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Environmental health influences in pregnancy and risk of gestational diabetes mellitus: a systematic review

BMC Public Health volume 22, Article number: 1572 (2022) Cite this article

Abstract

Background

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications globally. Environmental risk factors may lead to increased glucose levels and GDM, which in turn may affect not only the health of the mother but assuming hypotheses of "fetal programming", also the health of the offspring. In addition to traditional GDM risk factors, the evidence is growing that environmental influences might affect the development of GDM. We conducted a systematic review analyzing the association between several environmental health risk factors in pregnancy, including climate factors, chemicals and metals, and GDM.

Methods

We performed a systematic literature search in Medline (PubMed), EMBASE, CINAHL, Cochrane Library and Web of Science Core Collection databases for research articles published until March 2021. Epidemiological human and animal model studies that examined GDM as an outcome and / or glycemic outcomes and at least one environmental risk factor for GDM were included.

Results

Of n = 91 studies, we classified n = 28 air pollution, n = 18 persistent organic pollutants (POP), n = 11 arsenic, n = 9 phthalate n = 8 bisphenol A (BPA), n = 8 seasonality, n = 6 cadmium and n = 5 ambient temperature studies. In total, we identified two animal model studies. Whilst we found clear evidence for an association between GDM and air pollution, ambient temperature, season, cadmium, arsenic, POPs and phthalates, the findings regarding phenols were rather inconsistent. There were clear associations between adverse glycemic outcomes and air pollution, ambient temperature, season, POPs, phenols, and phthalates. Findings regarding cadmium and arsenic were heterogeneous (n = 2 publications in each case).

Conclusions

Environmental risk factors are important to consider in the management and prevention of GDM. In view of mechanisms of fetal programming, the environmental risk factors investigated may impair the health of mother and offspring in the short and long term. Further research is needed.

Peer Review reports

Background

Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications worldwide [1]. It is defined as any degree of glucose intolerance with onset or first recognition during pregnancy [1]. In the past 20 years, the prevalence of GDM has increased [1,2,3,4], so that up to ca. 14% of all pregnancies worldwide are complicated by GDM [5]. The association between GDM and adverse pregnancy maternal and neonatal outcomes like preeclampsia, caesarean section, preterm birth, macrosomia and neonatal hypoglycaemia, is well known [5, 6]. In addition, the evidence of long-term health impacts on mothers and offspring is increasing [7]. For example, there is a growing risk for the mother of developing type 2 diabetes (T2D) [8, 9] and cardiovascular disease (CV) [10, 11] in later life. However, there is an increased risk for offspring to develop obesity and abnormal glucose metabolism in mid-childhood [1, 6, 12]. This suggests that metabolic and cardiovascular conditions in the offspring may be associated with mechanisms of perinatal (or fetal or transgenerational) programming attributable to genomic and environmental influences of the mother during perigestation [13].

The “Thrifty Phenotype Hypothesis” suggests that physiological and metabolic adaptations may be induced, for example, by fetal malnutrition [14]. During critical periods of pregnancy, the nutrient supply of some organs is limited to ensure the supply of the brain or the heart. This may lead to a permanent programmed metabolism of the fetus which results in harmful long-term consequences triggered by nutritional abundance in later life [14]. Studies found an association between exposure to longer starving periods in utero and reduced glucose tolerance in adulthood [15], and between low birth weight and adult glucose and insulin metabolism [16].

The mechanisms behind the Thrifty Phenotype Hypothesis are diverse [13]: the genotype of the fetus may be influenced by mutations which result in impaired insulin secretion or in polymorphisms [17, 18]. Adverse intrauterine factors such as maternal food restriction, malnutrition, stress, placental dysfunction, obesity, gestational diabetes, hypertension, preeclampsia, and gestational weight gain are very important influences. Adverse postnatal environmental factors like malnutrition, inactivity or aging also play an important role [13].

The “oxidative stress” hypothesis is one other possible mechanism. In the sensitive state of pregnancy, the metabolic state can be easily disturbed, which results in dysblances towards oxidative stress, leading to insulin resistance, gestational diabetes, and gestational hypertension [19,20,21]. There seems to be a relation between higher rates of malformations and anomalies in offspring of diabetic mothers and mechanisms of oxidative stress, hyperglycemia-induced ROS, mitochondrial dysfunction and altered glycosylation [22,23,24].

Furthermore, according to Gluckman et al. and the “Predictive Adaptive Responses Hypothesis”, unfavourable environmental conditions induced by diseases such as GDM can lead to adapted developmental processes in utero [13, 14, 25]. Another hypothesis called the “Fetal Insulin Hypothesis” points out that genetically determined alterations within the insulin signaling cascades could be a reason for altered fetal development as well as long-term metabolic changes [14]. This hypothesis is supported by studies which showed that genetic influences like polymorphisms or mutations, and environmental effects on the fetus, which can result in altered glucose tolerance and insulin resistance, are strongly related to long-term consequences like metabolic and cardiovascular diseases in later life [14, 22, 26].

It is of great interest to better characterize the potential environmental risk factors for GDM and its ability to “program” short- and long-term consequences in offspring. The aetiology of GDM is multifactorial [27]. “Traditional” risk factors such as excess caloric consumption, lack of physical activity and increased BMI [28] do play an essential role, but evidence is growing that environmental exposures such as air pollution [29, 30], climate factors [31, 32] and endocrine disrupting chemicals [33,34,35] and metals [36, 37] affect the development of GDM as well. Therefore we aimed at performing a systematic review analyzing the association between environmental health risk factors in pregnancy, including climate factors, chemicals and metals, and gestational diabetes or adverse glycemic parameters. We provide an overview of potential environmental risk factors for GDM, considering a wide range of these factors including epidemiological human and animal model studies.

Methods

Search strategy

We conducted a systematic literature search in Medline (PubMed), EMBASE, CINAHL, Cochrane Library and Web of Science Core Collection databases for research articles published until March 2021. Keywords (gestational diabetes, bisphenol A, phthalate, persistent organic pollutants, cadmium, arsenic, air pollution, ambient temperature, season, air humidity) were selected from the Medical Subject and Embase Subject Headings and searched as titles/abstracts terms (Additional file 1). In general, the steps of literature search were:

  1. 1.

    Systematic search in databases.

  2. 2.

    Elimination of duplicates.

  3. 3.

    Screening of titles and abstracts and selection of studies.

  4. 4.

    Appraisal of selected studies for eligibility.

  5. 5.

    Selection of suitable studies and classification of these studies according to risk factors.

  6. 6.

    Extraction and synthesis of relevant data.

Study selection

Peer-reviewed epidemiological human studies and animal model studies with full-texts in English and German were included. We considered studies that examined GDM as an outcome and/or maternal glycemic outcomes and at least one environmental risk factor for GDM. In general, we included papers that analyzed the following environmental risk factors, as there were the most frequently investigated health influences: bisphenol A (BPA), phthalate, persistent organic pollutants (POP), cadmium, arsenic, air pollution, ambient temperature, season and air humidity.

Duplicates, other study designs (e.g. reviews, meta-analyses, case-reports), letter, comments, notes, editorials and conference proceedings were excluded. In addition, we excluded papers that analyzed GDM treatment methods, papers that focussed on women with pre-existing type 1 or type 2 diabetes, and publications that examined other pregnancy outcomes, for example gestational hypertension, preeclampsia, macrosomia and preterm birth. The PRISMA flow chart is provided in the Additional file 1.

Data synthesis and analysis

We extracted authors, year of publication, location, study design, sample sizes, characteristics of the subjects, methods, outcomes of interest, study limitations, diagnostic criteria for GDM, main findings and statistical calculations. Furthermore, we classified the publications according to the environmental risk factors and the outcomes (GDM vs. glycemic parameters).

Results

Our systematic literature research resulted in 819 articles (see Additional file 1). After excluding unsuitable articles and duplicates (n = 514), n = 105 full-text articles were assessed for eligibility based on our inclusion and exclusion criteria. In total, n = 91 studies were analyzed in this systematic review. We identified n = 28 air pollution, n = 18 POP, n = 11 arsenic, n = 9 phthalate n = 8 BPA, n = 8 season, n = 6 cadmium and n = 5 ambient temperature studies. We found no studies examining the association between air humidity and GDM and only n = 2 animal model studies.

The majority of studies were conducted in China (n = 32) and in the USA (n = 25). Five studies took place in Taiwan, n = 4 in UK, n = 4 in Canada, n = 4 in Australia, n = 3 in Iran, n = 2 in Spain, n = 2 in Greece, n = 2 in France, n = 2 in Sweden, n = 1 in Guadeloupe, n = 1 in Argentina, n = 1 in Chile, n = 1 in Japan, n = 1 in Israel and n = 1 in Denmark. Most studies were prospective cohort studies (n = 41) and retrospective cohort studies (n = 16). 13 publications were case–control studies and 12 were cross-sectional. 4 papers were conducted as longitudinal birth cohort studies, n = 2 were interventional studies, n = 2 population based cohort studies and n = 2 were semi-ecological-studies. Table 1 gives an overview of the included studies.

