men or < 50 mg/dL in women. Elevated fasting glucose (EFG) was defined as fasting glucose ≥100 mg/dL; for this analysis, EFG could also be defined by a selfreport of current use of insulin or oral hypoglycemic. The last criterion was abdominal obesity, defined as > 88 cm for women and > 102 cm for men. The details of the adult questionnaire, MEC examination, and laboratory tests for profiling MetS risk factors have been described on the CDC website (National Center for Health Statistics, 2014).
2. Methods
2.3.1. Ambient air pollution and weather data Predictions of daily ambient 24-h average PM2.5 (µg/m3) and 8-h maximum O3 levels (ppb) were obtained from the USEPA using a downscaling modeling approach (Berrocal et al., 2012). This downscaling approach uses Bayesian space-time modeling to combine air monitoring data and gridded numerical output from the Community Multi-Scale Air Quality Model (CMAQ) to produce point level daily air pollution predictions to the year 2000 US census tract centroids (United States Environmental Protection Agency, 2013). Daily predictions of O3 and PM2.5 were obtained from January 1, 2001–December 31, 2008 at the population weighted centroid (centers of population) of each year 2000 US census tract (CT) in the 48 continental states (United States Census Bureau, 2013). Meteorological data were obtained from the CDC WONDER North America Land Data Assimilation System Daily Air Temperatures and Heat Index (1979–2010) website (Centers for Disease Control and Prevention, 2013). Daily values of the maximum air temperature and maximum heat index (HI) for each county were extracted for the study time period (1/1/2001-12/31/2008). HI incorporates both temperature and relative humidity and is a better measure on days when air temperature > 80 F°. Maximum HI was provided for those days when air temperature was above 80 F° or 26.7 °C. CDC used a formula by Steadman to calculate the hourly HI, from which the daily maximum HI was computed (Steadman, 1979). For our analysis, we computed a daily maximum apparent air temperature which was defined as the daily maximum HI if provided; otherwise the daily maximum air temperature was used. The environmental database of daily pollution data and meteorological data was assembled for each CT in the 48 continental United States for the study time period. This large database contained
2.1. Health data We used health data from the NHANES conducted by the Centers for Disease Control and Prevention (CDC) for the period 2001–2008. Details about this survey and the specific measurement procedures and protocols have been described on the CDC website (National Center for Health Statistics, 2014). In brief, the NHANES follows a complex, stratified, and multistage probability sampling of the population of the US, with oversampling of minorities (African Americans and Mexican Americans) and the elderly (≥ 60 years of age). The survey consisted of an extensive household interview followed by a series of laboratory and other physical tests administered in a mobile examination center (MEC). Only those who completed the household interviews were invited for the MEC examination. Since 1999, the NHANES has been continuously conducted in two-year cycles. The NHANES public use data sets were accessed for the four cycles of 2001–2008. This time period coincided with the availability of USEPA modeled data by census tract for the continental US and provided an opportunity for the study. Study participants were all non-pregnant adults age > 20 with information available on all five criteria of MetS (see definitions below). After exclusions, including participants with missing data on biomarker levels and covariates of interest, there were 7134 and 7123 participants included in our analysis for CRP and WBC, respectively. 2.2. Markers of inflammation and covariates The cardiac inflammatory biomarkers examined in this study were CRP and WBC count, as these biomarkers were available in NHANES for the entire 2001–2008 period for which the US Environmental Protection Agency downscaler air pollution data was also available. 486
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predicted values of PM2.5 and O3 at the population weighted centroid of each CT based on the 2000 US Census data. Maximum apparent temperature for each county was assigned to the appropriate CT level. In addition to the daily levels (lag 0), we calculated the following for PM2.5 and O3: the level on the previous day (lag 1); two days prior (lag 2); three days prior (lag 3); the average of lags 0 and 1 (lag 0–1); average of lags 0, 1 and 2 (lag 0–2); average of lags 0, 1, 2, and 3 (lag 0–3); average of lags 1 and 2 (lag 1–2); and the average of lags 1, 2, and 3 (lag 1–3). The following long-term averages were also calculated: the average of the 30 days prior (30-day moving average), 60 days prior (60-day moving average), and annual average.
Table 1 Distribution of environmental variables.a
2.4. Merging of health and environmental data
Year
N
PM2.5 (µg/m3)
O3 (ppb)
Temperature (°C)
2001 2002 2003 2004 2005 2006 2007 2008 2001–2008
812 922 799 859 766 832 1054 1090 7134
11.00 ± 0.76 12.60 ± 1.66 12.20 ± 0.91 11.38 ± 0.92 11.78 ± 0.83 10.81 ± 0.73 12.80 ± 1.61 11.51 ± 0.75 11.74 ± 0.37
41.48 ± 1.79 47.00 ± 3.54 43.18 ± 2.21 38.93 ± 1.72 42.70 ± 2.60 39.56 ± 1.69 44.35 ± 3.25 43.03 ± 2.19 42.47 ± 0.98
23.57 ± 1.47 23.27 ± 1.27 21.76 ± 0.93 22.39 ± 1.59 21.04 ± 1.32 21.25 ± 1.09 21.78 ± 1.97 22.22 ± 1.20 22.14 ± 0.48
a Data is shown for the 7134 individuals included in the CRP analysis; there were 11 fewer of these individuals included in the WBC analysis (data not shown).
The CT (11 digit Federal Information Processing Standards code) of residence of each individual and the date of the NHANES examination were used to merge the NHANES data with the environmental dataset of air pollution and weather. Thus, each participant was assigned a daily PM2.5, O3, and temperature exposure based on the CT of residence at time of blood draw.
NC, US. Descriptive analyses were conducted using PROC SURVEYMEANS and PROC UNIVARIATE. All regression models were run accounting for the complex sampling design of the NHANES with the SAS SURVEYREG command by using the sample weights included in the datasets. P-values were 2-tailed, and < 0.05 was considered significant.
2.4.1. Statistical analysis We examined exposure to ambient PM2.5 as a predictor of each biomarker of interest, CRP and WBC, in separate regression models. CRP was log transformed to improve normality and stabilize the variance. To evaluate the short-term effects of PM2.5, we analyzed the effect of PM2.5 on the day of the blood draw (lag 0) as well on the day before (lag 1), two days before (lag 2), and three days before (lag 3) and averages of these time periods (lag 0–1, lag 0–2, lag 0–3, lag 1–2, and lag 1–3). In addition, we examined the long-term effects of PM2.5 on each biomarker by using the average PM2.5 in the 30 days prior, 60 days prior and annual average value. We also examined the long-term effects of PM2.5 after adjusting for short-term effects of air pollution (lag 0–3 of PM2.5 and O3). We used multiple linear regression models to assess the association of PM2.5 with each biomarker in subgroups: participants with and without MetS. The regression estimates were calculated for a 10 μg/m3 increase in PM2.5 after controlling for selected covariates based on prior biological and epidemiological knowledge of major determinants of cardiovascular health (Ong et al., 2013). Age was treated as a continuous variable, whereas gender, smoking (current smokers versus never and former smoker), and history of any CVD were treated as dichotomous variables in the models. Race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, Mexican American, and other races. Education was categorized as 1st–11th grade, high school graduate/GED or some college, and college graduate. Models were run adjusted for the co-pollutant O3 at the same lag or average as PM2.5. Shortterm models were adjusted for maximum apparent temperature (MAT) at the zero lag. The 30 day and 60 day moving average models were adjusted for a 30 day MAT. The annual average models were not adjusted for temperature. Quartiles of temperature were used to account for the non-linear relationship of temperature with biomarkers.
