Next Article in Journal
Data from the PASSI d’Argento Surveillance System on Difficulties Met by Older Adults in Accessing Health Services in Italy as Major Risk Factor to Health Outcomes
Next Article in Special Issue
Long-Acting Reversible Contraceptive Use by Rural–Urban Residence among Women in Nigeria, 2016–2018
Previous Article in Journal
Relationships between House Characteristics and Exposures to Metal(loid)s and Synthetic Organic Contaminants Evaluated Using Settled Indoor Dust
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 16
10.3390/ijerph191610339
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS)

by
Nirmin F. Juber
1,*,
Abdishakur Abdulle
1,
Abdulla AlJunaibi
2,
Abdulla AlNaeemi
3,
Amar Ahmad
1,
Andrea Leinberger-Jabari
1,
Ayesha S. Al Dhaheri
4,
Eiman AlZaabi
5,
Fatima Mezhal
1,
Fatma Al-Maskari
6,7,
Fatme AlAnouti
8,
Habiba Alsafar
9,10,11,
Juma Alkaabi
12,
Laila Abdel Wareth
13,
Mai Aljaber
14,
Marina Kazim
15,
Michael Weitzman
16,
Mohammad Al-Houqani
17,
Mohammed Hag Ali
18,
Naima Oumeziane
15,
Omar El-Shahawy
19,
Scott Sherman
19,
Sharifa AlBlooshi
20,
Syed M. Shah
6,
Tom Loney
21,
Wael Almahmeed
22,
Youssef Idaghdour
1 and
Raghib Ali
1,23,†add Show full author list remove Hide full author list
1
Public Health Research Center, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
2
Department of Pediatrics, Zayed Military Hospital, Abu Dhabi P.O. Box 72763, United Arab Emirates
3
Department of Cardiology, Zayed Military Hospital, Abu Dhabi P.O. Box 72763, United Arab Emirates
4
Department of Nutrition and Health, College of Medicine and Health Sciences, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates
5
Department of Pathology, Sheikh Shakhbout Medical City, Abu Dhabi P.O. Box 11001, United Arab Emirates
6
Institute of Public Health, College of Medicine and Health Sciences, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates
7
Zayed Center for Health Sciences, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates
8
College of Natural and Health Sciences, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates
9
Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
10
Department of Genetics and Molecular Biology, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
11
Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
12
Department of Internal Medicine, College of Medicine and Health Sciences, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates
13
The National Reference Laboratory, Abu Dhabi P.O. Box 92323, United Arab Emirates
14
Healthpoint Hospital, Abu Dhabi P.O. Box 112308, United Arab Emirates
15
Abu Dhabi Blood Bank Services, SEHA, Abu Dhabi P.O. Box 109090, United Arab Emirates
16
Department of Environmental Medicine, New York University of Medicine, New York, NY 10016, USA
17
Department of Medicine, College of Medicine and Health Sciences, UAE University, Al-Ain P.O. Box 15551, United Arab Emirates
18
Faculty of Health Sciences, Higher Colleges of Technology, Abu Dhabi P.O. Box 25026, United Arab Emirates
19
Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
20
College of Natural and Health Sciences, Zayed University, Dubai P.O. Box 19282, United Arab Emirates
21
College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai P.O. Box 505055, United Arab Emirates
22
Heart and Vascular Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi P.O. Box 112412, United Arab Emirates
23
MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 1TN, UK
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
Senior author.
Int. J. Environ. Res. Public Health 2022, 19(16), 10339; https://doi.org/10.3390/ijerph191610339
Submission received: 27 June 2022 / Revised: 10 August 2022 / Accepted: 17 August 2022 / Published: 19 August 2022
(This article belongs to the Special Issue Advances in Perinatal Epidemiology and Maternal/Child Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Limited studies have focused on maternal early-life risk factors and the later development of gestational diabetes mellitus (GDM). We aimed to estimate the GDM prevalence and examine the associations of maternal early-life risk factors, namely: maternal birthweight, parental smoking at birth, childhood urbanicity, ever-breastfed, parental education attainment, parental history of diabetes, childhood overall health, childhood body size, and childhood height, with later GDM. This was a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) baseline data (February 2016 to April 2022) on 702 ever-married women aged 18 to 67 years. We fitted a Poisson regression to estimate the risk ratio (RR) for later GDM and its 95% confidence interval (CI). The GDM prevalence was 5.1%. In the fully adjusted model, females with low birthweight were four times more likely (RR 4.04, 95% CI 1.36–12.0) and females with a parental history of diabetes were nearly three times more likely (RR 2.86, 95% CI 1.10–7.43) to report later GDM. In conclusion, maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group.

1. Introduction

Gestational diabetes mellitus (GDM) is a common and potentially serious pregnancy complication if left untreated that affects one in six pregnancies worldwide [1]. The American Diabetes Association (ADA) defines GDM as glucose intolerance resulting in hyperglycemia first diagnosed in the second or third trimester of pregnancy and it does not exclude the possibility of unrecognized or undiagnosed glucose intolerance in the pre-pregnancy period [2]. Data indicates that the burden of GDM has increased by 10–100% in various populations between 1987 to 2007, increasing the burden of metabolic complications in both mothers and offspring [3]. The median estimates of GDM range from 6 to 13% worldwide with the highest prevalence observed in the Middle East and North Africa (MENA) region [4]. In the MENA region, the GDM prevalence in the United Arab Emirates (UAE) was observed to be the third-highest (13.4%) [5].
GDM is a multifactorial disease in which genetic, epigenetic, and environmental factors contribute to its development [1]. GDM is also considered to be an acute condition with short-term effects for the mother and the offspring since glucose intolerance usually reverts back to normal levels after the pregnancy [6]. However, women with GDM history have an increased risk for diabetes occurrence, compared to those without GDM history [1]. Major risk factors for GDM include being overweight/obese, gaining excessive weight during pregnancy, consuming a western diet, belonging to certain ethnic groups (i.e., Middle Easterners), having advanced maternal age, having low/high maternal birthweight, and having a family history with diabetes [1,7]. Maternal metabolic disturbance, such as GDM, has been known to lead to subsequent fetal metabolic programming, hence, increasing the risk of cardiometabolic disorders in the offspring, including future GDM (transgenerational cycle) [8].
Previous studies have focused on the maternal later-life risk factors to tackle short-term pregnancy and delivery complications due to GDM; however, a shift to focus on earlier prevention is highly needed [6]. There are limited studies that have focused on maternal early-life risk factors and later GDM. To date, previous studies on maternal early-life risk factors and later GDM only focused on maternal birthweight [9,10], parental smoking [11], and parental history of diabetes [7]. To our knowledge, only one recent study has focused on childhood body height and later GDM [9], and two studies on childhood body mass index (BMI) and later GDM [10,12]. In addition, other domains of maternal early-life risk factors such as ever-breastfed history, overall health up to 10 years old, and urbanicity at birth have not been explored.
Our analysis of data from the UAE Healthy Future Study (UAEHFS) aimed to be the first population-based study characterizing the associations between several maternal early-life factors and later GDM in a single study. Our study aimed to estimate the GDM prevalence and to comprehensively examine the associations of several maternal early-life risk factors and later GDM, including commonly studied (maternal birthweight, parental smoking, and parental history of diabetes), less studied (parental education attainment, childhood body size, and height), and unstudied maternal early-life risk factors (urbanicity at birth, ever-breastfed, and childhood overall health), with later GDM.

