# Gestational diabetes mellitus and air pollution in the urban environment: Critical time windows of exposure across age and BMI classes

M. Pascard, F. Mauny, A.-S. Mariet, B. Martin, C. Quantin, P. Sagot, A.-L.

Parmentier, M. Boilleaut, S. Pujol, N. Bernard

► To cite this version:

M. Pascard, F. Mauny, A.-S. Mariet, B. Martin, C. Quantin, et al., Gestational diabetes mellitus and air pollution in the urban environment: Critical time windows of exposure across age and BMI classes. Building and Environment, 2024, 266, pp.112098. 10.1016/j.buildenv.2024.112098. hal-05039822

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# Gestational diabetes mellitus and air pollution in the urban environment: Critical time windows of exposure across age and BMI classes

M. Pascard<sup>a,b</sup>, F. Mauny<sup>a,b,*</sup>, A.-S. Mariet<sup>a,c,g</sup>, B. Martin<sup>b</sup>, H. Gauthier-Manuel<sup>a</sup>, C. Quantin<sup>c,g,h</sup>, P. Sagot<sup>d</sup>, A.-L. Parmentier<sup>a,b</sup>, M. Boilleaut<sup>e</sup>, S. Pujol<sup>a,b</sup>, N. Bernard<sup>a,f</sup>

## ARTICLE INFO

### Keywords:

Gestational diabetes

Air pollution

Particulate matter

Nitrogen dioxide

Critical time window

Disease susceptibility

## ABSTRACT

This exploratory study was designed to identify potential time windows of exposure to air pollution before and during early pregnancy that are more likely to be associated with GDM across age and body mass index (BMI) classes. The studied women were living in the urban region of Dijon (France) and delivered between 2005 and 2009. Data were obtained from medical records. Outdoor NO₂ and PM₁₀ cumulative home exposures were modelled over sliding 4-week exposure windows from 7 months before the last menstrual period to the 24th week of gestation. Logistic regression models estimating the odds ratio (OR) for GDM per 10 µg/m³ increase in pollutant concentrations were applied to two age classes and four prepregnancy BMI classes. Among the 5218 women, 311 women under 35 years of age and 136 women over 35 years old developed GDM. The number of women who developed GDM in the underweight, normal weight, overweight and obese classes was 21 (6.1 %), 251 (7.4 %), 96 (10.6 %) and 65 (25.3 %), respectively. When considering PM₁₀ exposure, ORs of up to 2.03 were observed in women under 35 years of age. The highest ORs were associated with the [-09; -06], [-13; -10] and [-15; -12] windows in underweight, overweight, and obese women, respectively. The ORs were nearly all centred on one for NO₂. For women living in an urban environment, our results suggest that critical 4-week windows of exposure to air pollution in both the pre- and postconception periods are associated with the development of GDM.

## 1. Introduction

Pregnancy is characterized by an increase in insulin resistance, which is correlated with advancing gestation. Beta cell production of insulin increases to maintain maternal normoglycaemia [1]. Women whose bodies are unable to adapt to these pregnancy-induced physiological changes develop gestational diabetes mellitus (GDM) [1], a disorder characterized by hyperglycaemia during pregnancy. GDM is the most common form of hyperglycaemia during pregnancy [2]. In 2021,

approximately 17 million live-born infants worldwide were exposed to hyperglycaemia during pregnancy due to the development of GDM [3]. The same year, the proportion of GDM pregnancies in France was estimated to reach 16.4 % [4].

GDM has adverse effects on both mothers and infants. Women with a history of GDM appear to have a nearly 10-fold greater risk of developing type 2 diabetes mellitus (T2DM) than those with a normoglycaemic pregnancy [5]. GDM is also an important step in the cycle of transgenerational obesity and diabetes, which impose great burdens on

<footnote><sup>a</sup> Université de Franche-Comté, CNRS, CHRONO-E (UMR 6249), F-25000, Besançon, France</footnote>

<footnote><sup>b</sup> CHU Besançon, Inserm, CIC (CIC 1431), uMETh, F-25000, Besançon, France</footnote>

<footnote><sup>c</sup> Service de Biostatistiques et d'Information Médicale (DIM), CHU Dijon Bourgogne, F21000, Dijon, France</footnote>

<footnote><sup>d</sup> Centre Hospitalier Universitaire de Dijon Bourgogne, Service de Gynécologie-Obstétrique, 14 rue Gaffarel, 21079, Dijon, France</footnote>

<footnote><sup>e</sup> Atmo Bourgogne-Franche-Comté, 37 rue Battant, 25000, Besançon, France</footnote>

<footnote><sup>f</sup> Université de Franche-Comté, CNRS, THÉMA (UMR 6049), F-25000, Besançon, France</footnote>

<footnote><sup>g</sup> CHU Dijon Bourgogne, Inserm, CIC 1432, Module Épidémiologie Clinique, F21000, Dijon, France</footnote>

<footnote><sup>h</sup> Université Paris-Saclay, UVSQ, Inserm, CESP, F-94807, Villejuif, France</footnote>

<footnote>* The corresponding author. uMETh, INSERM CIC 1431, Centre hospitalier universitaire de Besançon, 2 place Saint Jacques, 25030, Besançon, France.

E-mail addresses: madeline.pascard@gmail.com (M. Pascard), frederic.mauny@univ-fcomte.fr (F. Mauny), anne-sophie.mariet@chu-dijon.fr (A.-S. Mariet), bmartin@chu-besancon.fr (B. Martin), honorine.gauthier-manuel@univ-fcomte.fr (H. Gauthier-Manuel), catherine.quantin@chu-dijon.fr (C. Quantin), paul.sagot@chu-dijon.fr (P. Sagot), alparmentier@chu-besancon.fr (A.-L. Parmentier), mathieu.boilleaut@atmo-bfc.org (M. Boilleaut), sophie.pujol@univ-fcomte.fr (S. Pujol), nadine.bernard@univ-fcomte.fr (N. Bernard).</footnote>
patients, their families, and society as a whole [6,7]. Preventing the onset of gestational diabetes by acting on risk factors such as obesity is a great opportunity to break the cycle [7,8].

The impact of exposure to air pollution mixtures, including NO₂ and PM₁₀, has been extensively studied and appears to be complex. Several systematic reviews and meta-analyses have been published since 2020 [9–20]. The conclusions of these studies support the existence of an association between air pollution and gestational diabetes but diverge according to the gestational period and the atmospheric pollutants. These results are based on a partition of the duration of pregnancy into a priori defined gestational exposure periods (trimester, whole pregnancy), which are also used for different environmental exposures or outcomes [21–24].

In contrast to the abovementioned studies, a recent approach focuses on time windows that are i) not determined a priori and ii) narrower (one week) [12,25–35]. The results of these surveys also differed in terms of the presence, temporal location (particularly before or during pregnancy), length of the critical window of exposure, and strength of the association within this critical window. This discrepancy could be explained by a modulation of the association according to individual characteristics (e.g., age or BMI), the nature of the pollutants as identified by Niu et al. [31], and their concentrations. Few studies have investigated exposure prior to the prepregnancy trimester [32,36]. These studies identified a critical window of exposure approximately five months before pregnancy. In the present study, we hypothesize that the true relationship between GDM and air pollution is misunderstood, particularly because of the modulating effect of maternal age or prepregnancy BMI on both the amplitude and the temporal definition of the critical exposure window. The impact of bronchial inflammation on the diabetogenic process may differ according to BMI status.

The aim of our study was to explore the potential modulation of the association between GDM and exposure to air pollution by BMI and age and to explore the impact of air pollution exposure prior to conception. We focused on two pollutants: PM₁₀ (particulate matter) and NO₂ (gas).

## 2. Materials and methods

This study was a part of the PreCEE program (PREgnancy and Combined Environmental Exposure program), a research program covering over 10 000 deliveries promoted by the Besançon University Hospital and funded by the Agence de la Transition Ecologique, the Bourgogne-Franche-Comté Region, the Dijon-Bourgogne University Hospital and the Foundation de France. This program, which focuses on the relationship between the urban environment and pregnancy, has focused on preterm birth [37], foetal growth [38] and pregnancy vulnerability [39].

### 2.1. Study design and population

This exploratory retrospective study was based on 6320 deliveries that occurred at Dijon University Hospital (a medium-sized city in eastern France) between January 1, 2005 and December 31, 2009 among women living in the urban area of Dijon on the date of delivery. The inclusion criteria were as follows: mothers aged 18 years or older, mothers without pregestational diabetes, and mothers who gave birth to one newborn (stillborn or alive) after 22 completed weeks of gestation and/or whose newborn birth weight was 500 g or greater. Women who had induced abortions and women with missing or invalid data for their delivery date or address were excluded, as were women who gave birth without disclosing their identity (anonymous birth) and women who refused to participate in the study. Several women had more than one delivery during the study period; only one delivery was included based on random selection (Fig. S1).

### 2.2. Outcomes

Prior to 2014, GDM was defined as "carbohydrate intolerance resulting in hyperglycaemia of variable severity with onset or first recognition during pregnancy [...] whether or not insulin is used for treatment or the condition persists after pregnancy" [40,41]. The GDM data were extracted from the French database of hospital activity (French acronym PMSI) of Dijon University Hospital (France). The codes ICD-10, O24.4 (diabetes mellitus occurring during pregnancy) and P70.0 (syndrome of a child whose mother had diabetes during pregnancy) were retained. The data were checked by a practitioner of the Medical Information Department of Dijon University Hospital. Over the study period (2005–2009), the French High Health Authority (French acronym HAS) recommended systematic screening between the 24th and 28th weeks of gestation, with the exception of women with risk factors, who required a diagnostic test as quickly as possible [42]. The screening recommendations did not change over this period [43].

### 2.3. Exposure assessment

The two air pollutants considered in this study were based on reliable, validated models that were only available for NO₂ and PM₁₀ [44, 45]. NO₂ is a gaseous pollutant used as a marker for the cocktail of combustion-related pollutants, particularly those emitted by road traffic [46]. Particulate matter less than 10 µm in diameter (PM₁₀) is a mixture of airborne solid particles or liquid droplets of various compositions that are 10 µm and smaller. The residential exposure assessment has been described previously [37,38,47]. Briefly, for each woman, average NO₂ and PM₁₀ concentrations were calculated for a 50-m radius circular area around their residence building (Fig. 1).

The concentrations were estimated via two-step emission and diffusion modelling. This modelling was performed for the entire geographical area of the urban unit of Dijon. Emissions from road traffic were calculated via CIRCUL'AIR software, which was developed and is used by all French approved air quality monitoring associations (French acronym AASQA). For emissions from heating, industry, and long-range sources, the AASQA Pollution Emission Inventory was used. To model diffusion on a grid of points 2 m above the ground, ADMS-Urban© software (developed by researchers at CERC (Cambridge Environmental Research Consultant)) was used. ESRI arcGIS© software (V10.1) was used for spatial interpolation; the information was rasterized in the form of a 2 m × 2 m (4 m²) grid. In each pixel, the NO₂ and PM₁₀ concentrations were expressed in micrograms per cubic metre (µg/m³). These modelled concentrations were validated by measurement campaigns. Monthly maps of the concentrations were established from January 2004 to December 2009 to account for seasonal variations in air pollutants. The NO₂ and PM₁₀ exposures were assessed for sliding 4-week exposure windows from 7 months before the first day of the last menstrual period (LMP) to the 24th week of gestation. Such a preconception period has been defined to allow the evaluation of a signal at least as early as that already identified 5 months before the pregnancy [27,31]. Each exposure window was shifted by one week with respect to the previous window. The first window of exposure was thus from the 28th week to the 25th week before the LMP. The second window of exposure was from the 27th to the 24th week before the LMP and so on until the window of exposure extending from the 20th to the 23rd week after the LMP was reached. The number -01 was given to the week preceding the LMP, and the number +01 was given to the week following the LMP (there was no week 0). Forty-eight windows of 4 weeks were studied (Fig. 2).

### 2.4. Statistical analysis

Descriptive statistics are presented as the means and standard deviations (SDs) or as the frequencies and percentages (%). The analyses were stratified by maternal age (under 35 years of age and 35 years or
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Graphical representation of the residential area considered for the exposure quantification. Exposure was quantified via the residential value obtained by modelling air pollution in the immediate vicinity (the average level within a 50 m radius of the home).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Graphical representation of the relationship between the 4-week sliding window of exposure and the number of weeks from the LMP date. Abbreviations: LMP, first day of the last menstrual period.</figcaption></figure>

older) and prepregnancy BMI classes (underweight (BMI <18.5 kg/m²), normal weight (BMI between 18.5 and 24.9 kg/m²), overweight (BMI between 25 and 29.9 kg/m²) and obese (BMI ≥30 kg/m²)). Logistic regression models were iteratively used to assess the associations between GDM and the PM₁₀ and NO₂ exposure levels in each different subpopulation and each sliding 4-week exposure window. Models stratified by age class were adjusted for parity (parity of 0, 1 and more than 1) and prepregnancy BMI. BMI was treated as quantitative via restricted cubic splines to model a possible nonlinear BMI-outcome

association [48,49]. Models stratified by prepregnancy BMI classes were adjusted for parity and age (as a continuous variable and via restricted cubic splines). The restricted cubic splines revealed no evidence of nonlinear associations of GDM with air pollutants. Therefore, each pollutant was treated as a continuous variable and modelled linearly. The ORs and their 95 % CIs were expressed for an exposure increment of 10 µg/m³. Sensitivity analyses were performed using sliding 5- and 6-week exposure windows. R statistical software version 4.0.1 (2020-06-06) was used. No correction for multiple testing was applied in
this exploratory study.

## 2.5. Ethics

This study was approved by the French National Advisory Committee for the Treatment of Information in Health Research (CCTIRS) and by the French data protection authority (CNIL). An information letter was sent to each woman included in the study. Women who declined analysis of their data were excluded from the study.

## 3. Results

A total of 5218 women were included in the study. Twenty women with preexisting diabetes were excluded (7 with T2DM, and 13 with type 1 diabetes) (Fig. S1). This exclusion proportion was similar in the two age groups (0.3 % and 0.4 % for women under 35 years and women 35 years or older, respectively) but differed slightly across the BMI classes (0 %, 0.2 %, 0.7 % and 0.1 % for women who were underweight, normal weight, overweight and obese, respectively). The characteristics of the study population are presented in Table 1. GDM was diagnosed in 447 (8.6 %) women.

In the group of women under 35 years of age, 311 (7.3 %) had GDM,

<table><caption>**Table 1**
Distribution of participant characteristics.</caption><thead><tr><th>Characteristics</th><th>Total population</th></tr></thead><tbody><tr><td>N</td><td>5218</td></tr><tr><td>Maternal age in years (mean (SD))</td><td>29.52 (5.34)</td></tr><tr><td>Maternal age in years (n (%))</td><td></td></tr><tr><td>&lt; 35</td><td>4251 (81.5)</td></tr><tr><td>≥ 35</td><td>967 (18.5)</td></tr><tr><td>Body Mass Index in kg/m² (mean (SD))</td><td>23.12 (4.64)</td></tr><tr><td>Body Mass Index in kg/m² (n (%))</td><td></td></tr><tr><td>&lt; 18.5</td><td>343 (6.6)</td></tr><tr><td>18.5-24.9</td><td>3400 (65.2)</td></tr><tr><td>25.0-29.9</td><td>909 (17.4)</td></tr><tr><td>≥ 30</td><td>426 (8.2)</td></tr><tr><td>Unknown</td><td>140 (2.7)</td></tr><tr><td>Smoking during pregnancy (n (%))</td><td></td></tr><tr><td>No</td><td>4070 (78.0)</td></tr><tr><td>Yes</td><td>953 (18.3)</td></tr><tr><td>Unknown</td><td>195 (3.7)</td></tr><tr><td>Marital status (n (%))</td><td></td></tr><tr><td>In a relationship</td><td>4448 (85.2)</td></tr><tr><td>Single</td><td>377 (7.2)</td></tr><tr><td>Unknown</td><td>393 (7.5)</td></tr><tr><td>Occupation (n (%))</td><td></td></tr><tr><td>Unemployed</td><td>1381 (26.5)</td></tr><tr><td>Employed</td><td>3348 (64.2)</td></tr><tr><td>Unknown</td><td>489 (9.4)</td></tr><tr><td>Parity (n (%))</td><td></td></tr><tr><td>0</td><td>2789 (53.4)</td></tr><tr><td>1</td><td>1440 (27.6)</td></tr><tr><td>≥ 2</td><td>989 (19.0)</td></tr><tr><td>Year of conception (n (%))</td><td></td></tr><tr><td>2004</td><td>610 (11.7)</td></tr><tr><td>2005</td><td>964 (18.5)</td></tr><tr><td>2006</td><td>1048 (20.1)</td></tr><tr><td>2007</td><td>1088 (20.9)</td></tr><tr><td>2008</td><td>1170 (22.4)</td></tr><tr><td>2009</td><td>338 (6.5)</td></tr><tr><td>Season of conception (n (%))</td><td></td></tr><tr><td>Autumn</td><td>1347 (25.8)</td></tr><tr><td>Spring</td><td>1289 (24.7)</td></tr><tr><td>Summer</td><td>1242 (23.8)</td></tr><tr><td>Winter</td><td>1340 (25.7)</td></tr><tr><td>Use of assisted conception (n (%))</td><td></td></tr><tr><td>No</td><td>5103 (97.8)</td></tr><tr><td>Yes</td><td>115 (2.2)</td></tr><tr><td>Gestational diabetes mellitus (n (%))</td><td></td></tr><tr><td>No</td><td>4771 (91.4)</td></tr><tr><td>Yes</td><td>447 (8.6)</td></tr></tbody></table>

Abbreviations: SD, standard deviation.

and 136 (14.1 %) had GDM in the group of women 35 years or older (Table SI). The number of women who suffered from GDM was 21 (6.1 %), 251 (7.4 %), 96 (10.6 %) and 65 (25.3 %) among the underweight, normal weight, overweight and obese classes, respectively (Table SII).

Over the first 4 weeks of gestation, the median PM₁₀ and NO₂ exposures were 17.8 µg/m³ (minimum 8.8 µg/m³; maximum 42.9 µg/m³) and 26.5 µg/m³ (minimum 6.0 µg/m³, maximum 61.3 µg/m³), respectively (Fig. 3). These values were similar regardless of the 4-week window that was considered. The correlations between the PM₁₀ and NO₂ indices ranged from 0.42 to 0.47.

In the entire population, the ORs associated with a 10 µg/m³ increase in PM₁₀ ranged from 0.89 (95 % CI: 0.66–1.20) to 1.35 (95 % CI: 1.08–1.68), and the highest OR value was associated with the [−05; −02] window (Fig. 4A). The ORs associated with a 10 µg/m³ increase in NO₂ ranged from 0.95 (95 % CI: 0.84–1.08) to 1.08 (95 % CI: 0.95–1.22) (Fig. 4B).

For women under 35 years of age, the ORs associated with a 10 µg/m³ increase in PM₁₀ ranged from 0.79 (95 % CI: 0.59–1.06) to 1.50 (95 % CI: 1.17–1.94), and the highest OR value was associated with the [−05; −02] window (Fig. 5A). In women aged 35 years or over, the ORs associated with a 10 µg/m³ increase in PM₁₀ ranged from 0.96 (95 % CI: 0.54–1.68) to 1.38 (95 % CI: 0.91–2.10) (Fig. 5A). Regardless of the age class considered, the ORs associated with a 10 µg/m³ increase in NO₂ ranged from 0.95 (95 % CI: 0.74–1.21) to 1.14 (95 % CI: 0.90–1.44) (Fig. 5B).

The ORs associated with PM₁₀ among the BMI classes are presented in Fig. 6A. In underweight women, the ORs ranged from 0.46 (95 % CI: 0.12–1.76) to 1.82 (95 % CI: 0.61–5.42), and the highest OR value was associated with the [−09; −06] window (Fig. 6A). In normal weight women, the ORs ranged from 0.86 (95 % CI: 0.57–1.28) to 1.48 (95 % CI: 1.12–1.95), and the highest OR value was associated with the [−05; −02] window (Fig. 6A). In overweight women, the ORs ranged from 0.75 (95 % CI: 0.45–1.24) to 2.03 (95 % CI: 1.14–3.64), and the highest OR value was associated with the [−13; −10] window (Fig. 6A). In obese women, the ORs ranged from 0.67 (95 % CI: 0.33–1.36) to 1.94 (95 % CI: 0.86–4.38), and the highest OR value was associated with the [−15; −12] window (Fig. 6A).

The ORs associated with NO₂ among the BMI classes are presented in Fig. 6B. In underweight women, the ORs ranged from 1.04 (95 % CI: 0.61–1.76) to 1.46 (95 % CI: 0.85–2.51), and the highest OR value was related to the [+08; +11] window (Fig. 6B). In normal weight women, the ORs ranged from 0.94 (95 % CI: 0.79–1.11) to 1.13 (95 % CI: 0.96–1.32) (Fig. 6B); in overweight women, the ORs ranged from 1.03 (95 % CI: 0.79–1.34) to 1.28 (95 % CI: 0.96–1.69) (Fig. 6B); and in obese women, the ORs ranged from 0.72 (95 % CI: 0.50–1.03) to 1.04 (95 % CI: 0.73–1.49) (Fig. 6B).

Sensitivity analyses with 5-week windows and 6-week windows led to comparable results, but the OR values were closer to one value, especially for the 6-week critical windows (Fig. S2A-B, S3A-B, S4A, S4B, S5A, S5B, S6A, S6B, S7A and S7B).

## 4. Discussion

### 4.1. Main findings

To our knowledge, this is the first study to explore the critical time windows of exposure to air pollution that are more likely associated with GDM before and during early pregnancy across age and BMI classes for a European-type population. Odds ratios of up to 2.03 were observed in the pregestational or gestational period when PM₁₀ exposure was considered. The highest OR was associated with the [−05; −02] 4-week window in women under 35 years of age. The highest ORs were associated with the [−09; −06], [−13; −10] and [−15; −12] 4-week windows in underweight, overweight, and obese women, respectively.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Distribution of the studied pollutants, considering a 50 m radius buffer, during the 4-week window starting on the first day of the last menstrual period (LMP) date and ending 4 weeks later, which corresponds to the 4-week window [+01; +04].</figcaption></figure>

## 5. Discussion of methodology

The study population included women from a public university hospital. The characteristics of our sample were, however, consistent with those observed in the 2010 French Perinatal Survey [50] (in which the women's characteristics were similar to those in the French civil registry over a full year), especially for maternal age, BMI, prepregnancy diabetes, GDM and sociological characteristics such as the percentage of single women and the percentage of employed women. The two-stage emission and diffusion modelling used may have generated measurement errors, but when the mismeasured variable is a function of the true value plus error, the association is expected to be biased towards the null [51]. On the basis of medical records, our data may contain errors in outcomes; in particular, few GDM diagnoses should have been missed, but false-positive declarations of GDM seem very unlikely. This situation could result in negligible bias in the ratio measure [51]. The women were living in a middle-sized city, similar to the living conditions of 54 % of the European population [52]. Environmental exposures were directly assessed from the mothers' geocoded addresses via an entirely reproducible process, and special attention was given to identifying the actual home building of women at the time of delivery instead of the last address recorded in the hospital information system. Wilson et al. demonstrated that an approach based on three trimester average exposures (TAEs) could lead to the identification of biased and incorrect critical windows [53] and that this error could be eliminated when a distributed lag model (DLM) is employed. These authors recommended using a DLM approach when air pollution exposures are quantified at a fine temporal resolution (e.g., daily or weekly) [53]. Our time windows were longer (4 weeks) and overlapped to screen for the most likely critical window. According to the simulation study of Wilson et al., no

signal would be observed in the absence of an exposure effect, regardless of the model used. We cannot exclude that the critical windows suspected with the approach we used were partially incorrect, but it seems unlikely that our approach could have induced a complete false-positive signal. The association appeared to become increasingly attenuated as the window size increased; if confirmed, this could indicate that 4-week windows could be more relevant than 5- or 6-week windows for defining critical windows.

A recent meta-analysis revealed no associations between exposure to PM₁₀ and NO₂ and GDM [9–11] or T2DM incidence [54–56]; only one study reported an association between T2DM incidence and exposure to PM₁₀ [54]. In these studies, high heterogeneity was identified, and the sources of heterogeneity were unclear. In our study, we did not observe a clear signal for NO₂, whereas our results favour an association with PM₁₀ exposure over several critical windows. It is possible that the observed heterogeneity in the meta-analysis regarding PM₁₀ exposure could be partly explained by our results. If the critical windows differ according to age or BMI status, the comparison of results from populations that differ in terms of age and BMI composition may induce heterogeneity in the observed effect or even cancel the effect of a real critical exposure window.

This difference between NO₂ and PM₁₀ was not expected. However, the exposure levels for both air pollutants could be considered low. The difference in our results between PM₁₀ and NO₂ could be explained by the fact that the effects of PM₁₀ exposure could appear at a much lower level than the effects of NO₂ exposure, especially when the exposure levels are low.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Associations between gestational diabetes mellitus and PM₁₀ (A) and NO₂ (B) exposure in different 4-week windows in the entire population. Adjusted odds ratio estimates (95 % CIs) were calculated for an increase of 10 µg/m³ in air pollution exposure. The models were adjusted for prepregnancy body mass index and parity. The y-axis starts with "[-28; -25]", indicating the 4-week window starting 28 weeks before the first day of the last menstrual period and lasting 4 weeks, and ends with "[+20; +23]", indicating the 4-week window starting at the 20th week of gestation and lasting 4 weeks. Abbreviations: OR, odds ratio; CI, confidence interval.</figcaption></figure>

## 5.1. Potential mechanisms and pathways

The specific biological mechanisms whereby air pollution (especially PM or ozone) affects GDM risk remain to be elucidated [26,29]. A possible mechanism is that inhaled particles, via pulmonary inflammation, can induce 1. oxidative stress [26], which can lead to 2. insulin resistance [57,58], potentially via TNF or IL6 [59]; 3. the inhibition of circulating insulin [60]; 4. blood glucose increases and the inhibition of pancreatic β-cell secretion of insulin [26,61,62]; and 5. a potential reduction in hepatic glycogen storage resulting in glucose intolerance [63].

Mechanistic studies of GDM have revealed at least three separate underlying causes of β-cell dysfunction: autoimmune (generally <10 % of all women with GDM), genetic (approximately 1–5% of cases), and

metabolic causes via obesity and chronic insulin resistance [64]. The latter cause, which accounts for the majority of GDM cases, has led many clinicians to consider GDM as a form of evolving type 2 diabetes mellitus (T2DM) [64]. The pathophysiology of GDM is an evolving process that can begin long before pregnancy and continue long after pregnancy, and a meta-analysis published in 2018 indicated an association between air pollution and insulin resistance [65].

Our results suggest the existence of a modulation of the potential critical windows for PM₁₀ exposure across maternal age. A potential bias related to differential selection due to the exclusion of preexisting diabetes or a confounding effect due to BMI or parity status appears to be very unlikely. For age, Niu et al. also reported a variable effect and critical window depending on the pollutant and age category [26]. Differential susceptibility across maternal age could be explained by
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Associations between gestational diabetes mellitus and PM₁₀ (A) and NO₂ (B) exposure in different 4-week windows in women under 35 years of age and women 35 years or older. Adjusted odds ratio estimates (95 % CIs) were calculated for an increase of 10 µg/m³ in air pollution exposure. The models were adjusted for prepregnancy body mass index and parity. The y-axis starts with "[-28; -25]", indicating the 4-week window starting 28 weeks before the first day of the last menstrual period and lasting 4 weeks, and ends with "[+20; +23]", indicating the 4-week window starting at the 20th week of gestation and lasting 4 weeks. Abbreviations: OR, odds ratio; CI, confidence interval.</figcaption></figure>

differences in lifestyle (activity level, type of activities, time spent outdoors, etc.) or differences in geographical distribution among cities [66] combined with geographical heterogeneity in PM₁₀ composition [67]. This combination may have led women under 35 years of age to be exposed to a PM₁₀ composition that may be more harmful to health [68–71]. Among women under 35 years of age who develop diabetes, autoimmune and genetic mechanisms are likely to be more prevalent than they are among women 35 years or older [64], which could lead to a difference in the pathophysiology of diabetes between these two groups.

For women with a prepregnancy BMI outside the 18.5–25 range, our results support the existence of critical exposure windows up to 6 months before conception. This finding is consistent with previous findings [32,36] and the fact that this disease is a woman’s disease and not a pregnancy-related disease [72].

Our results suggest that the position of the potential critical time windows could vary among different BMI classes. The pathophysiology underlying the development of GDM may vary slightly across BMI classes. In obese women, insulin resistance may play a preponderant role, whereas in women with a normal weight or overweight, β-cell dysfunction may play a preponderant role [73,74]. Our results are also in agreement with the observation that women with higher BMIs develop GDM earlier in pregnancy than women with lower BMIs do [74].

In women under 35 years of age and underweight women, a signal in favour of a relationship between exposure before conception and the onset of GDM was observed [75,76]. These two groups are generally the least affected by GDM. We cannot exclude the possibility that maternal age or BMI could be such strong risk factors for GDM that the women

most at risk for GDM would develop the disease regardless of other factors, whereas women less at risk are more readily influenced by environmental exposures such as air pollution, or that this influence is more easily identifiable [77].

## 5.2. Limitations

GDM is now defined as “hyperglycaemia with blood glucose values above normal but below those diagnostic of diabetes” [78]. Our definition of GDM (which corresponded to the definition used at the time of our study) did not take thresholds into account and included unrecognized preexisting diabetes and diabetes occurring during pregnancy. However, ICD-11 coding still does not distinguish between diabetes and gestational diabetes (and uses the term “diabetes mellitus arising in pregnancy”) [79], so it would not be possible to improve on our strategy with electronic medical records today. Potential confounders such as diet and physical activity levels could not be collected, and additional sensitivity analyses adjusting for these factors could not be conducted.

The assessment of air pollution exposure was based on monthly values, and it did not seem appropriate to define critical exposure windows shorter than a 4-week window given the monthly precision of our exposure values (if we had taken shorter exposure windows, some successive windows would have artificially presented similar values); this could have induced a loss of precision in the exposure assessment. In this exploratory study, we built overlapping windows that allowed scanning over the whole period without predefined windows. However, this process produced an increased number of windows and their related odds ratios. This strategy did not allow us to test statistical significance. Our study remains exploratory, and we thus decided not to apply
<figure><img src="image_9.png" /><figcaption>**Fig. 6A.** Associations between gestational diabetes mellitus and PM₁₀ exposure in different 4-week windows in underweight, normal weight, overweight and obese women. Adjusted odds ratio estimates (95 % CIs) were calculated for an increase of 10 µg/m³ in PM₁₀. The models were adjusted for maternal age and parity. The y-axis starts with "[-28; -25]", indicating the 4-week window starting 28 weeks before the first day of the last menstrual period and lasting 4 weeks, and ends with "[+20; +23]", indicating the 4-week window starting at the 20th week of gestation and lasting 4 weeks. Abbreviations: OR, odds ratio; CI, confidence interval.</figcaption></figure>

correction for multiple testing. We specifically geocoded the home address declared at delivery via the address directory of the official French database. However, the data did not allow the identification of residential mobility during the preconception or pregnancy period, which could have induced errors in exposure quantification. These errors should primarily be nondifferential. A link between residential mobility and both the exposure level and the occurrence of GDM indeed appears unlikely. The time, location, and duration of the women's activity patterns along their daily trajectory were not considered in this study, and we cannot exclude the possibility that a measurement error could have occurred. A study of pregnant women living in Paris revealed that ignoring daily mobility, particularly modes of commuting, might lead to the differential misclassification of exposure according to social characteristics [80]. Compared with the situation in Paris, daily commuting time (equal to 45 min on average in Paris) and exposure heterogeneity among pregnant women would certainly be much lower at our study site, which could considerably reduce the risk of exposure misclassification.

### 5.3. Perspectives

Larger prospective studies involving populations exposed to greater air pollution and with comprehensive collection of potential confounders such as diet and physical activity would enable these results to be confirmed. Exposure data collected on a finer time scale (daily or weekly) would enable the DLM approach to be used.

If our results were to be confirmed, it would be necessary to improve the recommendations made to professionals in charge of monitoring pregnant women, to promote the adaptation of monitoring and referring pregnant women on the basis of identified risk situations (women who are overweight and obese but also thin) and to adapt prevention

messages for the public during pollution peaks.

## 6. Conclusions

Our results suggest that the critical windows of exposure to air pollution associated with the development of GDM are modulated by maternal age and preconception body mass index. These windows could be located both before gestation and during the early gestational period. These preliminary results need to be confirmed by larger prospective studies of populations exposed to more significant air pollution. If confirmed, these results may influence prevention and health promotion initiatives for pregnant women living in urban environments. Address this potential risk factor may represent an opportunity to break the transgenerational obesity and diabetes cycle.

### Funding information

This work was supported by the Agence de l'Environnement et de la Maitrise de l'Energie (ADEME) [1217C0065] as part of the National Environmental Research and Occupational Health Program 2012 from the French Agency for Food, Environmental and Occupational Health and Safety (ANSES) and the Fondation de France (00069262). The funding sources had no involvement in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.

### Data availability statement

Editors and reviewers of our manuscript who need access to source data beyond that which is contained in the manuscript for the purpose of evaluating our study may submit a request for data access to the
<figure><img src="image_10.png" /><figcaption>**Fig. 6B.** Associations between gestational diabetes mellitus and NO₂ exposure in different 4-week windows in underweight, normal weight, overweight and obese women. Adjusted odds ratio estimates (95 % CIs) were calculated for an increase of 10 µg/m³ in NO₂. The models were adjusted for maternal age and parity. The y-axis starts with "[-28; -25]", indicating the 4-week window starting 28 weeks before the first day of the last menstrual period and lasting 4 weeks, and ends with "[+20; +23]", indicating the 4-week window starting at the 20th week of gestation and lasting 4 weeks. Abbreviations: OR, odds ratio; CI, confidence interval.</figcaption></figure>

corresponding author.

## Ethics approval

This study was approved by the French National Advisory Committee for the Treatment of Information in Health Research (CCTIRS) (registration number 15.292, 2015 April 9th) and by the French data protection authority (CNIL) (registration number DR-2015-736, 2015 December 24th). An information letter was sent to each woman included in the study. Women who were opposed to the processing of their data were excluded from the study.

## CRediT authorship contribution statement

**M. Pascard:** Writing – original draft, Investigation, Formal analysis, Data curation. **F. Mauny:** Writing – review & editing, Methodology, Funding acquisition, Conceptualization. **A.-S. Mariet:** Writing – review & editing, Methodology, Funding acquisition. **B. Martin:** Data curation. **H. Gauthier-Manuel:** Methodology. **C. Quantin:** Writing – review & editing, Funding acquisition. **P. Sagot:** Methodology, Investigation. **A.-L. Parmentier:** Conceptualization. **M. Boilleaut:** Methodology. **S. Pujol:** Writing – review & editing, Methodology, Investigation, Funding acquisition, Conceptualization. **N. Bernard:** Writing – review & editing, Methodology, Funding acquisition, Conceptualization.

## Declaration of competing interest

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

## Data availability

Data will be made available on request.

## Acknowledgements

The authors acknowledge the local association for air quality monitoring: Atmo Bourgogne Franche-Comté; O. Allemandet, A. Bourtembourg, A. Dussaucy, A. Eckmann, K. Diallo, T. Pierre, M. Puyraveau and Gérard Thiriez from the Centre Hospitalier Universitaire (CHU) of Besançon; J. Besson and S. Belhadj from the CHU of Dijon; Marie Barba-Vasseur from the Bourgogne Franche-Comté's Regional Health Agencies; Didier Riethmuller from the CHU Grenoble. Computations were performed on the supercomputer facilities at the Mésocentre de Calcul de Franche-Comté.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.buildenv.2024.112098.

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