3. Results There were 7134 and 7123 non-pregnant adult participants for CRP and WBC analysis, respectively, with information available on all five MetS criteria, excluding participants with missing biomarker data and covariates of interest. Of these, N = 2789 (35.3% weighted frequency) for CRP analysis group and N = 2782 (35.0% weighted frequency) for WBC analysis group were defined as having MetS as they met three or more criteria per its definition (Alberti et al., 2009). Table 1 shows the distribution of environmental exposures to PM2.5, ozone and weather information by year from 2001 to 2008 at the level of participant's address on the day of blood draw. The mean ± standard error of PM2.5 (µg/m3), O3 (ppb), and maximum apparent temperature (°C) were 11.74 ± 0.37, 42.47 ± 0.98, and 22.14 ± 0.48, respectively. The data is shown for the 7134 individuals who were included in the CRP analysis; 7123 of these individuals were also included in the analysis of WBC. Table 2 shows the survey weighted descriptive statistics of biomarkers for non-pregnant adult participants with information available on all five MetS criteria, excluding participants with missing data on CRP and WBC levels and covariates of interest. The CRP levels were raised in Black, older, and male participants; whereas WBC count was higher in younger, male, and races other than Black. The levels of both biomarkers in individuals were elevated in current smokers, (lower education, and history of any CVD, rheumatoid arthritis, chronic obstructive pulmonary disease, and recent infections. In single and double pollutant PM2.5 models, there was an increased response of % change in CRP in participants who had MetS compared to those without the condition after adjusting for age, gender, race, education, smoking status, history of any CVD, and maximum apparent temperature (see Fig. 1(a), single pollutant model (PM2.5 and Fig. 1(b), bi-pollutant model). The increased response was significant for lag day zero indicating an acute response in the bi-pollutant models (i.e., adjusting for ozone). There was a significant positive association of lag 0 PM2.5 for participants with MetS. For every 10 µg/m3change in PM2.5, there was an increase of 10.1% (2.2–18.6%) for CRP. For those without MetS, the change in CRP was −1.3% (95% CI −8.8%, 6.8%). Similar to CRP, there was an increase in WBC count in participants who had MetS compared to those who did not for both short and longterm exposure in single pollutant PM2.5 models, after adjusting for age, gender, race, education, smoking status, history of any cardiovascular disease, and maximum apparent temperature. The increased response was more pronunced for long-term exposure, but did not reach
2.5. Sensitivity analyses Certain medical conditions (such as rheumatoid arthritis and COPD), acute infections, and presence of household smoking have been related to elevated levels of inflammatory markers (Venn and Britton, 2007; Sin and Man, 2003; Rooney et al., 2011; Morley and Kushner, 1982). Therefore, we investigated the sensitivity of our results to alternate ways of modeling by running separate models excluding people with histories of (a) rheumatoid arthritis; (b) COPD; (c) acute infection in last 30 days; and (d) household smoking. We also examined our results after controlling for season and year because temperature and pollutants show seasonal and yearly variations. All analyses were performed using SAS software, version 9.2, Cary, 487
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Table 2 Survey weighted descriptive statistics of biomarkers for non-pregnant adult participants with information available on all five criteria of metabolic syndrome. C - reactive protein
Overall Age (years) 20–39 40–59 60+ Gender Male Female Race/Ethnicity White Black Hispanic Other Education 1st–11th grade HS grad/GED or some college College graduate Smoking Never Former Current History of any CVD Yes No Recent Infection Yes No Rheumatoid Arthritis Yes No COPD Yes No Household Smoker Yes No
White blood cells
N (%) 7134(100)
Mean ± SE (mg/dL) 0.4 ± 0.0)
N (%) 7123(100)
Mean ± SE (103 cells/µL) 6.78 ± 0.0
2302(38.6) 2321(39.3) 2511(22.2)
0.35 ± 0.0 0.42 ± 0.0 0.46 ± 0.0
2299(38.5) 2318(39.3) 2506(22.2
6.85 ± 0.1 6.73 ± 0.1 6.77 ± 0.1
3651(49.7) 3483(50.4)
0.34 ± 0.0 0.47 ± 0.0
3645(49.7) 3478(50.33)
6.83 ± 0.1 6.74 ± 0.1
3722(72.4) 1363(10.9) 1381(7.6) 668(9.1)
0.39 ± 0.0 0.50 ± 0.0 0.41 ± 0.0 0.39 ± 0.0
3717(72.4) 1362(10.9) 1380(7.6) 664(9.1)
6.84 ± 0.1 6.25 ± 0.1 6.85 ± 0.1 6.89 ± 0.1
1993(17.8) 3708(56.4) 1433(25.8)
0.44 ± 0.0 0.43 ± 0.0 0.32 ± 0.0
1987(17.8) 3704(56.5) 1432(25.7)
7.12 ± 0.1 6.87 ± 0.1 6.37 ± 0.1
3653(51.1) 1894(24.6) 1587(24.3)
0.38 ± 0.0 0.41 ± 0.0 0.45 ± 0.0
3645(51.1) 1891(24.6) 1587(24.4)
6.38 ± 0.0 6.56 ± 0.1 7.85 ± 0.1
801(8.2) 6333(91.9)
0.56 ± 0.1 0.39 ± 0.0
798(8.2) 6325(91.9)
7.19 ± 0.1 6.75 ± 0.0
1904(27.1) 4981(72.9)
0.54 ± 0.0 0.36 ± 0.0
1903(27.1) 4972(72.9)
7.02 ± 0.1 6.69 ± 0.1
369(4.0) 6754(95.1)
0.57 ± 0.0 0.40 ± 0.0
367(4.0) 6745(95.1)
7.10 ± 0.2 6.77 ± 0.0
538(7.4) 6574(92.6)
0.65 ± 0.1 0.38 ± 0.0
539(7.5) 6562(92.5)
7.27 ± 0.1 6.74 ± 0.0
1376(20.3) 5711(79.7)
0.47 ± 0.0 0.39 ± 0.0
1376(20.3) 5700(79.7
7.67 ± 0.1 6.56 ± 0.0
Abbreviations: CVD, COPD, Cardiovascular Diseases.
