Abstract
Introduction:
Gestational diabetes mellitus (GDM), characterized by hyperglycemia during pregnancy, poses significant health risks to both mothers and their offspring. Its prevalence varies widely across regions and populations.
Objectives:
This study aimed to assess the prevalence of GDM and identify its associated risk factors.
Methods:
This a cross-sectional study conducted from October 2022 to October 2023 at a private hospital, 741 pregnant women underwent a 75-gram oral glucose tolerance test (OGTT) at 24–28 weeks of gestation. Participants adhered to normal dietary habits three days prior to testing and fasted for 8–14 hours overnight before the test. Fasting blood sugar levels were recorded, followed by glucose administration, and blood sugar levels were re-measured 2 hours later.
Results:
Among the participants, 585 were non-GDM, while 156 were diagnosed with GDM. In the GDM group, only 20.5% had no risk factors compared to 37.3% in the non-diabetic group. Key risk factors included maternal age over 35 years adjusted odds ratio [aOR]: (4.77, 95% CI: 2.89–8.09; P < 0.001), overweight status (BMI 25–29.9; aOR: 1.6, 95% CI: 1.03–2.54; P = 0.041), obesity (BMI ≥30; aOR: 2.23, 95% CI: 1.39–3.62; P = 0.001), and macrosomia in prior pregnancies (aOR: 2.41, 95% CI: 1.33–4.28; P = 0.003).
Conclusions:
This study highlights the critical nature of identifying specific risk factors for populations and recommends universal screening to ensure comprehensive care. Variations in prevalence and risk factors underscore the need for region-specific research to inform public health strategies.
Keywords: antenatal care, gestational diabetes mellitus, OGTT, oral glucose tolerance test, prevalence, risk factors
Introduction
Gestational diabetes mellitus (GDM), defined as hyperglycemia first identified during pregnancy, is one of the most common medical complications of pregnancy[1]. Globally, the prevalence of GDM ranges from 1% to 28% and factors including ethnicity and population cause this wide range of variation; for example, the prevalence is less than 5% in South Korea and between 5% and 6% in the US, although Southeast Asia has the highest frequency[2]. In addition to race, other well-known risk factors include advanced maternal age, obesity, high parity, family history of type 2 diabetes, history of GDM, delivery of a large baby, and previous intrauterine fetal death (IUFD)[3,4]. However, medical disorders, including hypothyroidism and polycystic ovarian syndrome (PCOS), have been found to increase the occurrence of GDM[5].
HIGHLIGHTS
Prevalence of gestational diabetes mellitus of 21.1% among the 741 pregnant women analyzed, with approximately 1 in 7 women affected.
Major risk factors for GDM were (Advanced maternal age (>35 years), Being overweight or obese, Multigravida status, History of macrosomia in previous pregnancies
Women aged 36-45 had a markedly higher GDM prevalence (46.8%)
The study found that 20.5% of women with GDM had no traditional risk factors, highlighting the need for universal GDM screening rather than only screening high-risk groups.
GDM poses serious health risks to both the mother and fetus[2]. Fetal complications include macrosomia, shoulder dystocia, intrauterine fetal death, infant hypoglycemia, hypomagnesemia, hyperbilirubinemia, polycythemia, and respiratory distress syndrome[5]. However, it also has long-term fetal complications, and children of mothers with diabetes are more likely to experience early onset metabolic syndrome, obesity, and diabetes mellitus[5]. It is generally established that GDM increases the risk of several maternal problems, such as preeclampsia, premature birth, and the need for caesarean sections[5]. Importantly, a history of GDM significantly increases the chances of the mother developing type 2 diabetes and cardiovascular problems in the future[6]. Given the consequences of hyperglycemia during pregnancy, diagnosis and appropriate treatment of GDM are imperative to prevent adverse pregnancy outcomes[6,7]. Currently, according to the International Association for Diabetes in Pregnancy Study Group (LADPSG), the oral glucose tolerance test (OGTT) is the gold standard test for diagnosing GDM; it usually involves fasting plasma glucose (FPG) measurement after an overnight fast, then administration of a 75 g glucose challenge, and a follow-up plasma glucose sample after 2 hours[8,9]. The NICE guidelines recommend screening for women at risk at 24–28 weeks[10]. The objectives of our study were to determine the prevalence of gestational diabetes among pregnant women, outline its relationship with risk factors, and decide whether it is important to screen all pregnant women for GDM or just those with high-risk factors.
