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Differential exposure to combustion by-products and allergens may partially explain the marked disparity in asthma prevalence (3%–18%) among New York City neighborhoods. Subclinical changes in airway inflammation can be measured by fractional exhaled nitric oxide (FeNO). FeNO could be used to test independent effects of these environmental exposures on airway inflammation. Seven and eight year-old children from neighborhoods with lower (range 3–9%, n=119) and higher (range 11–18%, n=121) asthma prevalence participated in an asthma case-control study. During home visits, FeNO was measured, and samples of bed dust (allergens) and air (black carbon) were collected. Neighborhood built-environment characteristics were assessed for the 500m surrounding participants’ homes. Airborne black carbon concentrations in homes correlated with neighborhood asthma prevalence (P<0.001) and neighborhood densities of truck routes (P<0.001) and buildings burning residual oil (P<0.001). FeNO concentrations were higher among asthmatics with compared to asthmatics without frequent wheeze (≥4 times/year) (P=0.002). FeNO concentrations correlated with domestic black carbon among children without seroatopy (P=0.012) and with dust mite allergen among children with seroatopy (P=0.020). The association between airborne black carbon in homes and both neighborhood asthma prevalence and FeNO suggest that further public health interventions on truck emissions standards and residual oil use are warranted.
Within New York City (NYC), the prevalence of pediatric asthma varies widely by neighborhood (3–18%), with low-income neighborhoods disproportionately burdened (Garg R, 2003). Neighborhood level differences in environmental exposures may partially explain these disparities (Eder et al., 2006; Postma and Boezen, 2004). Cockroach allergen, for example, has been linked to increased asthma morbidity among sensitized asthmatics and has been shown to be higher in high (HAPN) compared with low (LAPN) asthma prevalence neighborhoods in NYC (Olmedo et al., 2011; Rosenstreich et al., 1997).
Airborne particulate matter (PM) pollution has been shown to impact asthma morbidity and mortality in urban environments (Laden et al., 2006; Pope and Dockery, 2006; Pope et al., 2009). Associations are seen between combustion by-product exposure and airway inflammation (Behndig et al., 2006; Delfino et al., 2006; Holguin et al., 2007; McCreanor et al., 2007; Nadeau et al., 2010; Peters et al., 1999), as well as between living in close proximity to vehicle traffic and asthma morbidity (Mann et al., 2010; McConnell et al., 2010; Patel et al., 2010). In a recent study of school children, air pollution exposure as measured by school monitoring was associated with increased airway inflammation, an association that was more pronounced in atopic children (Flamant-Hulin et al., 2010).
In urban communities, airborne black carbon (BC) in the home penetrates readily from outdoor sources and is often interpreted as a surrogate for diesel exposure (Jung et al., 2010; Kinney et al., 2002). However, it is important to consider other incomplete combustion sources. In NYC, residual oil (#4 and #6 oil) is used commonly for residential and commercial heating. These fuels produce more PM than #2 oil or natural gas and contribute substantially to air pollution in NYC (Cromar and Schwartz, 2010b; Seamonds et al., 2009). Buildings that burn residual oil are distributed unevenly throughout NYC (www.edf.org, 2010) and could contribute to disparities in health (Cromar and Schwartz, 2010a). We are unaware of any studies linking buildings burning residual oil to levels of BC in nearby homes or associated asthma risks.
There is increasing recognition that asthma morbidity is related to multiple exposures; however, urban asthma studies often lack sufficient statistical power to test the independent effects of several exposures on the development or exacerbation of asthma. In addition, in population-based studies, the majority of asthmatics have mild disease with infrequent exacerbations, making temporal associations between exposures and exacerbations difficult. An objective biomarker to detect subclinical changes reflecting increased risk for exacerbation could help differentiate the effects of multiple exposures, including BC. Fractional exhaled nitric oxide (FeNO) is such a biomarker (Figure 1), as it reflects eosinophilic airway inflammation in response to known asthma triggers and has been associated with the development of asthma (Caudri et al., 2010). FeNO was proposed as a biomarker of airway inflammation in response to air pollutants more than a decade ago and subsequently has been successfully employed in epidemiology studies (Delfino et al., 2006; Holguin et al., 2007; McCreanor et al., 2007; van Amsterdam et al., 2000). Importantly, FeNO could be used to test independent effects of differential environmental exposures on airway inflammation.
