Effect of environmental pollutants particulate matter PM2.5, PM10, nitrogen dioxide (NO2) and ozone (O3) on obesity

Sultan Ayoub Meo; Nervana M. Bayoumy; Anusha Sultan Meo

doi:10.1016/j.jksus.2024.103146

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Full Length Article

05 2024

:36;

103146

doi:

10.1016/j.jksus.2024.103146

Effect of environmental pollutants particulate matter PM_2.5, PM₁₀, nitrogen dioxide (NO₂) and ozone (O₃) on obesity

Sultan Ayoub Meo^{^a,⁎}, Nervana M. Bayoumy^{^a}, Anusha Sultan Meo^{^b}

a Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia

b The School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Scotland, United Kingdom

⁎Corresponding author. smeo@ksu.edu.sa (Sultan Ayoub Meo)

Received: 2023-10-21, Accepted: 2024-2-26,

Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Abstract

Background

Environmental pollution is a highly challenging global health concern, affecting the natural ecosystem and human health. The obesity risk is allied to diverse factors, including genetics, behaviour, food, and socio-cultural conditions, however, literature is lacking in exploring the effect of environmental pollution on obesity and the causal pathways are not fully understood. Therefore, this study aimed to investigate the effect of environmental pollutants particulate matter PM_2.5 μm, PM₁₀ μm, Nitrogen Dioxide (NO₂), and ground level Ozone (O₃) on obesity.

Methods

This study recorded data on air pollutants and obesity using the electronic platforms Pub Med, Web of Science, Scopus, and Google Scholar. The keywords included for the literature search were based on a combination of two main aspects, which were used to represent exposure (air pollutants), and outcome (obesity). Initially, 324 articles and reports were identified, and after revising the abstracts and full articles, 12 studies were selected for a detailed analysis and discussion. The Odds Ratio (OR) and 95 % confidence intervals (CIs) were extracted to investigate the impact between air pollutants and obesity. The Cochrane chi-squared test (Chi²), fixed-effects design was used when I² < 50 % and P > 0.05; otherwise, a random-effects model was adopted.

Results

Environmental pollutants, particulate matter PM_2.5 (OR = 1.18; 95 % CI: 1.10–1.25p < 0.01), PM₁₀ (OR = 1.11; 95 % CI: 1.02–1.20; p < 0.01), Nitrogen Dioxide NO₂ (OR = 1.14; 95 % CI: 1.02–1.28; p = 0.03), ground-level Ozone O₃ (OR = 1.01; 95 % CI: 1.00–1.01; p = 0.01) were significantly and positively associated with obesity.

Conclusions

Environmental pollution could be a risk factor for obesity. The potential mechanisms involved in the pathogenesis of this relationship are oxidative stress, inflammation, hormonal disruption, and adipose tissue function alteration. Addressing this multifaceted issue requires collaboration between public health officials, policymakers, and the public at large. Moreover, raising public awareness is a crucial step towards mitigating the impact of environmental pollution on obesity.

Keywords

Environmental pollution

PM2.5

PM10

NO2

O3

Obesity

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1 Introduction

Worldwide, the escalating occurrence of obesity has reached an epidemic scale, causing a substantial burden on public healthcare systems and economies. Obesity has emerged as a critical global health challenge, affecting individuals of all ages, genders, socioeconomic backgrounds, and geographic locations. The obesity outburst is attributed to a complex relationship of genetics, behaviour, food, and socio-cultural conditions (Swinburn et al., 2011; Verde et al., 2023; Centers for Disease Control Prevention (CDC, 2023). Moreover, rapid urbanization, sedentary lifestyles, increased availability of calorie-dense foods and environmental pollution have exacerbated this issue, leading to an alarming rise in obesity-related comorbidities, including coronary artery disease, type 2 diabetes mellitus, neuropsychiatric disorders, and cancers (Swinburn et al., 2011; Verde et al., 2023; Stierman et al., 2021 ).

Worldwide, from 1975 to 2016, the prevalence of obesity has markedly increased. It has major public health consequences and is one of the leading risk factors for chronic debilitating diseases. The World Health Organization in 2016 reported that there were about 1.9 billion overweight adults, among them 650 million were obese, which equals 13 %, among them 10.8 % were males and 14.9 % were females (World Health Organization, 2023). In the US, the obesity prevalence was 41.9 %, ranked the state with the highest prevalence rates among the high-income nations (Stierman et al., 2021).

The most recent report published in the obesity atlas, 2023 reported that “over 4 billion people may be affected by 2035, compared with about 2.6 billion in 2020. This figure reflects an increased rate from 38 % of the world’s population in 2020 to over 50% by 2035”. Obesity causes 4.7 million premature deaths each year. The economic impact of overweight and obesity is estimated to surpass $4.32 trillion annually by 2035 if prevention measures are not implemented appropriately (World Obesity, 2023).

The prevalence and pathogenesis of obesity are complex and involve numerous factors. However, in recent years, the scientific community has highlighted the role of environmental pollution in the pathogenesis of obesity. Environmental pollution and obesity characterize significant public health concerns in this modern world. While literature highlights distinct issues, emerging research has uncovered a multifaceted relationship between air pollution and obesity. The literature is lacking in exploring the effect of environmental pollution on obesity and the causal pathways underlying mechanisms are not fully understood. Therefore, this study aimed to address the gaps in the literature by exploring the impact of environmental pollutants particulate matter PM_2.5, PM₁₀, Nitrogen Dioxide (NO₂), and ground level Ozone (O₃) on obesity.

2 Materials and methods

2.1

2.1 Search strategy

The present study was conducted in the “Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia.” For the selection of documents, we followed the protocol of PRISMA “Preferred Reporting Items for Systematic Reviews and Meta-Analyses.” The literature search was performed, and data were collected using electronic platforms such as PubMed, Web of Science, Scopus, and Google Scholar, to search the literature on the relationship between air pollutants and obesity from January 2000 to June 2023. The keywords included pollutants exposure (air pollutants), and outcome (obesity). We filtered the data using the key terms “environmental pollution, air pollution, particulate matter, PM2.5, PM10, Nitrogen Dioxide (NO₂), Ozone (O₃) and obesity”. While using both the terms “environmental pollution and obesity,” initially, 324 documents were identified; after the screening of the full documents, 18 studies were selected, out of which finally 12 studies were included in the analysis (Fig. 1).

PRISMA Flow Diagram for the selection of documents.

2.2

2.2 Inclusion and exclusion criteria

The inclusion criteria were constructed on the following principles (a) air pollution exposures (PM_2.5, PM₁₀, NO₂, and O₃); (b) outcome: obesity status by body mass index (BMI), or waist circumference (c) article type: original research; and (d) article language, English. Moreover, articles must demonstrate the relative risks (RRs)/odd ratios (ORs)/hazard ratios (HRs) and corresponding 95 % confidence intervals (CIs). The studies were excluded from the study if they met any of the criteria below, such as studies conducted on other air pollutants like studies on the effects of smoking, studies other than original articles, such as letters, editorials, brief communications, and review articles were excluded from the study.

2.3

2.3 Statistical analysis

In this study, the literature found was from cross-sectional studies, and few were cohort design studies. Therefore, we took into consideration the methodological differences of the studies and decided to only analyze the studies with the most prevalent study design, which was cross-sectional. The odds ratio (OR) with 95 % confidence intervals (CIs) were extracted from the included studies and pooled to investigate the relationship between air pollutants and obesity using the Mantel-Haenszel method (Fidler and Nagelkerke, 2013). For studies that reported more than one value for OR (for example, one for general obesity and one for abdominal obesity), both values were used as separate data points and were labelled (A) and (B). A p-value less than p < 0.05 was considered significant. The “Cochrane chi-squared test (Chi²) was used to evaluate heterogeneity among articles, a p-value < 0.05 indicates the existence of heterogeneity. To estimate the impact of heterogeneity on the analysis, the I² value was calculated. I² is a measure of heterogeneity and methods for calculating the associated 95 % CI. I² expresses the proportion of variability in the analysis. I² values ≥ 50 % and p < 0.05 indicated a moderate to high degree of heterogeneity among pooled studies. A fixed-effects design was used when I² < 50 % and p > 0.05; otherwise, a random-effects model was adopted” (Borenstein et al., 2010). The meta-analysis was conducted using RStudio version 2023.09.1 and package ‘meta” Egger's test was performed to evaluate publication bias and it was further assessed by the visual examination of the symmetry in funnel plots using comprehensive meta-analysis version 3, Biostat Inc, USA (Borenstein et al., 2010).

3 Results

3.1

3.1 Particulate matter PM 2.5 and obesity

Eleven cross-sectional studies reported the impact of PM_2.5 on obesity and were included in the analysis. The Cochrane chi-squared test and I² statistic revealed a significant heterogeneity (Chi² = 555.70, p < 0.01, I² = 97 %), so a random model was used. The forest plot analysis showed that the PM_2.5 pollutant was significantly associated with obesity (OR = 1.18; 95 % CI: 1.10–1.25; p < 0.01) (Table 1, Fig. 2).

Table 1 Impact of air pollutants PM_2.5, PM₁₀, NO₂, and O₃ on obesity.

Author, study year and Country	Study type	Sample Size	Pollutants	Pollutants Associated Obesity
Chen R et al., 2022, China	Cross-sectional	47,204	PM^2.5	Obesity: (OR 1.12 (95 % CI 1.09–1.14)
Zhang JS et al., 2021, China	Cross-sectional	9897	PM^2.5	Abdominal Obesity OR 1.40 (95 %CI: 1.19, 1.65)
			PM¹⁰	Abdominal Obesity OR 1.32 (95 %CI: 1.11, 1.55)
			NO₂	Abdominal Obesity OR 1.40 (95 %CI: 1.19, 1.65)
Cao S et al., 2021, China	Cross-sectional	91,052	PM^2.5	A: Obesity OR 1.08 (95 % CI: 1.07, 1.10) B: Abdominal obesity 1.10 (95 % CI: 1.09, 1.11)
De Bont et al., 2019, Spain	Cross-sectional	2660	PM^2.5	Overweight/obesity- OR = 1.05; 95 % CI = 0.96, 1.15
			PM¹⁰	Overweight/obese- OR = 1.10; 95 % CI = 1.00, 1.22
			NO₂	Overweight/obesity- OR = 1.05; 95 % CI = 0.96, 1.15
Yang Z et al., 2019a, China	Cross-sectional	13,414	PM_2.5	BMI: AOR 1.009 (95 % CI-1.003, 1.016)
			PM₁₀	AOR 1.007 (95 % CI-1.003, 1.011)
			NO₂	BMI: AOR 1.009 (95 % CI-1.003, 1.016)
			O₃	BMI: 1.006 (95 % CI-1.001, 1.011)
Zhang Z et al., 2021, China	Cross-sectional	44,718	PM_2.5	A: Obesity OR (95 % CI): 1.40 (1.26–1.55) B: Central obesity (95 % CI): 1.39 (1.26, 1.54)
			PM₁₀	OR (95 % CI): A: 1.25 (1.15, 1.37) B: 1.32 (1.21, 1.45)
			NO₂	A: Obesity OR (95 % CI): 1.40 (1.26–1.55) B: Central obesity (95 % CI): 1.39 (1.26, 1.54)
Yang BY et al., 2019, China	Cross-sectional	15,477	PM_2.5	Obesity OR (95 % CI): 1.04 (1.01–1.06)
			PM₁₀	Obesity/overweight OR (95 % CI): 1.04 (1.01–1.06)
			NO₂	Obesity/overweight OR (95 % CI): 1.04 (1.01–1.06)
			O₃	Obesity/overweight OR (95 % CI): 1.00 (0.97–1.03)
Wu et al., 2022, China	Cross-sectional	47,990	PM_2.5	Obesity OR (95 %CI): 1.29 (1.15, 1.44)
Wu et al., 2022, China	Cross-sectional	47,990	PM₁₀	Obesity OR (95 %CI): 1.32 (1.17, 1.49)
Zheng et al., 2021, China	Cross-sectional	36,456	PM_2.5	Obesity 1.185 (95 % CI: 1.054, 1.333)
			PM₁₀	OR: 1.030 (95 % CI: 0.972, 1.093)
			NO₂	Obesity OR 1.185 (95 % (CI): 1.054, 1.333)
			O₃	Obesity OR 1.041 (95 %CI: 1.001, 1.082)
Yang C et al., 2023, China	Cross-sectional	44,544	PM_2.5	A: Obesity: OR 1.27 (95 % CI: 1.23, 1.31), B: Obesity: ORs of 1.10 (95 % CI: 1.05, 1.14),
López-Gil et al., 2023, Spain	Cross-sectional	4378	PM_2.5	A: Medium PM2.5 (OR = 1.23; 95 % CI, 1.02–1.49) B: High PM2.5 (OR = 1.35; 95 % CI, 1.11–1.64)
Shin J et al., 2019, Korea	Cross-sectional	100,867	PM₁₀	OR (95 % CI) Obesity Male: 1.07 (1.01, 1.13) Female: 1.06 (1.00, 1.12)
			NO₂	OR (95 %CI) Obesity Male: 0.97 (0.91, 1.02) Female: 1.06 (0.99, 1.13)
			O₃	OR (95 %CI): Obesity Male:0.98 (0.93, 1.04) Female: 1.03 (1.00, 1.06)

Impact of particulate matter PM 2.5 on obesity. (A) and (B) are two different ORs within the same study that were used as separate data points.

3.2

3.2 Particulate matter PM₁₀ and obesity

For establishing the association between PM10 and obesity, eight cross-sectional studies reported the impact of PM₁₀ on obesity and were included in the analysis. The Cochrane chi-squared test and I² statistic revealed a significant heterogeneity (Chi² = 68.99, p < 0.01, I² = 88 %), so a random model was used. The forest plot analysis showed that the PM10 pollutant was significantly associated with obesity (OR = 1.11; 95 % CI: 1.02–1.20; p = 0.01) (Table 1, Fig. 3).

Impact of particulate matter PM10 on obesity. *(A) and (B) are two different ORs within the same study that were used as separate data points.

3.3

3.3 Nitrogen dioxide (NO₂) and obesity

While searching the literature about the association between NO₂ and obesity, seven cross-sectional studies reported the impact of NO₂ on obesity. The Cochrane chi-squared test and I² statistic revealed a significant heterogeneity (Chi² = 108.25, p < 0.01, I² = 93 %), so a random model was used. The forest plot analysis showed that the NO₂ pollutant was significantly associated with obesity (OR = 1.14;95 % CI:1.02–1.28; p = 0.03) (Table 1, Fig. 4).

Impact of NO2 on obesity. (A) and (B) are two different ORs within the same study that were used as separate data points.

3.4

3.4 Ozone (O₃) and obesity

There were 4 cross-sectional studies which were analyzed. The Cochrane chi-squared test and I² statistic did not reveal a significant heterogeneity (Chi² = 46.27, p = 0.18, I² = 36 %), so a fixed model was used. The forest plot analysis showed that the O₃ pollutant was significantly associated with obesity (OR = 1.01; 95 % CI: 1.00–1.01; p = 0.01) (Table 1, Fig. 5).

Impact of O3 on obesity. (A) and (B) are two different ORs within the same study that was used as separate data points.

4 Discussion

Environmental pollution is a global concern due to its detrimental effects on the environment, climate, and human health. The obesity risk is allied to numerous factors; however, literature is lacking in exploring the effect of environmental pollution on obesity, and the causal pathways' underlying mechanisms are not fully understood. The present study results revealed that environmental pollutants, particulate matter PM_2.5, PM₁₀, NO₂, and O₃ have a significant relationship with obesity. Environmental pollutants particulate matter PM_2.5, PM_10, NO_2, and O₃ are contagions of various biological, chemical, and physical elements, which adversely affect the everyday environment. Environmental pollutants directly or indirectly introduce gases, liquids, or solid substances that pollute the environment and make it hazardous to life and natural systems.

Tamayo-Ortiz et al. (2021) performed two surveys and analyzed the relationship between PM_2.5 exposure and the prevalence of obesity. The results showed that 10 μg/m³ increased in PM _2.5 concentration, and the odds ratios for obesity were increased. In another study conducted in China by (Liang et al., 2022) the authors adopted a distinctive methodology and established the association between decreasing levels of PM_2.5 following the implementation of environmental protection policies and the incidence of obesity among adolescents. The findings indicated a substantial decrease in BMI (β − 0.10 (95 % CI: −0.13, −0.07) as a direct outcome of reduced PM_2.5 exposure (Liang et al., 2022).

Furthermore, a cohort study conducted in the United States, focusing on the elderly veteran population, showed that PM2.5 exposure was linked with increased obesity risk (Bowe et al., 2021). Surprisingly, the analyses revealed an association at PM _2.5 concentrations. These associations remained consistent in both direction and magnitude across sensitivity analyses. Similarly, (Wang et al., 2022) provided a piece of evidence that exposure to PM2.5, NO₂, and O₃ was adversely linked with body composition including higher fat mass.

Another study was conducted in rural areas of China to explore the link between air pollution and obesity. This study assessed this relationship by looking at six different measurements related to body size in rural Chinese regions (Yang et al., 2019). Among the four types of pollutants studied, the study showed the strongest association of PM10 with a change in BMI. However, a cross-sectional study conducted on the Korean population did not find any significant associations when the data was analyzed across various subgroups stratified by sex and age (Hwang et al., 2019).

The emerging evidence suggests that NO₂ could play a role in the development and exacerbation of obesity. NO₂ is generated and released during combustion processes, motor vehicle emissions, industrial activities, and power plants. A researcher (de Bont et al., 2019) conducted a study that investigated the association between air pollution and overweight and obesity in school children and identified that exposure to NO2 at schools was associated with overweight and obesity. Similarly, (Yang Z et al., 2019) investigated the impact of PM _2.5μm, PM ₁₀μm, CO, NO₂, and O₃ on the BMI score. The results further revealed that the air quality index (AQI) was significantly and positively associated with the BMI score. (Zheng et al., 2021) identified that higher levels of NO₂ in the environment were linked to elevated prevalence of obesity. The findings of these studies (de Bont et al., 2019; Yang Z et al., 2019; and Zheng et al., 2021) provide convincing evidence of a causal relationship between NO₂ exposure and the development of obesity. Similarly, in the present study, we found that environmental pollutants, particulate matter PM_2.5 (OR = 1.18; 95 % CI: 1.10–1.25p < 0.01), PM₁₀ (OR = 1.11; 95 % CI: 1.02–1.20; p < 0.01), NO₂ (OR = 1.14; 95 % CI: 1.02–1.28; p = 0.03), O₃ (OR = 1.01; 95 % CI: 1.00–1.01; p = 0.01) were significantly associated with obesity.

The body of literature surrounding the link between ozone and obesity presents a diverse landscape of findings with contrasting findings. One longitudinal study conducted in schools across China aimed to discern the impact of prolonged exposure to air pollution on obesity rates among a large number of children and adolescents (Zheng et al., 2021). The results yielded a significant odds ratio of 1.041 (95 % CI: 1.001, 1.082) for obesity, corresponding to a 10 μg/m³ increase in ozone levels. Similarly, another longitudinal study targeting young adults investigated the effects of chronic ozone exposure on cardiometabolic health. After adjustments for age, ethnicity, and gender, this study also reported a significant odds ratio of 1.022 (95 % CI: 1.004.

The understanding of the mechanisms by which air pollutants, PM_2.5, PM₁₀, NO₂, and ozone (O₃), contribute to obesity involves several intricate pathways. Air pollution can initiate oxidative stress, disrupt the metabolism processes, enhance fat accumulation, and develop metabolic disorders and obesity (Sun et al., 2009; Xu et al., 2010). Exposure to air pollution can result in epigenetic modifications, including alterations in DNA methylation, histone modifications, and the expression of non-coding RNA. These epigenetic changes can affect the expression of genes related to inflammation, oxidative stress, cell cycle regulation, and signal transduction (Yang et al., 2019; Yang et al., 2023; Shi et al., 2022 ). All these facts support the hypothesis that environmental pollutants are the risk factors for developing obesity.

4.1

4.1 Study strengths and limitations

The present study’s strengths are that the analysis is based on the worldwide extensive literature with a large sample size. This study provides valuable insights for healthcare workers, policymakers, and researchers, informing them about the precise pieces of evidence on how environmental pollution causes obesity. Similar to other studies, this study has some limitations. This study analysis was based on cross-sectional studies as it was the most prevalent study design and there was a limited number of cohort studies. This study's findings are based on published studies from various regions of the world with different socioeconomic developments and air pollution. We were unable to exclude such confounding factors. While analyzing the study design, type, and homogeneity of the studies to minimize these confounding factors. Large sample-sized studies are needed to highlight more data from the various corners of the globe for a better understanding of the effect of environmental pollution on obesity.

5 Conclusions

Environmental pollutants PM_2.5, PM₁₀, NO₂, and O₃ were significantly associated with obesity. Environmental pollution could play a role in obesity through a complex interplay of mechanisms including lung inflammation, oxidative stress, and metabolic disorders which interact and reinforce each other, promoting obesity. Addressing this multifaceted issue requires collaboration between public health officials, policymakers, and the public at large to combat the epidemic of obesity that has become a prevalent pandemic and putting a significant burden on the world’s healthcare system and socioeconomic conditions.

Ethics approval

The data were recorded from publicly available literature and data-based sources and had no direct involvement of animals or humans; hence ethical approval is not required.

Informed consent

Not required.

Authors’ contribution

SAM, study concept, literature review, manuscript writing and editing, NB, ASM: literature review, data collection and analysis.

Acknowledgements

The authors extend their appreciation to the “Researchers Supporting Project (RSP-2024 R47), King Saud University, Riyadh, Saudi Arabia”.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Show Sections

[1] Borenstein M., Hedges L.V., Higgins J.P.T., Rothstein H.R., . A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Method. 2010;1:97-111.
[Google Scholar]

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