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Original Research Article

Fine particulate matter exposure and lipid levels among children in Mexico city

McGuinn, Laura A.a; Coull, Brent A.b,,c; Kloog, Itaia,,d; Just, Allan C.a; Tamayo-Ortiz, Marcelae,,f; Osorio-Yáñez, Citlallie; Baccarelli, Andrea A.g; Wright, Rosalind J.a; Téllez-Rojo, Martha M.e,,*; Wright, Robert O.a

Author Information
Environmental Epidemiology: April 2020 - Volume 4 - Issue 2 - p e088
doi: 10.1097/EE9.0000000000000088

Abstract

What this study adds

We evaluated the association between pre- and postnatal particulate matter <2.5 µm in diameter (PM2.5) exposure and lipid levels (total cholesterol [TC], Low-density lipoprotein cholesterol [LDL-C], non-HDL-C, high-density lipoprotein cholesterol [HDL-C], and triglycerides [TG]) in a cohort of children living in Mexico City. The findings from our study showed associations between prenatal, specifically third trimester, PM2.5 exposure and increased LDL-C, non-HDL-C, and total cholesterol levels in the child. We additionally observed an increasing trend across quantiles of the outcome distribution, with stronger associations seen for higher LDL-C levels. This is the first epidemiologic study to specifically address this research question in a younger study population—future studies are warranted to confirm these findings.

Introduction

An extensive body of literature exists on the associations between particulate matter <2.5 µm in diameter (PM2.5) and increases in cardiovascular (CVD) morbidity and mortality.1,2 The main hypothesized mechanistic pathways linking air pollution and progression of atherosclerosis are through increases in systemic inflammation, oxidative stress, and susceptibility to lipid oxidation.2,3 Assessing associations with risk factors, or markers, of atherosclerosis may help gain insight into the PM induced CVD health effects, and help us to better understand these complex mechanistic pathways. Low-density lipoprotein cholesterol (LDL-C) is one such risk factor for progression of atherosclerosis.4 Recent studies have identified associations between air pollution and lipid levels in adult populations5–10; however, little is known about the association between early life air pollution exposure and lipid levels in children.

Although once considered strictly an adult disease, cardiometabolic disease and its consequences may have its origins in very early life. David Barker first coined the term “fetal origins of disease” from his findings of the importance of the intrauterine environment.11 His findings on low birthweight and increased cardiometabolic risk later in life laid the groundwork for developing the concept of critical windows of susceptibility—life stages when an individual is more susceptible to an environmental factor. Exposures during these time periods may predict early life preclinical cardiometabolic disease better than exposures at other time periods. Such windows may be reflected by dysregulated lipid profiles from toxic exposures, and may lead to later life health effects. The concept is analogous to the predictive value of low birth weight on later life cardiometabolic disease.12,13

There is growing evidence that childhood lipid levels track into adulthood14 and are associated with cardiometabolic disease later in life.15 Taken together, these findings suggest that early life alterations in lipid levels may contribute to later life cardiometabolic disease. Early life represents an understudied, yet potentially important, window of susceptibility for development of CVD. Studying associations between early life environmental exposures, such as PM2.5, and lipid levels in children may aid in primary prevention measures and help to gain insight into the early origins of cardiometabolic disease.

Previous studies have used linear models to assess associations between air pollution exposure and continuous outcome measures, such as LDL-C. This particular analytical technique assesses the change in the mean of, for instance, the LDL-C distribution with each unit increase in air pollution exposure. While the change in the overall distribution of outcome measures is important, another critical question is whether air pollution exposure impacts different levels of the outcome distribution. Quantile regression is one alternative analytical approach that allows the assessment of the impacts of exposures on continuous outcome levels at different quantiles of the outcome. We applied this method to assess associations with early life air pollution for children at the low (i.e., 10th percentile) and high (i.e., 90th percentile) end of the lipid outcome distribution.

The objective of this study was to assess associations between early life PM2.5 exposure and childhood cholesterol and triglyceride levels and to investigate if associations between PM2.5 and lipid levels varied across lipid quantiles.

Methods

Study population

We used data from the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) longitudinal birth cohort in Mexico. Briefly, pregnant women were recruited between 2007 and 2011 at 12–24 weeks’ gestation through the Mexican Social Security System (IMSS). In order to be included in the study, pregnant women needed to be 18 years or older and plan to live in Mexico City. Additionally, women were eligible if they were <20 weeks gestation, had completed primary education, had no medical history of heart or kidney disease, and did not consume alcohol daily. In total, 948 women enrolled in the second trimester and delivered a live child who was then followed longitudinally. For this analysis, we used data from the age 4- to 6-year visit, the first at which a blood draw on the child was performed to allow lipid measurement. A total of 613 children were seen at this visit, of which 465 mother–child pairs had complete exposure, outcome, and covariate information available. Protocols were approved by the institutional review boards at the Icahn School of Medicine at Mount Sinai, Harvard School of Public Health, and Mexican National Institute of Public Health. All women provided informed consent.

Measurement of child lipids

Blood samples were collected from each child at the 4- to 6-year study visit. Children at this visit were not asked to fast due to their young age. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were measured using enzymatic methods (Roche Diagnostics, Indianapolis, IN). Low-density lipoprotein cholesterol was calculated using the Friedewald formula, [(total cholesterol) − (HDL-C) − (triglycerides/5). Studies have shown that childhood non-HDL cholesterol (non-HDL-C) levels persist over time into adulthood and are predictive of adult dyslipidemia.16 Therefore, we additionally included non-HDL-C as an outcome measure by subtracting HDL-C from total cholesterol.

Air pollution exposure assessment and data linking

Residential exposure to PM2.5 was estimated using a satellite-based exposure model recently developed by our team.17 Briefly, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived Aerosol Optical Depth (AOD) measurements were obtained and calibrated with ground monitored data, meteorological data, and land use regression variables (such as roadway density, temperature, relative humidity, planetary boundary layer, and daily precipitation) with output at a daily temporal and 1 km spatial resolution. The model additionally used mixed effect models with temporal and spatial predictors and day-specific random effects to account for temporal variation in the PM2.5-AOD relationship. Model performance was evaluated using monitor-level leave one out cross-validation with an R2 of 0.74. Further details on this model, including methods and performance, can be found elsewhere.17

The nearest 1km exposure grid was linked to each participant based on their residential address during pregnancy. Gestational age was used to link the air pollution exposures on time. Gestational age at birth was estimated based on last menstrual period, as reported by the mother. Average levels of PM2.5 were calculated for each trimester of pregnancy (first trimester: 1–13 weeks, second trimester: 14–27 weeks, third trimester: 28 weeks-delivery) and across the entire pregnancy period. We additionally investigated associations with first year of life PM2.5 exposure in order to assess the impact of early postnatal exposures.

Covariates

A directed acyclic graph (DAG) (eFigure 1; http://links.lww.com/EE/A75) was used to identify the minimally sufficient adjustment set. Covariate information was obtained from baseline questionnaires or field measurements. Covariates include, maternal age at enrollment (continuous), maternal education (less than high school, high school, or greater than high school), environmental tobacco smoke exposure during pregnancy (present or absent in home), pre-pregnancy body mass index (weight in kg/height in m2), child’s age at testing, and season of conception. Season of conception was categorized as cold-dry (November–February), warm-dry (March–April), and rainy (May–October). We additionally adjusted for child sex and gestational age in sensitivity analyses.

Statistical analyses

We first estimated associations between trimester-specific, entire pregnancy, and first year of life PM2.5 averages and childhood lipid levels using linear regression models. The estimates from these analyses can be interpreted as the difference in lipid level per unit increase in PM2.5. Next, we estimated associations between pre- and postnatal PM2.5 and childhood lipid levels using quantile regression at the 10th, 30th, 50th, 70th, and 90th percentiles of each outcome. Instead of modeling the change in outcome level at the mean as linear regression does, quantile regression models the effect estimate at specific quantiles and uses the entire outcome distribution. For example, we can compare associations with PM2.5 across the outcome distribution, by investigating the change in outcome level at the 90th percentile compared with the 10th or 50th.

Analyses were run separately for each outcome measure (total cholesterol, LDL-C, non-HDL-C, HDL-C, and triglycerides). Models were mutually adjusted for the other developmental windows of interest. Trimester-specific associations were mutually adjusted for exposures during the other trimesters and for concentrations during the first year of life.18 For example, estimates for first trimester PM2.5 exposure were mutually adjusted for PM2.5 concentrations during the second and third trimesters, as well as first year of life exposures. Average first year of life exposure was additionally adjusted for in entire pregnancy PM2.5 models; average entire pregnancy exposure was mutually adjusted for in the first year of life models. Regression coefficients from all models were scaled to the interquartile range (IQR) increases in PM2.5 concentrations averaged over the entire pregnancy period (3.8 μg/m3), to allow for comparison in results across all developmental windows. Finally, previous studies have found differences in effects of PM2.5 exposure for males and females. Therefore, as a sensitivity analysis, we assessed PM2.5-lipid associations stratified by child sex.

Results

Table 1 shows a description of the 465 mother–child pairs included in our study. Children were on average 4.8 years at the 4- to 6-year follow-up visit. The study population consisted of an even distribution of boys and girls. Mothers were on average 28 years old at enrollment and primarily lower educated and of lower socioeconomic status. About one-third of mothers reported exposure to a smoker in the home during the pregnancy period.

Table 1.
Table 1.:
Characteristics of mother–child dyads in the PROGRESS study

Table 2 includes the distribution of childhood lipid levels and pre- and postnatal PM2.5 concentrations. Levels are displayed for the mean and also by percentile of the outcome and exposure distribution (10th, 30th, 50th, 70th, and 90th). Overall, about 11% of the study population had high LDL-C (≥130 mg/dl) and total cholesterol (≥200 mg/dl) levels. The 10th percentile for LDL-C was 70.2 mg/dl, 50th percentile: 94.8 mg/dl, and 90th percentile: 131.0 mg/dl.

Table 2.
Table 2.:
Distribution of childhood lipid levels and pre- and postnatal PM2.5 concentrations

The average PM2.5 level for study participants averaged across the pregnancy period was 22.5 μg/m3 (range: 16.4–29.2 µg/m3), with an interquartile range of 3.8 (SD: 2.6). Entire pregnancy exposure averages were moderately correlated with each of the trimester-specific averages (correlations of 0.52–0.71) (eTable 1; http://links.lww.com/EE/A75); however, they were only weakly correlated with first year of life averages (0.25). Trimester-specific averages were not strongly correlated with each other (eTable 1; http://links.lww.com/EE/A75).

Trimester-specific, entire pregnancy, and first year of life associations between IQR (3.8 μg/m3) increases in PM2.5 and changes in lipid levels (TC, LDL-C, non-HDL-C, HDL-C, and TG) are presented in Table 3. In linear models, increased PM2.5 exposure during pregnancy was associated with increases in several of the lipid outcomes. In particular, effects were strongest for exposures during the third trimester with increases in total cholesterol (β: 3.02, 95% confidence interval [CI] = 0.26, 6.85), LDL-C (β: 4.49, 95% CI = 2.01, 6.97), and non-HDL-C (β: 3.99, 95% CI = 1.47, 6.52). There were additionally elevated effect estimates for first year of life exposures in relation to LDL-C and non-HDL-C, however the confidence intervals for the estimate included the null. We observed associations between increases in PM2.5 and decreases in HDL-C, particularly for exposures during the third trimester. Additionally, there were decreases in triglyceride levels for several of the exposure windows; however, the confidence intervals for these effect estimates include the null (Table 3). Overall, results were similar in crude, individual window models, and when adjusting for other covariates such as gestational age and child sex (eTable 2; http://links.lww.com/EE/A75).