The NYC Neighborhood Allergy and Asthma Study (NAAS) is a case-control study of asthma among 7–8 year-old children from middle-income households in both HAPN and LAPN. We hypothesized that BC would be higher in homes in HAPN than those in LAPN and would correlate with local vehicle traffic and proximity to buildings burning #4 and #6 oil. We further hypothesized that airborne BC in the home would be associated independently with FeNO among these children. Finally, the study sought to demonstrate that FeNO, as an indicator of subclinical changes in airway inflammation, is a useful biomarker in population-based studies for testing independent effects of multiple pollutants.
The NYC Neighborhood Asthma and Allergy Study is a case-control study of children with and without asthma, described previously (Olmedo et al., 2011). Parents of 7–8 year-old children were recruited through the Health Insurance Plan of New York (HIP), a provider used primarily by a middle-income population. Neighborhoods were selected based on zip code level asthma prevalence among 5-year old children as reported by the NYC Department of Health and Mental Hygiene (Garg R, 2003). All NYC neighborhoods in the Bronx, Brooklyn, Queens and Manhattan with asthma prevalence of 3–9% (LAPN) or 11–18% (HAPN) were selected for recruitment by mail for a home visit. The cut-points in asthma prevalence (e.g., ≤9%, ≥11% ) were selected to yield an approximately equal population of potential participants (i.e., HIP members with a 7–8 year old child) in the LAPN and HAPN groups. No matching strategy beyond this recruitment from similar population sizes was employed. All parents 1) with a child turning 7–8 during the study period, 2) belonging to HIP through an employer (rather than e.g., through Medicaid), and 3) living in the selected zip codes were contacted for participation in the study. Children who did not meet all of the above criteria or whose parents said that the children were unable to complete the breathing tests due to mental or physical disabilities were excluded from the study.
Consenting parents of eligible children completed a brief screening questionnaire. The initial study design called for inviting all children with asthma (identified by the screening questionnaire) and a matched number of randomly selected controls for a home visit. In practice, the recruitment method yielded an approximately equal number of children with and without asthma symptoms. Therefore, all interested families were invited to participate in a home visit, during which a caregiver was asked to complete a detailed questionnaire on the child’s health and the family’s demographics. Children were recruited from LAPN and HAPN simultaneously throughout the year.
Asthma cases were defined by questionnaire, including the modules for wheeze from the International Study of Asthma and Allergy in Childhood (ISAAC) (Asher et al., 1995). Children were classified as asthmatic if their parent reported at least one of the following for the child in the past 12 months: 1) wheeze, 2) awakened at night by cough without having a cold, 3) wheeze with exercise or 4) report of medication use for asthma (Olmedo et al., 2011). A child not classified as a case was classified as a control. ‘Frequent wheeze’ was defined as ≥4 episodes of wheeze in the past year. For sensitivity analyses controls also were compared to children with ‘frequent asthma symptoms’, as we have defined previously (Olmedo et al., 2011).
Fine particulate matter (PM2.5) was collected by sampling air in the child’s home at 1.5L/min for 7 days starting after the home visit during which FeNO was collected. BC was quantified on the filters using a recently developed multi-wavelength optical absorption technique, which has been described previously and validated for measuring airborne BC against traditional methods in both heating and non-heating seasons (Yan BZ, 2011 (in press)). The optical device consisted of a balanced deuterium tungsten halogen light source (DH-2000-BAL), an integrating sphere (ISP-50-8-R), a lab-made filter holder, and an Ocean Optics USB4000-VIS-NIR miniature fiber-optic spectrometer. Because of disruptions to the power supply of the sampling equipment, a subset of the subjects (20%) had their air sampled for more or less than 7 days.
For a subset of the subjects (n=161) daily BC data were available from a central NYC site (Columbia University Medical Campus, Northern Manhattan, Figure E1) where BC was measured continuously by Aethalometer® from outside of a window and daily averages were calculated (Yan BZ, 2011 (in press)). Average outdoor BC was calculated for the seven days before the home visit and for the seven-day period beginning on the the day of the home. Daily outdoor central site BC measurements were used for comparison of the outdoor BC for the week before with the week during home BC sampling.