Table 1 Overview of included studies
Full size table

GDM screening methods

Across the human studies, different methods of GDM screening and diagnosis were used. The most frequent screening methods involved a one-step approach where GDM is diagnosed based on results of a single fasting oral glucose tolerance test (OGTT), as well as a two-step approach where women are pre-screened, generally with a non-fasting glucose challenge test (GCT). In some studies GDM was self-reported. Five studies also evaluated various markers of insulin resistance (IR) for example the Matsuda index and the homeostasis model assessment of insulin (HOMA-IR, HOMA S) and beta cell function like insulinogenic index (IGI)/HOMA-IR, and HOMA B.

Environmental risk factors

Phenols and GDM (n = 5 studies)

Overall, the studies showed no clear and rather heterogenic results [38,39,40,41,42]. Hou et al. (n = 390 [38] and Zhang et al. (n = 1841) [42]) indicated significant associations (p < 0.01) for BPAF and 2-t-OP, whereas two prospective cohort studies (n = 535 [41], n = 620 [40]) and one case–control-study (n = 22 cases, 72 controls [39]) did not observe clear associations between BPA and GDM. In a cohort-study by Wang et al., the odds of GDM were even reduced by 27% (OR = 0.73; 95% CI = 0.56, 0.97) [40].

Phenols and glycemic outcomes (n = 4)

All n = 4 studies reported positive associations between glycemic outcomes and phenols in exposed women [38, 41, 43, 44]. Three studies, [(n = 350 [43], n = 245 [44], n = 535 [41]) found significant associations between BPA concentrations and glucose levels (p < 0.05 [43], p < 0.01 [44], p < 0.05 [41]). However, Bellavia et al. observed this association only on obese and overweight women [43].

Pthalates and GDM (n = 6)

An animal model study performed on rats (n = 24) showed significant positive associations for the progression of GDM through Di-n-butyl phthalate (p > 0.05 [45]).

Consistent with this, 3 of 5 human studies observed positive associations between certain phthalates (mono-iso-butylphthalate, mono-n-butylphthalate, mono-2-ethyl-5-oxohexyl phthalate (p < 0.05) [46], monoethyl phthalate (95% CI:1.61 (1.10, 2.36) [47], mono-(2-ethylhexyl) phthalate (p = 0.019) [48]) and GDM. Two cohort studies by Shapiro e al. (n = 1274 [49], n = 1795 [50]) did not find significant associations between phthalates (aOR third quartile = 2.0, 95% CI = 0.9–4.4; aOR fourth quartile = 2.0, 95% CI = 0.9–4.8) [49] or triclosan (aOR third quartile = 0.9, 95% CI (0.3–2.5) aOR fourth quartile = 0.9, 95% CI (0.4–2.5) [50] and GDM.

Phthaltes and glycemic outcomes (n = 7)

Most studies (n = 4) showed positive associations between phthalate exposure and adverse glycemic outcomes (p < 0.01 [48], p < 0.05 [47, 51, 52]. James Todd et al. (n = 350) showed that second trimester mono-ethyl phthalate was associated with increased odds of impaired glucose tolerance (adj. OR: 7.18; 95% CI: 1.97, 26.15). In contrast, n = 3 publications (n = 72 [53], n = 1274 [49], n = 1795 [50]) observed not significant associations and concluded that phthalate exposure might not be associated with adverse glycemic values.

POPs and GDM (n = 15)

The majority of the papers found that POPs exposure obviously increased the risk for GDM [35, 54,55,56,57,58,59,60,61,62,63,64,65,66,67]. In general, n = 10 out of n = 15 studies reported significant positive associations (p < 0.05 [55, 57,58,59,60, 62, 64, 67], p < 0.001 [63], p < 0.01 [66]) for POPs like PCBs [55, 60, 63, 67], PBDEs [55, 59, 62], EtP [57], PCAs [58], PFASs [66] and OCs [64]. In contrast, n = 4 studies found no significant associations between POPs and GDM [35, 56, 61, 65]. Alvarez-Silvares et al. [54] reported an inverse relationship between PBDE and PCB levels in placenta and GDM.

POPs and glycemic outcomes (n = 5)

All studies showed that POPs had clear negative effects on glycemic parameters in pregnant women [35, 58, 65, 68, 69]. In total, n = 3 of n = 5 studies reported that POPs (PB [68], PFCAs [58], PFAS [65]) resulted in elevated blood glucose levels (p < 0.05 [58, 65, 68]. Shapiro et al. (n = 1274) observed elevated odds of gestational IGT ((OR = 3.5, 95% CI = 1.4–8.9) [35]. Wang et al. (n = 217) found time-specific inverse associations (p < 0,05) between BP-3 and pregnancy glucose levels [69].

Arsenic and GDM (n = 10)

One animal model study by Bonventura et al. (rats) noted clearly that arsenic alters glucose homeostasis during pregnancy by altering beta-cell function, increasing the risk of developing gestational diabetes (p < 0.05) [70]. Overall, most human studies (n = 8 out of n = 9) found a positive relation between exposure to arsenic and GDM (p = 0.04 [71], p < 0.05 [37, 72,73,74,75], p < 0.001 [76], p = 0.37 [77]). Two of these studies [72, 73] found this association only in obese and overweight women. Contrary to this, Muñoz et al. reported no significant association between GDM and inorganic arsenic exposure tertiles [78]).

Arsenic and glycemic outcomes (n = 2)

Ettinger et al. (n = 532) reported a significant association for arsenic exposure and increased risk for impaired glucose tolerance (p = 0.008 [79]), whereas Farzan et al. (n = 1151) found no significant association [72].

Cadmium and GDM (n = 6)

In general, the majority of studies (n = 4 of n = 6) found significant positive associations for cadmium exposure and GDM (p = 0.003 [80], p = 0.03 [81], p < 0.05 [36, 82]). However, Oguri et al. found no association for elevated blood cadmium with increased GDM risk [83] and Romano et al. only found a slight association in women with normal weight (OR = 1.32, 95% CI 0.88–1.98) [84].

Cadmium and glycemic outcomes (n = 2)

Romano et al. observed no significant association for glucose intolerance (OR = 1.11, 95%CI 0.85–1.45) and Soomro et al. reported that cadmium was statistically related to having had a diagnosis of IGT (aOR = 1.61, 95% CI 1.05–2.48) [36].

Season and GDM (n = 7)

In general, most studies (n = 5 of n = 7) found clear seasonal variations in the prevalence of GDM. Strong relations were reported for GDM prevalence in spring and summer in n = 5 studies (p < 0.0001 [85] p = 0.027 [86], p = 0.01 [32], p < 0.001 [87], p = 0.02 [88]). However, Shen et al. and Petry et al. (n = 2120 [89], n = 1074 [90]) observed no significant associations with seasonality (p = 0.51 [89], p = 0.4 [90]).

Season and glycemic outcomes (n = 6)

In total, n = 5 of n = 6 studies outlined significant seasonal trends, with higher glucose levels in summer (p < 0.0001 [32, 85], p = 0.009 [89], p < 0.001 [91], p < 0.05 [88]). In contrast, Petry et al. revealed no significant association of OGTT fasting glucose concentrations and seasonality [amplitude: 21.3 (− 24.1, 66.6) [90]).

Ambient temperature and GDM (n = 5)

All publications found that rising environmental temperature was distinctly associated with an increased risk of GDM [31, 92,93,94,95]. Furthermore, a large cohort study by Zhang et al. reported that extreme low temperature exposure increased the risk of GDM as well (p < 0.05 [95]).

Ambient temperature and glycemic outcomes (n = 2)

Retnajaran et al. (n = 318) indicated that rising environmental temperature in the 3– 4 weeks prior to glucose tolerance testing in pregnancy was independently associated with maternal beta cell dysfunction and blood glucose levels [92] and Vasileiou et al. noted that temperatures above 25 °C might lead to increased glucose levels [94].

Air pollution and GDM (n = 25)

In summary, n = 22 of n = 25 studies showed that air pollution (through PM2.5 [29, 96,97,98,99,100,101,102,103,104,105,106,107], NO [30, 108,109,110,111,112], NO2 [30, 108, 109, 112,113,114], CO [30, 113], PM10 [98, 100, 104], BC [98], SO2 [30, 100, 103, 104, 115], O3 [30]) was clearly associated with an increased risk of GDM. Eight studies found a significant association (p < 0.01 [107, 109], p < 0.05 [103, 110, 111, 113], (aOR = 1.69 (1.41, 2.03) [108]). However, Fleisch et al. (n = 159.373) found higher odds of GDM in women less than 20 years old (95% CI: 1.08, 1.70) [116], whereas Padula et al. in a cross-sectional-study (n = 262.182) reported consistent inverse associations between exposure to air pollution during the first two trimesters and GDM (p < 0.01) [117].