2. Materials and Methods

2.1. Study Design, Participants, and Setting

This is a retrospective cross-sectional study using the UAE Healthy Future Study (UAEHFS) data collected from February 2016 to April 2022. We studied 702 ever-married women aged 18–67 years who had complete information on diabetes diagnosis history during their lifetime (Figure 1). The study design, questionnaire, and methodologies of the UAEHFS are described elsewhere [13]. In brief, the UAEHFS is a longitudinal population-based cohort study that aims to explore risk factors for non-communicable diseases (NCDs) among Emirati nationals aged 18 years or above. Emirati adults were asked to fill out the questionnaire and had some physical measurements taken at multiple centers across major cities in the UAE: Abu Dhabi, Al-Ain, Dubai, and Ras Al Khaimah. Due to the COVID-19 pandemic, study recruitment shifted to online enrollment starting from April 2020, and an online questionnaire was introduced to the new participants. Physical measurements, such as BMI, were taken in the participating centers for new participants that filled and returned the online questionnaire once in-person recruitment resumed in September 2020.

2.2. Measurements

2.2.1. Outcome Measure

We analyzed self-reported physician diagnoses for diabetes based on the questionnaire response to: “Has a doctor ever told you that you have diabetes?” (yes, no), and GDM as an outcome variable was extracted from the response to the following questionnaire item: “Did you only have diabetes during pregnancy?” (yes, no). GDM in this study was defined as females diagnosed with diabetes during pregnancy only and we excluded those with non-GDM diabetes [14].

2.2.2. Maternal Early-Life Risk Factors

We considered nine variables as predictors of interest: maternal birthweight, parental smoking at birth, urbanicity at birth, ever-breastfed history, parental education attainment, parental history of diabetes, overall health up to 10 years old, as well as body size and height at 10 years old. Maternal birthweight was determined based on the response to questionnaire item: “What was your birth weight (in kg)?”, and we categorized maternal birthweight into low maternal birthweight (<2.5 kg) and normal maternal birthweight (≥2.5 kg) [15]. Less than 1% of our study participants reported a maternal birthweight above 4 kg (high maternal birthweight), therefore we labeled the 2.5 kg and above as normal maternal birthweight category. Parental smoking (at birth) was constructed based on the questionnaire response to “Did your mother or father smoke regularly around the time when you were born?” (yes, no). Urbanicity at birth was determined based on the questionnaire response to: “Where do you and your family live around the time of your birth?”. We categorized city as an urban area and non-cities (village, desert, island, and others) as rural or other non-urban. Ever-breastfed history was determined based on the questionnaire response to: “Were you breastfed when you were a baby?” (yes, no). Parental education attainment was constructed based on the questionnaire response to: “What level of education did your father or mother complete?”, and we categorized parental education, based on the maximum level of either father or mother education reported, into 6 years or below, >6–12 years, and >12 years of schooling. We also studied maternal early-life factors during childhood which included overall health up to 10 years old, as well as body size and height at 10 years old. Overall health up 10 years old was determined based on the questionnaire response to: “In general, how was your health in childhood (less than 10 years old)?” (poor or fair, good or excellent). Body size and height at 10 years old were determined based on the respective questionnaire responses to: “When you were 10 years old, compared to average would you describe yourself as: about average, thinner, or plumper?”, and “When you were 10 years old, compared to average would you describe yourself as: about average, shorter, or taller?”. Lastly, parental history of diabetes was constructed based on the questionnaire response to” “Did your mother or father ever suffer from diabetes?” (yes, no). Due to a significant number of uncertain values (missing or prefer not to answer (PNA) or do not know (DN)), we used missing indicator by creating “Missing/PNA/DN” variable for each variable that contains uncertain values.

2.2.3. Sociodemographic Factors, Health Factors, and BMI

Age was constructed based on the questionnaire response to “What is your date of birth”. Urbanicity (current) was determined based on the questionnaire response to: “Where do you and your family live now?”. We categorized city as an urban area and non-cities (village, dessert, island, and others) as rural or other non-urban. Marital status was determined based on the questionnaire response to: “What is your marital status?” (married, divorced or separated, widow or widower). Education attainment was determined based on the response to the questionnaire item: “What is the highest level of education that you have completed?”. We then categorized education levels into three categories (6 years or below, >6 to 12 years, and >12 years). Health factors such as current overall health and history of poly-cystic ovarian syndrome were determined based on the questionnaire responses to: “In general, how would you rate your overall health now?” (poor or fair, good or excellent) and “Has a doctor ever told you that you have polycystic ovarian syndrome/disease” (yes, no). Lastly, BMI was calculated using the Tanita MC 780 (Tanita Inc., Tokyo, Japan) by nurses at the recruitment centers [12]. We also categorized BMI into three categories: normal or below (<25 kg/m2), overweight (25–<30 kg/m2), and obese (≥30 kg/m2) [16].

2.3. Statistical Analysis

Current and early-life characteristics of the study participants were evaluated using frequencies with percentage (n, %) for categorical variables and means with standard deviations (means ± SD) for continuous variables (Table 1 and Table 2). We used chi-squared tests for categorical variables and t-tests for continuous variables to compare distributions of study participants based on their GDM status (without vs. with GDM). For each maternal early-life risk factor included in this study, we fitted a Poisson regression model with robust variance to estimate the risk ratio (RR) and its 95% confidence interval (95% CI) to assess its association with later GDM [17]. We examined the RR and its 95% CI under three models: unadjusted (crude), age-and-parental history of diabetes (basic model), and full adjustment (fully adjusted) models. We adjusted for age-and parental history of diabetes since these factors have been known to strongly confound the associations involving GDM as an outcome [7]. We further added relevant potential confounding factors in the full adjustment model for each maternal early-life risk factor to better estimate the respective association between maternal early-life risk factor and later GDM (i.e., urbanicity at birth was adjusted for parental education, parental smoking, and birth weight). The complete list of relevant confounding factors is shown in Table 3. The missing indicator method was used to handle uncertain values from missing, prefer not to answer (PNA), and do not know (DN) responses. In addition, we also performed a sensitivity analysis to include only those with uncertain values of diabetes history to examine the pattern of missingness (Table 1). Analyses were carried out using STATA 17.0 (StataCorp, Brazos, TX, USA). p values < 0.05 were considered statistically significant.

2.4. Ethical Approval

The study and its procedures have been reviewed and approved by the Institutional Review Board at New York University Abu Dhabi, Dubai Health Authority, Ministry of Health and Prevention in the UAE, and Health Research and Technology Committee, reference number DOH/HQD/2020/516. Written consent was obtained from participants at the centers or by filling out an online consent form before data collection started.

