SO 300 Research Methods Paper - 2 Pages - APA 7e Format (DESCRIBE BOTH ARTICLES)

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January 2020, Volume 8, Issue 1, Number 17

Maryam Bahreynian1 , Marjan Mansourian2 , Nafiseh Mozaffarian3 , Parinaz Poursafa4 , Mehri Khoshhali3* , Roya Kelishadi3

Review Article: The Association Between Exposure to Ambient Particulate Matter and Childhood Obesity: A Systematic Review and Meta-analysis

Context: Physical environment contamination and in particular, air pollution might cause long-term adverse effects in child growth and a higher risk of catching non-communicable diseases later in life.

Objective: This study aimed to overview the human studies on the association of exposure to ambient Particulate Matter (PM) with childhood obesity.

Data Sources: We systematically searched human studies published until March 2018 in PubMed, Scopus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases.

Study Selection: All studies that explored the association between PM exposure and childhood obesity were assessed in the present study, and finally, 5 studies were used in the meta-analysis.

Data Extraction: Two independent researchers performed the data extraction procedure and quality assessment of the studies. The papers were qualitatively assessed by STROBE (Strengthening the Reporting of Observational studies in Epidemiology) statement checklist.

Results: The pooled analysis of PM exposure was significantly associated with increased Body Mass Index (BMI) (Fisher’s z-distribution=0. 028; 95% CI=0. 017, 0. 038) using the fixed effects model. We also used a random-effect model because we found a significant high heterogeneity of the included studies concerning the PM (I2=94. 4%; P<0. 001). PM exposure was associated with increased BMI (Fisher’s z-distribution=0. 022; 95% CI=-0. 057, 0. 102). However, the overall effect size was not significant, and heterogeneity of the included studies was similar to the fixed effect model.

Discussion: Our findings on the significant association between PM10 exposure and the increased BMI (r=0. 034; 95%CI=0. 007, 0. 061) without heterogeneity (I2=16. 6%, P=0. 274) (in the studies with PM10) suggest that the PM type might account for the heterogeneity among the studies.

Conclusion: The findings indicate that exposure to ambient PM10 might have significant effects on childhood obesity.

A B S T R A C T

Key Words: Air pollution, Particulate matter, Childhood obesity, Meta-analysis

Article info: Received: 10 Oct 2018 First Revision: 23 Feb 2019 Accepted: 09 Mar 2019 Published: 01 Jan 2020

1. Department of Nutrition Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis- eases, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran. 2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran. 3. Department of Pediatrics, Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis- eases, Isfahan University of Medical Sciences, Isfahan, Iran. 4. Environment Research Center, Research Institute for Primordial Prevention of Non-communicable Diseases, Isfahan University of Medical Sciences, Isfahan, Iran.

* Corresponding Author: Mehri Khoshhali, MD. Address: Department of Pediatrics, Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis- eases, Isfahan University of Medical Sciences, Isfahan, Iran. Tel: +98 (311) 7925215 E-mail: m. khoshhali@yahoo. com

Citation Bahreynian M, Mansourian M, Mozaffarian N, Poursafa P, Khoshhali M, Kelishadi R. The Association Between Exposure to Ambient Particulate Matter and Childhood Obesity: A Systematic Review and Meta-analysis. Journal of Pediatrics Review. 2020; 8(1):1-14. http://dx. doi. org/10. 32598/jpr. 8. 1. 1

: http://dx. doi. org/10. 32598/jpr. 8. 1. 1

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January 2020, Volume 8, Issue 1, Number 17

1. Context

hildhood obesity is a growing public health problem, even in developing countries (1, 2). It is associated with several health complications during childhood, which will usually extend to adulthood (3). It has several underlying causes, both genetic and environmental factors (4, 5).

Recently, researchers have paid attention to the as- sociation between air pollution and obesity, and some studies suggest that ambient air pollution may increase the risk of catching Non-communicable Diseases (NCDs) in adults, diseases such as cardiovascular diseases, dia- betes and cancer (6-8). However, little epidemiological evidence is available on the association of exposure to ambient air pollution with the development of child- hood obesity (9-11). Physical environment contamina- tion and in particular, air pollution might cause long- term adverse effects in child growth and a higher risk of developing NCDs later in life. The “Obesogenic Environ- ment” hypothesis discusses the impact of environmen- tal chemicals with endocrine disruption properties that can change child growth patterns and result in weight gain, obesity, and obesity-related NCDs (12).

Some previous studies reveal a positive association between exposure to Polycyclic Aromatic Hydrocar- bons (PAHs) and childhood obesity (11, 13). A recent study conducted in China reports that long-term ex- posure to air pollutants, including Particulate Matter (PM)

10 , NO

2 , SO

2 , and O

3 is associated with higher risk

of childhood obesity and hypertension (14). More- over, the association of residential traffic density and roadway proximity with rapid infant weight gain and childhood obesity has been documented in some pre- vious studies (15-18). A study on Latino children living in the US shows that higher exposure to NO

2 and PM

2.

5 is related to higher Body Mass Index (BMI) at the age

of 18 (19). However, some other studies report no as- sociation between exposure to vehicular traffic and pollutants and the risk of obesity and dyslipidemia in children (20, 21). Therefore, the overall evidence on obesogenic properties of air pollutants is controversial.

2. Objectives

Due to the high prevalence of childhood obesity, its multifactorial nature, and the importance of conducting preventive strategies, we aimed to provide a systematic review and meta-analysis on the association of expo- sure to ambient PM and childhood obesity.

3. Data Sources

We performed a systematic review and meta-analysis of human studies that explored the association be- tween PM exposure and childhood obesity. We con- sidered PECO as the following: Population (P): Children and adolescents; Exposure (E): PM exposure; Compari- son (C) (There is no comparison between exposed and non-exposed groups because we have reported the cor- relations of PM exposure and BMI); and Outcome (O): Childhood obesity (BMI).

We systematically searched human studies available on the study subject until March 2018 in PubMed, Sco- pus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases. All cross-sectional and co- hort studies were selected. We used the search terms of “Air Pollution” OR “Pollutants” OR “Particulate Matter” in combination with “Obesity” OR “Weight” OR “Body Mass Index” OR “BMI” OR “Overweight” OR “Cardio- metabolic” OR “Metabolic Syndrome” OR “Metabolic Syndrome X” OR “Mets” OR “Adiposity” AND “Child” OR “Adolescent” OR “School-aged” OR “Youth” OR “Teen- ager” OR “Boy” OR “Girl” OR “Student” OR “Pediatrics” in the form of Medical Subject Headings (MeSH) and truncations. The relevant articles were examined with- out any language restriction.

4. Study Selection

After removing the duplicates, the relevant papers were selected in three phases. In the first and the second phases, titles and abstracts of papers were screened, and the irrelevant papers were excluded. In the third phase, the full texts of the remaining papers were ex- plored carefully to select only the relevant papers. To find any additional pertinent study, the reference list of all reviews and related papers were screened as well.

The included studies had the following criteria: 1. Observational cross-sectional design; 2. Longitudinal cohort studies which report the study association; 3. Measurement of PM concentration as an index for air pollution exposure; and 4. Reporting the Odds Ratio (OR), Relative Risk (RR), and β-coefficient of PM with child obesity. In the final step, all statistics were changed to the correlation coefficient values.

5. Data Extraction

Two reviewers extracted the data independently using a data collection form, including the first author’s name, publication year, sample size, study design, as well as

C

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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January 2020, Volume 8, Issue 1, Number 17

age, exposure measurement, statistical analysis, and the variables adjusted in the analyses.

5. 1. Quality assessment

Two independent reviewers (MB and MKH) evalu- ated the methodological quality of each study. The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) checklist was used for the qual- ity assessment of the papers. According to STROBE (22 questions), the included studies were divided into three groups of high, medium, and low-quality. The studies scored one to eight were ranked as low-quality studies, 9-16 as medium-quality ones, and 17-22 as high-quality papers. The two reviewers agreed on (80%) of cases. The remaining discrepancies were resolved by consulta- tion and consensus.

5. 2. Statistical analysis

The effect sizes of RR, OR, and β-coefficient from all ar- ticles were extracted directly from the original reports. All effect sizes were transformed into (r: correlation), and Fisher z-transformation of the r value was applied for the pooled analysis (22, 23). The potential heteroge- neity across studies was evaluated using the Cochran’s Q test and was expressed using the I2 index. The pooled results for Fisher z-transformation were calculated by the fixed-effects model (for low heterogeneity) or the random-effects model (for high heterogeneity). Publica- tion bias was evaluated by the Egger’s and the Begg’s tests. Subgroup analyses and meta-regression were per- formed to seek the sources of heterogeneity. The sensi- tivity analyses were performed by omitting one study at a time to gauge the robustness of our results. All statisti- cal analyses were conducted in STATA V. 14. 0.

6. Results

We initially retrieved 4391 articles from the databases. Figure 1 represents the search results. After the initial study of the titles and abstracts, the duplicate papers were omitted, and out of 4276 papers, five articles remained. No additional references were identified through checking the reference lists of selected papers.

The main characteristics of the studies included in the systematic review are presented in Appendix 1. Overall, the studies reported data on 33825 subjects, and they were published between 2010 and 2018.

6. 1. Meta-analysis of the correlations

Figure 2 showed the pooled results using random ef- fect model. It showed that PM exposure was associat- ed with the increased BMI (Fisher-z= 0. 022; 95% CI (-0. 057, 0. 102)) that overall effect size was not significant and heterogeneity of the included studies was as same fixed effect model.

Table 1 presents the results of the meta-regression analysis. The univariate meta-regression analyses in- dicated that none of the factors, including mean age, sample size, study location (Europe, Asia, and the USA), study type (cross-sectional and cohort), and PM type (2. 5 and 10) contributed to the heterogeneity of meta- analysis (P>0. 05 for all).

Table 2 presents the results of subgroup analysis ac- cording to the study location, study type, and PM type. We observed significant association between PM

10 ex-

posure and the increased BMI (Fisher’s z=0. 034; 95% CI=0. 007, 0. 061) with no apparent heterogeneity (I2=16. 6%, P=0. 274) in the studies with PM

10 . It sug-

gests that the PM type may partially account for the het- erogeneity among the studies on BMI (Figure 3).

Begg’s test and Egger’s test revealed no obvious publi- cation bias among these studies. The P-values for these tests were higher than 0. 05 (P=0. 661 and 1. 0, respec- tively). The results of sensitivity analyses showed that with excluding the study of Fleisch AF et al. (7. 7 years), the pooled Fisher’s z for the subgroup PM

2. 5 increased.

Although this change was not significant, it decreased the overall heterogeneity (I2=83. 1%, P<0. 001) (Figure 4).

Figure 4 Forest plot of Fisher’s z values for the cor- relation between PM and BMI by PM type after exclud- ing the study of Fleisch AF et al. (7. 7 years) Table 3 presents the results of converting Fisher’s z values into correlation values. We found a significant relationship between PM

10 and BMI (r=0. 034, P=0. 015), but the

association of PM 2. 5

and BMI was not statistically sig- nificant (r=0. 035, P=0. 606).

7. Discussion

This systematic review and meta-analysis revealed a weak positive association between ambient PM

10 and child obe-

sity. However, the results for PM 2. 5

was not significant. A few meta-analysis or large sample size studies have explored the association of ambient PM with adult obesity or birth weight, but with childhood obesity (24-26).

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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January 2020, Volume 8, Issue 1, Number 17

NOTE: Weights are from random effects analysis

Overall (I-squared = 94.4%, p = 0.000)

Fioravanti S et al (PM2.5) (2018)

Poursafa P et al (2017)

Fleisch AF et al (3.3 years) (2016)

Study

Mao G et al (2017)

Fioravanti S et al (PM 10) (2018)

Fleisch AF et al (7.7 years) (2016)

Dong GH et al (2014)

ID

0.02 (-0.06, 0.10)

0.01 (-0.08, 0.09)

0.41 (0.27, 0.56)

-0.00 (-0.05, 0.05)

0.03 (-0.03, 0.08)

-0.01 (-0.09, 0.07)

-0.20 (-0.25, -0.15)

0.04 (0.03, 0.05)

ES (95% CI)

100.00

13.80

10.56

15.20

%

15.22

13.80

15.20

16.21

Weight

0.02 (-0.06, 0.10)

0.01 (-0.08, 0.09)

0.41 (0.27, 0.56)

-0.00 (-0.05, 0.05)

0.03 (-0.03, 0.08)

-0.01 (-0.09, 0.07)

-0.20 (-0.25, -0.15)

0.04 (0.03, 0.05)

ES (95% CI)

100.00

13.80

10.56

15.20

%

15.22

13.80

15.20

16.21

Weight

0-.557 0 .557

Figure 2. Forest plot of Fisher’s z values indicating the correlation between PM and BMI

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

Articles screened by title and abstract

(n=4276)

Excluded non- relevant articles

(n=4271)

Full text articles assessed for eligibility and studies included in the meta-analysis (n=5) (Two studies reported PM10 and five studies reported PM2.5 as the indicators of air pollution. Three studies reported PM2.5, one study reported PM10 and one study reported both PM2.5 and PM10.

Removed duplicates articles (n=115)

Articles identified through electronic database search

(n=4391)

(PubMed: 662; Scopus: 2600; ISI Web of Science: 1129)

Figure 1. The flowchart of the search results

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January 2020, Volume 8, Issue 1, Number 17

Table 1. Results of meta-regression analyses for the potential source of heterogeneity

Covariate B SE P 95% CI

Year of publication 0. 016 0. 056 0. 790 (-0. 128, 0. 159)

Mean age 0. 040 0. 030 0. 243 (-0. 038, 0. 118)

PM2. 5 (Ref, PM10) 0. 024 0. 162 0. 889 (-0. 392, 0. 440)

Sample size of study -0. 0000002 0. 000007 0. 974 (-0. 00002, 0. 00002)

Study location: (Ref. : Asia)

USA -0. 264 0. 144 0. 107 (-0. 664, 0. 136)

Europe -0. 207 0. 159 0. 109 (-0. 649, 0. 235)

Study type: Cohort (Ref: Case control) -0. 238 0. 121 0. 107 (-0. 550, 0. 074)

SE: Standard Error; CI: Confidence Interval

Table 2. Results of subgroup analysis on the association between PM and BMI

Variables Groups NO. of Study Effect Size (Fisher’ z) 95% CI P Heterogeneity

I2 (%) P

PM type 10 2 0. 034 (0. 007, 0. 061) 0. 015 16. 60 0. 274

2. 5 3 0. 035 (-0. 099, 0. 169) 0. 606 95. 30 < 0. 001

Study type Cross-sectional 2 0. 218 (-0. 148, 0. 583) 0. 243 96. 10 < 0. 001

cohort 3 -0. 037 (-0. 132, 0. 057) 0. 442 91. 60 < 0. 001

Study location

Asia 2 0. 218 (-0. 148, 0. 583) 0. 243 96. 10 < 0. 001

Europe 1 -0. 001 (-0. 06, 0. 057) 0. 961 0. 00 0. 818

USA 2 -0. 059 (-0. 2, 0. 083) 0. 416 95. 50 < 0. 001

Figure 3. Forest plot of Fisher’s z values indicating the correlation between PM and BMI by PM type

NOTE: Weights are from random effects analysis

.

.

Overall (I-squared = 94.4%, p = 0.000)

Fleisch AF et al (7.7 years) (2016)

Poursafa P et al (2017)

Fioravanti S et al (PM 10) (2018)

ID

Mao G et al (2017)

PM10

Fleisch AF et al (3.3 years) (2016)

Study

Fioravanti S et al (PM2.5) (2018)

PM2.5

Subtotal (I-squared = 95.3%, p = 0.000)

Dong GH et al (2014)

Subtotal (I-squared = 16.6%, p = 0.274)

0.02 (-0.06, 0.10)

-0.20 (-0.25, -0.15)

0.41 (0.27, 0.56)

-0.01 (-0.09, 0.07)

ES (95% CI)

0.03 (-0.03, 0.08)

-0.00 (-0.05, 0.05)

0.01 (-0.08, 0.09)

0.04 (-0.10, 0.17)

0.04 (0.03, 0.05)

0.03 (0.01, 0.06)

100.00

15.20

10.56

13.80

Weight

15.22

15.20

%

13.80

69.99

16.21

30.01

0.02 (-0.06, 0.10)

-0.20 (-0.25, -0.15)

0.41 (0.27, 0.56)

-0.01 (-0.09, 0.07)

ES (95% CI)

0.03 (-0.03, 0.08)

-0.00 (-0.05, 0.05)

0.01 (-0.08, 0.09)

0.04 (-0.10, 0.17)

0.04 (0.03, 0.05)

0.03 (0.01, 0.06)

100.00

15.20

10.56

13.80

Weight

15.22

15.20

%

13.80

69.99

16.21

30.01

0-.557 0 .557

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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January 2020, Volume 8, Issue 1, Number 17

These five studies investigated more than 33000 par- ticipants. The current literature provides conflicting re- sults on the association between air pollution and child- hood obesity (19-21, 27). We found a relatively weak positive relationship between exposure to PM

10 and

childhood BMI, consistent with most previous studies findings. Five of the seven studies included in the cur- rent meta-analysis reported the direct association of air pollution and child weight, whereas two cohort studies did not report such association (20, 21).

Such discrepancies among these studies results might be due to confounding factors like age, gender, ethnicity, physical activity, level of exposures, and some other fac- tors. These findings might be confounded by heterogene- ity due to multiple dispersions between studies such as study design, different techniques to measure PM con- centration, the way PM levels is reported, and other vari- ous confounders which were adjusted in the analysis.

Only a few cross-sectional studies have investigated the relation of air pollution and obesity in children (16, 17, 20, 27-29). In a longitudinal study, higher exposure to NO

2 and

NOTE: Weights are from random effects analysis

.

.

Overall (I-squared = 83.1%, p = 0.000)

Study

Dong GH et al (2014)

ID

Mao G et al (2017)

Poursafa P et al (2017)

Subtotal (I-squared = 16.6%, p = 0.274)

Subtotal (I-squared = 89.4%, p = 0.000)

Fioravanti S et al (PM 10) (2018)

Fioravanti S et al (PM2.5) (2018)

PM2.5

PM10

Fleisch AF et al (3.3 years) (2016)

0.05 (-0.00, 0.10)

0.04 (0.03, 0.05)

ES (95% CI)

0.03 (-0.03, 0.08)

0.41 (0.27, 0.56)

0.03 (0.01, 0.06)

0.09 (-0.02, 0.20)

-0.01 (-0.09, 0.07)

0.01 (-0.08, 0.09)

-0.00 (-0.05, 0.05)

100.00

%

23.11

Weight

19.21

8.64

38.06

61.94

14.95

14.95

19.14

0.05 (-0.00, 0.10)

0.04 (0.03, 0.05)

ES (95% CI)

0.03 (-0.03, 0.08)

0.41 (0.27, 0.56)

0.03 (0.01, 0.06)

0.09 (-0.02, 0.20)

-0.01 (-0.09, 0.07)

0.01 (-0.08, 0.09)

-0.00 (-0.05, 0.05)

100.00

%

23.11

Weight

19.21

8.64

38.06

61.94

14.95

14.95

19.14

0-.557 0 .557

Figure 4. Forest plot of Fisher’s z values, indicating the correlation between PM and BMI by PM type after excluding the study of Fleisch AF et al (7. 7 years)

Table 3. The correlation between PM exposure and BMI

Variables Effect Size Heterogeneity

Pooled r 95% CI P I2 P τ2

PM10 0. 034 (0. 007, 0. 061) 0. 015 16. 60% 0. 0002 0. 0002

PM2. 5 0. 035 (-0. 099, 0. 167) 0. 606 95. 30% 0. 0216 0. 022

overall 0. 022 (-0. 057, 0. 102) 0. 579 94. 40% 0. 0101 0. 010

τ2: Between-studies variance

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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January 2020, Volume 8, Issue 1, Number 17

PM 2. 5

was associated with higher BMI, body fat percent- age, and abdominal obesity during follow up and at the age of 18 in children who were overweight or obese at the study baseline (19). Another study conducted on over- weight and obese minority youths found that higher expo- sures to NO

2 and PM

2. 5 during one year before the study

was not associated with obesity, but it was related to low- er insulin sensitivity and higher acute insulin response to glucose, which might contribute to obesity (19, 30).

The mechanisms linking air pollution to obesity risk and type-2 diabetes are not entirely determined. The effects of air pollutants on immune response, oxidative stress, and insulin resistance might explain the results (31).

Air pollutants such as PM might increase the infiltra- tion and activation of immune-competent cells, includ- ing monocyte and macrophages, in body tissues (32). Previous findings also indicated that early life exposure to PM

2. 5 might result in insulin resistance and obesity

later in life, through NAD(P)H oxidase-derived super- oxide anions, which might cause changes in adipocyte numbers and size (33).

Animal studies suggest that higher exposure to air pol- lutants might result in increased adipose tissue inflam- mation, accumulation of glucose in skeletal muscles, and therefore it might contribute to metabolic dysfunc- tion and obesity (34, 35). Furthermore, previous studies indicate that long-term exposure to combustion-related air pollutants can increase systemic inflammation and oxidative stress (34).

Little information is available about the biological basis of the relationship between exposure to air pollutants and childhood obesity. There may be a potential for residual confounders, including socioeconomic status and physi- cal activity, which can be associated with both air pollu- tion exposure and children’s weight. Therefore, residual confounding may affect the study results due to the as- sociations of poor diet and low physical activity with child overweight and metabolic disruption. Also, these factors may be related to residential proximity to sources of air pollution (36, 37). For example, children living in areas with higher levels of air pollution usually belong to lower socio- economic families who often consume higher amounts of total or saturated fats (27, 38).

Lack of physical activity among the children living in pollut- ed regions (because of their parental control to restrict the children’s exposure to air pollution) may be another reason for the excess weight in children. It is documented that over- weight children usually have less frequent and shorter peri-

ods of activities compared to their normal weight peers (39, 40). However, the findings on the associations of exposure to air pollutants and childhood obesity are unlikely to be confounded by these factors, because many of these studies had adjusted these associations for socioeconomic status, as a strong predictor of dietary intake and physical activity (36).

Furthermore, misclassification of exposure to air pollut- ants might have occurred with residential-based estimates of pollutant exposure, which might decrease the observed effects (41). Some studies also lack information about oth- er potential confounders such as active and passive smok- ing as well as exposure to noise pollution (19). Previous studies suggest that tobacco exposure and near roadway air pollution contribute to synergistic effects on the devel- opment of child obesity (18).

The findings of the current study concerning the asso- ciation of exposure to ambient PM with childhood obesity should be considered with caution. The cross-sectional design of some studies used for this meta-analysis might preclude any causality. Another limitation is the high het- erogeneity between studies. Other potential risk factors like child physical activity, familial socioeconomic status, and climate conditions were not available in some studies.

8. Conclusions

This systematic review and meta-analysis indicate that exposure to ambient PM

10 has a weak positive as-

sociation with childhood obesity. This finding should be considered in future studies and preventive programs. Our results are also useful for health policymakers and health care providers to design health promotion inter- ventions and preventive strategies. More research is needed to clarify the effect of other ambient air pollut- ants on child health status.

Ethical Considerations

Compliance with ethical guidelines

All ethical principles were considered in this article.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for- profit sectors.

Authors contribution's

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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January 2020, Volume 8, Issue 1, Number 17

Conceptualization, methodology, and investigation: All au- thors; Writing-original draft: Maryam Bahreynian; Writing- review & editing: Maryam Bahreynian, Roya Kelishadi, Meh- ri Khoshhali, and Marjan Mansourian; Supervision: Roya Kelishadi and Mehri Khoshhali.

Conflicts of interest

The authors declared no conflict of interests.

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Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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A ppendix 1. Sum

m ary of exposure and outcom

e assessm ent strategies and an estim

ate of effect size in included studies

Author(s)/ D

ate Study

Location Study D

esign

Follow

U p

D ura-

tion

Sam ple

Size Age (y)

O utcom

e Exposure Assess-

m ent M

ethod Exposure

G roup /

Subgroups Effect Size CI (95%

) Adjustm

ent Factors

M ichael

Jerrett ,

2010

Southern California,

U SA

Cohort 8-year follow

- up

2889 10–18 years

Body M ass

Index (BM I)

Traffi c-related air

pollution

Annual average daily traffi

c (AADT) 150 m

B (SE): 0. 0039 (0. 0008)

G ender, cohorts variables

parental education, personal w

eekly sm oking,

second hand sm oke (cur-

rent + past), ever asthm a,

buffer population, gam m

a index, proportion of below

poverty people w

ithin census block, norm

alized difference vegetation

index (N DVI), foreign-born,

com m

unity-level violent crim

e rate, and having no food stores w

ithin 500-m

road netw ork buffer w

ith random

com m

unity effects

AADT 300 m 0. 0013 (0. 0008)

Pei, 2013 G

erm any

Cohort 10-year follow

- up

3121 Fem

ales (N

=1114), M

ales (N

=1158)

10 (W e predicted

standardized body m

ass index (BM I) at 10

years of age using stan - dardized BM

Is from

birth to 5 years. )

M aternal sm

oking dur- ing pregnancy

β (CI): 0. 13

(0. 03, 0. 22)

Parental education, fam -

ily incom e, and m

aternal sm

oking during pregnancy

M ichael Jer-

rett (2014)

Southern California,

U SA

Cohort 4-year follow

- up

N =4550

5–11 years

Β (SE)

Having asthm a,

parental education, im

m igrant status,

the m easure of green

cover, street connectivity, recreational program

m ing

w ithin 5 km

of the hom e,

and fast food access w ithin

500 m of the hom

e

Traffi c density

0. 0002 (0. 0001)

N on-Freew

ay N

O x

0. 0861 (0. 0255)

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

11

January 2020, Volume 8, Issue 1, Number 17

Author(s)/ D

ate Study

Location Study D

esign

Follow

U p

D ura-

tion

Sam ple

Size Age (y)

O utcom

e Exposure Assess-

m ent M

ethod Exposure

G roup /

Subgroups Effect Size CI (95%

) Adjustm

ent Factors

G uang-Hui

Dong, 2014

China

Cross- sectional

- 30056

2-14

O besity BM

I> 95th per -

centile

M easurem

ents of am bi -

ent PM 10 , SO

2 , N O

2, and O

3 concentrations from

2006 to 2008 w ere

obtained at m unicipal

air pollution m onitoring

stations.

PM 10 (μg/m

3) O

R(CI): 1. 19

(1. 11–1. 26) Age, gender, parental

education, breast feeding, low

birth w eight,

area of residence per person, house decorations, hom

e coal use, ventilation device in the kitchen, air

exchange in w inter, passive

sm oking exposure, and districts

SO 2 (ppb)

1. 11 (1. 03–1. 20)

N O

2 (ppb) 1. 13

(1. 04–1. 22)

O 3 (ppb)

1. 14 (1. 04–1. 24)

Rob M cCon -

nel, 2015

Southern California,

U SA

Cohort 8-year

3318 10-18

BM I

Residential near-road- w

ay pollution exposure (N

RP) w as estim

ated based on a line source

dispersion m odel

accounting for traffi c

volum e, proxim

ity, and m

eteorology

Secondhand sm

oke

BM I grow

th (95%

CI):

Ethnicity, sex, com m

unity, year of enrollm

ent, and age

O ne sm

oker at hom

e: 0. 48 (0. 16, 1. 12)

Tw o or m

ore than 2 sm

okers at hom

e: 1. 08 (0. 19, 1. 97)

M aternal sm

oking during pregnancy: 0. 72 (0. 14, 1. 31) N

RP: 1. 13 (0. 61, 1. 65).

The difference in the attained BM

I (95%

CI): O

ne sm oker at

hom e: 0. 95 (0. 42,

1. 47) ≥ 2 sm

okers at hom

e: 1. 77 (1. 04, 2. 51

M aternal sm

oking during pregnancy; 1. 14 (0. 66, 1. 62 N

RP: 1. 27 (0. 75, 1. 80)

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

12

January 2020, Volume 8, Issue 1, Number 17

Author(s)/ D

ate Study

Location Study D

esign

Follow

U p

D ura-

tion

Sam ple

Size Age (y)

O utcom

e Exposure Assess -

m ent M

ethod Exposure

G roup /

Subgroups Effect Size CI (95%

) Adjustm

ent Factors

Fleisch, 2016

Boston, U

SA

Cohort

Early child- hood (m

e- dian: 3. 3 years of age)

m id-

child- hood (m

e- dian: 7. 7 years of age)

1418

M ean (standard

deviation) of age at early childhood visit 3.

3 (0. 4),

and at m id-childhood

visit 8. 0 (0. 9)

BM I z-score

Spatiotem poral m

odels to estim

ate prenatal and early life residential PM

2. 5 and black carbon exposure as w

ell as traf- fic density and

roadw ay proxim

ity.

PM 2. 5 (μg/m

3)

3. 3 years

Child (age, sex and race/ ethnicity), m

other (age, education, and sm

ok- ing during pregnancy), neighborhood (census tract m

edian incom e),

season and date of health outcom

e

Third trim es -

ter 0. 0

(-0. 1, 0. 1)

Year prior to early child- hood visit

-0. 0 (-0. 1, 0. 1)

N ear-residence

traffi c density

Birth address 0. 0

(-0. 0, 0. 1)

Early child - hood address

0. 0 (-0. 0, 0. 1)

Proxim ity to

m ajor roadw

ay, birth address

Reference:≥200m

<50m 0. 3

(0. 0, 0. 7)

[50, 100m ]

-0. 0 (-0. 4,0. 3

[100, 200m ]

0. 4 (0. 1, 0. 6)

Proxim ity to

m ajor roadw

ay, early child -

hood address Reference:≥200m

<50 m 0. 1

(-0. 2, 0. 5)

[50, 100 m ]

-0. 0 (-0. 4, 0. 3)

[100, 200 m ]

0. 1 (-0. 2, 0. 3)

Yueh- HsiuM

athil - daChiua

2017

Boston, U

SA Cohort

4. 0±0. 7 years

239 BM

I, z- score

Prenatal daily PM 2.

5 exposure w as esti-

m ated using a validated

satellite-based spatio- tem

poral m

odel. Prenatal PM

2. 5 level (μg/m

3

M edian IQ

R 10. 7(9. 9─11. 4)

PM 2. 5 (μg/m

3)

G irls

-0. 12 (-0. 37,-0. 03)

M aternal age, race/

ethnicity, education, pre- pregnancy BM

I, and child’s age at anthropom

etric m

easurem ent

Boys 0. 21

(0. 003,-0. 37)

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

13

January 2020, Volume 8, Issue 1, Number 17

Author(s)/ D

ate Study

Location Study D

esign

Follow

U p

D ura-

tion

Sam ple

Size Age (y)

O utcom

e Exposure Assess-

m ent M

ethod Exposure

G roup /

Subgroups Effect Size CI (95%

) Adjustm

ent Factors

Tanya. Alde- rete, 2017

Los Ange- les, U

SA Cohort

3. 4 years

314 overw

eight and obese children

8-15 years BM

I

N O

2 and PM 2. 5 w

ere m

odeled as long-term

exposure using cum ula-

tive 12-m

onth averaged exposure during the

follow -up.

Estim ated effect esti-

m ates w

ere reported for a

A 5-ppb difference in N

O 2 and a 4-μg/m

3 difference in PM

2. 5

N O

2 (ppb)

2. 1 (0. 1, 4. 1)

Sex, Tanner stage, the season of testing

(w arm

/cold), prior year exposure at each follow

-up visit, social position, body

fat percentage (w here

appropriate), study w ave,

and study entry year

PM 2. 5 (μg/m

3) 3. 8

(1, 6. 6)

Poursafa, 2017

Iran Cross-

sectional ---

186 6-18

BM I

The air quality index (AQ

I) is used to describe the level of air pollution

w ith adverse health

effects. W e used PM

2.

5 data.

Β=0. 34 Age and gender

Sara Fiora- vant, 2018

Italy Cohort

8-year follow

- up

581 Birth-8 years

Prevalence of over- w

eight/ obesity w

as 9. 3%

and 36. 9%

Air pollution w as as-

sessed at the residential address

N O

2 (per 10 μg/ m

3) 0. 99

(0. 86, 1. 12) M

aternal and paternal education, m

aternal pre- pregnancy BM

I, m aternal

sm oking during pregnancy,

gestational diabetes, m

aternal age at delivery, gestational age, childbirth w

eight, breast- feeding duration, age (in

m onths) at w

eaning

N O

X (per 20 μg/ m

3) 0. 98

(0. 86, 1. 12)

PM 10 (per 10 μg/

m 3)

0. 971 (0. 77, 1. 23)

PM 2. 5 (per 5 μg/

m 3)

1. 02 (0. 75, 1. 40)

PM coarse (per 5

μg/m 3)

0. 91 (0. 77, 1. 09)

PM 2. 5 abs (per 1 μg/m

3) 1. 10

(0. 88, 1. 37)

G uangyun

M ao, 2017

Boston, U

SA Cohort

2-year follow

- up

1,446 first 2 years of life

Com paring the

highest and low -

est quartiles of PM

(2:5 μg/m 3)

The adjusted Rela- tive Risks (RRs)

1. 3 (1. 1, 1. 5)

M aternal age at delivery,

race/ ethnicity, education level, sm

oking status, diabetes, m

arriage status, household incom

e per year, the season of child- birth, preterm

birth, birth w

eight, and breastfeeding

Bahreynian M, et al. Exposure to Ambient Particulate Matter and Childhood Obesity. Association Between Exposure to Ambient Particulate. J Pediatr Rev. 2020; 8(1):1-14.

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