Table 3.
Table 3.:
Adjusteda associations between early life PM2.5 exposure and childhood lipid levels

Figure 1 shows the associations between IQR increases in PM2.5 concentrations during each trimester of pregnancy, the entire pregnancy period, and first year of life for each of the lipid outcome measures (TC, LDL-C, non-HDL-C, HDL-C, and TG) (see eTable 3; http://links.lww.com/EE/A75, for numeric results). Results are presented for the 10th, 30th, 50th, 70th, and 90th percentiles of the lipid outcome distributions using quantile regression. In quantile regression analyses, there was an increasing trend across quantiles of the LDL-C, non-HDL-C, and total cholesterol outcome distributions for exposures during the third trimester and across the entire pregnancy period (Figure 1). Increases in LDL-C, non-HDL-C, and total cholesterol levels were primarily seen for children with lipid levels above the median. For example, the β for LDL-C for the 30th percentile was 3.55 (95% CI = 1.02, 6.08); for the 90th percentile, the β was 10.2 (95% CI = 5.62, 14.8)) (Figure 1; eTable 3; http://links.lww.com/EE/A75). There were no consistent associations across quantiles of the HDL-C outcome distribution. There appeared to be a decreasing trend in triglyceride levels for exposures during the second trimester and entire pregnancy. Overall, there were no consistent trends for any of the outcomes for first year of life exposures (Figure 1).

Figure 1.
Figure 1.:
Trimester-specific, entire pregnancy, and first year of life quantile regression estimates (β and 95% CI) for associations between IQR (3.8 µg/m3) increases in PM2.5 exposure and childhood lipid levels (TC, non-HDL-C, LDL-C, HDL-C, and TG). Results are shown for the 10th, 30th, 50th, 70th, and 90th quantiles. Models are adjusted for maternal education, maternal age at enrollment, maternal BMI, child’s age at testing, season of conception, and prenatal environmental tobacco smoke exposure. Trimester-specific effect estimates are mutually adjusted for other trimester and first year of life exposure averages; pregnancy estimates are mutually adjusted for first year of life PM2.5 averages; first year of life estimates are mutually adjusted for average pregnancy exposure.

Finally, we assessed associations between PM2.5 exposure averaged across the entire pregnancy and childhood lipid levels, stratified by child sex. Overall, results did not significantly differ by child sex (eFigure 2; http://links.lww.com/EE/A75).

Discussion

In our prospective birth cohort study, we found associations between late pregnancy PM2.5 exposure and increases in LDL-C, non-HDL-C, and total cholesterol in the child. We additionally assessed associations between prenatal PM2.5 and quantiles of the lipid outcome distributions. We demonstrated associations between PM2.5 and an increasing trend of higher lipids with the strongest results seen for the higher quantiles of the LDL-C, non-HDL-C, and total cholesterol outcome distribution.

To our knowledge, this is the first epidemiologic study to assess associations between early life PM2.5 exposure and childhood lipid levels. Our findings of an association with prenatal PM2.5 exposure and elevated LDL-C and total cholesterol levels, and decreased HDL-C levels are in accordance with previous studies in adult populations.5,9,10 We additionally observed associations between increases in PM2.5 exposure and decreases in triglyceride levels, which is consistent with a recent study in adults,19 but conflicting with a few other previous studies in adult populations.6,8 Although no previous study has assessed associations with air pollution and lipid levels in children, a few previous studies have assessed associations between other environmental chemicals and lipid levels in children, with several finding inverse associations between prenatal exposures (such as phthalates and polyfluoroalkyl chemicals) and childhood lipid levels.20,21

We found stronger associations with PM2.5 for several of our outcomes for levels above the 50th outcome percentile. Using quantile regression, we were able to use the entire outcome distribution, instead of modeling the change from the mean as is commonly done with continuous outcomes. A few previous studies have assessed associations with air pollution exposure and continuous outcomes using quantile regression.6 A recent US-based analysis in a cohort of cardiac catheterization patients found associations between long-term PM2.5 exposure and increases in LDL concentrations and sizes in adults, with the strongest results observed for those with the highest LDL levels.10

The main mechanistic pathway linking prenatal air pollution exposure with changes in lipid levels may be through an inflammatory response in the mother and offspring. Studies suggest PM2.5 can increase systemic inflammation and interfere with lipid metabolism and oxidation.3,22 A recent study in mothers and their newborns found associations with prenatal air pollution exposure and increases in oxidative DNA damage and lipid peroxidation in the newborns, but not the mothers.23 Further, a recent PROGRESS study assessed associations between prenatal PM2.5 exposure and changes in mitochondrial DNA content in cord blood, as a marker of cumulative oxidative stress.24 Findings from this study showed decreases in mitochondrial DNA, particularly for exposures during the third trimester, which is similar to the developmental window we observed in the current study. A potential unifying theory that brings together observations that higher in utero air pollution is associated with child obesity25,26 as well as adult cardiovascular disease is that air pollution may operate through both chronic and acute inflammatory pathways.27,28 Chronically, air pollution inflammation in utero may promote obesity in children, with pregnancy exposures representing a critical exposure window. This increase in obesity rates from in utero PM2.5 exposure in turn elevates serum lipid levels in children beginning in the preschool years. These increased lipid levels may add to an increased risk for later life cardiovascular disease as the child transitions to adulthood.

A few previous studies have found conflicting results for prenatal vs childhood environmental exposures and childhood lipid levels.20 We were unable to assess the impact of childhood air pollution exposures in the current study as our model has not yet been extended beyond 2014; however, this is of interest in future studies using PROGRESS data. Due to their young age, children in the study were not asked to fast. This may have overestimated the triglyceride levels in the child. However, studies have shown minimal differences in fasting and non-fasting lipid levels,29,30 and that non-fasting lipid values may be more reflective of one’s actual daily levels and equally predictive of future CVD events.31,32 Also, the temporality of recent eating and the timing of the blood draw for lipids is likely random with respect to air pollution in pregnancy, and therefore would have induced nondifferential misclassification into our analysis. Finally, we used one address to link air pollution exposure to participants for the entire pregnancy period and first year of life, and did not assess residential mobility during pregnancy. This may have resulted in exposure misclassification; however, previous studies have found minimal differences in air pollution exposure assignment and resulting effect estimates when using the full address history during pregnancy.33,34

Despite these limitations, our study has several strengths. To our knowledge, this is the first epidemiologic study to assess associations between early life air pollution exposure and childhood lipid levels. Our study additionally makes use of a state-of-the-art air pollution model at a fine temporal and spatial scale. We addressed this research question using data from a prospective birth cohort in Mexico City, an area with considerably high PM2.5 levels. Women in our study population had average PM2.5 levels during pregnancy of 22.5 µg/m3. For comparison, the World Health Organization PM2.5 annual mean standard is 10 µg/m3.35 Previous studies have cautioned against analyzing trimester-specific averages in separate models, as this can induce bias in the resulting estimates.18 Thus, an additional strength of the current study is that our trimester models were mutually adjusted for the other trimesters of interest. Finally, in addition to linear models, we assessed associations at different percentiles of the outcome distribution using quantile regression.

Conclusions

In conclusion, we found associations between late pregnancy PM2.5 exposure and increased LDL-C, non-HDL-C, and total cholesterol levels in the child. We additionally observed an increasing trend across quantiles of the outcome distribution, with stronger associations seen for higher LDL-C levels. Results were inconsistent for first year of life exposures. This study begins to reveal potential associations between early life air pollution exposure and early life CVD risk factors. Future studies using PROGRESS data will examine associations between PM2.5 and other CVD risk factors in children and adolescents, including carotid intima-media thickness.