More study in this vulnerable population is needed. Our objective was to examine the association of PM2.5 air pollution exposure with inflammatory biomarkers, specifically CRP and WBC, of cardiovascular risk in adult NHANES participants with MetS compared to participants without the condition. We found that participants with MetS had a significant increase in CRP with increase in PM2.5 air pollution at lag time 0 compared to participants without MetS. In the multiethnic study of atherosclerosis (MESA), higher levels of PM2.5 on the day of the blood draw were associated with higher CRP concentration (1% increase (95% CI: 0–3%) increase for each 5 micrograms difference in PM2.5) (Hajat et al., 2015). We cannot explain why there was no observed increase in single lag day 1, 2, or 3 in our study. The major difference of our study compared to the majority of investigations of PM2.5 and biomarkers is that the metabolic syndrome population applied a rigorous definition of who qualified for the condition, having met three of the five criteria at the time of their exam and blood draw. The presence of metabolic syndrome may result in a more acute response to fine particulate matter. This study supports the finding that pre-existing cardio-metabolic diseases may confer susceptibility to particle-induced systemic inflammation. Previous epidemiological studies have found stronger effects of air pollution on inflammatory markers among diabetics (Dubowsky et al., 2006; Ostro et al., 2014; Emmerechts et al., 2012) and the obese (Zeka et al., 2006). Controlled human exposure studies in MetS participants (Devlin et al., 2014) found significant positive associations of CRP to particle air pollution compared to studies in healthy adults (Samet et al., 2009; Ghio et al., 2003; Graff et al., 2009).
statistical significance. In bi-pollutant models (adjusted for ozone), the point estimates were enhanced (Fig. 2b). Also shown in Figs. 1 and 2, are percent change in CRP and WBC by metabolic syndrome status for longer time periods of PM2.5 exposure (30 and 60 days and annual average). It can be noted that in all exposure categories, those individuals with metabolic syndrome have a greater response to PM2.5 than those without metabolic syndrome after adjustment for covariates. The confidence intervals for these measures reflect a longer time period of observation and therefore greater variability. 3.1. Sensitivity analysis The results of separate sensitivity analyses excluding individuals with history of (a) rheumatoid arthritis; (b) chronic obstructive pulmonary disease; (c) acute infection in last 30 days; and (d) household smoking were largely similar. Additionally, adjusting for season and year led to similar results (Data not shown). 4. Discussion PM2.5 was found to be significantly related to CRP after the adjustment for ozone and other factors (10.1% increase (95% CI: 2.2–18.6%) on lag day 0. Although the results were not statistically significant, there was an overall positive increase in WBC associated with for PM2.5 on lag day 0 − 6.9% increase (95% CI: −0.06–0.26%)”. The risk estimates are in the same direction but CRP may be a more specific marker of inflammation in those with metabolic syndrome. 488
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Fig. 1. (a) Effect Modification of associations between PM2.5 and C - reactive protein (CRP) by metabolic syndrome. Models are adjusted for age, gender, race/ethnicity, education, smoking status, history of cardiovascular disease, and maximum apparent temperature. 1(b) Models were also adjusted for the same lag ozone.
studies (Schwartz, 2001). As a result, this investigation suggests that even at a low level of air pollution, those with MetS are at increased risk of higher levels of inflammatory markers associated with cardiovascular disease when exposed to particulate matter air pollution. Although there is potential of exposure misclassification in rural areas as air quality monitors are fewer in number increasing the distance between the locations of monitors, Berrocal et al. has shown that by applying the downscaler methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, they obtained respectively, a 5% and a 15% predictive gain in overall predictive mean square error over their earlier downscaler model (Berrocal et al., 2012). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites. The effect on the results presented in this paper would be to increase the precision of the exposure estimates in more rural areas that are farther away from USEPA monitors.
However, there are only a few studies that have examined the relationship of PM2.5 with CRP and WBC count in people with MetS in a non-controlled setting and none with as large a sample size and complete ascertainment of air pollution levels. Dubowsky et al. (2006) found consistently significant positive associations of CRP with moving averages of 1–7 days of PM2.5 exposure by presence of diabetes or obesity individually, or diabetes, obesity and hypertension together in a panel study of 44 elderly participants (Dubowsky et al., 2006). In the same study, there was a non-significant increased response for WBC count. The stronger association of PM2.5 exposure with biomarkers of cardiovascular risk in individuals with MetS may indicate that those who are already at higher risk of CVD are already primed with cellular machinery for the generation of excess reactive oxygen species and proinflammatory responses (Roberts et al., 2006). The acute response on lag day 0 for CRP noted in this study is consistent with this potential mechanism. To our knowledge, this is the first nationwide population-based study examining the association of short and long-term exposure to PM2.5 air pollution with inflammatory biomarkers of cardiovascular risk in individuals with MetS. The PM2.5 exposure was assessed by using pollutant predictions at the population weighted centroid of the CT using a downscaling model approach from the Environmental Protection Agency (Berrocal et al., 2012). This approach allows use of health data for the majority of the US instead of being limited to urban areas due to its ability to predict air pollutant concentrations for a large spatial extent and makes study findings more generalizable. It also better predicts temporal variability indicative of air pollutant concentrations measured at air quality monitors compared with earlier CMAQ models and spatial interpolation methods (Berrocal et al., 2012; Sacks et al., 2014). Our study was able to consider health effects at the lower end of ambient particulate matter exposure compared to previous
5. Conclusion Non-pregnant adults with MetS, compared to those without MetS, showed a stronger positive response in systemic inflammatory markers associated with cardiovascular disease, as manifested by an acute elevation in CRP on lag day 0, in association with increases in PM2.5 particulate air pollution. With one third of the U.S. population meeting criteria for MetS, the health impact of particulate air pollution in this sensitive population has the potential to be significant. Acknowledgements We would like to acknowledge Dr. David Holland of the Environmental Protection Agency for providing us the population weighted census tract level daily predictions of O3 and PM2.5. 489
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Fig. 2. (a) Effect Modification of associations between PM2.5 and White Blood Cell (WBC) counts by metabolic syndrome. Models are adjusted for age, gender, race/ethnicity, education, smoking status, history of cardiovascular disease, and maximum apparent temperature. Fig. 2(b) Models were also adjusted for the same lag ozone.