Allergens were measured in dust samples collected from the child’s bed as described previously (Olmedo et al., 2011). Der f 1 (dust mite), Fel d 1 (cat), Can f 1 (dog), Mus m 1 (mouse) were measured by multiplex bead immunoassays.(Earle et al., 2007) Bla g 2 (cockroach) was measured by ELISA (Indoor Biotechnologies, Charlottesville, VA) (Pollart et al., 1991). All results are based on the universal allergen standard curve (Chapman et al., 2009). For results below the limit of detection, values of half of the limit of detection were used in analyses.
FeNO was collected during the home visit by the offline method using a collection device described previously (#CBSK 01400-01, GE Instruments, Boulder, CO) that included a scrubber to remove ambient NO from the air that the children inspired and a noisemaker to confirm proper inhalation through the device (2005; Perzanowski et al., 2008). A minimum of three breath samples was collected at a flow of 83 ml/s from each child in individual Mylar balloons, monitoring exhalation flow and inhalation through the device. Two ambient air samples were collected at the time of testing for each child. Within 4 hours of collection, breath and ambient samples were assayed using an NO analyzer (GE Instruments, Boulder, CO). Ambient NO during collection was measured. Breath samples were considered valid only if: 1) the child exhaled at a consistent, correct flow AND, 2) EITHER the child inhaled correctly through the NO sampling device OR the ambient NO was less than 20 ppb. Previous studies have indicated contamination from ambient sources with offline collection (Linn et al., 2009). Analyses were conducted to determine a cut-point above which NO would substantially contaminate breath samples. Samples collected above this ambient NO cut-point were excluded from analyses. Ambient NO concentrations were included in all multivariable analyses. The mean of the acceptable tests was used in the analyses.
PFT were conducted in the home using a portable spirometer (Koko, nSpire Health, Longmont, CA). PFT were scored based on acceptability criteria developed by the study pulmonologists (AGC, RBM) according to ATS and ERS guidelines (Beydon et al., 2007; Loeb et al., 2008; Stanojevic et al., 2008; Stocks, 2006). Tests were considered acceptable if they met the following criteria: 1) rapid upstroke, 2) volume extrapolated <5% of forced vital capacity, 3) minimal premature termination of exhalation (premature termination = termination at >15% of peak flow), and 4) smooth exhalatory limb. A priori, FEV1/FVC was selected as the primary PFT outcome (see Discussion section).
Serum IgE antibodies against German cockroach, mouse urine, D. farinea, cat dander, dog dander, common ragweed, and mixed tree (Tx8) and grass (Gx2) pollen were measured by ImmunoCAP (Phadia, Uppsala, Sweden). Children with specific IgE ≥0.35 IU/mL against any of the allergens tested were considered seroatopic.
Children’s home addresses were geocoded and linked to a comprehensive GIS database described previously (Lovasi et al., 2009). Radial buffers were created by plotting a circle with a 500m Euclidean radius around each subject’s address. Data on residential and commercial heating oil use was obtained from the NYC Department of Environmental Protection database, which includes buildings with annually renewed permits to use boilers (www.edf.org, 2010; www.nyc.gov, 2011). The 500m geography was selected a priori as a relevant distance of density of pollutants (truck routes and buildings burning residual oil), but a smaller geography of 250m was also tested in sensitivity analyses and yielded similar results.
As FeNO, BC and allergen concentrations were log normally distributed, logarithmically transformed values were used in the analyses and geometric means reported. Neighborhood asthma prevalence, GIS variables and FEV1/FVC were not normally distributed. Thus, medians are reported and non-parametric tests were performed. Multivariable models with variables related to local outdoor and indoor sources of combustion predicting BC were tested using a generalized estimating equation model with community district used as a cluster variable and an unstructured correlation matrix with a robust estimator covariance matrix. Effect modification by season was tested with a previously defined heating season (Jung et al., 2010). Confounding by neighborhood income was also tested. Linear regression models with FeNO as the dependent variable and BC and allergens as independent variables were developed, controlling for potential confounders conventionally included in the analyses of FeNO as an outcome (e.g., sex of the child, race/ethnicity, smoker in the home, ambient NO) and have tested for effect modification by seroatopy and case status. Similar models were built using logistic regression with FEV1/FVC in the lowest quartile as the dependent variable. Data were analyzed in SPSS version 17 (Chicago, IL).