Air pollution and glycemic outcomes (n = 9)

Overall, the most articles pointed out that exposure to air pollution (through PM2.5 [98,99,100, 105, 107, 118,119,120,121], PM10 [98, 100, 119, 121], PM1 [121], BC [98], NO2 [119], CO [119], SO2 [100]) was able to increase blood glucose levels in pregnant women. In n = 7 of n = 9 studies, air pollution was significantly associated (p < 0.05 [98, 99, 105, 119, 120], p < 0.01 [107, 121]) with elevated fasting blood glucose concentrations. The remaining n = 2 studies by Fleisch et al. (n = 2.093) [118] and Lin et al. (n = 12.842) [100]) also found associations, but they were not significant.

Discussion

In this systematic review, the current evidence assessing environmental risk factors and GDM has been analyzed. Whilst we found clear evidence for an association between GDM and air pollution [29, 96,97,98,99,100,101,102,103,104,105,106,107], ambient temperature [31, 92,93,94,95], season [32, 85,86,87,88], cadmium [36, 80,81,82], arsenic [37, 70,71,72,73,74,75,76,77], POPs [55, 57,58,59,60, 62,63,64, 66, 67] and phthalates [45,46,47,48], the findings regarding phenols were rather heterogeneous. The results for adverse glycemic outcomes also showed clear associations regarding air pollution [98,99,100, 105, 107, 118,119,120,121], ambient temperature [92, 94], season [32, 85, 88, 89, 91], POPs [35, 58, 65, 68], phenols [38, 41, 43, 44] and phthalates [47, 48, 51, 52]. Our findings regarding cadmium and arsenic were inconsistent with only n = 2 publications in each case.

The results suggest that environmental health influences such as bisphenol A (BPA), phthalate, persistent organic pollutants (POPs), cadmium, arsenic, air pollution, ambient temperature and season might be associated with an increased risk for GDM and with adverse glycemic values. These results suggest that environmental risk factors are important to consider in the management and prevention of GDM, especially in the context of the growing dissemination of environmental pollutants and toxins. In view of key theses on fetal (or transgenerational) programming, which were mentioned at the beginning, the environmental factors examined may impair the health of mother and offspring in the short and long-term and lead to the development of diseases. Furthermore, the mechanisms underlying the potential correlation of environmental influences with GDM remain obscure and need more research. However, in previous research, it was found that air pollution exposure during the second trimester was significantly associated with GDM. Substances like SO2, oxynitride (NOX, NO2, NO), CO, and O3 all showed a linear trend effect on GDM [30]. On the other hand, the data on fine particular matter (PM) is inconsistent. Some research studies report that greater exposure to fine particulate matter and other traffic-related pollutants during pregnancy was not associated with GDM [118]. Other studies observed that PM in the 2nd trimester were associated with higher odds of GDM [29].

Moreover, an association between ambient temperature / seasonality and type 1 and/or type 2 diabetes was found in meta-analyses and observational studies [122,123,124]. There was discussion about whether this association could be transferred to GDM. Very cold or very high ambient temperature may lead to improved insulin sensitivity, due to activation of brown adipose tissue [125], or to spurious increased blood glucose concentrations, because of dehydration and hemoconcentration [91]. Another systematic review also found that the seasonality of GDM was consistent across studies, with higher prevalence of GDM generally observed in the summer months and with clear associations between ambient temperature and GDM [126]. Future studies need more consistency in exposure and outcome assessment and diverse study populations need to be included to identify potential high-risk population subgroups.

Furthermore, other studies also suggest that chemical exposure has a negative effect on women’s health [127, 128]. Chemicals like polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), BPA and some pesticides currently in use are widespread in consumer and personal care products or food, yet have an endocrine disrupting property which can lead to adverse pregnancy outcomes such as GDM [129, 130]. Maternal susceptibility for developing GDM might be increased through chemicals that disrupt or damage pancreatic β cells and environmental chemicals that interfere with the peroxisome proliferator-activated receptor signalling pathway, which mediates placental development and is fundamental to lipid metabolism [131, 132]. Some animal and human studies also indicated that endocrine disrupting chemicals are diaplacental [133,134,135,136] and thus result in adverse pregnancy outcomes like fetal growth restriction or preterm birth [134, 135]. Even though endocrine disrupting chemicals seem to have an extensive impact on women’s and offspring’s health, the data on their effect on GDM is still limited.

Besides, metals can persist in the environment and some heavy metals such as cadmium have biological half-lives of more than ten years. Therefore, they have been a public health concern for many years [137]. Metals including arsenic and cadmium are classified as endocrine disrupting substances because they seem to have estrogenic activity [138]. High level of arsenic in maternal blood or meconium might be associated with increased risk of GDM [75, 76]. Other metals like nickel, antimony, cobalt or vanadium also showed positive associations with an increased risk of GDM [37]. Just like endocrine disrupting chemicals, metals are easily transported through the placenta [139, 140] and might cause adverse pregnancy outcomes like adverse. glycemic parameters, small for gestational age and stillbirths [140,141,142].

Limitations

In some cases and depending on the risk factors, the sample sizes were small, which is a limitation as there was limited statistical significance. With regard to arsenic and cadmium and glycemic values, we were able to identify only very few studies. In general, further studies are required for all risk factors examined in order to reproduce the results. Furthermore, we included studies that are observational in nature and therefore cannot confirm whether the observed association is causal. Potential confounders, such as family history of diabetes, diet, physical activity and other exogenous compounds with similar exposure sources were mostly not measured and considered, but might influence the findings. Probably the major limitation of the manuscript is the lack of clear evidence and transfer related to clinical consultation. Nevertheless, it is important to point out a possible overarching relationship. More research needs to be done.

Conclusion

This systematic review found that environmental health influences such as bisphenol A (BPA), phthalate, persistent organic pollutants (POP), cadmium, arsenic, air pollution, ambient temperature and seasonality might be associated with an increased risk of GDM. Therefore, environmental risk factors must be considered in the prevention and management of GDM – in clinical practice and research.

Metabolic conditions in offspring might be a result of fetal programming due to maternal perigestational genomic and environmental conditions. As far as fetal programming is concerned, environmental health influences can impair the health of mother and child during pregnancy and later in life. Our findings need to be replicated in other studies. There is a need for further research in particular with regard to the association between GDM and phenols and with regard to glycemic values and arsenic and cadmium. Furthermore, we identified very few animal studies and none on humidity and GDM. Overall, more research is required to analyze this increasingly important research topic in more detail. Besides, the mechanisms underlying the potential correlation of environmental influences with GDM need to be analyzed.

Availability of data and materials

All data are included within the article and its supplementary information files.

References

  1. American Diabetes Association. Gestational diabetes mellitus. Diabetes Care. 2003;26(Suppl 1):S103–5. https://doi.org/10.2337/diacare.26.2007.s103.

    Article  Google Scholar 

  2. Behboudi-Gandevani S, Amiri M, Bidhendi Yarandi R, Ramezani TF. The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis. Diabetol Metab Syndr. 2019;11:11. https://doi.org/10.1186/s13098-019-0406-1.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Deputy NP, Kim SY, Conrey EJ, Bullard KM. Prevalence and Changes in Preexisting Diabetes and Gestational Diabetes Among Women Who Had a Live Birth - United States, 2012–2016. MMWR Morb Mortal Wkly Rep. 2018;67:1201–7. https://doi.org/10.15585/mmwr.mm6743a2.

    Article  PubMed  PubMed Central  Google Scholar 

  4. DeSisto CL, Kim SY, Sharma AJ. Prevalence estimates of gestational diabetes mellitus in the United States, Pregnancy Risk Assessment Monitoring System (PRAMS), 2007–2010. Prev Chronic Dis. 2014;11:E104. https://doi.org/10.5888/pcd11.130415.

    Article  PubMed  PubMed Central  Google Scholar 

  5. American College of Obstetricians and Gynecologists. ACOG practice bulletin no 190: gestational diabetes mellitus. Obstet Gynecol. 2018;131:e49–64. https://doi.org/10.1097/AOG.0000000000002501.

    Article  Google Scholar 

  6. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002. https://doi.org/10.1056/NEJMoa0707943.

    Article  PubMed  Google Scholar 

  7. Farrar D, Simmonds M, Bryant M, Sheldon TA, Tuffnell D, Golder S, et al. Hyperglycaemia and risk of adverse perinatal outcomes: systematic review and meta-analysis. BMJ (Clinical research ed). 2016;354:4694. https://doi.org/10.1136/bmj.i4694.

    Article  Google Scholar 

  8. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002;25:1862–8. https://doi.org/10.2337/diacare.25.10.1862.

    Article  PubMed  Google Scholar 

  9. Lowe WL, Scholtens DM, Lowe LP, Kuang A, Nodzenski M, Talbot O, et al. Association of gestational diabetes with maternal disorders of glucose metabolism and childhood adiposity. JAMA. 2018;320:1005–16. https://doi.org/10.1001/jama.2018.11628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019;62:905–14. https://doi.org/10.1007/s00125-019-4840-2.