3. Results

Table 1 summarizes the current characteristics of the study participants at baseline based on GDM status. Out of 702 ever-married females in our study, 36 females reported ever being diagnosed with diabetes only during pregnancy (GDM prevalence = 5.1%). Compared to those without GDM, females with GDM were older (36.5 ± 8.5 vs. 33.0 ± 7.5 years), had higher BMI (31.2 ± 6.0 vs. 27.5 ± 5.8 kg/m2), a greater proportion were classified as obese (47.2% vs. 24.0), had lower levels of education (50.0% vs. 61.7% > 12 years of schooling), and a higher proportion being in poor or fair health (41.7% vs. 22.8%).
Table 2 shows the early-life characteristics of the study participants based on GDM status. Compared to those without GDM, a higher proportion of females with GDM reported having low maternal birthweight of <2.5 kg (61.1% vs. 54.2%) and having a parental history of diabetes (86.1% vs. 64.3%). Our sensitivity analysis (Table 1 and Table 2) revealed non-systematic differences in missingness among those excluded due to missing values of diabetes status.
Table 3 presents the risk ratios (RRs) of the associations between maternal early-life risk factors and later GDM. Under three models, compared to those with normal maternal birthweight (≥2.5 kg), those with low maternal birthweight (<2.5 kg) were four times more likely to report GDM (RR = 3.67, 95% CI: 1.26–10.7 in the crude model; RR = 4.13, 95% CI: 1.42–12.1 in the basic model; RR = 4.04, 95% CI: 1.36–12.0 in fully adjusted model). Similarly, those with parental history of diabetes were three times more likely to report GDM in all models (RR = 3.28, 95% CI: 1.29–8.34 in the crude model, and RR = 2.86, 95% CI: 1.10–7.43 in basic or fully adjusted model), compared to those without a parental history of diabetes. Other domains of maternal early-life risk factors were shown to be not statistically significant in all models, however, parental smoking at birth as well as body size and height at 10 years old were shown to have a strong magnitude of association with later GDM. In the fully adjusted model, compared to those without parental smoking at birth, females with parental smoking at birth had a 24% increased risk of later GDM. Females with plumper body size and shorter height at 10 years old were associated with 38% and 37% increased risk of later GDM, compared to those with average body size and height at 10 years old, respectively. Interestingly, those who reported taller height at 10 years old were associated with a 56% decreased risk of later GDM, compared to females that reported average height at 10 years old.
Figure 2 illustrates the prevalence of GDM by each maternal early-life factor in our study. GDM prevalence for each variables of maternal early-life risk factors as follows. Maternal birthweight: <2.5 kg (8.6%), ≥2.5 kg (2.3%), and missing/DN/PNA (5.7%). Parental smoking at birth: no (4.7%), yes (6.1), and missing/DN/PNA (6.7%). Parental history of diabetes: no (2.1%), and yes (6.8%). Parental education: ≤12 years (7.3%), >12 years (7.3%), and missing/DN/PNA (3.6%). Body size at 10 years old: thinner (5.2%), about average (4.7%), plumper (6.1%), and missing/DN/PNA (5.4%). Body height at 10 years old: shorter (6.8%), about average (5.1%), taller (2.2%), and missing/DN/PNA (8.5%). Urbanicity at birth: rural or other non-urban (4.2%), urban (4.9%), and missing/DN/PNA (6.4%). Ever-breastfed: no (5.6%), yes (5.0%), and missing/DN/PNA (5.4%). Overall health up to 10 years old: poor or fair (7.8%), and good or excellent (4.9%).

4. Discussion

In this retrospective cross-sectional study, we found that 5.1% of ever-married women reported ever being diagnosed with diabetes only during pregnancy (GDM). The prevalence of GDM by each maternal early-life factor in our study ranged from 2.1% to 8.6% (Figure 2). The GDM prevalence in our study was shown to be lower than national estimates across the MENA region and in the UAE, which revealed approximately 13% of GDM prevalence [4,5]. Previous studies have found that Asian women were among the high-risk group for developing GDM [31,32]. Our study included females aged 18 to 67 years, and a previous study has shown that GDM prevalence in women aged ≥ 30 years was 2.26 times higher compared to women aged 15–29 years [5]. Advanced maternal age is known to be a significant risk factor for GDM, as the risk of developing GDM was 7 to 10 times higher in pregnant women aged 24 years and above compared to their younger counterparts [33]. Furthermore, outcome ascertainment utilized self-reported and may have also contributed to the lower prevalence estimate in our study. For comparison, two Omani studies using self-reported and medical records data revealed the GDM prevalence of 3.3% and 7.6%, respectively [34,35].
We found significant associations between low maternal birthweight (<2.5 kg) and later GDM in the basic model and fully adjusted model, compared to females with normal maternal birthweight history (≥2.5 kg). Our results are consistent with previous studies that have identified the association between low maternal birthweight and later GDM [1,36]. Previous studies also found “U-shaped” associations between maternal birthweight and later GDM, in which high maternal birthweight (>4 kg) as well as low maternal birthweight (<2.0 kg or <2.5 kg,) were shown to be significantly associated with GDM, compared to normal maternal birthweight [9]. In our study, less than one percent reported maternal birthweight above 4 kg, therefore we could not analyze the association between high maternal birthweight and later GDM, and we included those with >4 kg of maternal birthweight in the normal maternal birthweight category (≥2.5 kg). Another study that was able to evaluate low and high maternal birthweight only found a significant association between low birthweight and later GDM [37]. Low maternal birthweight and GDM are linked due to insulin resistance, as low maternal birthweight reflects undernutrition in the womb and therefore alters the expression of genes, hence leading to future metabolic consequences including GDM [1].
Our study also found significant associations between parental history of diabetes and later GDM in all models. Our results are in general agreement with previous studies that have revealed similar associations with GDM [7,38,39,40]. One meta-analysis study pooled 33 studies on family history with diabetes and GDM showed an overall odds ratio of 3.46 for the association between family history with diabetes and later GDM [40], similar to our study. To date, the level of evidence on the association between parental/family history with diabetes and later GDM is shown to be highly suggestive [38], and the link between parental/family history with diabetes and later GDM lies in genetic and lifestyle factors with metabolic programming due to family/parental history of diabetes proposed as one mechanism [8,41]. Furthermore, a previous study has shown an intergenerational cycle of GDM in which females with GDM mothers are more likely to experience GDM in their pregnancies [42]. Family history of diabetes is an important risk factor for later GDM, and a previous study revealed that 40% of women with GDM were more likely to have had a first-degree relative with diabetes [43].
We found no statistically significant associations between parental smoking at birth and later GDM in all models. A previous study has shown that paternal smoking during pregnancy was not associated with the risk of GDM in the daughter and maternal heavy smoking was shown to be significantly associated with later GDM [10]. A previous study from the UAE Health Future (UAEHFS) Pilot Study that used biochemical verification for smoking status revealed that 42% of males and 9% of females had recent tobacco use [44]. We believe that the low prevalence of maternal smoking in our study population contributed to non-significant associations between parental smoking at birth and later GDM.
We found non-significant associations between parental education and later GDM; however, the magnitude of associations between high parental education and later GDM was shown to have an 8–9% increase for GDM risk in the basic and fully adjusted models, compared to those with the lowest parental education level (6 years or below of schooling). In our study, parental education contained many missing values (>50%), therefore, a small sample size, especially with four categorizations of parental education that we used, led to low statistical power to detect differences. We have similar findings to a recent study that revealed a non-significant association between maternal education attainment and GDM risk [45]. Parental education attainment has been known to be a proxy for family background [46], but the role of parental education and GDM risk is not fully known.
Our study also examined other domains of early maternal life-risk factors that were less commonly studied, namely childhood body size and height at 10 years old. We did not find any significant association in the aforementioned domains of maternal early-life factors due to a low catchment of those with GDM in our study, hence low statistical power to detect differences. Regarding the magnitude of associations, we found those who reported having thinner or plumper body size at 10 years old to be positively associated with later GDM, compared to those with average body size. While two recent studies have confirmed a positive association between childhood or adolescent obesity and later GDM [10,12], the “U-shaped” relationship between childhood body size and later GDM in our study provided new information and is worthy of further investigation. We also found that compared to those with average body height at 10 years old, those who reported having a shorter height at 10 years old were positively associated with later GDM. Conversely, those reported having a taller height at 10 years old were negatively associated with later GDM. One recent study has confirmed the association between shorter childhood height and later GDM [10]; however, more studies are needed to confirm the association between taller childhood height and later GDM. Childhood body size and height are proxies for fetal growth and nutrition and are shown to be important determinants not just for childhood health but also for future health in adulthood [47], so more studies are needed to elucidate the exact mechanism of childhood body size and height with GDM risk.
In this study, we proposed novel maternal early-life risk factors namely: ever-breastfed history, overall health up to 10 years old, and urbanicity at birth. Despite non-significant relationships in all models for these maternal early-life risk factors, the magnitudes of associations from these domains were shown to be worth further investigation. We found that those with ever-breastfed history had a weak negative association with later GDM, compared to those with never-breastfed history. We proposed a new direction of the association between ever-breastfed and later GDM. A previous study found that mothers with GDM had a shorter breastfeeding duration compared to mothers without GDM, but breastfeeding was found to be beneficial for mothers with GDM as their lipid and glucose metabolic profiles improved [48]. We also found that those who reported having good or excellent health status up to 10 years old were negatively associated with later GDM, compared to those with poor or fair health. Overall health status has been known as one of the comorbidity indices commonly used in health research [49]. Lastly, we found that those born in urban areas had a weak positive association with later GDM, compared to those in rural or other non-urban regions. A previous study found an increased odds of GDM among women living in urban areas compared to those in rural areas [50], and proposed urban lifestyle factors (western diet, low physical activity, and biochemical risk factors) as possible mechanisms [51].

Strengths and Limitations

To our knowledge, this is the first comprehensive population-based study examining the associations between several maternal early-life factors and later GDM, including previously unstudied maternal early-life risk factors such as overall health status, body size, and height during childhood. We were able to control for confounding factors for each maternal early-life risk factor that is relevant as stated in the literature. We excluded never married females and were therefore able to address immortal-time bias, as they do not have the same chance to experience GDM based on cultural and religious norms in the UAE country setting [52]. Similarly, to separate the effect of non-pregnancy-related diabetes diagnosis, we excluded those with non-GDM diabetes, since the genetic risk factors and pathogenesis of these two diseases are known to differ [53]. Finally, we tried to maximize the representativeness of the sample by recruiting participants from multiple centers in four major cities in the UAE. Our study was subject to several limitations. One major limitation was self-reported GDM status, raising the concern of disease ascertainment accuracy. A previously validated study has shown self-reported GDM to be accurate with 94% of the self-reported GDM cases being confirmed by a physician [54]. In addition, maternal early-life factors in our study were assessed based on self-reporting, hence, we cannot rule out recall error. However, we adjusted for age in our basic and fully adjusted models to address the possible age effects of recall error. We used ever-married females as a proxy for ever-pregnant females for the GDM outcome in our study. A previous study in the UAE country setting in 2008 revealed that more than 95% of ever-married women of reproductive age (15–49 years of age) reported having one or more children [55]. Furthermore, we had a lot of uncertain or missing values for some predictors; however, our sensitivity analyses revealed non-systematic mechanisms in the missingness pattern. Lastly, our study may be prone to residual confounding factors due to the nature of the observational research.

5. Conclusions

Maternal birthweight and parental history of diabetes were significantly associated with later GDM. Close glucose monitoring during pregnancy among females with either a low birth weight and/or parental history of diabetes might help to prevent GDM among this high-risk group.

Author Contributions

Conceptualization, N.F.J., A.A. (Amar Ahmad) and R.A.; formal analysis, N.F.J.; data curation, N.F.J.; writing—original draft preparation, N.F.J., T.L. and O.E.-S.; writing—review and editing, A.A. (Abdishakur Abdulle), A.A. (Abdulla AlJunaibi), A.A. (Abdulla AlNaeemi), A.A. (Amar Ahmad), A.L.-J., A.S.A.D., E.A., F.M., F.A., F.A.-M., H.A., J.A., L.A.W., M.A., M.K., M.W., M.A.-H., M.H.A., N.O., O.E.-S., S.S., S.A., S.M.S., T.L., W.A., Y.I. and R.A.; funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript.

Funding

This publication is based upon works supported by Tamkeen under Research Institute Grant No. G1206.

Institutional Review Board Statement

The study and its procedures have been reviewed and approved by the Institutional Review Board at New York University Abu Dhabi, Dubai Health Authority, Ministry of Health and Prevention in the UAE, and Health Research and Technology Committee, reference number DOH/ HQD/2020/516.

Informed Consent Statement

Written consent was obtained from participants at the centers or by filling out an online consent form before data collection started.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the senior author on reasonable request.

Acknowledgments

The authors would like to thank all members of the Public Health Research Center at New York University Abu Dhabi and those who are actively involved in the UAE Healthy Future Study (UAEHFS). This study would not have been possible without their collective effort.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. “Abu Dhabi Blood Bank Services, SEHA” had no role in the design of the study; in the analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Plows, J.F.; Stanley, J.L.; Baker, P.N.; Reynolds, C.M.; Vickers, M.H. The pathophysiology of gestational diabetes mellitus. Int. J. Mol. Sci. 2018, 19, 3342. [Google Scholar] [CrossRef] [PubMed]
  2. American Diabetes Association. Standards of Medical Care in Diabetes. Diabetes Care 2011, 34 (Suppl. 1), S11–S61. [Google Scholar] [CrossRef] [PubMed]
  3. Ferrara, A. Increasing prevalence of gestational diabetes mellitus: A public health perspective. Diabetes Care 2007, 30, S141–S146. [Google Scholar] [CrossRef] [PubMed]
  4. Zhu, Y.; Zhang, C. Prevalence of gestational diabetes and risk of progression to type 2 diabetes: A global perspective. Curr. Diabetes Rep. 2016, 16, 7. [Google Scholar] [CrossRef]
  5. Al-Rifai, R.H.; Abdo, N.M.; Paulo, M.S.; Saha, S.; Ahmed, L.A. Prevalence of gestational diabetes mellitus in the middle east and North Africa, 2000–2019: A Systematic Review, Meta-Analysis, and Meta-Regression. Front. Endocrinol. 2021, 12, 1010. [Google Scholar] [CrossRef] [PubMed]
  6. Saravanan, P.; Magee, L.A.; Banerjee, A.; Coleman, M.A.; Von Dadelszen, P.; Denison, F.; Farmer, A.; Finer, S.; Fox-Rushby, J.; Holt, R.; et al. Gestational diabetes: Opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 2020, 8, 793–800. [Google Scholar] [CrossRef]
  7. Larrabure-Torrealva, G.T.; Martinez, S.; Luque-Fernandez, M.A.; Sanchez, S.E.; Mascaro, P.A.; Ingar, H.; Castillo, W.; Zumaeta, R.; Grande, M.; Motta, V.; et al. Prevalence and risk factors of gestational diabetes mellitus: Findings from a universal screening feasibility program in Lima, Peru. BMC Pregnancy Childbirth 2018, 18, 303. [Google Scholar] [CrossRef]
  8. McIntyre, H.D.; Catalano, P.; Zhang, C.; Desoye, G.; Mathiesen, E.R.; Damm, P. Gestational diabetes mellitus. Nat. Rev. Dis. Prim. 2019, 5, 47. [Google Scholar] [CrossRef]
  9. Innes, K.E.; Byers, T.E.; Marshall, J.A.; Barón, A.; Orleans, M.; Hamman, R.F. Association of a woman’s own birth weight with subsequent risk for gestational diabetes. JAMA 2002, 287, 2534–2541. [Google Scholar] [CrossRef]
  10. Pedersen, D.C.; Bjerregaard, L.G.; Rasmussen, K.M.; Nohr, E.A.; Baker, J.L. Risk of gestational diabetes mellitus in nulliparous women—Associations with early life body size and change in body mass index from childhood to adulthood. Diabetes Res. Clin. Pr. 2020, 171, 108564. [Google Scholar] [CrossRef]
  11. Bao, W.; Michels, K.B.; Tobias, D.K.; Li, S.; Chavarro, J.E.; Gaskins, A.J.; Vaag, A.A.; Hu, F.B.; Zhang, C. Parental smoking during pregnancy and the risk of gestational diabetes in the daughter. Int. J. Epidemiol. 2016, 45, 160–169. [Google Scholar] [CrossRef] [PubMed]
  12. Tsviban, A.; Frenkel, A.; Schvartz, N.; Tzur, D.; Klaitman, V.; Walfisch, A. The association between adolescent obesity and later gestational diabetes in military personnel: A retrospective cohort study. Diabetes Res. Clin. Pract. 2022, 189, 109883. [Google Scholar] [CrossRef] [PubMed]
  13. Abdulle, A.; Alnaeemi, A.; Aljunaibi, A.; Al Ali, A.; Al Saedi, K.; Al Zaabi, E.; Oumeziane, N.; Al Bastaki, M.; Al-Houqani, M.; Al Maskari, F.; et al. The UAE healthy future study: A pilot for a prospective cohort study of 20,000 United Arab Emirates nationals. BMC Public Health 2018, 18, 101. [Google Scholar] [CrossRef]
  14. Spaight, C.; Gross, J.; Horsch, A.; Puder, J.J. Gestational Diabetes Mellitus. Nov. Diabetes 2016, 31, 163–178. [Google Scholar] [CrossRef]
  15. Iguacel, I.; On behalf of the CALINA Study Group; Escartín, L.; Fernández-Alvira, J.M.; Iglesia, I.; Labayen, I.; Moreno, L.A.; Samper, M.P.; Rodríguez, G. Early life risk factors and their cumulative effects as predictors of overweight in Spanish children. Int. J. Public Health 2018, 63, 501–512. [Google Scholar] [CrossRef] [PubMed]
  16. World Health Organization Consultation on Obesity. Obesity: Preventing and Managing the Global Epidemic: Report of a WHO Consultation; World Health Organization: Geneva, Switzerland, 2000; Available online: https://apps.who.int/iris/handle/10665/42330 (accessed on 10 March 2022).
  17. Barros, A.J.D.; Hirakata, V.N.; Barros, A.J.D.; Hirakata, V.N. Alternatives for logistic regression in cross-sectional studies: An empirical comparison of models that directly estimate the prevalence ratio. BMC Med. Res. Methodol. 2003, 3, 21. [Google Scholar] [CrossRef] [PubMed]
  18. Di Napoli, A.; Di Lallo, D.; Pezzotti, P.; Forastiere, F.; Porta, D. Effects of parental smoking and level of education on initiation and duration of breastfeeding. Acta Paediatr. 2006, 95, 678–685. [Google Scholar] [CrossRef]
  19. Andriani, H.; Kuo, H.-W. Adverse effects of parental smoking during pregnancy in urban and rural areas. BMC Pregnancy Childbirth 2014, 14, 414. [Google Scholar] [CrossRef]
  20. Mohan, V.; Mathur, P.; Deepa, R.; Deepa, M.; Shukla, D.; Menon, G.; Anand, K.; Desai, N.G.; Joshi, P.P.; Mahanta, J.; et al. Urban rural differences in prevalence of self-reported diabetes in India—The WHO–ICMR Indian NCD risk factor surveillance. Diabetes Res. Clin. Pract. 2008, 80, 159–168. [Google Scholar] [CrossRef]
  21. Hummel, S.; Winkler, C.; Schoen, S.; Knopff, A.; Marienfeld, S.; Bonifacio, E.; Ziegler, A.G. Breastfeeding habits in families with Type 1 diabetes. Diabet. Med. 2007, 24, 671–676. [Google Scholar] [CrossRef]
  22. Tomioka, K.; Kurumatani, N.; Saeki, K. The association between education and smoking prevalence, independent of occupation: A nationally representative survey in Japan. J. Epidemiol. 2020, 30, 136–142. [Google Scholar] [CrossRef] [PubMed]
  23. Leybovitz-Haleluya, N.; Wainstock, T.; Landau, D.; Sheiner, E. Maternal gestational diabetes mellitus and the risk of subsequent pediatric cardiovascular diseases of the offspring: A population-based cohort study with up to 18 years of follow up. Acta Diabetol. 2018, 55, 1037–1042. [Google Scholar] [CrossRef] [PubMed]
  24. Yang, T.Y.O.; on behalf of the Million Women Study; Cairns, B.J.; Allen, N.; Sweetland, S.; Reeves, G.K.; Beral, V. Postmenopausal endometrial cancer risk and body size in early life and middle age: Prospective cohort study. Br. J. Cancer 2012, 107, 169–175. [Google Scholar] [CrossRef] [PubMed]
  25. Babu, G.R.; Deepa, R.; Lewis, M.G.; Lobo, E.; Krishnan, A.; Ana, Y.; Katon, J.G.; Enquobahrie, D.A.; Arah, O.A.; Kinra, S.; et al. Do gestational obesity and gestational diabetes have an independent effect on neonatal adiposity? Results of mediation analysis from a cohort study in South India. Clin. Epidemiol. 2019, 11, 1067–1080. [Google Scholar] [CrossRef]
  26. Kristiansen, A.L.; Bjelland, M.; Brantsæter, A.L.; Haugen, M.; Meltzer, H.M.; Nystad, W.; Andersen, L.F. Tracking of body size from birth to 7 years of age and factors associated with maintenance of a high body size from birth to 7 years of Age—The Norwegian Mother and Child Cohort study (MoBa). Public Health Nutr. 2014, 18, 1746–1755. [Google Scholar] [CrossRef]
  27. Lowe, W.L.; Scholtens, D.M.; Lowe, L.P.; Kuang, A.; Nodzenski, M.; Talbot, O.; Catalano, P.M.; Linder, B.; Brickman, W.J.; Clayton, P.; et al. Association of gestational diabetes with maternal disorders of glucose metabolism and childhood adiposity. JAMA 2018, 320, 1005–1016. [Google Scholar] [CrossRef]
  28. Brite, J.; Shiroma, E.J.; Bowers, K.; Yeung, E.; Laughon, S.K.; Grewal, J.G.; Zhang, C. Height and the risk of gestational diabetes: Variations by race/ethnicity. Diabet. Med. 2013, 31, 332–340. [Google Scholar] [CrossRef]
  29. Ntenda, P.A.M. Association of low birth weight with undernutrition in preschool-aged children in Malawi. Nutr. J. 2019, 18, 1–15. [Google Scholar] [CrossRef]
  30. Rani, M.; Rajput, R.; Rajput, M.; Garg, R. Etiological profile of short stature in children and adolescents. Indian J. Endocrinol. Metab. 2021, 25, 247. [Google Scholar] [CrossRef]
  31. Alzaim, M.; Wood, R.J. Vitamin D and gestational diabetes mellitus. Nutr. Rev. 2013, 71, 158–167. [Google Scholar] [CrossRef]
  32. Yuen, L. Gestational diabetes mellitus: Challenges for different ethnic groups. World J. Diabetes 2015, 6, 1024–1032. [Google Scholar] [CrossRef] [PubMed]
  33. Reece, E.A.; Leguizamón, G.; Wiznitzer, A. Gestational diabetes: The need for a common ground. Lancet 2009, 373, 1789–1797. [Google Scholar] [CrossRef]
  34. Islam, M.M.; Bakheit, C.S. Advanced maternal age and risks for adverse pregnancy outcomes: A population-based study in oman. Health Care Women Int. 2015, 36, 1081–1103. [Google Scholar] [CrossRef] [PubMed]
  35. Zutshi, A.; Santhosh, J.; Sheikh, J.; Naeem, F.; Al-Hamedi, A.; Khan, S.; Al-Said, E. Implications of Early Pregnancy Obesity on Maternal, Fetal and Neonatal Health: Retrospective cohort study from Oman. Sultan Qaboos Univ. Med. J. 2018, 18, 47–53. [Google Scholar] [CrossRef]
  36. Pettitt, D.J.; Jovanovic, L. Low birth weight as a risk factor for gestational diabetes, diabetes, and impaired glucose tolerance during pregnancy. Diabetes Care 2007, 30 (Suppl. 2), S147–S149. [Google Scholar] [CrossRef] [PubMed]
  37. Ogonowski, J.; Miazgowski, T.; Engel, K.; Celewicz, Z. Birth weight predicts the risk of gestational diabetes mellitus and pregravid obesity. Nutrition 2014, 30, 39–43. [Google Scholar] [CrossRef] [PubMed]
  38. Giannakou, K.; Evangelou, E.; Yiallouros, P.; Christophi, C.A.; Middleton, N.; Papatheodorou, E.; Papatheodorou, S.I. Risk factors for gestational diabetes: An umbrella review of meta-analyses of observational studies. PLoS ONE 2019, 14, e0215372. [Google Scholar] [CrossRef]
  39. Lin, P.-C.; Hung, C.-H.; Chan, T.-F.; Lin, K.-C.; Hsu, Y.-Y.; Tzeng, Y.-L. The risk factors for gestational diabetes mellitus: A retrospective study. Midwifery 2016, 42, 16–20. [Google Scholar] [CrossRef]
  40. Moosazadeh, M.; Asemi, Z.; Lankarani, K.B.; Tabrizi, R.; Maharlouei, N.; Naghibzadeh-Tahami, A.; Yousefzadeh, G.; Sadeghi, R.; Khatibi, S.R.; Afshari, M.; et al. Family history of diabetes and the risk of gestational diabetes mellitus in Iran: A systematic review and meta-analysis. Diabetes Metab. Syndr. Clin. Res. Rev. 2017, 11, S99–S104. [Google Scholar] [CrossRef]
  41. Schleger, F.; Linder, K.; Walter, L.; Heni, M.; Brändle, J.; Brucker, S.; Pauluschke-Fröhlich, J.; Weiss, M.; Häring, H.-U.; Preissl, H.; et al. Family history of diabetes is associated with delayed fetal postprandial brain activity. Front. Endocrinol. 2018, 9, 673. [Google Scholar] [CrossRef]
  42. Lee, S.C.; Pu, Y.B.; Chow, C.C.; Yeung, V.T.; Ko, G.T.; So, W.Y.; Li, J.K.; Chan, W.B.; Ma, R.C.; Critchley, J.A.; et al. Diabetes in Hong Kong Chinese: Evidence for familial clustering and parental effects. Diabetes Care 2000, 23, 1365–1368. [Google Scholar] [CrossRef] [PubMed]
  43. Cypryk, K.; Szymczak, W.; Czupryniak, L.; Sobczak, M.; Lewiński, A. Gestational diabetes Mellitus—An analysis of risk factors. Endokrynol. Polska 2008, 59, 393–397. [Google Scholar]
  44. Al-Houqani, M.; Leinberger-Jabari, A.; Al Naeemi, A.; Al Junaibi, A.; Al Zaabi, E.; Oumeziane, N.; Kazim, M.; Al-Maskari, F.; Al Dhaheri, A.; Wareth, L.A.; et al. Patterns of tobacco use in the United Arab Emirates Healthy Future (UAEHFS) pilot study. PLoS ONE 2018, 13, e0198119. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, J.-W.; Wang, Q.; Wang, X.-Q.; Wang, M.; Cao, S.-S.; Wang, J.-N. Association between maternal education level and gestational diabetes mellitus: A meta-analysis. J. Matern. Neonatal Med. 2019, 34, 580–587. [Google Scholar] [CrossRef]
  46. Erola, J.S.; Jalonen, H. Lehti, Parental education, class and income over early life course and children’s achievement. Res. Soc. Stratif. Mobil. 2016, 44, 33–43. [Google Scholar] [CrossRef]
  47. NCD Risk Factor Collaboration (NCD-RisC). A century of trends in adult human height. eLife 2016, 5, e13410. [Google Scholar] [CrossRef]
  48. Much, D.; Beyerlein, A.; Roßbauer, M.; Hummel, S.; Ziegler, A.-G. Beneficial effects of breastfeeding in women with gestational diabetes mellitus. Mol. Metab. 2014, 3, 284–292. [Google Scholar] [CrossRef]
  49. Baser, O.; Palmer, L.; Stephenson, J. The Estimation Power of Alternative Comorbidity Indices. Value Health 2008, 11, 946–955. [Google Scholar] [CrossRef]
  50. Mazumder, T.; Akter, E.; Rahman, S.M.; Islam, T.; Talukder, M.R. Prevalence and Risk Factors of Gestational Diabetes Mellitus in Bangladesh: Findings from Demographic Health Survey 2017–2018. Int. J. Environ. Res. Public Health 2022, 19, 2583. [Google Scholar] [CrossRef]
  51. Riaz, B.K.; Islam, Z.; Islam, A.N.M.S.; Zaman, M.M.; Hossain, A.; Rahman, M.; Khanam, F.; Amin, K.M.B.; Noor, I.N. Risk factors for non-communicable diseases in Bangladesh: Findings of the population-based cross-sectional national survey 2018. BMJ Open 2020, 10, e041334. [Google Scholar] [CrossRef]
  52. Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries. Middle East Fertil. Soc. J. 2019, 24, 1–15. [Google Scholar] [CrossRef]
  53. Petry, C.J. Gestational diabetes: Risk factors and recent advances in its genetics and treatment. Br. J. Nutr. 2010, 104, 775–787. [Google Scholar] [CrossRef] [PubMed]
  54. Solomon, C.G.; Willett, W.C.; Rich-Edwards, J.; Hunter, D.J.; Stampfer, M.J.; Colditz, G.A.; Manson, J.E. Variability in Diagnostic Evaluation and Criteria for Gestational Diabetes. Diabetes Care 1996, 19, 12–16. [Google Scholar] [CrossRef] [PubMed]
  55. AlAwad, M.; Chartouni, C. Explaining the decline in fertility among citizens of the GCC countries: The case of the UAE. Educ. Bus. Soc. Contemp. Middle East. Issues 2014, 7, 82–97. [Google Scholar] [CrossRef]
Ijerph 19 10339 g001 550
Figure 1. Flowchart of the final analytical sample included in the study.
Figure 1. Flowchart of the final analytical sample included in the study.
Ijerph 19 10339 g001
Ijerph 19 10339 g002 550
Figure 2. Prevalence of GDM by each maternal early−life risk factor. Point estimates and 95% CI are reported.
Figure 2. Prevalence of GDM by each maternal early−life risk factor. Point estimates and 95% CI are reported.