Funding
Grundy, S.M., 2008. Metabolic syndrome pandemic. Arterioscler. Thromb. Vasc. Biol. 28 (4), 629–636. Hajat, A., Allison, M., Diez-Roux, A.V., et al., 2015. Long-term exposure to air pollution and markers of inflammation, coagulation, and endothelial activation: a repeatmeasures analysis in the Multi-Ethnic Study of Atherosclerosis (MESA). Epidemiology 26 (3), 310–320. Morley, J.J., Kushner, I., 1982. Serum C-reactive protein levels in disease. Ann. N. Y. Acad. Sci. 389, 406–418. National Center for Health Statistics, 2014. National Health and Nutrition Examination Survey 2001–2008 questionnaires, datasets, and related documentation. 〈http:// www.cdc.gov/nchs/nhanes.htm〉 (Accessed January 2014). Noda, H., Iso, H., Saito, I., Konishi, M., Inoue, M., Tsugane, S., 2009. The impact of the metabolic syndrome and its components on the incidence of ischemic heart disease and stroke: the Japan public health center-based study. Hypertens. Res. 32 (4), 289–298. Ong, K.L., Allison, M.A., Cheung, B.M., Wu, B.J., Barter, P.J., Rye, K.A., 2013. Trends in C-reactive protein levels in US adults from 1999 to 2010. Am. J. Epidemiol. 177 (12), 1430–1442. Ostro, B., Malig, B., Broadwin, R., et al., 2014. Chronic PM2.5 exposure and inflammation: determining sensitive subgroups in mid-life women. Environ. Res. 132, 168–175. Park, S.K., Auchincloss, A.H., O'Neill, M.S., et al., 2010. Particulate air pollution, metabolic syndrome, and heart rate variability: the multi-ethnic study of atherosclerosis (MESA). Environ. Health Perspect. 118 (10), 1406–1411. Roberts, C.K., Barnard, R.J., Sindhu, R.K., Jurczak, M., Ehdaie, A., Vaziri, N.D., 2006. Oxidative stress and dysregulation of NAD(P)H oxidase and antioxidant enzymes in diet-induced metabolic syndrome. Metab.: Clin. Exp. 55 (7), 928–934. Rooney, T., Scherzer, R., Shigenaga, J.K., Graf, J., Imboden, J.B., Grunfeld, C., 2011. Levels of plasma fibrinogen are elevated in well-controlled rheumatoid arthritis. Rheumatology 50 (8), 1458–1465. Sacks, J.D., Rappold, A.G., Davis Jr., J.A., Richardson, D.B., Waller, A.E., Luben, T.J., 2014. Influence of urbanicity and county characteristics on the association between ozone and asthma emergency department visits in North Carolina. Environ. Health Perspect. 122 (5), 506–512. Samet, J.M., Rappold, A., Graff, D., et al., 2009. Concentrated ambient ultrafine particle exposure induces cardiac changes in young healthy volunteers. Am. J. Respir. Crit. Care Med. 179 (11), 1034–1042. Schwartz, J., 2001. Air pollution and blood markers of cardiovascular risk. EHP Toxicogenom.: J. Natl. Inst. Environ. Health Sci. 109 (suppl 3), 405. Sin, D.D., Man, S.F., 2003. Why are patients with chronic obstructive pulmonary disease at increased risk of cardiovascular diseases? The potential role of systemic
This study has been funded by the US Centers for Disease Control (CDC - Environmental Public Health Tracking - 200-2010-3744300004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Alberti, K.G., Eckel, R.H., Grundy, S.M., et al., 2009. Harmonizing the metabolic syndrome: a joint interim statement of the International diabetes Federation Task force on Epidemiology and prevention; National heart, lung, and blood Institute; American heart Association; World heart Federation; International atherosclerosis Society; and International Association for the study of obesity. Circulation 120 (16), 1640–1645. Arnlov, J., Ingelsson, E., Sundstrom, J., Lind, L., 2010. Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation 121 (2), 230–236. Berrocal, V.J., Gelfand, A.E., Holland, D.M., 2012. Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics 68 (3), 837–848. Centers for Disease Control and Prevention, 2013. CDC WONDER. 〈http://wonder.cdc. gov/nasa-nldas.html〉. Chen, J.C., Schwartz, J., 2008. Metabolic syndrome and inflammatory responses to longterm particulate air pollutants. Environ. Health Perspect. 116 (5), 612–617. Devlin, R.B., Smith, C.B., Schmitt, M.T., et al., 2014. Controlled exposure of humans with metabolic syndrome to concentrated ultrafine ambient particulate matter causes cardiovascular effects. Toxicol. Sci.: Off. J. Soc. Toxicol. 140 (1), 61–72. Dubowsky, S.D., Suh, H., Schwartz, J., Coull, B.A., Gold, D.R., 2006. Diabetes, obesity, and hypertension may enhance associations between air pollution and markers of systemic inflammation. Environ. Health Perspect. 114 (7), 992–998. Emmerechts, J., Jacobs, L., Van Kerckhoven, S., et al., 2012. Air pollution-associated procoagulant changes: the role of circulating microvesicles. J. Thromb. Haemost.: JTH 10 (1), 96–106. Ghio, A.J., Hall, A., Bassett, M.A., Cascio, W.E., Devlin, R.B., 2003. Exposure to concentrated ambient air particles alters hematologic indices in humans. Inhal. Toxicol. 15 (14), 1465–1478. Graff, D.W., Cascio, W.E., Rappold, A., Zhou, H., Huang, Y.C., Devlin, R.B., 2009. Exposure to concentrated coarse air pollution particles causes mild cardiopulmonary effects in healthy young adults. Environ. Health Perspect. 117 (7), 1089–1094.
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United States Environmental Protection Agency, 2013. Fused Air Quality Surfaces Using Downscaling. 〈http://www.epa.gov/esd/land-sci/lcb/lcb_faqsd.html〉. Venn, A., Britton, J., 2007. Exposure to secondhand smoke and biomarkers of cardiovascular disease risk in never-smoking adults. Circulation 115 (8), 990–995. Wilson, P.W., Kannel, W.B., Silbershatz, H., D'Agostino, R.B., 1999. Clustering of metabolic factors and coronary heart disease. Arch. Intern. Med. 159 (10), 1104–1109. Zeka, A., Sullivan, J.R., Vokonas, P.S., Sparrow, D., Schwartz, J., 2006. Inflammatory markers and particulate air pollution: characterizing the pathway to disease. Int. J. Epidemiol. 35 (5), 1347–1354.
inflammation in chronic obstructive pulmonary disease. Circulation 107 (11), 1514–1519. Steadman, R.G., 1979. The assessment of sultriness. Part I: a temperature-humidity index based on human physiology and clothing science. J. Appl. Meteorol. 18 (7), 861–873. Suzuki, T., Hirata, K., Elkind, M.S., et al., 2008. Metabolic syndrome, endothelial dysfunction, and risk of cardiovascular events: the Northern Manhattan Study (NOMAS). Am. Heart J. 156 (2), 405–410. United States Census Bureau, 2013. Centers of Population for Census 2000. 〈https:// www.census.gov/geo/reference/centersofpop2000.html〉.
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Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome
T
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Arvind Dabassa, Evelyn O. Talbotta, , Judith R. Ragera, Gary M. Marshb, Arvind Venkatc, Fernando Holguind, Ravi K. Sharmae a
University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA, USA University of Pittsburgh, Graduate School of Public Health, Department of Biostatistics, Pittsburgh, PA, USA Allegheny Health Network, Department of Emergency Medicine, Pittsburgh, PA, USA d University of Colorado, School of Medicine, Pulmonary Disease and Critical Care Medicine, Aurora, CO, USA e University of Pittsburgh, Graduate School of Public Health, Department of Behavioral and Community Health Sciences, Pittsburgh, PA, USA b c
A R T I C L E I N F O
A B S T R A C T
Keywords: Air pollution Cardiovascular disease Metabolic syndrome PM2.5 Environmental public health tracking
Background: There has been no investigation to date of adults with metabolic syndrome examining the association of short and long-term exposure to fine particulate matter (PM2.5) air pollution with cardiovasculardisease related inflammatory marker (WBC and CRP) levels in a nationally representative sample. The goal of this study is to assess the susceptibility of adults with metabolic syndrome to PM2.5 exposure as suggested by increased cardiovascular-disease related inflammatory marker levels. Methods: A cross sectional analysis of adult National Health and Nutrition Examination Survey (NHANES) participants (2000–2008) was carried out with linkage of CDC WONDER meteorological data and downscaler modeled USEPA air pollution data for census tracts in the continental United States. Participants were non-pregnant NHANES adults (2000–2008) with complete data for evaluating presence of metabolic syndrome and laboratory data on WBC and CRP. Exposures studied included short (lags 0–3 days and their averages), long-term (30 and 60 day moving and annual averages) PM2.5 exposure levels at the census tract level in the continental United States. The main outcomes included CRP and WBC levels the day of NHANES study visit analyzed using multiple linear regression, adjusting for age, gender, race, education, smoking status, history of any cardiovascular disease, maximum apparent temperature and ozone level, for participants with and without metabolic syndrome. Results: A total of 7134 NHANES participants (35% with metabolic syndrome) met the inclusion criteria. After adjusting for confounders, we observed a significant effect of PM2.5 acutely at lag day 0 on CRP level; a 10 µg/m3 rise in lag day 0 PM2.5 level was associated with a 10.1% increase (95% CI: 2.2–18.6%) in CRP levels for participants with metabolic syndrome. For those without metabolic syndrome, the change in CRP was −1.3% (95% CI −8.8%, 6.8%). There were no significant associations for WBC count. In this first national study of the effect of PM2.5 air pollution on levels of cardiovascular-disease related inflammatory markers in adults with metabolic syndrome, CRP levels were found to be significantly increased in those with this condition with increased fine particulate matter levels at lag day 0. With one third of US adults with metabolic syndrome, the health impact of PM2.5 in this sensitive population may be significant.
1. Introduction Metabolic syndrome (MetS), defined by a combination of individual cardiovascular risk factors (hypertension, dyslipidemia (elevated triglycerides and lowered high-density lipoprotein cholesterol), raised fasting glucose, and central obesity), increases the likelihood of
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cardiovascular disease (CVD) (Grundy, 2008; Wilson et al., 1999; Suzuki et al., 2008) and is associated with systemic inflammation (Alberti et al., 2009). MetS increases the risk of CVD to an extent greater than that conferred by any of its individual components (Wilson et al., 1999; Arnlov et al., 2010; Noda et al., 2009). Individuals with MetS have greater susceptibility to autonomic dysfunction (e.g. heart
Correspondence to: A526 Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA 15261, USA. E-mail address: [email protected] (E.O. Talbott).
https://doi.org/10.1016/j.envres.2017.11.042 Received 19 July 2017; Received in revised form 31 October 2017; Accepted 23 November 2017 0013-9351/ © 2017 Elsevier Inc. All rights reserved.
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In addition to these biomarkers, we also obtained data on demographic variables (including age, gender, race/ethnicity, and education) and potential risk factors for cardiovascular disease, such as smoking status and history of any cardiovascular diseases (congestive heart failure, coronary heart diseases, angina/angina pectoris, heart attack, or stroke). Smoking status was defined as follows: current-presently smoking cigarettes or serum cotinine levels were greater than or equal to 10 ng/mL; former-have smoked 100 cigarettes in life but currently not smoking; never-had not smoked at least 100 cigarettes in life. We also obtained data from NHANES on infections within the last 30 days (cold, gastrointestinal illness, flu/pneumonia/ear infection), chronic obstructive pulmonary disease (COPD) (includes emphysema and chronic bronchitis), household smoker presence, and rheumatoid arthritis for conducting sensitivity analyses.
rate variability) in response to PM2.5 exposure (Park et al., 2010). Previous studies have shown that the association between particulate matter (PM) exposure and systemic inflammation may be stronger among participants with diabetes, obesity, and hypertension (Dubowsky et al., 2006; Ostro et al., 2014; Emmerechts et al., 2012; Zeka et al., 2006; Chen and Schwartz, 2008). Dubowsky et al. studied 44 people with hypertension, diabetes or obesity and examined the association of exposure to ambient PM2.5 during the previous week on WBC and CRP. They noted associations between PM2.5 and WBC, with a 5.5% [95% CI, 0.10–11%)] increase per interquartile increase (5.4 μg/ m3) of PM2.5 averaged over the previous week. This was based on a very small sample, however. Chen and Schwartz (2008) studied the effect of longer term exposure to PM10 on WBC among NHANES survey 1988–1994 participants with and without MetS, linking the residence of those in urban areas to an air monitor. They noted increased WBC levels related to increased one-year average PM10 exposure among individuals with MetS. No study to date, however, has considered the association of both short-term and longer term exposure to PM2.5 on the inflammatory markers WBC and CRP with all adult participants of a nationally representative (NHANES) population. Our objective is to use data from the National Health and Nutrition Examination Survey (NHANES) for 2001–2008, a US based national sample, to investigate the potential susceptibility of participants with MetS to acute and longer term exposure to PM2.5 air pollution for markers of systemic inflammation, specifically C-reactive protein (CRP) and white blood cell (WBC) count, compared to participants without MetS. Our hypothesis is that acute and/or longer term exposure to PM2.5 particulate air pollution will result in a greater inflammatory response among those with MS than among those without the condition. Unlike previous investigations, all individuals in this large sample will be assigned an exposure level based on nationwide modeled USEPA PM2.5 data with the national sample population weighted to represent the continental US as a whole.
2.3. Definition of metabolic syndrome MetS was defined as per the Joint Scientific Statement Harmonizing the Metabolic Syndrome (Alberti et al., 2009). According to these guidelines, MetS is defined as the presence of three or more of the following five conditions: high blood pressure, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), elevated fasting glucose, and abdominal obesity. High blood pressure was defined as systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg. For this analysis, high blood pressure could also be defined by a self-report of current use of antihypertensive medication. Hypertriglyceridemia was identified based on triglycerides ≥150 mg/ dL. Low HDL-C was a level of < 40 mg/dL in men or < 50 mg/dL in women. Elevated fasting glucose (EFG) was defined as fasting glucose ≥100 mg/dL; for this analysis, EFG could also be defined by a selfreport of current use of insulin or oral hypoglycemic. The last criterion was abdominal obesity, defined as > 88 cm for women and > 102 cm for men. The details of the adult questionnaire, MEC examination, and laboratory tests for profiling MetS risk factors have been described on the CDC website (National Center for Health Statistics, 2014).
2. Methods
2.3.1. Ambient air pollution and weather data Predictions of daily ambient 24-h average PM2.5 (µg/m3) and 8-h maximum O3 levels (ppb) were obtained from the USEPA using a downscaling modeling approach (Berrocal et al., 2012). This downscaling approach uses Bayesian space-time modeling to combine air monitoring data and gridded numerical output from the Community Multi-Scale Air Quality Model (CMAQ) to produce point level daily air pollution predictions to the year 2000 US census tract centroids (United States Environmental Protection Agency, 2013). Daily predictions of O3 and PM2.5 were obtained from January 1, 2001–December 31, 2008 at the population weighted centroid (centers of population) of each year 2000 US census tract (CT) in the 48 continental states (United States Census Bureau, 2013). Meteorological data were obtained from the CDC WONDER North America Land Data Assimilation System Daily Air Temperatures and Heat Index (1979–2010) website (Centers for Disease Control and Prevention, 2013). Daily values of the maximum air temperature and maximum heat index (HI) for each county were extracted for the study time period (1/1/2001-12/31/2008). HI incorporates both temperature and relative humidity and is a better measure on days when air temperature > 80 F°. Maximum HI was provided for those days when air temperature was above 80 F° or 26.7 °C. CDC used a formula by Steadman to calculate the hourly HI, from which the daily maximum HI was computed (Steadman, 1979). For our analysis, we computed a daily maximum apparent air temperature which was defined as the daily maximum HI if provided; otherwise the daily maximum air temperature was used. The environmental database of daily pollution data and meteorological data was assembled for each CT in the 48 continental United States for the study time period. This large database contained
2.1. Health data We used health data from the NHANES conducted by the Centers for Disease Control and Prevention (CDC) for the period 2001–2008. Details about this survey and the specific measurement procedures and protocols have been described on the CDC website (National Center for Health Statistics, 2014). In brief, the NHANES follows a complex, stratified, and multistage probability sampling of the population of the US, with oversampling of minorities (African Americans and Mexican Americans) and the elderly (≥ 60 years of age). The survey consisted of an extensive household interview followed by a series of laboratory and other physical tests administered in a mobile examination center (MEC). Only those who completed the household interviews were invited for the MEC examination. Since 1999, the NHANES has been continuously conducted in two-year cycles. The NHANES public use data sets were accessed for the four cycles of 2001–2008. This time period coincided with the availability of USEPA modeled data by census tract for the continental US and provided an opportunity for the study. Study participants were all non-pregnant adults age > 20 with information available on all five criteria of MetS (see definitions below). After exclusions, including participants with missing data on biomarker levels and covariates of interest, there were 7134 and 7123 participants included in our analysis for CRP and WBC, respectively. 2.2. Markers of inflammation and covariates The cardiac inflammatory biomarkers examined in this study were CRP and WBC count, as these biomarkers were available in NHANES for the entire 2001–2008 period for which the US Environmental Protection Agency downscaler air pollution data was also available. 486
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predicted values of PM2.5 and O3 at the population weighted centroid of each CT based on the 2000 US Census data. Maximum apparent temperature for each county was assigned to the appropriate CT level. In addition to the daily levels (lag 0), we calculated the following for PM2.5 and O3: the level on the previous day (lag 1); two days prior (lag 2); three days prior (lag 3); the average of lags 0 and 1 (lag 0–1); average of lags 0, 1 and 2 (lag 0–2); average of lags 0, 1, 2, and 3 (lag 0–3); average of lags 1 and 2 (lag 1–2); and the average of lags 1, 2, and 3 (lag 1–3). The following long-term averages were also calculated: the average of the 30 days prior (30-day moving average), 60 days prior (60-day moving average), and annual average.
Table 1 Distribution of environmental variables.a
2.4. Merging of health and environmental data
Year
N
PM2.5 (µg/m3)
O3 (ppb)
Temperature (°C)
2001 2002 2003 2004 2005 2006 2007 2008 2001–2008
812 922 799 859 766 832 1054 1090 7134
11.00 ± 0.76 12.60 ± 1.66 12.20 ± 0.91 11.38 ± 0.92 11.78 ± 0.83 10.81 ± 0.73 12.80 ± 1.61 11.51 ± 0.75 11.74 ± 0.37
41.48 ± 1.79 47.00 ± 3.54 43.18 ± 2.21 38.93 ± 1.72 42.70 ± 2.60 39.56 ± 1.69 44.35 ± 3.25 43.03 ± 2.19 42.47 ± 0.98
23.57 ± 1.47 23.27 ± 1.27 21.76 ± 0.93 22.39 ± 1.59 21.04 ± 1.32 21.25 ± 1.09 21.78 ± 1.97 22.22 ± 1.20 22.14 ± 0.48
a Data is shown for the 7134 individuals included in the CRP analysis; there were 11 fewer of these individuals included in the WBC analysis (data not shown).
The CT (11 digit Federal Information Processing Standards code) of residence of each individual and the date of the NHANES examination were used to merge the NHANES data with the environmental dataset of air pollution and weather. Thus, each participant was assigned a daily PM2.5, O3, and temperature exposure based on the CT of residence at time of blood draw.
NC, US. Descriptive analyses were conducted using PROC SURVEYMEANS and PROC UNIVARIATE. All regression models were run accounting for the complex sampling design of the NHANES with the SAS SURVEYREG command by using the sample weights included in the datasets. P-values were 2-tailed, and < 0.05 was considered significant.
2.4.1. Statistical analysis We examined exposure to ambient PM2.5 as a predictor of each biomarker of interest, CRP and WBC, in separate regression models. CRP was log transformed to improve normality and stabilize the variance. To evaluate the short-term effects of PM2.5, we analyzed the effect of PM2.5 on the day of the blood draw (lag 0) as well on the day before (lag 1), two days before (lag 2), and three days before (lag 3) and averages of these time periods (lag 0–1, lag 0–2, lag 0–3, lag 1–2, and lag 1–3). In addition, we examined the long-term effects of PM2.5 on each biomarker by using the average PM2.5 in the 30 days prior, 60 days prior and annual average value. We also examined the long-term effects of PM2.5 after adjusting for short-term effects of air pollution (lag 0–3 of PM2.5 and O3). We used multiple linear regression models to assess the association of PM2.5 with each biomarker in subgroups: participants with and without MetS. The regression estimates were calculated for a 10 μg/m3 increase in PM2.5 after controlling for selected covariates based on prior biological and epidemiological knowledge of major determinants of cardiovascular health (Ong et al., 2013). Age was treated as a continuous variable, whereas gender, smoking (current smokers versus never and former smoker), and history of any CVD were treated as dichotomous variables in the models. Race/ethnicity was categorized as non-Hispanic White, non-Hispanic Black, Mexican American, and other races. Education was categorized as 1st–11th grade, high school graduate/GED or some college, and college graduate. Models were run adjusted for the co-pollutant O3 at the same lag or average as PM2.5. Shortterm models were adjusted for maximum apparent temperature (MAT) at the zero lag. The 30 day and 60 day moving average models were adjusted for a 30 day MAT. The annual average models were not adjusted for temperature. Quartiles of temperature were used to account for the non-linear relationship of temperature with biomarkers.
3. Results There were 7134 and 7123 non-pregnant adult participants for CRP and WBC analysis, respectively, with information available on all five MetS criteria, excluding participants with missing biomarker data and covariates of interest. Of these, N = 2789 (35.3% weighted frequency) for CRP analysis group and N = 2782 (35.0% weighted frequency) for WBC analysis group were defined as having MetS as they met three or more criteria per its definition (Alberti et al., 2009). Table 1 shows the distribution of environmental exposures to PM2.5, ozone and weather information by year from 2001 to 2008 at the level of participant's address on the day of blood draw. The mean ± standard error of PM2.5 (µg/m3), O3 (ppb), and maximum apparent temperature (°C) were 11.74 ± 0.37, 42.47 ± 0.98, and 22.14 ± 0.48, respectively. The data is shown for the 7134 individuals who were included in the CRP analysis; 7123 of these individuals were also included in the analysis of WBC. Table 2 shows the survey weighted descriptive statistics of biomarkers for non-pregnant adult participants with information available on all five MetS criteria, excluding participants with missing data on CRP and WBC levels and covariates of interest. The CRP levels were raised in Black, older, and male participants; whereas WBC count was higher in younger, male, and races other than Black. The levels of both biomarkers in individuals were elevated in current smokers, (lower education, and history of any CVD, rheumatoid arthritis, chronic obstructive pulmonary disease, and recent infections. In single and double pollutant PM2.5 models, there was an increased response of % change in CRP in participants who had MetS compared to those without the condition after adjusting for age, gender, race, education, smoking status, history of any CVD, and maximum apparent temperature (see Fig. 1(a), single pollutant model (PM2.5 and Fig. 1(b), bi-pollutant model). The increased response was significant for lag day zero indicating an acute response in the bi-pollutant models (i.e., adjusting for ozone). There was a significant positive association of lag 0 PM2.5 for participants with MetS. For every 10 µg/m3change in PM2.5, there was an increase of 10.1% (2.2–18.6%) for CRP. For those without MetS, the change in CRP was −1.3% (95% CI −8.8%, 6.8%). Similar to CRP, there was an increase in WBC count in participants who had MetS compared to those who did not for both short and longterm exposure in single pollutant PM2.5 models, after adjusting for age, gender, race, education, smoking status, history of any cardiovascular disease, and maximum apparent temperature. The increased response was more pronunced for long-term exposure, but did not reach
2.5. Sensitivity analyses Certain medical conditions (such as rheumatoid arthritis and COPD), acute infections, and presence of household smoking have been related to elevated levels of inflammatory markers (Venn and Britton, 2007; Sin and Man, 2003; Rooney et al., 2011; Morley and Kushner, 1982). Therefore, we investigated the sensitivity of our results to alternate ways of modeling by running separate models excluding people with histories of (a) rheumatoid arthritis; (b) COPD; (c) acute infection in last 30 days; and (d) household smoking. We also examined our results after controlling for season and year because temperature and pollutants show seasonal and yearly variations. All analyses were performed using SAS software, version 9.2, Cary, 487
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Table 2 Survey weighted descriptive statistics of biomarkers for non-pregnant adult participants with information available on all five criteria of metabolic syndrome. C - reactive protein
Overall Age (years) 20–39 40–59 60+ Gender Male Female Race/Ethnicity White Black Hispanic Other Education 1st–11th grade HS grad/GED or some college College graduate Smoking Never Former Current History of any CVD Yes No Recent Infection Yes No Rheumatoid Arthritis Yes No COPD Yes No Household Smoker Yes No
White blood cells
N (%) 7134(100)
Mean ± SE (mg/dL) 0.4 ± 0.0)
N (%) 7123(100)
Mean ± SE (103 cells/µL) 6.78 ± 0.0
2302(38.6) 2321(39.3) 2511(22.2)
0.35 ± 0.0 0.42 ± 0.0 0.46 ± 0.0
2299(38.5) 2318(39.3) 2506(22.2
6.85 ± 0.1 6.73 ± 0.1 6.77 ± 0.1
3651(49.7) 3483(50.4)
0.34 ± 0.0 0.47 ± 0.0
3645(49.7) 3478(50.33)
6.83 ± 0.1 6.74 ± 0.1
3722(72.4) 1363(10.9) 1381(7.6) 668(9.1)
0.39 ± 0.0 0.50 ± 0.0 0.41 ± 0.0 0.39 ± 0.0
3717(72.4) 1362(10.9) 1380(7.6) 664(9.1)
6.84 ± 0.1 6.25 ± 0.1 6.85 ± 0.1 6.89 ± 0.1
1993(17.8) 3708(56.4) 1433(25.8)
0.44 ± 0.0 0.43 ± 0.0 0.32 ± 0.0
1987(17.8) 3704(56.5) 1432(25.7)
7.12 ± 0.1 6.87 ± 0.1 6.37 ± 0.1
3653(51.1) 1894(24.6) 1587(24.3)
0.38 ± 0.0 0.41 ± 0.0 0.45 ± 0.0
3645(51.1) 1891(24.6) 1587(24.4)
6.38 ± 0.0 6.56 ± 0.1 7.85 ± 0.1
801(8.2) 6333(91.9)
0.56 ± 0.1 0.39 ± 0.0
798(8.2) 6325(91.9)
7.19 ± 0.1 6.75 ± 0.0
1904(27.1) 4981(72.9)
0.54 ± 0.0 0.36 ± 0.0
1903(27.1) 4972(72.9)
7.02 ± 0.1 6.69 ± 0.1
369(4.0) 6754(95.1)
0.57 ± 0.0 0.40 ± 0.0
367(4.0) 6745(95.1)
7.10 ± 0.2 6.77 ± 0.0
538(7.4) 6574(92.6)
0.65 ± 0.1 0.38 ± 0.0
539(7.5) 6562(92.5)
7.27 ± 0.1 6.74 ± 0.0
1376(20.3) 5711(79.7)
0.47 ± 0.0 0.39 ± 0.0
1376(20.3) 5700(79.7
7.67 ± 0.1 6.56 ± 0.0
Abbreviations: CVD, COPD, Cardiovascular Diseases.
More study in this vulnerable population is needed. Our objective was to examine the association of PM2.5 air pollution exposure with inflammatory biomarkers, specifically CRP and WBC, of cardiovascular risk in adult NHANES participants with MetS compared to participants without the condition. We found that participants with MetS had a significant increase in CRP with increase in PM2.5 air pollution at lag time 0 compared to participants without MetS. In the multiethnic study of atherosclerosis (MESA), higher levels of PM2.5 on the day of the blood draw were associated with higher CRP concentration (1% increase (95% CI: 0–3%) increase for each 5 micrograms difference in PM2.5) (Hajat et al., 2015). We cannot explain why there was no observed increase in single lag day 1, 2, or 3 in our study. The major difference of our study compared to the majority of investigations of PM2.5 and biomarkers is that the metabolic syndrome population applied a rigorous definition of who qualified for the condition, having met three of the five criteria at the time of their exam and blood draw. The presence of metabolic syndrome may result in a more acute response to fine particulate matter. This study supports the finding that pre-existing cardio-metabolic diseases may confer susceptibility to particle-induced systemic inflammation. Previous epidemiological studies have found stronger effects of air pollution on inflammatory markers among diabetics (Dubowsky et al., 2006; Ostro et al., 2014; Emmerechts et al., 2012) and the obese (Zeka et al., 2006). Controlled human exposure studies in MetS participants (Devlin et al., 2014) found significant positive associations of CRP to particle air pollution compared to studies in healthy adults (Samet et al., 2009; Ghio et al., 2003; Graff et al., 2009).
statistical significance. In bi-pollutant models (adjusted for ozone), the point estimates were enhanced (Fig. 2b). Also shown in Figs. 1 and 2, are percent change in CRP and WBC by metabolic syndrome status for longer time periods of PM2.5 exposure (30 and 60 days and annual average). It can be noted that in all exposure categories, those individuals with metabolic syndrome have a greater response to PM2.5 than those without metabolic syndrome after adjustment for covariates. The confidence intervals for these measures reflect a longer time period of observation and therefore greater variability. 3.1. Sensitivity analysis The results of separate sensitivity analyses excluding individuals with history of (a) rheumatoid arthritis; (b) chronic obstructive pulmonary disease; (c) acute infection in last 30 days; and (d) household smoking were largely similar. Additionally, adjusting for season and year led to similar results (Data not shown). 4. Discussion PM2.5 was found to be significantly related to CRP after the adjustment for ozone and other factors (10.1% increase (95% CI: 2.2–18.6%) on lag day 0. Although the results were not statistically significant, there was an overall positive increase in WBC associated with for PM2.5 on lag day 0 − 6.9% increase (95% CI: −0.06–0.26%)”. The risk estimates are in the same direction but CRP may be a more specific marker of inflammation in those with metabolic syndrome. 488
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Fig. 1. (a) Effect Modification of associations between PM2.5 and C - reactive protein (CRP) by metabolic syndrome. Models are adjusted for age, gender, race/ethnicity, education, smoking status, history of cardiovascular disease, and maximum apparent temperature. 1(b) Models were also adjusted for the same lag ozone.
studies (Schwartz, 2001). As a result, this investigation suggests that even at a low level of air pollution, those with MetS are at increased risk of higher levels of inflammatory markers associated with cardiovascular disease when exposed to particulate matter air pollution. Although there is potential of exposure misclassification in rural areas as air quality monitors are fewer in number increasing the distance between the locations of monitors, Berrocal et al. has shown that by applying the downscaler methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, they obtained respectively, a 5% and a 15% predictive gain in overall predictive mean square error over their earlier downscaler model (Berrocal et al., 2012). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites. The effect on the results presented in this paper would be to increase the precision of the exposure estimates in more rural areas that are farther away from USEPA monitors.
However, there are only a few studies that have examined the relationship of PM2.5 with CRP and WBC count in people with MetS in a non-controlled setting and none with as large a sample size and complete ascertainment of air pollution levels. Dubowsky et al. (2006) found consistently significant positive associations of CRP with moving averages of 1–7 days of PM2.5 exposure by presence of diabetes or obesity individually, or diabetes, obesity and hypertension together in a panel study of 44 elderly participants (Dubowsky et al., 2006). In the same study, there was a non-significant increased response for WBC count. The stronger association of PM2.5 exposure with biomarkers of cardiovascular risk in individuals with MetS may indicate that those who are already at higher risk of CVD are already primed with cellular machinery for the generation of excess reactive oxygen species and proinflammatory responses (Roberts et al., 2006). The acute response on lag day 0 for CRP noted in this study is consistent with this potential mechanism. To our knowledge, this is the first nationwide population-based study examining the association of short and long-term exposure to PM2.5 air pollution with inflammatory biomarkers of cardiovascular risk in individuals with MetS. The PM2.5 exposure was assessed by using pollutant predictions at the population weighted centroid of the CT using a downscaling model approach from the Environmental Protection Agency (Berrocal et al., 2012). This approach allows use of health data for the majority of the US instead of being limited to urban areas due to its ability to predict air pollutant concentrations for a large spatial extent and makes study findings more generalizable. It also better predicts temporal variability indicative of air pollutant concentrations measured at air quality monitors compared with earlier CMAQ models and spatial interpolation methods (Berrocal et al., 2012; Sacks et al., 2014). Our study was able to consider health effects at the lower end of ambient particulate matter exposure compared to previous
5. Conclusion Non-pregnant adults with MetS, compared to those without MetS, showed a stronger positive response in systemic inflammatory markers associated with cardiovascular disease, as manifested by an acute elevation in CRP on lag day 0, in association with increases in PM2.5 particulate air pollution. With one third of the U.S. population meeting criteria for MetS, the health impact of particulate air pollution in this sensitive population has the potential to be significant. Acknowledgements We would like to acknowledge Dr. David Holland of the Environmental Protection Agency for providing us the population weighted census tract level daily predictions of O3 and PM2.5. 489
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Fig. 2. (a) Effect Modification of associations between PM2.5 and White Blood Cell (WBC) counts by metabolic syndrome. Models are adjusted for age, gender, race/ethnicity, education, smoking status, history of cardiovascular disease, and maximum apparent temperature. Fig. 2(b) Models were also adjusted for the same lag ozone.
Funding
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