Conflict of interest statement

The authors declare that they have no financial conflict of interest with regard to the content of this report.

References

1. Brook RD, Newby DE, Rajagopalan S. Air pollution and cardiometabolic disease: an Update and Call for Clinical Trials. Am J Hypertens. 2017; 31:1–10
2. Brook RD, Rajagopalan S, Pope CA 3rd, et al.; American Heart Association Council on Epidemiology and Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, Physical Activity and MetabolismParticulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation. 2010; 121:2331–2378
3. Araujo JA. Particulate air pollution, systemic oxidative stress, inflammation, and atherosclerosis. Air Qual Atmos Health. 2010; 4:79–93
4. Zaid M, Miura K, Fujiyoshi A, et al.; SESSA Research GroupAssociations of serum LDL particle concentration with carotid intima-media thickness and coronary artery calcification. J Clin Lipidol. 2016; 10:1195–1202.e1
5. Bell G, Mora S, Greenland P, Tsai M, Gill E, Kaufman JD. Association of air pollution exposures with high-density lipoprotein cholesterol and particle number: the multi-ethnic study of atherosclerosis. Arterioscler Thromb Vasc Biol. 2017; 37:976–982
6. Bind MA, Peters A, Koutrakis P, Coull B, Vokonas P, Schwartz J. Quantile regression analysis of the distributional effects of air pollution on blood pressure, heart rate variability, blood lipids, and biomarkers of inflammation in elderly American Men: the Normative Aging Study. Environ Health Perspect. 2016; 124:1189–1198
7. Chuang KJ, Yan YH, Chiu SY, Cheng TJ. Long-term air pollution exposure and risk factors for cardiovascular diseases among the elderly in Taiwan. Occup Environ Med. 2011; 68:64–68
8. Shanley RP, Hayes RB, Cromar KR, Ito K, Gordon T, Ahn J. Particulate air pollution and clinical cardiovascular disease risk factors. Epidemiology. 2016; 27:291–298
9. Yitshak Sade M, Kloog I, Liberty IF, Schwartz J, Novack V. The association between air pollution exposure and glucose and lipids levels. J Clin Endocrinol Metab. 2016; 101:2460–2467
10. McGuinn LA, Schneider A, McGarrah RW, et al. Association of long-term PM2.5 exposure with traditional and novel lipid measures related to cardiovascular disease risk. Environ Int. 2019; 122:193–200
11. Barker DJ. The developmental origins of adult disease. J Am Coll Nutr. 2004; 236 suppl588S–595S
12. Dalziel SR, Parag V, Rodgers A, Harding JE. Cardiovascular risk factors at age 30 following pre-term birth. Int J Epidemiol. 2007; 36:907–915
13. Hovi P, Kajantie E, Soininen P, et al. Lipoprotein subclass profiles in young adults born preterm at very low birth weight. Lipids Health Dis. 2013; 12:57
14. Juhola J, Magnussen CG, Viikari JS, et al. Tracking of serum lipid levels, blood pressure, and body mass index from childhood to adulthood: the Cardiovascular Risk in Young Finns Study. J Pediatr. 2011; 159:584–590
15. Raitakari OT, Juonala M, Kähönen M, et al. Cardiovascular risk factors in childhood and carotid artery intima-media thickness in adulthood: the Cardiovascular Risk in Young Finns Study. JAMA. 2003; 290:2277–2283
16. Srinivasan SR, Frontini MG, Xu J, Berenson GS. Utility of childhood non-high-density lipoprotein cholesterol levels in predicting adult dyslipidemia and other cardiovascular risks: the Bogalusa Heart Study. Pediatrics. 2006; 118:201–206
17. Just AC, Wright RO, Schwartz J, et al. Using high-resolution satellite aerosol optical depth to estimate daily PM2.5 geographical distribution in Mexico City. Environ Sci Technol. 2015; 49:8576–8584
18. Wilson A, Chiu YM, Hsu HL, Wright RO, Wright RJ, Coull BA. Potential for bias when estimating critical windows for air pollution in children’s health. Am J Epidemiol. 2017; 186:1281–1289
19. Mao S, Chen G, Liu F, et al. Long-term effects of ambient air pollutants to blood lipids and dyslipidemias in a Chinese rural population. Environ Pollut. 2020; 256:113403
20. Mora AM, Fleisch AF, Rifas-Shiman SL, et al. Early life exposure to per- and polyfluoroalkyl substances and mid-childhood lipid and alanine aminotransferase levels. Environ Int. 2018; 111:1–13
21. Perng W, Watkins DJ, Cantoral A, et al. Exposure to phthalates is associated with lipid profile in peripubertal Mexican youth. Environ Res. 2017; 154:311–317
22. Rao X, Zhong J, Brook RD, Rajagopalan S. Effect of particulate matter air pollution on cardiovascular oxidative stress pathways. Antioxid Redox Signal. 2018; 28:797–818
23. Ambroz A, Vlkova V, Rossner P Jr, et al. Impact of air pollution on oxidative DNA damage and lipid peroxidation in mothers and their newborns. Int J Hyg Environ Health. 2016; 219:545–556
24. Rosa MJ, Just AC, Guerra MS, et al. Identifying sensitive windows for prenatal particulate air pollution exposure and mitochondrial DNA content in cord blood. Environ Int. 2017; 98:198–203
25. Rundle AG, Gallagher D, Herbstman JB, et al. Prenatal exposure to airborne polycyclic aromatic hydrocarbons and childhood growth trajectories from age 5-14 years. Environ Res. 2019; 177:108595
26. Kim JS, Alderete TL, Chen Z, et al. Longitudinal associations of in utero and early life near-roadway air pollution with trajectories of childhood body mass index. Environ Health. 2018; 17:64
27. Li W, Dorans KS, Wilker EH, et al. Short-term exposure to ambient air pollution and biomarkers of systemic inflammation: the Framingham Heart Study. Arterioscler Thromb Vasc Biol. 2017; 37:1793–1800
28. Adams RA, Potter S, Bérubé K, Higgins TP, Jones TP, Evans SA. Prolonged systemic inflammation and damage to the vascular endothelium following intratracheal instillation of air pollution nanoparticles in rats. Clin Hemorheol Microcirc. 2019; 72:1–10
29. Sidhu D, Naugler C. Fasting time and lipid levels in a community-based population: a cross-sectional study. Arch Intern Med. 2012; 172:1707–1710
30. Steiner MJ, Skinner AC, Perrin EM. Fasting might not be necessary before lipid screening: a nationally representative cross-sectional study. Pediatrics. 2011; 128:463–470
31. Langsted A, Freiberg JJ, Nordestgaard BG. Fasting and nonfasting lipid levels: influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation. 2008; 118:2047–2056
32. Mora S, Chang CL, Moorthy MV, Sever PS. Association of nonfasting vs fasting lipid levels with risk of major coronary events in the anglo-scandinavian cardiac outcomes trial-lipid lowering arm. JAMA Intern Med. 2019; 179:898–905
33. Pereira G, Bracken MB, Bell ML. Particulate air pollution, fetal growth and gestational length: the influence of residential mobility in pregnancy. Environ Res. 2016; 147:269–274
34. Warren JL, Son JY, Pereira G, Leaderer BP, Bell ML. Investigating the impact of maternal residential mobility on identifying critical windows of susceptibility to ambient air pollution during pregnancy. Am J Epidemiol. 2018; 187:992–1000
35. WHOAmbient (outdoor) air pollution. 2018. Available at: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. Accessed January 10 2020

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