Caregivers completed telephone-screening questionnaires on 499 children. Home visits with BC, dust mite allergen and FeNO measurements were completed for 275 children. An additional 36 homes were visited, but due to technical problems, we were unable to measure either BC, dust allergens, or FeNO. Among those 275, 2% were unable to complete a valid FeNO maneuver. Ten percent (29/275) of the children were excluded from analyses due to ambient NO measured during the home visit exceeding 100ppb, a potential source of contamination (see next section). The 240 children that had a valid FeNO collected under acceptable ambient NO conditions were included in the primary analyses and were similar to those that were not included, except that they were more likely to be Hispanic and were less likely to have a low household income (Table E1 in Supplementary Information online). Seroatopic status, acceptable PFT, and neighborhood GIS variables were available for 228/240 (i.e., 95%), 217/240 (90%) and 235/240 (98%) children, respectively.
Among the subjects in our study, we observed a significant correlation between FeNO and ambient NO at NO concentrations above 100 ppb (r=0.64, P<0.001, n=29), suggesting contamination. A statistically significant correlation was not observed at ambient NO concentrations 50–100 ppb (r=0.005, P=0.98, n=37) or <50 ppb (r=0.13, P=0.073, n=203). Based on these findings, we excluded any FeNO test collected when the ambient NO exceeded 100 ppb. For those subjects with an FeNO collected at <100 ppb, ambient NO was included in all multivariable models as a potential confounder. Compared with the 240 children who were included, the 29 children excluded because of high ambient NO, had a similar proportion of cases (16/29 (55%) vs. 132/24 (55%), P= 0.99), a similar prevalence of frequent wheeze among asthmatics (3/16 (19%) vs. 28/132 (21%), P=0.82), and similar geometric mean BC over the sampling period (1.39 µg/m3 [1.12–1.72] vs. 1.36 µg/m3 [1.27–1.45], P=0.85).
The number of children from HAPN (n=121) and LAPN (n=119) were approximately equal (Figure E1). Compared with children living in LAPN, those in HAPN were more likely to be of African-American race or Hispanic ethnicity (Table 1). Few caregivers reported a household income below the poverty line, and the median [interquartile range] reported income for the cohort was $50–60K [$30–40K - $80–90K]. In general, HAPN had more Hispanic and African-American residents, as well as lower household incomes as compared with LAPN. Major truck routes, heavy daily traffic, and buildings burning residual oil were more common around homes in HAPN compared with those in LAPN. The asthmatics in the two neighborhood groups did not differ significantly in frequency of symptoms, prevalence of seroatopy, median FEV1/FVC or geometric mean FeNO. The proportion of study visits conducted during the heating season were similar in the LAPN (48%) and HAPN (45%).
To determine whether indoor BC exposure was associated with neighborhood asthma prevalence and neighborhood outdoor sources of BC, air samples were collected in homes. Geometric mean airborne BC was higher in HAPN (1.59 µg/m3 [95% C.I. 1.45–1.73]) than in LAPN (1.16 µg/m3 [1.06–1.27]) homes (P<0.001). BC correlated significantly with neighborhood asthma prevalence, density of buildings burning residual oil, and density of truck routes (Figure 2). BC also was elevated with the report of having a smoker in the home (P<0.001) and in homes visited during the heating season (P=0.040) and was correlated with street density (R=0.25, P<0.001) and distance from a major highway (R= −0.23, P<0.001). A final multivariable model after stepwise removal of non-significant variables included the independent variables 1) density of residual oil burners (β=0.004 [95% C.I, 0.002–0.007], P<0.001), 2) truck route density (β=0.096 [0.052–0.14], P<0.001), 3) report of smoker in the home (β=0.36 [0.22–0.51], P<0.001) and 4) sampling during the heating season (β=0.11 [0.023– 0.21], P=0.014). In sensitivity analyses, truck and residual oil burner density variables calculated for areas within 250m from the home and distance to residual oil burners yielded similar, although smaller correlation coefficients with BC than those with 500m boundaries.
To confirm the validity of using home BC measurements (that were collected during the week after FeNO measurement) to represent the BC exposure prior to FeNO measurement, central site BC measures were assessed among the subjects for whom data were available (n=161). Average outdoor central site BC during the home air-sampling week correlated modestly with measured home BC concentrations (R=0.34, P<0.001). There was a good correlation between average outdoor BC at a NYC central site during the week before and the week following the home visit (i.e. the week of indoor home air sampling) (R=0.55, P<0.001).
As previously reported, subjects in HAPN homes had higher cockroach, mouse, and cat and lower dust mite allergens in their bed dust than in LAPN homes (Olmedo et al., 2011). Geometric mean allergen concentrations for the 240 children included in the analyses for this paper are presented in the Supplementary Information online (Table E2).
As a result of participants unplugging air pumps, 17/240 (7.1%) homes had air sampled for less than 7 days (minimum=2.1 days). Similarly, unplugging and re-plugging in air pumps (which resets the counter) lead to 31/240 (12.9%) homes having an air sample that exceeded 7 days (maximum=15.9 days). Restricting the regression model to homes with exactly 7 days collected (n=186) yielded similar β estimates to those that included all of the homes sampled: density of residual oil burners (β=0.006 [95% C.I, 0.004–0.009], P<0.001), truck route density (β=0.099 [0.061–0.14], P<0.001) and report of smoker in the home (β=0.31 [0.18–0.45], P<0.001). All models included adjustment for home heating season (Jung et al., 2010). Local neighborhood income did not alter the associations between the main predictors and BC.
To validate FeNO as a biomarker for risk of asthma exacerbation, FeNO levels collected during the home visit were tested for correlation with symptoms, independent of lung function and seroatopy. The geometric mean FeNO concentration was 10.0 ppb [95% C.I., 9.2–10.8]. Cases had higher FeNO levels than controls (P=0.027 in unadjusted logistic regression, Figure E2A). The median [25%–75%] FEV1/FVC was 88% [84%–92%] and was associated inversely with the probability of being an asthma case (P=0.010, Figure E2B). The majority of asthmatics had normal lung function, with only 12/118 (10.2%) having an FEV1/FVC <80%. Among asthmatic children, FeNO and FEV1/FVC were associated with frequent wheeze (≥4 times past year) (Figures E2C and E2D). Among asthmatic children with normal lung function (FEV1/FVC ≥80%, n=106), FeNO was associated with probability of frequent wheeze (P=0.049, Figure E2E). FeNO levels were higher among seroatopic (12.1 ppb [11.1–13.1]) than among non-seroatopic children (7.9 ppb [7.2–8.7], P<0.001), and seroatopic asthmatics were more likely to have frequent wheeze (26.6%) than non-seroatopic asthmatics (10.9%, P=0.037). However, examining seroatopic asthmatics alone, FeNO remained significantly associated with frequent wheeze (P=0.006, Figure E2F).
To test the hypothesis that BC exposure was associated with airway inflammation, we explored the relationship in univariate and multivariable models. In the multivariable model, both dust mite allergen and BC were significant independent predictors of FeNO (Table 2A). Stratification analyses by seroatopy revealed that BC was associated with FeNO among the non-seroatopic children, while dust mite was associated with FeNO among the atopic children (Figure 3). For non-atopic children, geometric mean FeNO concentrations were significantly higher among the children in the highest quartile of BC exposure compared with those with lower exposure (9.9 ppb [0.85–11.6] vs. 7.2 [6.4–8.0], P=0.002). Among the atopic children, FeNO was significantly higher among the children in the highest quartile of Der f 1 in bed dust compared with those with lower exposure (15.3 ppb [12.2–19.2] vs. 11.1 ppb [9.7–12.8], P=0.026). There was no significant difference between FeNO concentrations for non-atopic children in the highest quartile of BC exposure and atopic children in the lower 75% of exposure to dust mite (P=0.38).
In multivariable models, the association between BC and FeNO appeared to be somewhat stronger among asthma cases (β =0.27 [0.041–0.50], P=0.021) and children with ‘frequent asthma symptoms’ (Olmedo et al., 2011) (n=57, β =0.36 [−0.015–0.72], P=0.059) than among controls (β=0.078 [−0.16–0.31], P=0.51) and stronger among boys (β=0.28 [0.030–0.53], P=0.029) than among girls (β=0.089 [−0.14–0.32], P=0.45). The BC association was statistically significant for subjects sampled during the heating season (β =0.26 [0.036–0.49], P=0.024), but not for those sampled outside of the heating season (β =0.14 [−0.098–0.38], P=0.24). Confirming the univariate findings illustrated in Figure 3 in multivariable models, BC was associated with FeNO among the non-seroatopic (β=0.23 [0.051–0.41, P=0.012), but not seroatopic children (β=0.13 [−0.16–0.41], P=0.37), and Der f 1 was associated with FeNO among the seroatopic (β=0.11 [0.018–0.21], P=0.020) but not non-seroatopic children (β=0.001 [−0.080–0.82], P=0.98).
There was no association between either BC or any of the allergens in the bed dust and having an FEV1/FVC in the lowest quartile (≤83.5%) (Table 2B). Rerunning the multivariable model from Table 2A, using an FEV1/FVC ratio of ≤80% instead of the lowest quartile of our data (≤83.5%) yielded similar results. The associations with BC (OR 1.26 [0.45–3.50], P=0.66) and dust mite (OR 0.80 [0.49–1.29], P=0.35) remained non-significant. Seroatopy was of borderline statistical significance (OR 2.97 [0.92–9.53], P=0.068).
When the regression model predicting FeNO was restricted to children who had exactly 7 days of air sampling (i.e., excluding the 20% of children with a greater or lesser sampling time) the association with BC was similar (β=0.24 [0.066–0.42], P=0.007). Repeating the linear regression model with FeNO as the dependent variable, but restricting the analyses to the children who also had a valid PFT (n=198) yielded similar estimates for BC (β=0.22 [0.055–0.39], P=0.009), dust mite allergen (β=0.099 [0.032–0.17], P=0.004) and seroatopy (β=0.44 [0.26–0.63], P<0.001). In a logistic regression model with FeNO in the highest quartile (>=14.8 ppb) as the dependent variable and the same covariates as were included for other multivariable models (Table 2), BC (OR 2.11 [0.99–4.51], P=0.054) and dust mite allergen (OR 1.31 [1.02– 1.68]. P=0.033) were associated with elevated FeNO. Cooking with natural gas leads to high levels of NO and other combustion byproducts. Having a gas stove in the home was not associated with FeNO and did not alter the main associations between BC and Der f 1 and FeNO. In sensitivity analyses, local neighborhood income was neither associated with FeNO nor altered the association between BC and Der f 1 and FeNO.
Our study demonstrates uneven geographic distribution of environmental exposures that can affect airway inflammation and asthma morbidity in 7–8 year-old children in NYC. We further characterized specific local sources of BC from residential heating oil and truck traffic. FeNO correlated independently with multiple environmental exposures. It was also associated with asthma symptoms. Combined these findings support the use of FeNO as a subclinical biomarker of the airway effects of multiple environmental exposures.
A novel aspect of this study is the focus on geographically adjacent neighborhoods with both high and low asthma prevalence. Higher asthma prevalence in urban areas is often attributed to low socioeconomic status, as well as a higher proportion of racial and ethnic minorities. The NYC NAAS study design minimized the socioeconomic heterogeneity of the cohort by recruiting through an employer-based health insurance plan, resulting in primarily middle-income study subjects. Living below the poverty line was rare, even amongst those living in HAPN that overall had a lower neighborhood household income. Given this relative homogeneity in terms of household income, the study findings are less vulnerable to confounding by socioeconomic status, unlike many other urban asthma cohort studies.
BC exposure was associated with airway inflammation. While we cannot discern whether this represents an acute, sub-acute or chronic exposure, it is compelling given the association between increasing FeNO and asthma symptoms. A limitation of our study was that FeNO was measured prior to the weeklong assessment of BC in the home with the assumption that the BC exposure would be similar before and after FeNO measurement. In urban communities, indoor BC has significant outdoor sources that vary relative to changes in source production (e.g., increased heating oil use in winter months) and in dispersion of BC between the source and home (Kinney et al., 2002). The latter is principally a function of weather (e.g., wind direction and speed) (Spalding et al., 2011). Weather patterns can differ across weeks, but, in general, follow 6–7 day patterns (Wolff and Lioy, 1978), so average outdoor sources of indoor BC for consecutive weeks should be similar. We observed that central site outdoor BC measured during the week of home BC measurement was well-correlated with both home BC measures and central site BC measured in the week prior to home measurement, supporting our assumption that indoor consecutive weekly concentrations also would be correlated. With respect to seasonal changes in exposure, it is compelling that an association between BC and FeNO was observed only during the heating months, suggesting that the heating oil contribution to BC, or important heating oil co-pollutants (see below), may be particularly relevant to airway inflammation. Exposure misclassification of BC most likely would have biased our findings toward the null.
The association between BC and FeNO was observed only among the non-seroatopic children. Atopy appears to be one of the strongest drivers of FeNO (2005). Thus, among the atopic children, the allergic component of FeNO production may overwhelm the more modest signal associated with BC. Among the non-atopic children exposed to the highest level of BC, FeNO concentrations approach levels seen for atopic children with low dust mite exposure. There is evidence that diesel combustion by-products can act as adjuvants of the allergic response in the airway (Behndig et al., 2006; Bosson et al., 2008; Bosson et al., 2007). Also, PM exposure has been associated with decreased DNA demethylation of the inducible nitric oxide synthase gene promoter, the primary enzyme involved in NO production in the lung, presumably diminishing gene silencing (Tarantini et al., 2009). Building on these findings, our observation of airway inflammation in non-atopic children similar to levels measured among atopic children could represent an inflammatory step that precedes the development of allergic airway inflammation. However, prospective studies are needed to test this hypothesis.
NYC has failed to meet National Ambient Air Quality Standards for PM2.5 and received a failing grade for short-term high particle pollution days from the American Lung Association (www.stateoftheair.org, 2011). PM2.5 has been a focus of interest in urban air pollution studies (Dockery et al., 1993), as particles this size can deposit deep in the lungs. BC (1–2µg/m3) comprises approximately 10–20% of PM2.5 in NYC (10 µg/m3). Importantly, a recent longitudinal study of adolescents in NYC and the surrounding suburbs found that increases of the BC component of PM2.5 were associated with increases in wheeze and respiratory symptoms (Patel et al., 2010). In a recent study of asthmatics exposed to high vehicle traffic exhaust, FeNO was increased several hours after exposure to elemental carbon, pointing to a possible acute-on-chronic effect in some of the subjects (McCreanor et al., 2007).
To our knowledge, ours is the first study to demonstrate that BC in homes is related to the density of buildings burning residual oil and major truck routes in their immediate surroundings,; although this has recently been observed for outdoor samples in NYC (2011). Strikingly, most of the homes near residual oil burners are in neighborhoods with a higher asthma prevalence (Figure 2B). However, our study is under-represented with children from the Upper East and West Sides of Manhattan, that have a high density of buildings burning residual oil and a moderate (6–13%) prevalence of asthma and higher socioeconomic status. There has been some evidence of a decrease for NYC residents to polycyclic aromatic hydrocarbons, additional incomplete combustion by-products (Narvaez et al., 2008). While further study is needed to characterize better the exposure, the finding of elevated BC with closer proximity to a building burning residual oil, coupled with the association between increased BC and increased FeNO, lends support for transitioning from residual oil to #2 oil, ultralow sulfur heating oil, or natural gas for space and hot water heating.
Metals, specifically nickel (Ni) and vanadium (V) are co-pollutants with BC and are an important potential unmeasured confounder in the relationship between BC and FeNO. Residual oil can have elevated levels of Ni and V, resulting in higher levels of these metals in NYC compared to New Jersey and Connecticut (Peltier and Lippmann, 2010). Several studies have pointed to ambient variations in soot (BC), Ni, and sometimes V as being associated with negative respiratory and/or cardiovascular health outcomes (Lippmann et al., 2006; Patel et al., 2009; Peng et al., 2009). As such, the association observed in this study between FeNO and BC may be driven in part by one or both of these metal co-pollutants.
Measuring an intermediary biological marker of exposure related to risk of disease is well accepted (e.g., DNA adducts for cancer). The evidence for employing FeNO to assess responses or impacts of environmental exposures is increasing. Our results showing the association of dust mite allergen exposure among allergic individuals and BC exposure in non-atopic individuals with elevated FeNO are in keeping with current studies (Baraldi et al., 1999; de Kluijver et al., 2002; Hohlfeld et al., 2010; Piacentini et al., 2001). Moreover, the findings from the Dutch PIAMA study, where FeNO at age 4 was a risk for asthma at age 8, suggest that FeNO may be an important predictor of asthma development (Caudri et al., 2010). While FeNO was clearly associated with increased risk for current symptoms in this cohort, it will be important to confirm the long-term relevance of elevated FeNO at age 7–8 for these children.
We did not observe associations between either allergen or BC exposure and FEV1/FVC, despite a clear association between FeNO and FEV1/FVC. A priori, we selected FEV1/FVC in our multi-ethnic population, instead of percent-predicted FEV1, as the primary outcome indicating lower airflow obstruction, because it is less sensitive to ethnic differences. One explanation may be that asthma is characterized by intermittent diseases in obstructive physiology, especially in the milder cases. In contrast to our findings, a population-based study in southern California found that children living in communities with higher PM2.5 exposure had worse lung function than children living in lower exposed communities (Peters et al., 1999). Another explanation is that the study was underpowered to detect such an association with BC and lung function. Moreover, findings from the Dutch PIAMA study (Caudri et al., 2010) suggest that FeNO elevation may precede the development of asthma, and thus impaired lung function.
A limitation of our study was that for 20% of the subjects, the BC sampling time was not exactly 7 days; however, restriction of the sample to subjects with exactly 7 days yielded similar results. Also, study subjects for whom ambient NO was high in the home on the day of the test were excluded. High ambient NO in the home is often related to combustion of gas for cooking. Given the combustion related source, one could imagine a potential for confounding. However, as compared with children included, those excluded because of high ambient NO had a similar frequency of asthma cases, prevalence of frequent wheeze among asthmatics and BC concentrations. By design this study was limited to middle-income households and thus the findings may be less generalizable to children from upper and lower income homes. Also, as compared to children who were screened but did not have a home visit, those who did were more likely to be of Hispanic ethnicity and live in a home with greater truck density, limiting generalizability. Additionally, we were limited by sample size to test potential effect modifiers of the association between BC and FeNO for statistical significance.
These findings support the hypothesis that unequal distribution of incomplete combustion byproduct sources within a single city can lead to differences in exposure even among communities of similar socioeconomic status, and that these differences in exposure could be one aspect leading to differences in asthma prevalence and morbidity, as manifested by increased symptoms and biomarkers reflecting airway inflammation. Our findings support the use of FeNO to test independent effects of multiple exposures that could contribute to airway inflammation, and thus asthma exacerbations. Further public health interventions on residual oil use and increasing the pace of adopting truck emissions standards may be warranted.
This study was supported by the National Institute of Environmental Health Sciences (NIEHS) (grant # R01 ES 014400, P50 ES015905, P30 ES 009089, P01ES09600, R01 ES008977) and the Environmental Protection Agency (R827027).. This project would not have been possible without our collaborators at HIP Health Plan. Beatriz Jaramillo, DrPH was instrumental in the design and initial implementation of the study and Michael Byrne, MA, has ensured the continued success of the recruitment for the study. We would like to thank the NYC Neighborhood Asthma and Allergy Study field team for their hard work. We would like to thank Stephanie Soucier for artistic design of recruitment material. We would also like to thank the families who have participated in the study.
Supplementary information is available at the Journal of Exposure Science and Environmental Epidemiology.
Conflict of interest
The authors declare no conflicts of interest.