Materials and methods
This cross-sectional study was conducted in a private hospital with three obstetric and gynecological clinics from 1/October 2022 to 1/October 2023. After receiving informed consent, pregnant women who visited clinics for antenatal care were enrolled in the study. The inclusion criteria were women who visited clinics in the early first trimester; who regularly and frequently had antenatal appointments; who did not take diabetogenic medications, such as steroids, thiazide, beta-blockers, or antidepressants; and who did not have obvious diabetes. To rule out undiagnosed pre-existing diabetes, women who were at high risk for type 2 diabetes were sent for an OGTT early in pregnancy. Women who attended antenatal care later had trouble taking glucose solutions or were already diagnosed with diabetes before pregnancy were excluded from the study. During the initial appointment in early pregnancy, complete demographic information was provided, including age, occupation, place of residence, level of education, early pregnancy or pre-pregnancy, and body mass index (BMI) was classified according to the American Institute of Medicine (IOM) in 2009, issued guidelines, with BMI <18.5 as underweight, 18.5–24.9 as normal weight, 25-29.9 as overweight, and > 30 as obese[11]. Determine the risk factors associated with GDM as follows: age, multipara, BMI, previous abnormal baby, macrosomic baby weighing 4.5 kg or more, previous GDM, polycystic ovary (PCOS), hypothyroidism, family history of type 2 diabetes, and distribution of risk levels within each Gestational Diabetes group. The methodology in this study has been reported according to STROCSS guidelines[12].
Data assessment
At 24 to 28 weeks of gestation, 75 g of OGTTs were administered to all women. Three days before the test, the patients were asked to eat normally or consume 150 g of carbohydrates per day, and on the day of the test, the women fasted overnight for at least 8 h but no more than 14 h[13]. The women were required to arrive at the hospital and after 30 minutes of rest, blood was drawn from a vein to measure fasting blood sugar. The patient was then given 75 g of glucose powder dissolved in 250 cc of water to drink[14]. She was instructed to do so in front of a laboratory employee to ensure that the medication was consumed and that the woman should rest at the hospital. Two hours later, blood was drawn again to check blood sugar levels. Glucose was measured via the hexokinase method on the (Cobas system) analyzer in the hospital laboratory. Values of one or two of the following were considered GDM: fasting blood sugar 100 mg/dl or more, 2 hours glucose 140 mg/dl or more[15].
Sample size justification
Statistical Formulas and Considerations. Normally, in descriptive cross-sectional studies aimed at obtaining the prevalence rate, the formulas to determine sample size are as follows: The sample size was determined using a single population. proportion formula by considering the following statistical assumptions: 95% confidence interval (CI), 50% proportion (as there are no recent studies), and 5% marginal error. Hence, a minimum of 385 participants were needed for the study. To increase the statistical power, 741 participants were recruited for the study.
Statistical analysis
Data were analyzed with Statistical Package for Social Sciences (SPSS) version 25 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were used to analyze the study variables. Continuous variables are represented by means (± standard deviations), while categorical variables are represented by numbers (%). Odds ratios (ORs) and their associated 95% confidence intervals (CIs), P values <0.05 considered to be statistically significant were estimated using logistic regression models. The unadjusted associations between several maternal characteristics and GDM status were explored. To obtain adjusted associations and find a set of predictor variables that gives a model with a good fit, a stepwise selection method was applied. The associations between GDM status and risk factors were assessed. For each outcome variable, unadjusted and adjusted models were evaluated[16].
Results
This study analyzed data from 741 patients, which indicated a prevalence of 585 (79%) non-GDM cases and 156 (21.1%) GDM cases, indicating that approximately 1 in 7 pregnant women in the study population were affected by GDM (Fig. 1). The mean age for non-GDM women was 29.5 ± 5.9 years, compared to 33.8 ± 6.6 years for those with GDM, indicating that GDM prevalence increases with age. In particular, the oldest age group (36–45 years) showed a markedly higher prevalence of GDM (46.8%) than the younger groups. BMI averages were higher in the GDM group (28.4 ± 4.6) than in the non-GDM group (26.8 ± 4.6). Obesity rates were also higher in the GDM group (34%) than in the non-GDM group (23.7%). In terms of gravidity, 88.5% of the women with GDM were multigravida (multiple pregnancies), which was higher than 72% in the non-GDM group. Parity showed a similar trend, where multipara women were more common in the GDM group (71.2%) than in the non-GDM group (36.9%).
Figure 1.
Prevalence of gestational diabetes mellitus (GDM) among study participants.
Education levels varied, with a higher percentage of women with GDM having secondary education (23.7%) than those without GDM (18.9%). However, women with a high school education and above were more prevalent in the non-GDM group (53.2%) than in the GDM group (14.1%). Women with GDM had a higher fasting blood sugar level (94.5 ± 13.3 mg/dL) and 2-hour blood sugar level (150.3 ± 24.1 mg/dL) compared to those without GDM (83.8 ± 7.1 mg/dL and 99.2 ± 17.9 mg/dL, respectively). The incidence of polycystic ovary syndrome (PCOS) was similar in both groups (7.7%). The prevalence of hypothyroidism was slightly lower in the GDM group (7.7%) than in the non-GDM group (8.9%). The number of previous pregnancies affected by GDM and abnormal baby outcomes was marginally higher in the GDM group, reinforcing the need for targeted monitoring of subsequent pregnancies.
Macrosomia (large for gestational-age babies) and intrauterine fetal death rates were notably higher in the GDM group (14.1% and 6.4%, respectively) than in the non-GDM group (5.6% and 1.5%, respectively). Employment rates were identical in both groups (62.8%), but the proportion of housewives was lower and that of students was higher in the GDM group, reflecting possible lifestyle differences influencing GDM risk (Table 1).
Table 1.
Comparison of baseline characteristics between the GDM and non-GDM groups
| Variables | N = 741 total cases | |
|---|---|---|
| N = 585 (75.9%) non-GDM | N = 156 (20.2%) GDM | |
| Age (years) | 29.5 ± 5.9 | 33.8 ± 6.6 |
| 16–25 | 184(31.5%) | 25(16%) |
| 26–30 | 151(25.8%) | 23(14.17%) |
| 31–35 | 138(23.6%) | 35(22.4%) |
| 36–45 | 112(19.15) | 73(46.8%) |
| BMI (Mean ± SD) | 26.8 ± 4.6 | 28.4 ± 4.6 |
| Normal weight (18.5–24.9) | 12(2.1%) | 2(1.3%) |
| Underweight (<18.5) | 202(34.6%) | 35(22.4%) |
| Overweight (25–29.9) | 231(39.6%) | 66(42.3) |
| Obese (>30) | 138(23.7%) | 53(34%) |
| Gravida | ||
| Primigravida | 163(27.9%) | 18(11.5%) |
| Multigravida | 422(72%) | 138(88.5%) |
| Parity | ||
| Primipara | 187(32%) | 21(13.5%) |
| Multipara | 216(36.9%) | 111(71.2%) |
| Education | ||
| Illiterate | 45(7.7%) | 4(2.6%) |
| Primary | 117(20%) | 31(19.9%) |
| Secondary | 111(18.9%) | 37(23.7%) |
| High school and above | 312(53.2%) | 22(14.1%) |
| Abortion | ||
| Yes | 104(17.8) | 41(26.3%) |
| No | 438(74.9) | 95(60.9%) |
| Fasting blood sugar | 83.8 ± 7.1 | 94.5 ± 13.3 |
| 2 hours blood sugar | 99.2 ± 17.9 | 150.3 ± 24.1 |
| PCOS | ||
| Yes | 45(7.7%) | 12(7.7%) |
| No | 540(92.2%) | 144(92.3%) |
| Macrosomia fetal | ||
| Yes | 33(5.6%) | 22(14.1%) |
| No | 552(94.4%) | 134(85.9%) |
| Intrauterine fetus death | ||
| Yes | 9(1.5%) | 10(6.4%) |
| No | 576(98.5) | 146(93.6%) |
| Previous gestation diabetic | ||
| Yes | 36(6.2%) | 10(6.4%) |
| No | 549(93.8%) | 146(93.6%) |
| Hypothyroidism | 0.9 ± 0.2 | |
| Yes | 52(8.9%) | 12(7.7%) |
| No | 533(91%) | 144(93.3%) |
| Previous abnormal baby | ||
| Yes | 11(1.9) | 6(3.8%) |
| No | 574(98.1%) | 150(96.2%) |
| Occupation | ||
| Housewife | 197(33.7%) | 36(23.1%) |
| Employed | 368(62.8%) | 98(62.8%) |
| Student | 20(3.4%) | 22(14.1%) |
Women in the age group of 31–35 years had an unadjusted odds ratio (aOR) of 1.87 (95% CI: 1.07–3.29) with a P value of 0.029, indicating a statistically significant moderate increase in risk. The risk sharply escalates in the 36–45 age group, with an aOR of 4.77 (95% CI: 2.89–8.09), and a P value of <0.001, statistically significant with a strong association between advancing age and GDM. Women aged 45 years and older also showed a high aOR of 7.36, although the confidence interval was wide (0.29–190.09); however, the association was not statistically significant (P = 0.163). Being overweight or obese is a significant risk factor for GDM. Overweight individuals (BMI 25–29.9) had an aOR of 1.6 (95% CI: 1.03–2.54, P = 0.041), while obese individuals (BMI >30) had a more pronounced risk with an aOR of 2.23 (95% CI: 1.39–3.62, P = 0.001). Both groups retained significance in adjusted models, confirming that moderate overweight and obesity are strongly associated with GDM risk. The number of pregnancies (gravidity) was strongly and negatively associated with GDM. Multigravida women (those who had been pregnant more than once) had a significant association with GDM compared to primigravida women, with an adjusted aOR of 0.4 (95% CI: 0.23–0.67, P = 0.001). Unlike gravidity, parity (number of births) did not show a significant association with GDM, with an adjusted aOR of 0.89 (95% CI: 0.69–1.14, P = 0.351) for multipara (women who have given birth more than once) compared to primipara (first-time mothers). Previous history of GDM did not significantly impact the likelihood of subsequent GDM, with an adjusted aOR of 0.96 (95% CI: 0.44–1.93, P = 0.917). Having a previous abnormal baby showed an increased unadjusted aOR of 2.09; however, this association weakened and was not statistically significant in the adjusted model (aOR 1.77, P = 0.274). The presence of macrosomia in previous pregnancies is associated with a higher risk of GDM, with an adjusted aOR of 2.41 (95% CI: 1.33–4.28, P = 0.003), suggesting a significant correlation. Neither thyroid disease (adjusted aOR 0.85, P = 0.618) nor polycystic ovary syndrome (PCOS) (adjusted aOR 0.88, P = 0.713) showed a significant association with GDM in the analysis (Table 2).
Table 2.
Associations between gestational diabetes mellitus and risk factors
| Variable | Unadjusted aOR(95% CI) | P value | Adjusted aOR (95% CI) | P value |
|---|---|---|---|---|
| Age group (%) | ||||
| 16–25 | Ref (1) | – | Ref (1) | – |
| 26–30 | 1.12 (0.61–2.06) | 0.712 | 1.09 (0.59–2.01) | 0.771 |
| 31–35 | 1.87 (1.07–3.29) | 0.029 | – | – |
| 36–45 | 4.77 (2.89–8.09) | 0 | – | – |
| 45+ | 7.36(0.29–90.09) | 0.163 | – | – |
| BMI group (%) | ||||
| Normal weight (18.5–24.9) | Ref (1) | – | Ref (1) | – |
| Underweight (<18.5) | 0.96 (0.14–3.71) | 0.956 | 0.99 (0.15–3.85) | 0.989 |
| Overweight (25–29.9) | 1.6 (1.03–2.54) | 0.041 | 1.58 (1.01–2.5) | 0.05 |
| Obese (>30) | 2.23 (1.39–3.62) | 0.001 | 2.19 (1.36–3.56) | 0.001 |
| Gravid (%) | ||||
| Primigravida | Ref (1) | – | Ref (1) | – |
| Multigravida | 0.34 (0.19–0.56) | 0 | 0.4 (0.23–0.67) | 0.001 |
| Para (%) | ||||
| Primipara | Ref (1) | – | Ref (1) | – |
| Multipara | 0.95 (0.75–1.21) | 0.68 | 0.89 (0.69–1.14) | 0.351 |
| Previous GDM (%) | ||||
| No | Ref (1) | – | Ref (1) | – |
| Yes | 1.04 (0.48–2.08) | 0.906 | 0.96 (0.44–1.93) | 0.917 |
| Previous abnormal baby (%) | ||||
| No | Ref (1) | – | Ref (1) | – |
| Yes | 2.09 (0.71–5.58) | 0.154 | 1.77 (0.59–4.82) | 0.274 |
| Macrosomia (%) | ||||
| No | Ref (1) | – | Ref (1) | – |
| Yes | 2.75 (1.53–4.84) | 0.001 | 2.41 (1.33–4.28) | 0.003 |
| Thyroid disease (%) | ||||
| No | Ref (1) | – | Ref (1) | – |
| Yes | 0.85 (0.43–1.59) | 0.637 | 0.85 (0.42–1.58) | 0.618 |
| PCOS (%) | ||||
| No | