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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: Curr Epidemiol Rep. 2018 Apr 10;5(2):79–91. doi: 10.1007/s40471-018-0140-5

Ambient and Traffic-Related Air Pollution Exposures as Novel Risk Factors for Metabolic Dysfunction and Type 2 Diabetes

Tanya L Alderete 1,*, Zhanghua Chen 1,*, Claudia M Toledo-Corral 1,2,*, Zuelma A Contreras 1, Jeniffer S Kim 1, Rima Habre 1, Leda Chatzi 1, Theresa Bastain 1, Carrie V Breton 1, Frank D Gilliland 1
PMCID: PMC6178230  NIHMSID: NIHMS958534  PMID: 30319933

Abstract

Purpose of Review

Diabetes mellitus is a top contributor to the global burden of mortality and disability in adults. There has also been a slow, but steady rise in prediabetes and type 2 diabetes in youth. The current review summarizes recent findings regarding the impact of increased exposure to air pollutants on the type 2 diabetes epidemic.

Recent Findings

Human and animal studies provide strong evidence that exposure to ambient and traffic-related air pollutants such as particulate matter (PM), nitrogen dioxide (NO2), and nitrogen oxides (NOx) play an important role in metabolic dysfunction and type 2 diabetes etiology. This work is supported by recent findings that have observed similar effect sizes for increased exposure to air pollutants on clinical measures of risk for type 2 diabetes in children and adults. Further, studies indicate that these effects may be more pronounced among individuals with existing risk factors, including obesity and prediabetes.

Summary

Current epidemiological evidence suggests that increased air pollution exposure contributes to alterations in insulin signaling, glucose metabolism, and beta (β)-cell function. Future work is needed to identify the specific detrimental pollutants that alter glucose metabolism. Additionally, advanced tools and new areas of investigation present unique opportunities to study the underlying mechanisms, including intermediate pathways, that link increased air pollution exposure with type 2 diabetes onset.

Keywords: air pollution, type 2 diabetes, insulin resistance, beta-cell function

Introduction

The prevalence of diabetes mellitus remains high and is a top contributor to the global burden of mortality and disability [1]. Although type 2 diabetes has been traditionally regarded as an adult disease, there has been a slow yet steady increase in youth. For example, by 2050 the number of youth with type 2 diabetes is projected to increase 4-fold [2,3], illustrating that prevention of type 2 diabetes over the life course is an enormous public health priority. While studies have shown that type 2 diabetes is strongly linked with traditional risk factors such as poor diet and low physical activity and socio-economic status, recent work suggests that ambient and traffic-related air pollution exposures may also play an important role in disease development. The detrimental impact of air pollution exposure on chronic respiratory, cardiovascular, and cerebrovascular morbidity and mortality has been extensively studied [4–6], but the relationship between exposure to air pollutants and type 2 diabetes risk is a relatively new field of study in the past decade. This targeted review provides an update of the most recent epidemiological findings regarding the impact of air pollution exposure on diabetes morbidity in adults and children between the years 2012 and 2017.

Type 2 diabetes is characterized by high peripheral glucose concentrations caused by insulin resistance and a relative deficiency of insulin secretion from pancreatic beta (β)-cells to compensate insulin resistance. In clinical practice, the diagnosis of pre-diabetes and type 2 diabetes is made based upon blood markers of altered glucose metabolism, which include elevated levels of fasting glucose, post-prandial glucose, or glycated hemoglobin (HbA1c). Additionally, insulin resistance, hyperinsulinemia, and β-cell dysfunction can serve as early indicators of risk for developing type 2 diabetes. Collectively, these metabolic markers assist researchers and clinicians in monitoring individual risk for developing type 2 diabetes and present opportunities for early intervention. In this review, the term “metabolic dysfunction” has been used to encompass these metabolic markers as they relate to type 2 diabetes progression.

Methods

In this narrative review, we performed a comprehensive review of the literature between 2012–2017. Searches were performed in August 2017. PubMed database was searched for articles that contained the terms in the title and/or abstract that were relevant to the current review. The terms included: “air pollution” AND (“diabetes,” or “type 2 diabetes,” or “prediabetes,” or “metabolic dysfunction,” or “fasting glucose,” or “fasting insulin,” or “insulin resistance,” or “insulin sensitivity,” or “HOMA-IR,” or “beta-cell function,” or “HbA1c”). We also examined bibliographies of relevant articles, and papers previously known to the authors. This resulted in 170 articles from PubMed that were further evaluated for their relevance. Of these, we included 21 articles for diabetes prevalence or incidence, 6 articles on risk factors for type 2 diabetes among children, and 14 articles on risk factors for type 2 diabetes in adults.

Air pollution and diabetes morbidity in adults

Between 2012 and 2017, thirteen studies have examined the associations between chronic ambient and/or traffic-related air pollution exposures and diabetes morbidity in adults (Table 1). Eleven of these thirteen cross-sectional studies observed positive associations between diabetes prevalence and air pollutants, predominantly with particulate matter (including PM less than 10 [PM10] and 2.5 micrometers [PM2.5]), NO2, nitrogen oxides (NOx) [7–19]. Additionally, two of these studies examined traffic density and proximity to roadways as proxies for residential traffic-related air pollution exposure. One of these studies observed a positive association between diabetes prevalence and self-reported traffic density perception, yet the other did not find an association between diabetes and proximity to major roads [7,17]. Most longitudinal studies in adults [20–25], but not all [19,26,27], provide evidence that increased exposure to air pollution contributes to diabetes incidence. For example, cohort studies in Denmark and Germany found that exposure to pollutants, including PM, NO2, and traffic-related air pollutants (i.e. traffic density, distance to roadways), were associated with an elevated risk for developing diabetes [21,23]. Furthermore, results from the German cohort indicated that traffic exposures may account for the largest detrimental effects on metabolic risk where traffic- specific fine particulate matter (PM2.5) derived from a source-specific dispersion and chemistry transport model was more strongly associated with incident diabetes than total PM2.5 [23]. Lastly, only one longitudinal study found an increased risk of diabetes with greater ozone (O3) exposure among African-American women [25]. Among these recent studies, there appears to be specific subgroups that are more vulnerable to the effects of air pollution exposure, including nonsmokers, obese women, physically active older adults and those with heart disease [20,21]. Further, many of the studies that found positive associations between diabetes and traffic-related or ambient air pollution were in female-only cohorts or reported stronger effect sizes in women [19–22,24,25,28].

Table 1.

Summary of recent studies between ambient and traffic-related exposures with type 2 diabetes incidence or prevalence

Reference Study Design Location Sample Size Pollutants Main Findings
Eze et. al. (2017) Longitudinal Switzerland 2,631 NO2 An IQR increase in NO2 (15 μg/m3) was not associated with incident diabetes [RR=0.87, 95%CI: 0.62, 1.21].
Honda et. al. (2017) Cross-sectional United States 4,121 PM2.5, NO2 An IQR increase in one-year moving average PM2.5 (3.9 μg/m3) was associated with diabetes prevalence [POR= 1.35, 95% CI: 1.19, 1.53]. An IQR increase in NO2 (8.6 ppb) was associated with diabetes prevalence [POR=1.27, 95% CI: 1.10, 1.48].
Jerrett et. al. (2017) Longitudinal United States 43,003 O3 An IQR increase in O3 (6.7 ppb) was associated with incident T2D [HR=1.18, 95%CI: 1.04, 1.34].
Mazidi et. al. (2017) Cross- sectional/ecologic United States 3106 counties or equivalents from the continental USA, reflecting a population of ~170 million adults PM2.5 Average PM2.5 (μg/m3) explained 8.3% of the residual variation in T2D prevalence in males (P < 0.0001) and 11.5% in females (P < 0.0001) after correcting for obesity, race, poverty, education and temperature,
O’Donovan et. al. (2017) Cross-sectional United Kingdom 10,443 PM2.5, PM10, NO2 NO2 (μg/m3) was not associated with T2D prevalence after adjustment for demographic factors [OR= 1.08; 95% CI: 0.91, 1.29], further adjustment for lifestyle factors [1.10, 95% CI: 0.92, 1.32], and further adjustment for neighborhood green space [OR=0.91, 95% CI: 0.72, 1.16]. PM2.5 and PM10 were not significantly associated with T2D prevalence (p-value >0.05).
Requia et. al. (2017) Cross- sectional/ecologic Canada 117 health regions PM2.5 A 2-year increase of 10 μg/m3 in PM2.5 was associated with a 5.43% increase in incidence of diabetes [95%CI: 2.28%, 12.53%].
Sohn et. al. (2017) Cross-sectional South Korea 96,608 PM10, SO2 Exposure to PM10 (μg/m3) and SO2 (10−3 ppm) was associated with the prevalence of T2D among women [OR=1.01, 95%CI: 1.003, 1.013; OR=1.032, 95% CI: 1.004, 1.062, respectively], but not among men [OR=1.003, 95% CI: 0.998, 1.008; OR=0.98, 95% CI: 0.952, 1.006].
Strak et. al. (2017) Cross-sectional Netherlands 387,195 PM10, PM2.5, PM10 - 2.5, NO2, OPDDT, OPESR All pollutants, except PM2.5, were associated with diabetes prevalence. An IQR increase in NO2 (7.76 μg/m3) and OPDTT (0.28 nmol DTT/min/m3) was associated with diabetes prevalence [OR=1.07, 95%CI:1.05, 1.09; 1.08, 95% CI: 1.05, 1.10, respectively].
Coogan et. al. (2016) Longitudinal United States 43,003 NO2 NO2 was not associated with diabetes incidence (p-value >0.05).
Dzhambov et. al. (2016) Cross-sectional Plovdiv, Bulgaria 513 PM2.5, BaP, traffic density No significant associations with T2D for any pollutants (p-value >0.05).
Hansen et. al. (2016) Longitudinal Denmark 24,174 PM10, PM2.5, NOx, NO2 An IQR increase in PM2.5 (3.1 μg/m3) increased diabetes incidence [HR=1.11, 95% CI: 1.02, 1.22]. An IQR increase in PM10 (2.8 μg/m3) [HR=1.06, 95% CI: 0.98, 1.14], NO2 (7.5 μg/m3) [HR=1.05, 95%CI: 0.99, 1.12] and NOX (10.2 μg/m3) [HR=1.01, 95% CI: 0.98, 1.05] was weakly associated with diabetes incidence. Associations with PM2.5 enhanced in non-smokers, obese women, and heart disease patients.
Lazarevic et. al. (2015) Cross-sectional Australia 26,991 NO2, distance to major/minor road No significant associations were found between any pollutant and diabetes prevalence (p-value >0.05).
Liu et. al. (2016) Cross-sectional China 11,847 PM2.5 An IQR increase in PM2.5 (41.1 μg/m3) was associated with increased T2D prevalence [PR: 1.14, 95% CI: 1.08, 1.20].
Park et. al. (2015) Longitudinal 6 US sites* 5,839 PM2.5, NOx An IQR increase in PM2.5 (2.43 μg/m3) and NOx (47.1 pbb) was associated with T2D prevalence [OR=1.09, 95%CI: 1.00, 1.17; OR=1.18, 95% CI: 1.00, 1.38, respectively].
To et. al. (2015) Cross-sectional Canada 29,549 PM2.5 A 10 μg/m3 increase in PM2.5 was associated with diabetes [PR=1.28, 95% CI: 1.16, 1.41]. Risks elevated in the obese.
Weinmayr et. al. (2015) Longitudinal Germany 3,607 PM10, PM2.5 An increase of 1 μg/m3 in PM10 [RR=1.05, 95% CI: 1.00, 1.10) and PM2.5 [RR=1.03, 95% CI: 0.95, 1.12] was associated with incident T2D. Traffic- specific PM10 and PM2.5 were more strongly associated with T2D [RR=1.36, 95%CI: 0.98, 1.89; RR=1.36, 95% CI: 0.97, 1.89, respectively]. Individuals closer than 100m to busy road had a higher risk of incident T2D [RR=1.37, 95% CI: 1.04, 1.81].
Chien et. al. (2014) Cross- sectional/ecologic United States 3109 counties in the 48 contiguous states PM2.5 An increase in 1 μg/m3 PM2.5 increased the relative risk percentage for diabetes from −5.47 (95% credible interval: −6.14, −4.77) to 2.34% (95% credible interval: 2.01, 2.70) where 1323 of 3109 counties (42.55%) displayed diabetes vulnerability with significantly positive risk percentages.
Eze et. al. (2014) Cross-sectional Switzerland 6,392 PM10, NO2 PM10 and NO2 were associated with prevalent diabetes [OR= 1.40, 95% CI: 1.17, 1.67; OR=1.19, 95% CI: 1.03, 1.38, respectively] per 10 μg/m3 increase in average home outdoor level.
Chen et. al. (2013) Longitudinal Ontario, Canada 62,012 PM2.5 A 10 μg/m3 increase in PM2.5 was associated with incident diabetes 1.11 (95% CI:1.02, 1.21).
Andersen et. al. (2012) Longitudinal Denmark 57,053 NO2, traffic density/proximity An IQR increase in NO2 (4.9 μg/m3) was associated with confirmed diabetes incidence [HR=1.04, 95% CI:1.00–1.08]. Traffic load within 100 m was associated with confirmed diabetes incidence [HR=1.02, 95%CI: 1.00, 1.04]. NO2 effects were enhanced in nonsmokers, [HR=1.12, 95% CI: 1.05, 1.20] and physically active people [HR=1.10, 95% CI:1.03, 1.16].
Coogan et. al. (2012) Longitudinal Los Angeles, California 3,992 NOx, PM2.5 A 10 μg/m3 in PM2.5 and an IQR increase in NOx (12.4 parts ppb) was associated with diabetes [IRR=1.63, 95% CI: 0.78, 3.44; IRR=1.25, 95% CI: 1.07, 1.46, respectively].
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Summarizes the main findings from studies in adults between 2012 and 2017 that were included in this review. Bap: benzo alpha pyrene, CI: confidence interval, DTT: dithiothreitol, HR: hazard ratio, IQR: interquartile range, IRR: incidence rate ratio, nmol: nanomole, NO2: nitrogen dioxide, NOx: nitrogen oxide, O3: ozone, OPDTT: oxidative potential dithiothreitol OPESR: oxidative potential electron spin resonance, OR: odds ratio, PM: particulate matter, POR: prevalence, ppb: parts per billion, ppm: parts per million, PR: prevalence ratio, RR: risk ratio, SO2: sulfur dioxide, T2D: type 2 diabetes.

*

Six US sites included Baltimore, Maryland; Chicago, Illinois; Forsyth County, North Carolina; Los Angeles County, California; New York, New York; St. Paul, Minnesota

Air pollution and metabolic dysfunction among adults

Beyond diabetes morbidity, recent studies indicate that exposure to air pollutants may negatively impact early indicators of metabolic dysfunction (Table 2). Among fourteen recent reports, nine cross-sectional studies found that increased exposure to three ambient air pollutants (PM, NO2, and NOx) were associated with fasting blood levels of glucose, insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and/or HbA1c [11,12,28–34]. In a large study among 11,847 Chinese adults, exposure to PM2.5 was estimated using a spatial model incorporating satellite remote sensing data and an interquartile range increase in PM2.5 exposure (41.1 μg/m3) in the 10 months prior to blood testing was associated with an elevated fasting glucose (4.68 mg/dL) and HbA1c (0.08%) [12]. Another study in 1,023 predominantly obese Mexican-American women found that up to 58 days of cumulative lagged exposure to PM2.5 was associated with higher fasting insulin and glucose levels as well as HOMA-IR [28]. In addition to ambient pollutants, distance to major roadways has been used as a proxy of residential exposure to the complex mixture of traffic pollutants. Among 371 Chinese men and women, those living within 50 meters of a major road had 1.30 times higher HOMA-IR and 1.95 μU/ml higher fasting insulin levels compared to those living more than 200 meters away, yet fasting glucose levels did not differ between these two groups [30]. In a study among 363 women from Germany, land-use regression was used to asses exposures to NO2 and NOx 10 to 20 years prior their clinical visit, which were found to be positively associated with impaired glucose tolerance (2-hour glucose levels ≥140–199 mg/dL) [34]. To date, only one adult study has used robust measures of risk factors for type 2 diabetes [28], which includes whole-body insulin sensitivity (SI) and β-cell function from a frequently sampled intravenous glucose tolerance test (FSIVGTT) with minimal modeling [35]. This study found that short-term ambient exposure to PM2.5 and NO2 (two-months and up to 37 days prior to testing, respectively) was associated with lower SI among the 1,023 Mexican-American previously described [28]. Results from this study were robust to multi-pollutant models and further indicated that PM2.5 may have a larger effect on insulin resistance among those with increased obesity [28]. Although this study found strong inverse associations between ambient pollutants and SI, exposure to PM2.5 and NO2 was not associated with β-cell function. Overall, results from these studies suggest that increased exposure to ambient and traffic-related air pollutants have adverse effects on altered glucose metabolism through insulin-dependent pathways.

Table 2.

Summary of recent adult studies that found significant associations between ambient and traffic-related exposures with metabolic dysfunction

Reference Study Design Location Sample Size Pollutants with Significant Associations* Main Findings
Honda et. al. (2017) Longitudinal USA (Regions: North Atlantic, South, Great Lakes region, Plains States, Pacific) 4,121 PM2.5, NO2 ↑ HbA1c
Wallwork et. al. (2017) Longitudinal Eastern Massachusetts, southern New Hampshire, and southern Maine, USA 587 PM2.5 ↑ Fasting glucose, ↑ Hypertriglyceridemia, ↑ Risk of developing metabolic syndrome
Brook et (2016) Panel study (longitudinal) Beijing Area, China 65 PM2.5, BC ↑ HOMA-IR
Chen L et. al. (2016) Longitudinal Kailuan community, Tangshan City, China 27,685 NO2, PM10, SO2 ↑ Fasting glucose
Chen Z et. al. (2016) Cross-sectional Southern California, USA 1,023 NO2, PM2.5 ↑ Fasting glucose, ↑ Fasting insulin, ↑ HOMA-IR, ↓SI, ↓ HDL-to-LDL cholesterol ratio
Jiang et. al. (2016) Cross-sectional Urban residential area in Shanghai, China 371 PM2.5, Residential distance to major road ↑ Fasting insulin, ↑ HOMA-IR, ↑ LDL-C
Liu et. al. (2016) Cross-sectional China (nationally representative sample) 11,847 PM2.5 ↑ Fasting glucose, ↑HbA1C
Peng et. al. (2016) Longitudinal Greater Boston Area, MA, USA 551 PM2.5 ↑ Fasting glucose, ↑ Odds of IFG
Sade et. al. (2016) Cross-sectional southern Israel 73,117 PM10, PM2.5 ↑ Fasting glucose, ↑ HbA1C, ↑ LDL, ↑ TAG, ↓ HDL
Wolf et. al. (2016) Cross-sectional Augsburg, Germany and two adjacent rural counties (southern Germany) 2,944 PM10, PMcoarse,PM2.5, NO2, NOx ↑ HOMA-IR, ↑ Fasting glucose, ↑Fasting insulin
Eze et. al. (2015) Cross-sectional Eight Swiss communities representing a wide range of environmental conditions in Switzerland 3,769 PM10, ↑ Odds of MetS-W, MetS-I and MetS-A, ↑ Impaired fasting glycemia,
Sade et. al. (2015) Longitudinal southern Israel 131,882 NO2, SO2 ↑ Fasting glucose
Brook et. al. (2013) Experimental Michigan, USA 25 Environmental Mixture (only PM2.5 measured) ↑ HOMA-IR, ↓ Heart rate variability
Teichert et. al. (2013) Cross-sectional North-Rhine Westphalia (West Germany) 363 NO2, NOx ↑ Odds impaired glucose metabolism (IFG+T2D)
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Summarizes the main findings from adult studies between 2012 and 2017 that were included in this review. Pollutants listed are those found to be significantly associated with at least one measure of metabolic dysfunction. BC: black carbon, HbA1c: hemoglobin A1C, HDL: high density lipoprotein, HOMA-IR: homeostatic model assessment of insulin resistance, IFG: impaired fasting glucose, LDL: low density lipoprotein, NO2: nitrogen dioxide, PM: particulate matter, SI: insulin sensitivity, SO2: sulfur dioxide, TAG: triglycerides; MetS: metabolic syndrome, T2D: type 2 diabetes.

*

Statistically significant associations at a p-value <0.05.

Numerous studies have shown that increased exposure to air pollutants is associated with measures of type 2 diabetes risk, yet it remains uncertain as to whether these associations are independent of pre-existing states of metabolic dysfunction in susceptible populations. Four recent studies examined this question by conducting stratified analyses based on metabolic health [11,31–33] or restricting to a population of participants with metabolic syndrome (MetS), which is a constellation of metabolic complications associated with insulin resistance [29]. In one of the largest studies of this kind, researchers examined 73,117 adults in southern Israel. Results from this study found that average three-month concentrations of PM10, but not one- to seven-day exposure, was associated with increased fasting glucose levels and HbA1c. Positive associations were observed amongst all participants; however, the strongest association was present in diabetic patients where an interquartile range increase in PM10 (20 μg/m3) and PM2.5 (7 μg/m3) was associated with a 3.6% and 2.9% increase in HbA1c, respectively [31]. A German cohort study examined associations between an array of pollutants (e.g., PM10, PM2.5, NO2, NOx) in 2,944 participants who did not have diabetes, had prediabetes (impaired fasting glucose: ≥100–125 mg/dL or impaired glucose tolerance), or had diabetes. Among all participants, PMcoarse (PM2.5-10), PM10, PM2.5, NO2, and NOx were each associated with HOMA-IR and fasting insulin levels. In a stratified analysis, the effect sizes for these pollutants were much larger and highly statistically significant among those with prediabetes compared to those who were normal in fasting glucose concentrations [32]. Further, no associations were observed between air pollutants and HbA1c levels, and only increased PM2.5 and NO2 exposure were modestly associated with higher fasting glucose levels among all participants [32]. In another study, prior 3-month NO2 exposure was associated with fasting glucose levels among 131,882 adults, yet the effect sizes of these associations differed by glycemic status. For example, a 6.4 ppb (parts per billion) increase in NO2 exposure (24–72 hours prior to testing) was associated with a 0.4%, 0.6%, and 1.1% increase in fasting glucose levels among those with normal glucose, impaired fasting glucose, and diabetes, respectively [33]. In a large cohort of 4,121 older United Sates (U.S.) adults, 2–5 year moving averages of PM2.5 and NO2 exposure was associated with higher HbA1c levels in diabetic participants, while only NO2 was significantly associated with HbA1c in non-diabetic participants [11]. Additionally, significant dose response relationships were identified for both pollutants in diabetic participants and only for NO2 in non-diabetic participants [11]. Finally, in 65 nonsmoking adults with MetS from Beijing, four- and five-day exposure lags to exposure to ambient PM2.5 were significantly associated with an increased HOMA-IR. Specifically, a one-standard deviation (SD) increase in PM2.5 (67.2 μg/m3) exposure that was estimated from urban and local monitor sites was associated with a 0.22 unit increase in HOMA-IR [29]. Results from these studies suggest that individuals with underlying type 2 diabetes risk may be more susceptible to air pollution exposure by exacerbating insulin resistance and/or impairing insulin signaling. However, additional studies are needed in order to determine how such exposures impact whole body SI and β-cell function among susceptible populations. Despite this, associations between increased air pollution exposure and metabolic dysfunction have been observed in healthy populations, suggesting that air pollutants play an important role in type 2 diabetes development and progression.

Recent literature suggests that increased exposure to air pollutants negatively alters glucose metabolism. However, such cross-sectional studies are limited in that they are unable to determine causality. As such, longitudinal and intervention studies provide additional evidence, suggesting a causal role of air pollutants in type 2 diabetes. For example, four recent longitudinal studies [16,36–38] and one intervention study [39] found that PM10 and NO2 exposures negatively impacted metabolic health, including fasting glucose and MetS. In 27,685 Chinese adults, associations between 4-day average PM10 and NO2 exposure with fasting glucose levels were examined over four years of follow-up. This study found that a 100 μg/m3 increase in PM10 and NO2 was associated with 1.98 mg/dL and 9.6 mg/dL increase in fasting glucose levels, respectively. Furthermore, the effects of air pollutants on fasting glucose levels were stronger in females, the elderly, and overweight participants [36]. Amongst 3,769 participants, the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults revealed that per every 10 μ/m3 increase in 10-year mean PM10 the odds for developing MetS increased between 18–72% depending on the MetS definition. Interestingly, amongst all the MetS components these associations appeared to be driven by impaired fasting glucose [16]. Another study followed 551 nondiabetic US adults for a median of 2 years and found that an interquartile range increase in 1-, 7-, and 28-day PM2.5 exposure was associated with 0.6 mg/dL, 1.0 mg/dL, and 0.9 mg/dL higher fasting glucose level, respectively. The same PM2.5 exposures were associated with 13%, 27%, and 32% higher odds of impaired fasting glucose respectively [37]. The same group of researchers investigated 587 men with visits every 3–7 years (average number of visits: 2) in an effort to examine associations between PM2.5 and MetS as well as its components. This study found that a 1-μg/m3 increase in mean annual PM2.5 concentrations were associated with a 1.1 times higher risk of developing MetS and a 1.2 times higher risk of having an elevated fasting blood glucose level (defined as ≥100 mg/dL or medication to treat elevated blood glucose) [38]. Notably, an intervention study among 25 healthy adults in rural Michigan found that a 10 μg/m3 increase in sub-acute PM2.5 exposure for 5 consecutive days (with 4–5 hours/day of ambient exposures in an urban environment) was associated with increased HOMA-IR [39]. Results from these studies provide strong evidence that PM2.5 and NO2 exposures contribute to glucose dysregulation.

Air pollution and metabolic dysfunction among children

Meanwhile, early onset type 2 diabetes in children and youth is increasingly prevalent [40] with a heightened risk of microvascular and macrovascular complications in adult life [41]. There has been a growing body of evidence linking ambient and traffic-related air pollution exposures with metabolic dysfunction in children (Table 3). Of these, three cross-sectional studies found that increased exposure to ambient and traffic-related air pollution was associated with higher fasting insulin levels and higher HOMA-IR [42–44]. For example, among 837 adolescents from Germany, average prior year exposure to PM10 and NO2 was associated with increased HOMA-IR where a 2-SD increase in PM10 (6.7 μg/m3) and NO2 (8.9 μg/m3) were each associated with 11.4% higher HOMA-IR. Interestingly, in a multi-pollutant model including PM2.5 and NO2, only NO2 exposure remained significantly associated with HOMA-IR [42]. In an earlier study in 397 German children, the same group found that HOMA-IR increased by 17.0% and 18.7% for every 2-SD increase in ambient NO2 (6 μg/m3) and PM10 (3.7 μg/m3) exposure, respectively. Additionally, proximity to the nearest major road increased HOMA-IR by 7.2% per 500 meters [44]. The third cross-sectional study examined 54 children from the Mexico City Metropolitan Area (MCMA) and compared them to 26 controls matched on age, sex, weight, height, BMI, and socioeconomic status. Importantly, this control group lived in areas of Mexico with air pollution levels at or below air quality attainment levels. Compared to control children, MCMA children had higher fasting glucose levels but did not differ in fasting insulin levels or HOMA-IR [43]. Lastly, intervention studies provide additional evidence that air pollutants have negative effects on glucose homeostasis. For example, a clinical intervention study of 75 obese adolescents examined the metabolic benefits of laparoscopic adjustable gastric banding in the context of exposure to air pollutants. This study found that increased exposure to PM2.5 and NO2 attenuated the magnitude of HbA1c reduction, a known metabolic benefit of gastric banding [45]. As such, studies in children indicate that exposure to air pollutants may disrupt glucose homeostasis and/or hinder preventive methods to improve glucose metabolism.

Table 3.

Summary of recent studies in children that found significant associations between ambient and traffic-related exposures with metabolic dysfunction

Reference Study Design Location Sample Size Pollutants with Significant Associations* Main Findings
Alderete et. al. (2017) Longitudinal Los Angeles, CA, USA 314 NO2, PM2.5 ↓ SI, ↓ β-cell function (DI)
Ghosh et. al. (2017) Prospective /Intervention New York Area, USA 75 NO2, PM2.5 ↓ Metabolic benefits (e.g., HbA1c) of laparoscopic adjustable gastric banding
Thiering et. al. (2016) Cross-sectional Southern and Western Germany 837 PM10, NO2 ↑ HOMA-IR
Toledo-Corral & Alderete et. al. (2016) Cross-sectional Los Angeles, CA, USA 429 PM2.5, NO2, NOX ↑ Fasting glucose, ↓ Fasting insulin, ↓ SI, ↑ AIRg
Caldero n -Garciduen as et. al. (2015) Cross-sectional Mexico City Metropolitan Area (MCMA) and Polotitlán, Mexico (Control City) 54 MCMA and 26 Controls Matched case vs. control for high vs. low exposure in Mexico Compared to control children, MCMA had ↑ Fasting glucose levels
Thiering et. al. (2013) Cross-sectional Munich, South Germany, and Wesel, West Germany, 397 NO2, PM2.5, Proximity to Roadway ↑ HOMA-IR
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Summarizes the main findings from studies in children between 2012 and 2017 that were included in this review. Pollutants listed are those found to be significantly associated with at least one measure of metabolic dysfunction. AIRg: acute insulin response to glucose, DI: disposition index, HbA1c: hemoglobin A1C, HOMA-IR: homeostatic model assessment of insulin resistance, NO2: nitrogen dioxide, PM: particulate matter, SI: insulin sensitivity, MCMA: Mexico City Metropolitan Area.

*

Statistically significant associations at a p-value <0.05.

To our knowledge, only two studies in children have investigated the impact of increased air pollution exposure using the FSIVGTT with minimal modeling in order to describe SI, acute insulin response to glucose (AIRg), and β-cell function [46,47]. The first was a cross-sectional study among 429 overweight and obese African American and Latino children living in urban Los Angeles, California. This study found that higher prior year exposure to ambient and traffic-related air pollutants was positively associated with adverse effects on glucose metabolism independent of body fat percent. For example, a 1-SD increase in PM2.5 exposure (5.2 μg/m3) was associated with 25.0% higher fasting insulin, 8.3% lower SI, 14.7% higher AIRg, and 1.7% higher fasting glucose. Similar associations were observed for increased NO2 exposure. Additionally, a 1-SD increase in traffic-related air pollution exposure from non-freeway roads (4.8 ppb of NOx) was also associated with 12.1% higher fasting insulin, 6.9% lower SI, 10.8% higher AIRg, and 0.7% higher fasting glucose [46]. A recent longitudinal study built on this work by examining a cohort of 314 overweight and obese Latino youth from urban Los Angeles, California that was followed for an average of 3.4 years [47]. Results from this study found that higher NO2 and PM2.5 exposure over follow-up was associated with faster declines in SI and β-cell function. As an example, a 1-SD increase in NO2 exposure (5 ppb) over follow-up was associated with a 13% lower SI and 13% lower β-cell function at age 18 years [47]. Although these studies included only overweight and obese minority youth, their results suggest that increased air pollution exposure affects the underlying pathophysiology of type 2 diabetes, including insulin resistance and β-cell dysfunction in children.

Mechanisms linking air pollution with metabolic dysfunction

While the exact mechanisms underlying the associations between increased air pollution exposure and greater risk of type 2 diabetes remain uncertain, most hypothesized mechanisms include inflammatory or oxidative-stress responses. Exposure-induced inflammation in the lungs, may lead to spill-over of pro-inflammatory cytokines and chemokines to other tissues [48–54] or it may trigger neuronal responses in the brain. Either can cause a cascade of events that may lead to metabolic dysfunction. Additionally, PM components such as transition metals and lipopolysaccharides may penetrate into the systemic vasculature and/or activate toll-like receptors, [55] leading to increased inflammation. Exposure to air pollutants may also alter basal metabolism, including increased white adipose tissue accumulation relative to metabolically active brown adipose tissue, [56,57] inhibition of lipolysis [58], and/or increased adipose tissue inflammation [59]. Finally, inhaled or ingested PM can result in intestinal inflammation and increasing metabolic susceptibilities. These hypothesized mechanisms are largely derived from animal studies and suggest that the effects of increased air pollution exposure on diabetes etiology are complex and multifactorial.

Diabetes is characterized by an altered metabolism of key molecules and pathways that regulate insulin sensitivity and glycemic control. Metabolomics studies [60] suggest that exposure to air pollutants may alter these molecules and/or endogenous metabolites, which may contribute to increased inflammation and diabetes development. In a cohort of cardiac catheterization patients in the U.S. [61], researchers found that one-day lagged exposure to PM2.5 and O3 was associated with changes in amino acid concentrations of the glycineornithine-arginine metabolic axis, as well as increased levels of medium- and long-chain acylcarnitines, which indicated the involvement of oxidative stress [62] and mitochondrial dysfunction [63]. Another study in London [64] found that higher long-term exposure to PM10 and PM2.5 was associated with lower levels of asparagine and glycine. Interestingly, decreased glycine concentrations and increased levels of acylcarnitines have been related with insulin resistance and increased risk of type 2 diabetes [63,65–67]. In addition, a meta-analysis of targeted metabolomics across four cohorts in Germany [61] suggested that higher lagged 5-day averaged exposure to PM2.5, NO2, and O3 were associated with higher levels of lysophosphatidylcholines, which are associated with oxidative stress and increased oxidation of LDL [68]. Finally, non-targeted metabolomics studies of O3 suggest that acute (0–1 hour lagged) exposure can rapidly increase lipolysis and incomplete fatty acid oxidation in rats and humans [69,70]. Evidence in rats also suggest that short- and long-term exposure to air pollutants, including PM2.5 and O3, can increase lipid peroxidation and result in dyslipidemia and insulin resistance [69,71–73]. Overall, metabolomic studies suggest that PM2.5, NO2, and O3 exposure may contribute to metabolic dysfunction.

The neuroendocrine system may also play a role in air pollution-induced metabolic dysfunction via central nervous system (CNS) activation and downstream effects on psycho-behavioral pathways. A recent study in mice found that weight gain resulting from exposure to diesel exhaust was paralleled by changes in neuro-inflammation and neuronal structure in cognitive and emotional brain areas, suggesting that air pollution exposure directly alters the CNS [74]. It has also been shown that hunger and satiety signals interact with the hypothalamus to regulate energy status, feeding behaviors, and metabolism [75]. Moreover, air pollution may also act on the hypothalamus-pituitary-adrenal (HPA) system to alter the hormonal stress response [76]. In rats, for example, it has been shown that acute O3 exposure induces the activation of nucleus tractus solitarius neurons through the vagal nerves and promotes neuronal activation in stress-responsive regions of the CNS [77]. In humans, acute O3 exposure resulted in increased serum corticosterone and cortisol as well as lipid dysregulation [70]. These studies suggest O3-induced effects on the stress response through the CNS, which may ultimately affect metabolic regulation.

An emerging area of research suggests that increased exposure to air pollution may alter the composition and/or function of the gut microbiome where particles may reach the intestine through inhalation and diffusion from the lungs into systemic circulation or ingestion of inhaled particles following mucociliary clearance from the airways [78–81]. For example, studies in rodents have shown that ingestion of airborne sources of PM alter the gut microbiome and increase intestinal inflammation [82–84]. Studies in mice also indicate that exposure to PM alters resident bacteria, promotes intestinal inflammation, disrupts gut barrier integrity, and increases gut bacterial translocation [81,84,85]. As such, exposure-induced alterations in the gut microbiome may decrease gut barrier integrity, resulting in increased gut bacterial translocation, and a chronic low-grade level of inflammation that has been linked with insulin resistance and decreased glucose utilization [86–88]. Studies examining associations between air pollution exposure and chronic intestinal disease further support effects of air pollution on the gut [78]. One study found that adolescents who lived in regions with greater NO2 concentrations were more likely to be diagnosed with Crohn’s disease [89] and when indicators of air pollution (NO2, PM2.5) were elevated, adolescents and young adults visited emergency rooms more often for intestinal bowel disease-related pain [90]. Recently, work in overweight and obese adolescents found that increased exposure to traffic-related air pollutants was correlated with gut bacterial taxa and fasting glucose levels, suggesting that exposure to air pollutants may contribute to metabolic dysfunction through alterations in the gut microbiota [91]. Lastly, the gut and CNS have strong connections via the gut-brain axis, which is comprised of multiple sensing and signaling pathways that are thought to convey enteric signals to the brain. These signals can be mediated by the composition of the gut microbiome through alterations in the HPA axis in the form of gut hormones, through microbial-derived neurotransmitters, and/or gut bacterial translocation that may result in increased levels of systemic inflammation and increased risk of type 2 diabetes [92].

Conclusions

Human and animal studies provide strong evidence that short- and long-term exposures to ambient and traffic-related air pollutants, namely PM, NO2, NOx, play a role in glucose metabolism and type 2 diabetes etiology. This work is supported by recent findings that have observed similar effect sizes for increased exposure to air pollutants on clinical measures of risk for type 2 diabetes in children and adults. Emerging evidence also indicates that exposure to air pollutants has stronger effects in susceptible populations, including females and those with obesity and existing metabolic dysfunction. Despite recent advances in our understanding of the effects of air pollution exposure on human health, few long-term follow-up studies have examined the chronic and dynamic impacts of air pollution on increased diabetes risk. Additionally, most recent epidemiological studies have relied on air pollution exposure estimated from central monitors and/or model predictions. In order to fully understand the mechanics linking air pollution exposure with risk for type 2 diabetes, future studies should characterize the sources of air pollution exposure taking into account the multipollutant nature of the mixture and its varying chemical composition and physical properties that could lead to differential toxicity. Beyond these approaches, advanced tools (e.g., metabolomics) and new areas of investigation such as the CNS and the microbiome present distinct opportunities to generate additional evidence for causality by constructing the potential biological pathways linking air pollution exposure with type 2 diabetes. In summary, the strength of the current evidence linking air pollution exposure with metabolic dysfunction and diabetes risk warrants broader thinking about including the environment in the prevention and treatment of diabetes.

Footnotes

Compliance with Ethical Standards

Conflict of Interest

Tanya L. Alderete, Zhanghua Chen, Claudia M. Toledo-Corral, Zuelma A. Contreras, Jeniffer S. Kim, Rima Habre, Leda Chatzi, and Frank D. Gilliland declare no conflicts of interest.

Theresa Bastain reports grants from NIH, during the conduct of the study. Carrie V. Breton reports grants from NIH, outside the submitted work.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

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