Ijerph 19 10339 g002
Table
Table 1. Current characteristics of the participants at questionnaire based on GDM (N = 702).
Table 1. Current characteristics of the participants at questionnaire based on GDM (N = 702).
CharacteristicsWithout GDM (N = 666) *With GDM
(N = 36)		Missing/PNA/DN on Diabetes History
(N = 194)		p-Value *
Age year (mean ± SD)33.0 ± 7.536.5 ± 8.536.5 ± 8.30.022
BMI, year (kg/m2 ± SD)27.5 ± 5.831.2 ± 6.028.7 ± 6.30.002
BMI levels, n (%) 0.008
 Normal or below (<25 kg/m2)201 (30.2)4 (11.1)29 (15.0)
 Overweight (25 to <30 kg/m2)194 (29.1)9 (25.0)48 (24.7)
 Obese (30+ kg/m2)160 (24.0)17 (47.2)43 (22.2)
 Missing111 (16.7)6 (16.7)74 (38.1)
Marital status, n (%) 0.148
 Married545 (81.8)34 (94.4)163 (84.0)
 Divorced or separated111 (16.7)2 (5.6)27 (13.9)
 Widow or widower10 (1.5)0 (0)4 (2.1)
Urbanicity, n (%) 0.696
 Rural or other non–urbans61 (9.2)2 (5.6)9 (4.6)
 Urban (city)547 (82.1)30 (83.3)87 (44.9)
 Missing/PNA/DN58 (8.7)4 (11.1)98 (50.5)
Education attainment, n (%) 0.002
 6 years of schooling or below20 (3.0)5 (13.9)67 (34.5)
 >6–12 years of schooling235 (35.3)13 (36.1)46 (23.7)
 >12 years of schooling411 (61.7)18 (50.0)81 (41.8)
Current overal health, n (%) 0.010
 Poor or fair152 (22.8)15 (41.7)177 (91.24)
 Good or excellent514 (77.2)21 (58.3)17 (8.76)
With poly–cystic ovarian syndrome history, n (%) 0.237
 No460 (69.1)21 (58.3)6 (3.1)
 Yes127 (19.1)11 (30.6)4 (2.1)
 Missing/PNA/DN79 (11.9)4 (11.1)184 (94.9)
* Compares without and with GDM groups using the t-test for continuous variables and the χ2 for categorical variables.
Table
Table 2. Early-life characteristics of the participants based on GDM (N = 702).
Table 2. Early-life characteristics of the participants based on GDM (N = 702).
Maternal Early-Life FactorsWithout GDM (N = 666) *With GDM (N = 36)Missing/PNA/DN on Diabetes History
(N = 194)		p-Value *
Own birthweight, n (%) 0.043
  2.5+ kg (normal birthweight)209 (31.4)5 (13.9)8 (4.1)
  <2.5 kg (low birthweight)96 (14.4)9 (25.0)1 (0.5)
  Missing/PNA/DN361 (54.2)22 (61.1)185 (95.4)
Parental smoking at birth, n (%) 0.709
  No451 (67.7)22 (61.1)14 (7.2)
  Yes201 (30.2)13 (36.1)6 (3.1)
  Missing/PNA/DN14 (2.1)1 (2.8)174 (89.7)
Urbanicity at birth, n (%) 0.654
  Rural or other non-urbans115 (17.3)5 (13.9)18 (9.3)
  Urban (city)390 (58.6)20 (55.6)59 (30.4)
  Missing/PNA/DN161 (24.2)11 (30.6)117 (60.3)
Ever-breasfed history, n (%) 0.976
  No68 (10.2)4 (11.1)1 (0.5)
  Yes528 (79.3)28 (77.8)19 (9.8)
  Missing/PNA/DN70 (10.5)4 (11.1)174 (89.7)
Parental education, n (%) 0.163
  6 years of schooling or below88 (13.2)8 (22.2)3 (1.6)
  >6–12 years of schooling90 (13.5)6 (16.7)11 (5.7)
  >12 years of schooling89 (13.4)7 (19.4)7 (3.6)
  Missing/PNA/DN399 (59.9)15 (41.7)173 (89.2)
Overal health up to 10 years old, n (%) 0.307
  Poor or fair59 (8.9)5 (13.9)19 (9.8)
  Good or excellent607 (91.1)31 (86.1)1 (0.5)
  Missing/PNA/DN 174 (89.7)
Body size at 10 years of age, n (%) 0.971
  About average225 (33.8)11 (30.6)9 (4.6)
  Thinner326 (49.0)18 (50.0)9 (4.6)
  Plumper62 (9.3)4 (11.1)2 (1.0)
  Missing/PNA/DN53 (8.0)3 (8.3)174 (89.7)
Body height at 10 years of age, n (%) 0.206
  About average355 (53.3)19 (52.8)12 (6.2)
  Shorter123 (18.5)9 (25.0)6 (3.1)
  Taller134 (20.1)3 (8.3)2 (1.0)
  Missing/PNA/DN54 (8.1)5 (13.9)174 (89.7)
Parental history with diabetes, n (%) 0.007
  No238 (35.7)5 (13.9)
  Yes428 (64.3)31 (86.1)13 (6.7)
  Missing/PNA/DN 181 (93.3)
* Compares without and with GDM groups using the t-test for continuous variables and the χ2 for categorical variables.
Table
Table 3. Associations between maternal early-life risk factors and later GDM among 702 ever-married females.
Table 3. Associations between maternal early-life risk factors and later GDM among 702 ever-married females.
Early Life Risk FactorsCrude ModelAge and Parental Diabetes Adjusted Model Confounding Factors Adjusted Model aConfounding Factors
RR [95% CI]p-ValueRR [95% CI]p-ValueRR [95% CI]p-Value
Maternal birthweight Parental education, parental smoking [9,15].
   2.5+ kg (normal birthweight)(Reference) (Reference) (Reference)
   <2.5 kg (low birthweight)3.67 [1.26–10.7]0.0174.13 [1.42–12.1]0.0094.04 [1.36–12.0]0.012
   Missing/PNA/DN2.46 [0.94–6.40]0.0652.08 [0.80–5.42]0.1322.06 [0.80–5.33]0.137
Parental smoking at birth, n (%) Parental education, birthweight, ever-breastfed history [9,18].
   No(Reference) (Reference) (Reference)
   Yes1.31 [0.67–2.54]0.4331.14 [0.59–2.19]0.7051.24 [0.65–2.40]0.508
   Missing/PNA/DN1.43 [0.21–9.96]0.7161.43 [0.19–10.5]0.7271.77 [0.29–11.0]0.538
Urbanicity at birth, n (%) Parental education, parental smoking, birthweight [15,19,20].
   Rural or other non-urbans(Reference) (Reference) (Reference)
   Urban (city)1.17 [0.45–3.06]0.7470.98 [0.38–2.54]0.9601.03 [0.39–2.67]0.960
   Missing/PNA/DN1.54 [0.55–4.31]0.4160.83 [0.27–2.51]0.7370.65 [0.21–2.04]0.462
Ever-breastfed history, n (%) Parental education, parental smoking [18,21].
   No(Reference) (Reference) (Reference)
   Yes0.91 [0.33–2.51]0.8500.91 [0.33–2.50]0.8570.95 [0.35–2.58]0.915
   Missing/PNA/DN0.97 [0.25–3.75]0.9680.96 [0.25–3.62]0.9500.99 [0.27–3.70]0.992
Parental education, n (%) Parental smoking [22].
   6 years of schooling or below(Reference) (Reference) (Reference)
   >6–12 years of schooling0.75 [0.27–2.08]0.5810.86 [0.31–2.41]0.7770.85 [0.30–2.37]0.753
   >12 years of schooling0.88 [0.33–2.32]0.7881.09 [0.41–2.89]0.8591.08 [0.41–2.84]0.884
   Missing/PNA/DN0.44 [0.19–0.99]0.0490.59 [0.25–1.40]0.2290.56 [0.23–1.34]0.193
Overall health up to 10 years old, n (%) Parental education, parental smoking, birthweight, body size at 10 years old [15,23,24].
   Poor or fair(Reference) (Reference) (Reference)
   Good or excellent0.62 [0.25–1.55]0.3060.64 [0.26–1.56]0.3260.68 [0.28–1.61]0.374
Body size at 10 years of age, n (%)
   About average(Reference) (Reference) (Reference) Parental education, parental smoking, birthweight, all health at 10 years old [10,24,25,26,27].
   Thinner1.12 [0.54–2.33]0.7571.02 [0.50–2.10]0.9501.05 [0.51–2.19]0.894
   Plumper1.30 [0.43–3.95]0.6441.23 [0.40–3.78]0.7241.38 [0.46–4.16]0.564
   Missing/PNA/DN1.15 [0.33–3.99]0.8260.93 [0.26–3.28]0.9061.01 [0.25–4.11]0.984
Body height at 10 years of age, n (%)
   About average(Reference) (Reference) (Reference) Birthweight, overall health at 10 years old [28,29,30].
   Shorter1.34 [6.22–2.89]0.4531.43 [0.67–3.07]0.3581.37 [0.64–2.91]0.416
   Taller0.43 [0.13–1.44]0.1700.42 [0.13–1.40]0.1590.44 [0.14–1.44]0.176
   Missing/PNA/DN1.67 [0.65–4.30]0.2891.66 [0.64–4.26]0.2961.54 [0.57–4.11]0.393
Parental history with diabetes, n (%) None
   No(Reference) (Reference)
   Yes3.28 [1.29–8.34]0.0122.86 [1.10–7.43]0.031
RR: risk ratio; CI: confidence interval; PNA: prefer not to answer; DN: do not know. a Basic model plus additional adjustments.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Juber, N.F.; Abdulle, A.; AlJunaibi, A.; AlNaeemi, A.; Ahmad, A.; Leinberger-Jabari, A.; Al Dhaheri, A.S.; AlZaabi, E.; Mezhal, F.; Al-Maskari, F.; et al. Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS). Int. J. Environ. Res. Public Health 2022, 19, 10339. https://doi.org/10.3390/ijerph191610339

AMA Style

Juber NF, Abdulle A, AlJunaibi A, AlNaeemi A, Ahmad A, Leinberger-Jabari A, Al Dhaheri AS, AlZaabi E, Mezhal F, Al-Maskari F, et al. Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS). International Journal of Environmental Research and Public Health. 2022; 19(16):10339. https://doi.org/10.3390/ijerph191610339

Chicago/Turabian Style

Juber, Nirmin F., Abdishakur Abdulle, Abdulla AlJunaibi, Abdulla AlNaeemi, Amar Ahmad, Andrea Leinberger-Jabari, Ayesha S. Al Dhaheri, Eiman AlZaabi, Fatima Mezhal, Fatma Al-Maskari, and et al. 2022. "Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS)" International Journal of Environmental Research and Public Health 19, no. 16: 10339. https://doi.org/10.3390/ijerph191610339

APA Style

Juber, N. F., Abdulle, A., AlJunaibi, A., AlNaeemi, A., Ahmad, A., Leinberger-Jabari, A., Al Dhaheri, A. S., AlZaabi, E., Mezhal, F., Al-Maskari, F., AlAnouti, F., Alsafar, H., Alkaabi, J., Wareth, L. A., Aljaber, M., Kazim, M., Weitzman, M., Al-Houqani, M., Ali, M. H., ... Ali, R. (2022). Maternal Early-Life Risk Factors and Later Gestational Diabetes Mellitus: A Cross-Sectional Analysis of the UAE Healthy Future Study (UAEHFS). International Journal of Environmental Research and Public Health, 19(16), 10339. https://doi.org/10.3390/ijerph191610339

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop