Outdoor Air Pollution and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis.

Background
During the past 20 years, the prevalence of gestational diabetes mellitus (GDM) has increased by ∼10%-100% in several race/ethnicity groups. There is an association between ambient air pollution (AAP) and GDM. This study aimed to summarize the evidence about the association between AAP and GDM.


Methods
PubMed, Embase, Scopus, Web of Science and Cochrane Library were searched from inception till Oct 2017. Studies about the association between ambient air pollutants levels and GDM were included. Pooled effect estimates and their 95% confidence interval (CI) were calculated using R.


Results
Eight studies met the inclusion criteria. The odds of developing GDM upon exposure to CO (per 1 ppm), NO (per 1 ppb), NO2 (per 10 μg/m3), NOx (per 1 ppb), O3 (per 10 ppb), SO2 (per 10 ppb), PM10 (per 10 μg/m3) and PM2.5 (per 10 μg/m3) were 1.47 (95% CI 0.88-2.06), 1.04 (95% CI 1.03-1.06), 1 (95% CI 0.93-1.08), 1.02 (95% CI 1-1.04), 1.05 (95% CI 0.94-1.16), 1.39 (95% CI 1.04-1.73), 0.97 (95% CI 0.94-0.99) and 1.12 (95% CI 0.93-1.31), respectively.


Conclusion
The current literature showed evidence for an association between AAP and GDM. However, further well-designed studies are needed.


Introduction
Pregnancy is a vulnerable period for women because of the increased insulin resistance by the placenta diabetogenic effects in order to ensure more available glucose to the fetus (1). Gestational diabetes mellitus (GDM) is a condition defined as any degree of glucose intolerance during pregnancy that resolves postpartum (2). GDM complicates nearly more than 10% of all pregnancies in the USA and 17% of pregnancies in Iran (3). During the past 20 years, the prevalence of GDM has increased by ∼10%-100% in several race/ethnicity groups (4). The rise in GDM along with type 2 diabetes (T2D) and obesity worldwide has become of particular concern (5). In different populations and geographical regions, the risk of developing T2D is 7.4 higher among women with GDM in comparison with women without GDM, both postpartum and later in life (6)(7)(8). In addition, young women with GDM are at greater risk of developing cardiovascular diseases (CVD) and coronary artery diseases (CAD), much attributable to the development of T2D (9,10). Moreover, there is reportedly increased risk of preeclampsia, asymptomatic bacteriuria, pyelonephritis and cesarean delivery among women with GDM (11,12). Besides the adverse effects of GDM on the mother both in the short and long-terms, GDM was linked with many fetal and neonatal complications including macrosomia (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), shoulder dystocia (17), neonatal hypoglycemia (18) and congenital malformation (19)(20)(21).
With 92% of the global population living in areas not meeting the WHO air quality guidelines levels, ambient (outdoor) air pollution (AAP) is being considered a major risk to the public health (22). In 2012, AAP caused 3 million premature death worldwide, with 88% of these premature deaths in low and middle-income (LMI) countries (22). AAP was associated with many adverse health conditions including cardiopulmonary disease, lung cancer and acute lower respiratory infection (23,24). In addition, air pollution was significantly associated with insulin resistance and diabetes-related mortality (25)(26)(27). This association may be gender-dependent, being more distinct among women than men. (28)(29)(30)(31). Although the biological mechanisms underlying this association are still unclear, animal studies have shown that high levels of air pollution may be equivalent to a high-fat diet in terms of its effects, involving immune activation, endoplasmic reticulum (ER) stress, oxidative stress and CNS inflammation (32,33). Some studies have investigated the association between GDM and air pollutant including nitric oxides (NOx), sulfur dioxide (SO2), ozone (O3), particulate matter with diameter ≤ 10µm (PM10) and particulate matter with diameter ≤ 2.5 (PM2.5). However, the evidence remains controversial and inconsistent (34). This systematic review and meta-analysis aimed to summarize the current evidence regarding the association between ambient air pollution and gestational diabetes mellitus. In addition, it will help to provide further rationale for improving air quality standard and the public health.

Guidelines
The Meta-analysis of observational studies in epidemiology (MOOSE) statement was used in reporting this meta-analysis (35).

Search Strategy
The search process was carried out using the following keywords: (gestational diabetes OR pregnancy induced diabetes* OR pregnancy diabetes* OR gestational diabetes* OR GDM) and (air pol-lution OR outdoor air pollution OR ambient air pollution OR traffic pollution OR air pollutants OR outdoor air pollutants OR nitrogen dioxide OR particulate matter OR sulfur dioxide OR ozone). No language nor publication type filters were used.

Data sources
An electronic search on PubMed, Embase (via ovidSP), Web of Science, Scopus and Cochrane library databases was conducted from their dates of inception till Oct 2, 2017. Moreover, references of included articles were handsearched for relevant records.

Criteria for selecting studies
Retrieved records were screened in two steps: title and abstract screening then full-text reviewing. Records were screened for meeting the inclusion criteria: 1) peer-reviewed, published article 2) human population 3) observational study providing data about the association between GDM and AAP 4) levels of air pollutants were monitored. Exclusion criteria were: 1) conference abstracts, editorial, commentaries or reviews 2) in vitro or animal study 3) not measuring levels of air pollutants or use proximity to roads as an index for air quality 4) indoor air pollution study.

Data extraction
A data extraction form was prepared to collect the following data: study ID (first author name, year of publication), country, duration of the study, data source of the study, sample size, number of GDM cases, date of diagnosing GDM, exposure measurement method, investigated pollutants and data type.

Quality assessment
A modified version of the Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses was used to assess the quality of the included studies (36). Each study could attain a maximum of four points. Studies with ≥ 3 points were considered of good quality. Those with ≤ 2 points were considered of poor quality. The scale rated the pa-pers according to 1) sample representativeness 2) use of reliable GDM diagnostic method 3) residential-level air quality monitoring 4) adjustment for potential covariates; maternal age and BMI.

Data analysis
Effect estimates of the included studies were pooled and meta-analyzed using the 'meta' package, R (version 3.4.0) (37). Because most studies used odds ratio (OR) as their effect estimate, the pooled effect estimates were reported as OR with their 95% confidence interval (CI). Heterogeneity among included studies was tested by the Cochran-Q test and quantified its extent by the Isquare test. When a significant heterogeneity (P<0.1) was found, the pooled effect estimate was calculated under the random-effect model (38). Since included studies reported effect estimate (EE) with different pollutant increments, commonly used standardized increments (10 part per billion (ppb) in O3, 10 µg/m 3 in PM10, 10 µg/m 3 in PM2.5, 1 part per million (ppm) in CO, 1 ppb in NO, 10 µg/m 3 in NO2, 1 ppb in NOx and 10 ppb in SO2) were calculated for each study using the following equation: EE(standardized)=EE(original) increment(10)/increment(original) (27,39,40). Because the number of the included studies was <10, publication bias assessment was not reliable according to Egger and his colleagues (41).

Characteristics of the included studies
The included studies were carried out in four countries: 4 studies in the USA, 2 in Taiwan, 1 in Sweden and 1 in Denmark. Seven of the included studies were cohort studies with core aim of in-vestigating the association between AAP and GDM (43)(44)(45)(46)(47)(48)(49). And, one study was case-control seeking to assess the effect of developing GDM among women giving preterm birth. The sample size ranged from 2093 to 410267. GDM cases in the included studies were 35605. Six studies used residential-level exposure measurement to estimate the level of the pollutants (42)(43)(44)(45)(46)48). Women exposure were linked to her hospital referral region. Moreover, air pollutants levels were estimated from some distributed air quality monitoring stations.

Carbon monoxide (CO) (per 1 ppm) and GDM
Only two studies investigated the association between GDM and CO (42,47). No higher risk of developing GDM was showed during the 1 st trimester. However, the association between GDM and CO were analyzed during the 1 st , 2 nd and 3 rd trimesters and showed higher odds of GDM during them (Fig. 2). The pooled EE was 1.47 (95% CI 0.88-2.06). However, there is a high degree of heterogeneity with I-square of 79%. This could be assigned to the difference in the population, sample size, data sources and exposure measurement methods. Data were utilized from a consortium cohort study including 19 hospitals in the USA linking women exposure to her hospital referral region. Whereas data were used from a national birth registration database in Taiwan measuring air pollutants levels at the residential level.
In addition, the sample size number of GDM cases were much more in one study than in other. Moreover, the Taiwanese study accounted for many covariates not adjusted in the USA study including pre-pregnancy body mass index (BMI), weight gain, parity, education and household income (42,47).

Nitric oxide (NO) (per 1 ppb) and GDM
Only one study investigated the association between NO and GDM. It found significantly high odds of GDM in all trimesters (Fig. 2). The pooled EE was 1.04 (95% CI 1.03-1.06).

Nitric oxides (NOx) (per 1ppb) and GDM
Three studies reported the association between NOx and GDM (42,(46)(47). All studies showed a slightly positive association between NOx and the development of GDM (Fig. 2). The pooled EE was 1.02 (95% CI 1-1.04), I-squared = 91%. In an attempt to clarify the cause of this substantial heterogeneity, each study was removed at a time. When either of the first two studies were removed, substantial heterogeneity still existed (42,47). However, when the third study was removed, heterogeneity resolved (P=0.15) (46). In each case, the odds of GDM was marginally positive. The difference could be discussed in terms of geographical regions differences and study design. Study (46) was undertaken in Sweden, where air pollutants levels were generally below the WHO air quality guidelines levels. Therefore, the effect of lifetime exposure could attribute the difference. In contrast to third study (46), the first two studies (42,47) studies investigate more than one pollutant in their studies.

Ozone (O3) (per 10 ppb) and GDM
Four studies included the relationship between O3 and GDM. All studies, but study (42) showed either no or a positive association between O3 and GDM (Fig. 2). The pooled EE was 1.05 (95% CI 0.94-1.16). However, heterogeneity was substantial with I-squared=96%. To investigate it, each study was removed at a time. Heterogeneity remained high each time, except when removing study (45) (I-squared=78%). Despite testing whether the difference in the exposure measurement method, country or by adjusting for season of conception could underlie this, heterogeneity remained substantial. The reason behind the decrease in I-squared when Hu et al., study was removed could be attributed to its sample inclusion and exclusion criteria. Hu et al., applied various exclusion criteria including excluding women whose address could not be geo-coded, preterm birth and births with weight < 400.

Sulfur dioxide (SO2) (per 10 ppb) and GDM
Two studies measured and investigated the association between SO2 and GDM (42,47). Both studies showed high odds of GDM in relation to SO2 (Fig. 2). The pooled EE was 1.39 (95% CI 1.04-1.73), with I-squared = 64%). The moderate degree of heterogeneity could be explained by the difference in population, region, data sources and adjustment for covariates. In contrast to study (47), study (42) adjusted its model for BMI, weight gain, and parity.

Quality assessment
Using the modified version of the NOS for quality assessment, four studies got a score of four (42,43,44,46), two studies got a score of three (45,48), one study scored two (47) and one study got a score of one (49). Thus, six of the included studies were of good quality, whereas two studies were of poor quality.

Discussion
This systematic review and meta-analysis included a summary of the current evidence regarding the association between ambient air pollution (AAP) and the development of gestational diabetes mellitus (GDM). The effect estimates of the relationship between GDM and air pollutants ranged from 0.97 (95% CI 0.94-0.99) for PM10 to 1.47 (95% CI 0.88-2.06) for CO. However, only NO and SO2 showed statistically significant effect estimates. In most studies, the second trimester was the most vulnerable period. Although the biological mechanisms by which AAP contributes to the development of insulin resistance (IR) and glucose intolerance remain unclear, recent experimental and epidemiological have uncovered many insights into the role of AAP-mediated insulin resistance (50). The role of environmental pollutants was evident from the consistent report of the role of persistent organic phosphate pollutants in the development of IR (51)(52)(53)(54)(55). In addition, exposure to AAP has been reported to be associated with endothelial dysfunction elevated levels of inflammatory mediators including tumor necrosis factor (TNF) α, prostaglandin (PG) E2, C-reactive protein, interleukin-1β, and endothelin-1 (56-58). The increased level of inflammation mediator has been shown to interrupt and inhibit insulin signaling and transaction (59)(60)(61). Moreover, experimental studies link AAP exposure and endoplasmic reticulum (ER) stress-induced apoptosis in the lung and liver tissue along with brown adipose tissue dysfunction (62)(63)(64). ER stress enables the unfolded protein response (UPR) which contributes to the development of IR via inflammation, lipid accumulation, insulin biosynthesis and β-cell apoptosis (65)(66)(67)(68). Thus, the accumulative experimental and epidemiological evidence strongly suggest a biological association between AAP and IR.

Limitations
Some limitations should be taken into consideration when interpreting the results of this review. First, the number of available studies was limited which may lead to restricting the ability to get a more precise estimate with minimal heterogeneity. Second, two of the included studies did not use residential-level pollutant estimation. Thus, this may have led to misclassification of the exact estimate. Finally, most studies have not accounted for many potential covariates and have not applied multipollutant models. Thus, the results of this meta-analysis should be interpreted carefully.

Conclusion
Some ambient air pollutants may contribute to the development of gestational diabetes mellitus. Reflecting the increasing prevalence of GDM in the context of T2D and obesity necessitates promoting awareness among pregnant women about how air pollution could affect their health and their newborns, especially in regions with limited health care. Besides, this review provides additional evidence about the importance of implementing government-level actions to improve air quality.

Recommendations
More studies using large, representative sample size and residential-level air monitoring are needed. In addition, they need to take into consideration potential covariates including occupational exposure, indoor air pollution, BMI, ethnicity/race, physical activity, socio-economic background, diet, mother mobility during pregnancy, previous family history of diabetes, and effects of multipollutant rather than one pollutant.