    Article  PubMed  Google Scholar 

  11. Li J, Song C, Li C, Liu P, Sun Z, Yang X. Increased risk of cardiovascular disease in women with prior gestational diabetes: a systematic review and meta-analysis. Diabetes Res Clin Pract. 2018;140:324–38. https://doi.org/10.1016/j.diabres.2018.03.054.

    Article  PubMed  Google Scholar 

  12. Lowe WL, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Gestational Diabetes Mellitus and Childhood Glucose Metabolism. Diabetes Care. 2019;42:372–80. https://doi.org/10.2337/dc18-1646.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Eberle C, Ament C. Diabetic and metabolic programming: mechanisms altering the intrauterine milieu. ISRN pediatrics. 2012;2012:975685. https://doi.org/10.5402/2012/975685.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fernandez-Twinn DS, Ozanne SE. Mechanisms by which poor early growth programs type-2 diabetes, obesity and the metabolic syndrome. Physiol Behav. 2006;88:234–43. https://doi.org/10.1016/j.physbeh.2006.05.039.

    Article  CAS  PubMed  Google Scholar 

  15. Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, Bleker OP. Glucose tolerance in adults after prenatal exposure to famine. The Lancet. 1998;351:173–7. https://doi.org/10.1016/s0140-6736(97)07244-9.

    Article  CAS  Google Scholar 

  16. Newsome CA, Shiell AW, Fall CHD, Phillips DIW, Shier R, Law CM. Is birth weight related to later glucose and insulin metabolism?–A systematic review. Diabetic Med. 2003;20:339–48. https://doi.org/10.1046/j.1464-5491.2003.00871.x.

    Article  CAS  PubMed  Google Scholar 

  17. Bogdarina I, Murphy HC, Burns SP, Clark AJL. Investigation of the role of epigenetic modification of the rat glucokinase gene in fetal programming. Life Sci. 2004;74:1407–15. https://doi.org/10.1016/j.lfs.2003.08.017.

    Article  CAS  PubMed  Google Scholar 

  18. Bennett AJ, Sovio U, Ruokonen A, Martikainen H, Pouta A, Taponen S, et al. Variation at the Insulin Gene VNTR (Variable Number Tandem Repeat) Polymorphism and Early Growth: Studies in a Large Finnish Birth Cohort. Diabetes. 2004;53:2126–31. https://doi.org/10.2337/diabetes.53.8.2126.

    Article  CAS  PubMed  Google Scholar 

  19. Casanueva E, Viteri FE. Iron and oxidative stress in pregnancy. J Nutr. 2003;133:1700S-1708S. https://doi.org/10.1093/jn/133.5.1700S.

    Article  CAS  PubMed  Google Scholar 

  20. Yoshioka T, Ando M, Taniguchi K, Yamasaki F, Motoyama H. Lipoperoxidation and antioxidant substances in the human placenta during gestation. Nihon Sanka Fujinka Gakkai zasshi. 1990;42:1634–40.

    CAS  PubMed  Google Scholar 

  21. Li R, Chase M, Jung S-K, Smith PJS, Loeken MR. Hypoxic stress in diabetic pregnancy contributes to impaired embryo gene expression and defective development by inducing oxidative stress. Am J Physiol Endocrinol Metab. 2005;289:E591-9. https://doi.org/10.1152/ajpendo.00441.2004.

    Article  CAS  PubMed  Google Scholar 

  22. Simmons RA. Developmental origins of diabetes: the role of oxidative stress. Free Radical Biol Med. 2006;40:917–22. https://doi.org/10.1016/j.freeradbiomed.2005.12.018.

    Article  CAS  Google Scholar 

  23. Eriksson UJ. The pathogenesis of congenital malformations in diabetic pregnancy. Diabetes Metab Rev. 1995;11:63–82. https://doi.org/10.1002/dmr.5610110106.

    Article  CAS  PubMed  Google Scholar 

  24. Horal M, Zhang Z, Stanton R, Virkamäki A, Loeken MR. Activation of the hexosamine pathway causes oxidative stress and abnormal embryo gene expression: involvement in diabetic teratogenesis. Birth Defects Res A Clin Mol Teratol. 2004;70:519–27. https://doi.org/10.1002/bdra.20056.

    Article  CAS  PubMed  Google Scholar 

  25. Gluckman PD, Hanson MA. Maternal constraint of fetal growth and its consequences. Semin Fetal Neonatal Med. 2004;9:419–25. https://doi.org/10.1016/j.siny.2004.03.001.

    Article  PubMed  Google Scholar 

  26. Lindsay RS, Lindsay RM, Waddell BJ, Seckl JR. Prenatal glucocorticoid exposure leads to offspring hyperglycaemia in the rat: studies with the 11 beta-hydroxysteroid dehydrogenase inhibitor carbenoxolone. Diabetologia. 1996;39:1299–305. https://doi.org/10.1007/s001250050573.

    Article  CAS  PubMed  Google Scholar 

  27. Pelletier C, Dai S, Roberts KC, Bienek A, Onysko J, Pelletier L, Report summary. Diabetes in Canada: facts and figures from a public health perspective. Chronic Dis Inj Can. 2012;33:53–4.

    Article  CAS  Google Scholar 

  28. Bezek S, Ujházy E, Mach M, Navarová J, Dubovický M. Developmental origin of chronic diseases: toxicological implication. Interdiscip Toxicol. 2008;1:29–31. https://doi.org/10.2478/v10102-010-0029-8.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Choe S-A, Eliot MN, Savitz DA, Wellenius GA. Ambient air pollution during pregnancy and risk of gestational diabetes in New York City. Environ Res. 2019;175:414–20. https://doi.org/10.1016/j.envres.2019.04.030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhang H, Zhao Y. Ambient air pollution exposure during pregnancy and gestational diabetes mellitus in Shenyang, China: a prospective cohort study. Environ Sci Pollut Res Int. 2021;28:7806–14. https://doi.org/10.1007/s11356-020-11143-x.

    Article  CAS  PubMed  Google Scholar 

  31. Molina-Vega M, Gutiérrez-Repiso C, Muñoz-Garach A, Lima-Rubio F, Morcillo S, Tinahones FJ, Picón-César MJ. Relationship between environmental temperature and the diagnosis and treatment of gestational diabetes mellitus: An observational retrospective study. The Science of the total environment. 2020. https://doi.org/10.1016/j.scitotenv.2020.140994.

  32. Moses RG, Wong V, Lambert K, Morris GJ, Gil FS. Seasonal changes in the prevalence of gestational diabetes mellitus. Diabetes Care. 2016;39:1218–21. https://doi.org/10.2337/dc16-0451.

    Article  PubMed  Google Scholar 

  33. Ehrlich S, Lambers D, Baccarelli A, Khoury J, Macaluso M, Ho S-M. Endocrine disruptors: a potential risk factor for gestational diabetes mellitus. Am J Perinatol. 2016;33:1313–8. https://doi.org/10.1055/s-0036-1586500.

    Article  PubMed  Google Scholar 

  34. Kuo C-C, Moon K, Thayer KA, Navas-Acien A. Environmental chemicals and type 2 diabetes: an updated systematic review of the epidemiologic evidence. Curr DiabRep. 2013;13:831–49. https://doi.org/10.1007/s11892-013-0432-6.

    Article  CAS  Google Scholar 

  35. Shapiro GD, Dodds L, Arbuckle TE, Ashley-Martin J, Ettinger AS, Fisher M, et al. Exposure to organophosphorus and organochlorine pesticides, perfluoroalkyl substances, and polychlorinated biphenyls in pregnancy and the association with impaired glucose tolerance and gestational diabetes mellitus: the MIREC Study. Environ Res. 2016;147:71–81. https://doi.org/10.1016/j.envres.2016.01.040.

    Article  CAS  PubMed  Google Scholar 

  36. Soomro MH, Baiz N, Huel G, Yazbeck C, Botton J, Heude B, et al. Exposure to heavy metals during pregnancy related to gestational diabetes mellitus in diabetes-free mothers. Sci Total Environ. 2019;656:870–6. https://doi.org/10.1016/j.scitotenv.2018.11.422.

    Article  CAS  PubMed  Google Scholar 

  37. Wang X, Gao D, Zhang G, Zhang X, Li Q, Gao Q, et al. Exposure to multiple metals in early pregnancy and gestational diabetes mellitus: A prospective cohort study. Environ Int. 2020. https://doi.org/10.1016/j.envint.2019.105370.

  38. Hou Y, Li S, Xia L, Yang Q, Zhang L, Zhang X, et al. Associations of urinary phenolic environmental estrogens exposure with blood glucose levels and gestational diabetes mellitus in Chinese pregnant women. Sci Total Environ. 2021. https://doi.org/10.1016/j.scitotenv.2020.142085.

  39. Robledo C, Peck JD, Stoner JA, Carabin H, Cowan L, Koch HM, Goodman JR. Is bisphenol-A exposure during pregnancy associated with blood glucose levels or diagnosis of gestational diabetes? J Toxicol Environ Health A. 2013;76:865–73. https://doi.org/10.1080/15287394.2013.824395.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang X, Chen Q, Luo Z-C, Zhao S, Wang W, Zhang H-J, et al. Urinary bisphenol a concentration and gestational diabetes mellitus in Chinese women. Epidemiology. 2017;28:S41–7. https://doi.org/10.1097/EDE.0000000000000730.

    Article  PubMed  Google Scholar 

  41. Yang J, Wang H, Du H, Xu L, Liu S, Yi J, et al. Serum Bisphenol A, glucose homeostasis, and gestational diabetes mellitus in Chinese pregnant women: a prospective study. Environ Sci Pollut Res Int. 2021;28:12546–54. https://doi.org/10.1007/s11356-020-11263-4.

    Article  CAS  PubMed  Google Scholar 

  42. Zhang W, Xia W, Liu W, Li X, Hu J, Zhang B, et al. Exposure to bisphenol A substitutes and gestational diabetes mellitus: A prospective cohort study in China. Front Endocrinol. 2019. https://doi.org/10.3389/fendo.2019.00262.

  43. Bellavia A, Cantonwine DE, Meeker JD, Hauser R, Seely EW, McElrath TF, James-Todd T. Pregnancy urinary bisphenol-A concentrations and glucose levels across BMI categories. Environ Int. 2018;113:35–41. https://doi.org/10.1016/j.envint.2018.01.012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chiu Y-H, Mínguez-Alarcón L, Ford JB, Keller M, Seely EW, Messerlian C, et al. Trimester-specific urinary bisphenol a concentrations and blood glucose levels among pregnant women from a fertility clinic. J Clin Endocrinol Metab. 2017;102:1350–7. https://doi.org/10.1210/jc.2017-00022.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Chen M, Zhao S, Guo W-H, Zhu Y-P, Pan L, Xie Z-W, et al. Maternal exposure to Di-n-butyl phthalate (DBP) aggravate gestational diabetes mellitus via FoxM1 suppression by pSTAT1 signalling. Ecotoxicol Environ Saf. 2020;205:111154. https://doi.org/10.1016/j.ecoenv.2020.111154.

    Article  CAS  PubMed  Google Scholar 

  46. Guo J, Wu M, Gao X, Chen J, Li S, Chen B, Dong R. Meconium Exposure to Phthalates, Sex and Thyroid Hormones, Birth Size and Pregnancy Outcomes in 251 Mother-Infant Pairs from Shanghai. Int J Environ Res Public Health. 2020. https://doi.org/10.3390/ijerph17217711.

  47. Shaffer RM, Ferguson KK, Sheppard L, James-Todd T, Butts S, Chandrasekaran S, et al. Maternal urinary phthalate metabolites in relation to gestational diabetes and glucose intolerance during pregnancy. Environ Int. 2019;123:588–96. https://doi.org/10.1016/j.envint.2018.12.021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fisher BG, Frederiksen H, Andersson A-M, Juul A, Thankamony A, Ong KK, et al. Serum phthalate and triclosan levels have opposing associations with risk factors for gestational diabetes mellitus. Front Endocrinol. 2018. https://doi.org/10.3389/fendo.2018.00099.

  49. Shapiro GD, Dodds L, Arbuckle TE, Ashley-Martin J, Fraser W, Fisher M, et al. Exposure to phthalates, bisphenol A and metals in pregnancy and the association with impaired glucose tolerance and gestational diabetes mellitus: The MIREC study. Environ Int. 2015;83:63–71. https://doi.org/10.1016/j.envint.2015.05.016.

    Article  CAS  PubMed  Google Scholar 

  50. Shapiro GD, Arbuckle TE, Ashley-Martin J, Fraser WD, Fisher M, Bouchard MF, et al. Associations between maternal triclosan concentrations in early pregnancy and gestational diabetes mellitus, impaired glucose tolerance, gestational weight gain and fetal markers of metabolic function. Environ Res. 2018;161:554–61. https://doi.org/10.1016/j.envres.2017.12.001.

    Article  CAS  PubMed  Google Scholar 

  51. James-Todd TM, Chiu Y-H, Messerlian C, Minguez-Alarcon L, Ford JB, Keller M, et al. Environmental Health Trimester-specific phthalate concentrations and glucose levels among women from a fertility clinic. Environ Health. 2018. https://doi.org/10.1186/s12940-018-0399-5.

  52. James-Todd TM, Meeker JD, Huang T, Hauser R, Ferguson KK, Rich-Edwards JW, et al. Pregnancy urinary phthalate metabolite concentrations and gestational diabetes risk factors. Environ Int. 2016;96:118–26. https://doi.org/10.1016/j.envint.2016.09.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Robledo CA, Peck JD, Stoner J, Calafat AM, Carabin H, Cowan L, Goodman JR. Urinary phthalate metabolite concentrations and blood glucose levels during pregnancy. Int J Hyg Environ Health. 2015;218:324–30. https://doi.org/10.1016/j.ijheh.2015.01.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Alvarez-Silvares E, Fernández-Cruz T, Domínguez-Vigo P, Rubio-Cid P, Seoane-Pillado T, Martínez-Carballo E. Association between placenta concentrations polybrominated and polychlorinated biphenyls and gestational diabetes mellitus: a case-control study in northwestern Spain. Environ Sci Pollut Res Int. 2021;28:10292–301. https://doi.org/10.1007/s11356-021-12377-z.

    Article  CAS  PubMed  Google Scholar 

  55. Eslami B, Naddafi K, Rastkari N, Rashidi BH, Djazayeri A, Malekafzali H. Association between serum concentrations of persistent organic pollutants and gestational diabetes mellitus in primiparous women. Environ Res. 2016;151:706–12. https://doi.org/10.1016/j.envres.2016.09.002.

    Article  CAS  PubMed  Google Scholar 

  56. Jaacks LM, Barr DB, Sundaram R, Maisog JM, Zhang C, Buck Louis GM. Pre-pregnancy maternal exposure to polybrominated and polychlorinated biphenyls and gestational diabetes: a prospective cohort study. Environ Health. 2016;15:11. https://doi.org/10.1186/s12940-016-0092-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Liu W, Zhou Y, Li J, Sun X, Liu H, Jiang Y, et al. Parabens exposure in early pregnancy and gestational diabetes mellitus. Environ Int. 2019;126:468–75. https://doi.org/10.1016/j.envint.2019.02.040.

    Article  CAS  PubMed  Google Scholar 

  58. Liu X, Zhang L, Chen L, Li J, Wang Y, Wang J, et al. Structure-based investigation on the association between perfluoroalkyl acids exposure and both gestational diabetes mellitus and glucose homeostasis in pregnant women. Environ Int. 2019;127:85–93. https://doi.org/10.1016/j.envint.2019.03.035.

    Article  CAS  PubMed  Google Scholar 

  59. Liu X, Zhang L, Li J, Meng G, Chi M, Li T, et al. A nested case-control study of the association between exposure to polybrominated diphenyl ethers and the risk of gestational diabetes mellitus. Environ Int. 2018;119:232–8. https://doi.org/10.1016/j.envint.2018.06.029.

    Article  CAS  PubMed  Google Scholar 

  60. Rahman ML, Zhang C, Smarr MM, Lee S, Honda M, Kannan K, et al. Persistent organic pollutants and gestational diabetes: A multi-center prospective cohort study of healthy US women. Environ Int. 2019;124:249–58. https://doi.org/10.1016/j.envint.2019.01.027.

    Article  CAS  PubMed  Google Scholar 

  61. Saunders L, Kadhel P, Costet N, Rouget F, Monfort C, Thome J-P, et al. Hypertensive disorders of pregnancy and gestational diabetes mellitus among French Caribbean women chronically exposed to chlordecone. Environ Int. 2014;68:171–6. https://doi.org/10.1016/j.envint.2014.03.024.

    Article  CAS  PubMed  Google Scholar 

  62. Smarr MM, Grantz KL, Zhang C, Sundaram R, Maisog JM, Barr DB, Louis GMB. Persistent organic pollutants and pregnancy complications. Sci Total Environ. 2016;551:285–91. https://doi.org/10.1016/j.scitotenv.2016.02.030.

    Article  CAS  PubMed  Google Scholar 

  63. Vafeiadi M, Roumeliotaki T, Chalkiadaki G, Rantakokko P, Kiviranta H, Fthenou E, et al. Persistent organic pollutants in early pregnancy and risk of gestational diabetes mellitus. Environ Int. 2017;98:89–95. https://doi.org/10.1016/j.envint.2016.10.005.

    Article  CAS  PubMed  Google Scholar 

  64. Valvi D, Oulhote Y, Weihe P, Dalgård C, Bjerve KS, Steuerwald U, Grandjean P. Gestational diabetes and offspring birth size at elevated environmental pollutant exposures. Environ Int. 2017;107:205–15. https://doi.org/10.1016/j.envint.2017.07.016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Wang Y, Zhang L, Teng Y, Zhang J, Yang L, Li J, et al. Association of serum levels of perfluoroalkyl substances with gestational diabetes mellitus and postpartum blood glucose. J Environ Sci. 2018;69:5–11. https://doi.org/10.1016/j.jes.2018.03.016.

    Article  CAS  Google Scholar 

  66. Xu H, Zhou Q, Zhang J, Chen X, Zhao H, Lu H, et al. Exposure to elevated per- and polyfluoroalkyl substances in early pregnancy is related to increased risk of gestational diabetes mellitus: A nested case-control study in Shanghai, China. Environ Int. 2020. https://doi.org/10.1016/j.envint.2020.105952.

  67. Zhang L, Liu X, Meng G, Chi M, Li J, Yin S, et al. Non-dioxin-like polychlorinated biphenyls in early pregnancy and risk of gestational diabetes mellitus. Environ Int. 2018;115:127–32. https://doi.org/10.1016/j.envint.2018.03.012.

    Article  CAS  PubMed  Google Scholar 

  68. Bellavia A, Chiu Y-H, Brown FM, Minguez-Alarcon L, Ford JB, Keller M, et al. Urinary concentrations of parabens mixture and pregnancy glucose levels among women from a fertility clinic. Environ Res. 2019;168:389–96. https://doi.org/10.1016/j.envres.2018.10.009.

    Article  CAS  PubMed  Google Scholar 

  69. Wang Z, Mínguez-Alarcón L, Williams PL, Bellavia A, Ford JB, Keller M, et al. Perinatal urinary benzophenone-3 concentrations and glucose levels among women from a fertility clinic. Environ Health. 2020;19(1):45. https://doi.org/10.1186/s12940-020-00598-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Bonaventura, Marta, Maria, Soledad Bourguignon N, Bizzozzero M, Rodriguez D, Ventura C, Cocca C, et al. Arsenite in drinking water produces glucose intolerance in pregnant rats and their female offspring. Food Chem Toxicol. 2017;100:207-16. https://doi.org/10.1016/j.fct.2016.12.025.

  71. Ashley-Martin J, Dodds L, Arbuckle TE, Bouchard MF, Shapiro GD, Fisher M, et al. Association between maternal urinary speciated arsenic concentrations and gestational diabetes in a cohort of Canadian women. Environ Int. 2018;121:714–20. https://doi.org/10.1016/j.envint.2018.10.008.

    Article  CAS  PubMed  Google Scholar 

  72. Farzan SF, Gossai A, Chen Y, Chasan-Taber L, Baker E, Karagas M. Maternal arsenic exposure and gestational diabetes and glucose intolerance in the New Hampshire birth cohort study. Environ Health. 2016;15:106. https://doi.org/10.1186/s12940-016-0194-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Marie C, Léger S, Guttmann A, Rivière O, Marchiset N, Lémery D, et al. Exposure to arsenic in tap water and gestational diabetes: a French semi-ecological study. Environ Res. 2018;161:248–55. https://doi.org/10.1016/j.envres.2017.11.016.

    Article  CAS  PubMed  Google Scholar 

  74. Wu Y, Zhang J, Peng S, Wang X, Luo L, Liu L, et al. Multiple elements related to metabolic markers in the context of gestational diabetes mellitus in meconium. Environ Int. 2018:1227-34. https://doi.org/10.1016/j.envint.2018.10.044.

  75. Xia X, Liang C, Sheng J, Yan S, Huang K, Li Z, et al. Association between serum arsenic levels and gestational diabetes mellitus: A population-based birth cohort study. Environ Pollut (Barking, Essex : 1987). 2018;235:850–6. https://doi.org/10.1016/j.envpol.2018.01.016.

  76. Peng S, Liu L, Zhang X, Heinrich J, Zhang J, Schramm K-W, et al. A nested case-control study indicating heavy metal residues in meconium associate with maternal gestational diabetes mellitus risk. Environ Health. 2015;14:19. https://doi.org/10.1186/s12940-015-0004-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Wang Y, Zhang P, Chen X, Wu W, Feng Y, Yang H, et al. Multiple metal concentrations and gestational diabetes mellitus in Taiyuan, China. Chemosphere. 2019. https://doi.org/10.1016/j.chemosphere.2019.124412.

  78. Muñoz MP, Valdés M, Muñoz-Quezada MT, Lucero B, Rubilar P, Pino P, Iglesias V. Urinary Inorganic Arsenic Concentration and Gestational Diabetes Mellitus in Pregnant Women from Arica, Chile. Int J Environ Res Public Health. 2018;15(7):1418. https://doi.org/10.3390/ijerph15071418.

    Article  CAS  PubMed Central  Google Scholar 

  79. Ettinger AS, Zota AR, Amarasiriwardena CJ, Hopkins MR, Schwartz J, Hu H, Wright RO. Maternal arsenic exposure and impaired glucose tolerance during pregnancy. Environ Health Perspect. 2009;117:1059–64. https://doi.org/10.1289/ehp0800533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Li X, Huang Y, Xing Y, Hu C, Zhang W, Tang Y, et al. Association of urinary cadmium, circulating fatty acids, and risk of gestational diabetes mellitus: a nested case-control study in China. Environ Int. 2020;137:105527. https://doi.org/10.1016/j.envint.2020.105527.

    Article  CAS  PubMed  Google Scholar 

  81. Liu W, Zhang B, Huang Z, Pan X, Chen X, Hu C, et al. Cadmium body burden and gestational diabetes mellitus: a prospective study. Environ Health Perspect. 2018;126:27006. https://doi.org/10.1289/EHP2716.

    Article  Google Scholar 

  82. Xing Y, Xia W, Zhang B, Zhou A, Huang Z, Zhang H, et al. Relation between cadmium exposure and gestational diabetes mellitus. Environ Int. 2018;113:300–5. https://doi.org/10.1016/j.envint.2018.01.001.

    Article  CAS  PubMed  Google Scholar 

  83. Oguri T, Ebara T, Nakayama SF, Sugiura-Ogasawara M, Kamijima M. Association between maternal blood cadmium and lead concentrations and gestational diabetes mellitus in the Japan Environment and Children’s Study. Int Arch Occup Environ Health. 2019;92:209–17. https://doi.org/10.1007/s00420-018-1367-7.

  84. Romano ME, Gallagher LG, Jackson BP, Baker E, Karagas MR. Maternal urinary cadmium, glucose intolerance and gestational diabetes in the New Hampshire Birth Cohort Study. Environ Res. 2019;179:108733. https://doi.org/10.1016/j.envres.2019.108733.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Katsarou A, Claesson R, Ignell C, Shaat N, Berntorp K. Seasonal Pattern in the Diagnosis of Gestational Diabetes Mellitus in Southern Sweden. J Diabetes Res. 2016. https://doi.org/10.1155/2016/8905474.

  86. Meek CL, Devoy B, Simmons D, Patient CJ, Aiken AR, Murphy HR, Aiken CE. Seasonal variations in incidence and maternal–fetal outcomes of gestational diabetes. Diabet Med. 2020;37:674–80. https://doi.org/10.1111/dme.14236.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Verburg PE, Tucker G, Scheil W, Erwich J, Dekker GA, Roberts CT. Seasonality of gestational diabetes mellitus: a South Australian population study. BMJ Open Diabetes Res Care. 2016;4(1):e000286. https://doi.org/10.1136/bmjdrc-2016-000286.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Wang P, Wu C-S, Li C-Y, Yang C-P, Lu M-C. Seasonality of gestational diabetes mellitus and maternal blood glucose levels: Evidence from Taiwan. Medicine. 2020;99:e22684. https://doi.org/10.1097/MD.0000000000022684.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Shen EX, Moses RG, Oats J, Lowe J, McIntyre HD. Seasonality, temperature and pregnancy oral glucose tolerance test results in Australia. BMC Pregnancy Childbirth. 2019;19:263. https://doi.org/10.1186/s12884-019-2413-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Petry CJ, Fisher BG, Ong KK, Hughes IA, Acerini CL, Dunger DB. Temporal trends without seasonal effects on gestational diabetes incidence relate to reductions in indices of insulin secretion: the Cambridge Baby Growth Study. Acta Diabetol. 2019;56:1133–40. https://doi.org/10.1007/s00592-019-01354-1.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Wainstock T, Yoles I. Pregnant women may be sweeter in the summer: Seasonal changes in glucose challenge tests results. A population-based study. Diabetes Res Clin Pract. 2019;147:134–7. https://doi.org/10.1016/j.diabres.2018.11.020.

    Article  CAS  PubMed  Google Scholar 

  92. Retnakaran R, Ye C, Kramer CK, Hanley AJ, Connelly PW, Sermer M, Zinman B. Impact of daily incremental change in environmental temperature on beta cell function and the risk of gestational diabetes in pregnant women. Diabetologia. 2018;61:2633–42. https://doi.org/10.1007/s00125-018-4710-3.

    Article  PubMed  Google Scholar 

  93. Su W-L, Lu C-L, Martini S, Hsu Y-H, Li C-Y. A population-based study on the prevalence of gestational diabetes mellitus in association with temperature in Taiwan. Sci Total Environ. 2020;714:136747. https://doi.org/10.1016/j.scitotenv.2020.136747.

    Article  CAS  PubMed  Google Scholar 

  94. Vasileiou V, Kyratzoglou E, Paschou SA, Kyprianou M, Anastasiou E. The impact of environmental temperature on the diagnosis of gestational diabetes mellitus. Eur J Endocrinol. 2018;178:209–14. https://doi.org/10.1530/EJE-17-0730.

    Article  CAS  PubMed  Google Scholar 

  95. Zhang H, Wang Q, Benmarhnia T, Jalaludin B, Shen X, Yu Z, et al. Assessing the effects of non-optimal temperature on risk of gestational diabetes mellitus in a cohort of pregnant women in Guangzhou, China. Environ Int. 2021;152:106457. https://doi.org/10.1016/j.envint.2021.106457.

    Article  PubMed  Google Scholar 

  96. Choe S-A, Kauderer S, Eliot MN, Glazer KB, Kingsley SL, Carlson L, et al. Air pollution, land use, and complications of pregnancy. Sci Total Environ. 2018;645:1057–64. https://doi.org/10.1016/j.scitotenv.2018.07.237.

    Article  CAS  PubMed  Google Scholar 

  97. Hu H, Ha S, Henderson BH, Warner TD, Roth J, Kan H, Xu X. Association of atmospheric particulate matter and ozone with gestational diabetes mellitus. Environ Health Perspect. 2015;123:853–9. https://doi.org/10.1289/ehp.1408456.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Hu Q, Wang D, Yue D, Xu C, Hu B, Cheng P, et al. Association of ambient particle pollution with gestational diabetes mellitus and fasting blood glucose levels in pregnant women from two Chinese birth cohorts. Sci Total Environ. 2021;762:143176. https://doi.org/10.1016/j.scitotenv.2020.143176.

    Article  CAS  PubMed  Google Scholar 

  99. Kang J, Liao J, Xu S, Xia W, Li Y, Chen S, Lu B. Associations of exposure to fine particulate matter during pregnancy with maternal blood glucose levels and gestational diabetes mellitus: Potential effect modification by ABO blood group. Ecotoxicol Environ Saf. 2020;198:110673. https://doi.org/10.1016/j.ecoenv.2020.110673.

    Article  CAS  PubMed  Google Scholar 

  100. Lin Q, Zhang S, Liang Y, Wang C, Wang C, Wu X, et al. Ambient air pollution exposure associated with glucose homeostasis during pregnancy and gestational diabetes mellitus. Environ Res. 2020;190:109990. https://doi.org/10.1016/j.envres.2020.109990.

    Article  CAS  PubMed  Google Scholar 

  101. Melody SM, Ford JB, Wills K, Venn A, Johnston FH. Maternal exposure to fine particulate matter from a large coal mine fire is associated with gestational diabetes mellitus: a prospective cohort study. Environ Res. 2020;183:108956. https://doi.org/10.1016/j.envres.2019.108956.

    Article  CAS  PubMed  Google Scholar 

  102. Rammah A, Whitworth KW, Symanski E. Particle air pollution and gestational diabetes mellitus in Houston, Texas. Environ Res. 2020;190:109988. https://doi.org/10.1016/j.envres.2020.109988.

    Article  CAS  PubMed  Google Scholar 

  103. Shen H-N, Hua S-Y, Chiu C-T, Li C-Y. Maternal exposure to air pollutants and risk of gestational diabetes mellitus in Taiwan. Int J Environ Res Public Health. 2017;14(12):1604. https://doi.org/10.3390/ijerph14121604.

    Article  CAS  PubMed Central  Google Scholar 

  104. Yao M, Liu Y, Jin D, Yin W, Ma S, Tao R, et al. Relationship betweentemporal distribution of air pollution exposure and glucose homeostasis during pregnancy. Environ Res. 2020;185:109456. https://doi.org/10.1016/j.envres.2020.109456.

    Article  CAS  PubMed  Google Scholar 

  105. Ye B, Zhong C, Li Q, Xu S, Zhang Y, Zhang X, et al. The associations of ambient fine particulate matter exposure during pregnancy with blood glucose levels and gestational diabetes mellitus risk: a prospective cohort study in Wuhan, China. Am J Epidemiol. 2020;189:1306–15. https://doi.org/10.1093/aje/kwaa056.

    Article  PubMed  Google Scholar 

  106. Yu G, Ao J, Cai J, Luo Z, Martin R, van Donkelaar A, et al. Fine particular matter and its constituents in air pollution and gestational diabetes mellitus. Environ Int. 2020;142:105880. https://doi.org/10.1016/j.envint.2020.105880.

    Article  CAS  PubMed  Google Scholar 

  107. Zhang M, Wang X, Yang X, Dong T, Hu W, Guan Q, et al. Increased risk of gestational diabetes mellitus in women with higher prepregnancy ambient PM(2.5) exposure. Sci Total Environ. 2020;730:138982. https://doi.org/10.1016/j.scitotenv.2020.138982.

    Article  CAS  PubMed  Google Scholar 

  108. Malmqvist E, Jakobsson K, Tinnerberg H, Rignell-Hydbom A, Rylander L. Gestational diabetes and preeclampsia in association with air pollution at levels below current air quality guidelines. Environ Health Perspect. 2013;121:488–93. https://doi.org/10.1289/ehp.1205736.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Dastoorpoor M, Khanjani N, Moradgholi A, Sarizadeh R, Cheraghi M, Estebsari F. Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model. Int Arch Occup Environ Health. 2021;94:309–24. https://doi.org/10.1007/s00420-020-01577-8.

    Article  CAS  PubMed  Google Scholar 

  110. Pan S-C, Huang C-C, Lin S-J, Chen B-Y, Chang C-C, Leon Guo Y-L. Gestational diabetes mellitus was related to ambient air pollutant nitric oxide during early gestation. Environ Res. 2017;158:318–23. https://doi.org/10.1016/j.envres.2017.06.005.

    Article  CAS  PubMed  Google Scholar 

  111. Pedersen M, Olsen SF, Halldorsson TI, Zhang C, Hjortebjerg D, Ketzel M, et al. Gestational diabetes mellitus and exposure to ambient air pollution and road traffic noise: a cohort study. Environ Int. 2017;108:253–60. https://doi.org/10.1016/j.envint.2017.09.003.

    Article  CAS  PubMed  Google Scholar 

  112. Robledo CA, Mendola P, Yeung E, Männistö T, Sundaram R, Liu D, et al. Preconception and early pregnancy air pollution exposures and risk of gestational diabetes mellitus. Environ Res. 2015;137:316–22. https://doi.org/10.1016/j.envres.2014.12.020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Hehua Z, Yang X, Qing C, Shanyan G, Yuhong Z. Dietary patterns and associations between air pollution and gestational diabetes mellitus. Environ Int. 2021;147:106347. https://doi.org/10.1016/j.envint.2020.106347.

    Article  CAS  PubMed  Google Scholar 

  114. Jo H, Eckel SP, Chen J-C, Cockburn M, Martinez MP, Chow T, et al. Associations of gestational diabetes mellitus with residential air pollution exposure in a large Southern California pregnancy cohort. Environ Int. 2019;130:104933. https://doi.org/10.1016/j.envint.2019.104933.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Zhang H, Dong H, Ren M, Liang Q, Shen X, Wang Q, et al. Ambient air pollution exposure and gestational diabetes mellitus in Guangzhou, China: a prospective cohort study. Sci Total Environ. 2020;699:134390. https://doi.org/10.1016/j.scitotenv.2019.134390.

    Article  CAS  PubMed  Google Scholar 

  116. Fleisch AF, Kloog I, Luttmann-Gibson H, Gold DR, Oken E, Schwartz JD. Air pollution exposure and gestational diabetes mellitus among pregnant women in Massachusetts: a cohort study. Environ Health. 2016;15:40. https://doi.org/10.1186/s12940-016-0121-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Padula AM, Yang W, Lurmann FW, Balmes J, Hammond SK, Shaw GM. Prenatal exposure to air pollution, maternal diabetes and preterm birth. Environ Res. 2019;170:160–7. https://doi.org/10.1016/j.envres.2018.12.031.

    Article  CAS  PubMed  Google Scholar 

  118. Fleisch AF, Gold DR, Rifas-Shiman SL, Koutrakis P, Schwartz JD, Kloog I, et al. Air pollution exposure and abnormal glucose tolerance during pregnancy: the project Viva cohort. Environ Health Perspect. 2014;122:378–83. https://doi.org/10.1289/ehp.1307065.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Li D, Wang J, Yu Z, Lin H, Chen K. Air pollutants concentration and variation of blood glucose level among pregnant women in China: A cross-sectional study. Atmospheric Environment. 2020. https://doi.org/10.1016/j.atmosenv.2019.117191.

  120. Lu M-C, Wang P, Cheng T-J, Yang C-P, Yan Y-H. Association of temporal distribution of fine particulate matter with glucose homeostasis during pregnancy in women of Chiayi City. Taiwan Environmental research. 2017;152:81–7. https://doi.org/10.1016/j.envres.2016.09.023.

    Article  CAS  PubMed  Google Scholar 

  121. Najafi ML, Zarei M, Gohari A, Haghighi L, Heydari H, Miri M. Preconception air pollution exposure and glucose tolerance in healthy pregnant women in a middle-income country. Environ Health. 2020. https://doi.org/10.1186/s12940-020-00682-y.

  122. Blauw LL, Aziz NA, Tannemaat MR, Blauw CA, de Craen AJ, Pijl H, Rensen PCN. Diabetes incidence and glucose intolerance prevalence increase with higher outdoor temperature. BMJ Open Diabetes Res Care. 2017;5:e000317. https://doi.org/10.1136/bmjdrc-2016-000317.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Moltchanova EV, Schreier N, Lammi N, Karvonen M. Seasonal variation of diagnosis of Type 1 diabetes mellitus in children worldwide. Diabetic Med. 2009;26:673–8. https://doi.org/10.1111/j.1464-5491.2009.02743.x.

    Article  CAS  PubMed  Google Scholar 

  124. Tyrovolas S, Chalkias C, Morena M, Kalogeropoulos K, Tsakountakis N, Zeimbekis A, et al. High relative environmental humidity is associated with diabetes among elders living in Mediterranean islands. J Diabetes Metab Disord. 2014;13:25. https://doi.org/10.1186/2251-6581-13-25.

    Article  PubMed  PubMed Central  Google Scholar 

  125. Virtanen KA, Lidell ME, Orava J, Heglind M, Westergren R, Niemi T, et al. Functional brown adipose tissue in healthy adults. N Engl J Med. 2009;360:1518–25. https://doi.org/10.1056/NEJMoa0808949.

    Article  CAS  PubMed  Google Scholar 

  126. Preston EV, Eberle C, Brown FM, James-Todd T. Climate factors and gestational diabetes mellitus risk - a systematic review. Environ Health. 2020;19:112. https://doi.org/10.1186/s12940-020-00668-w.

    Article  PubMed  PubMed Central  Google Scholar 

  127. The American College of Obstetricians and Gynecologists Committee on Health Care for Underserved Women. ACOG Committee Opinion No. 575: Exposure to toxic environmental agents. ObstetGynecol. 2013;122:931–5. https://doi.org/10.1097/01.AOG.0000435416.21944.54.

  128. Di Renzo GC, Conry JA, Blake J, DeFrancesco MS, DeNicola N, Martin JN, et al. International Federation of Gynecology and Obstetrics opinion on reproductive health impacts of exposure to toxic environmental chemicals. Int J Gynaecol Obstet. 2015;131:219–25. https://doi.org/10.1016/j.ijgo.2015.09.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Wang A, Padula A, Sirota M, Woodruff TJ. Environmental influences on reproductive health: the importance of chemical exposures. Fertil Steril. 2016;106:905–29. https://doi.org/10.1016/j.fertnstert.2016.07.1076.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Woodruff TJ, Zota AR, Schwartz JM. Environmental chemicals in pregnant women in the United States: NHANES 2003–2004. Environ Health Perspect. 2011;119:878–85. https://doi.org/10.1289/ehp.1002727.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Holdsworth-Carson SJ, Lim R, Mitton A, Whitehead C, Rice GE, Permezel M, Lappas M. Peroxisome proliferator-activated receptors are altered in pathologies of the human placenta: gestational diabetes mellitus, intrauterine growth restriction and preeclampsia. Placenta. 2010;31:222–9. https://doi.org/10.1016/j.placenta.2009.12.009.

    Article  CAS  PubMed  Google Scholar 

  132. Varshavsky J, Smith A, Wang A, Hom E, Izano M, Huang H, et al. Heightened susceptibility: A review of how pregnancy and chemical exposures influence maternal health. Reprod Toxicol (Elmsford, NY). 2020;92:14–56. https://doi.org/10.1016/j.reprotox.2019.04.004.

    Article  CAS  Google Scholar 

  133. Hong E-J, Choi K-C, Jeung E-B. Maternal-fetal transfer of endocrine disruptors in the induction of Calbindin-D9k mRNA and protein during pregnancy in rat model. Mol Cell Endocrinol. 2003;212:63–72. https://doi.org/10.1016/j.mce.2003.08.011.

    Article  CAS  PubMed  Google Scholar 

  134. Souza M, Saraiva M, Honda M, Barbieri MA, Bettiol H, Barbosa F, Kannan K. Exposure to per- and polyfluorinated alkyl substances in pregnant Brazilian women and its association with fetal growth. Environ Res. 2020;187:109585. https://doi.org/10.1016/j.envres.2020.109585.

    Article  CAS  PubMed  Google Scholar 

  135. Pan Y, Deng M, Li J, Du B, Lan S, Liang X, Zeng L. Occurrence and Maternal Transfer of Multiple Bisphenols, including an Emerging Derivative with Unexpectedly High Concentrations, in the Human Maternal-Fetal-Placental Unit. Environ Sci Technol. 2020;54:3476–86. https://doi.org/10.1021/acs.est.0c00206.

    Article  CAS  PubMed  Google Scholar 

  136. Zhang W, Cai Y, Sheng G, Chen D, Fu J. Tissue distribution of decabrominated diphenyl ether (BDE-209) and its metabolites in sucking rat pups after prenatal and/or postnatal exposure. Toxicology. 2011;283:49–54. https://doi.org/10.1016/j.tox.2011.02.003.

    Article  CAS  PubMed  Google Scholar 

  137. Haines DA, Saravanabhavan G, Werry K, Khoury C. An overview of human biomonitoring of environmental chemicals in the Canadian Health Measures Survey: 2007–2019. Int J Hyg Environ Health. 2017;220:13–28. https://doi.org/10.1016/j.ijheh.2016.08.002.

    Article  CAS  PubMed  Google Scholar 

  138. Iavicoli I, Fontana L, Bergamaschi A. The effects of metals as endocrine disruptors. J Toxicol Environ Health B Crit Rev. 2009;12:206–23. https://doi.org/10.1080/10937400902902062.

    Article  CAS  PubMed  Google Scholar 

  139. Abdel Hameed ER, Shehata MA, Waheed H, Abdel Samie OM, Ahmed HH, Sherif LS, Ahmed A. Heavy metals can either aid or oppose the protective function of the placental barrier. Open Access Maced J Med Sci. 2019;7:2814–7. https://doi.org/10.3889/oamjms.2019.709.

    Article  Google Scholar 

  140. Sabra S, Malmqvist E, Saborit A, Gratacós E, Gomez Roig MD. Heavy metals exposure levels and their correlation with different clinical forms of fetal growth restriction. PLoS One. 2017;12(10):e0185645. https://doi.org/10.1371/journal.pone.0185645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Hameed ERA, Shehata MA, Ahmed HH, Sherif LS, Elnady HG, Waheed H. Relation of Heavy Metals in Cord and Maternal Blood to Neonatal Anthropometric Indices. J Clin Diagnostic Res. 2019;13:SC01–5. https://doi.org/10.7860/JCDR/2019/39929.12653.

    Article  Google Scholar 

  142. Kuntz WD, Pitkin RM, Bostrom AW, Hughes MS. Maternal and cord blood background mercury levels: a longitudinal surveillance. Am J Obstet Gynecol. 1982;143:440–3. https://doi.org/10.1016/0002-9378(82)90087-4.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank Anna Schultz for her support of this project.

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  1. Medicine With Specialization in Internal Medicine and General Medicine, Hochschule Fulda, University of Applied Sciences, Leipziger Strasse 123, 36037, Fulda, Germany

    Claudia Eberle & Stefanie Stichling

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C.E. designed the systematic review. C.E. and S.S. performed the systematic literature search, C.E. and S.S. performed the data extraction of all included studies and wrote the manuscript. C.E. provided guidance in synthesizing and interpreting the study results. C.E. reviewed the manuscript for intellectual content and approved the final manuscript. The author(s) read and approved the final manuscript.

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Eberle, C., Stichling, S. Environmental health influences in pregnancy and risk of gestational diabetes mellitus: a systematic review. BMC Public Health 22, 1572 (2022). https://doi.org/10.1186/s12889-022-13965-5

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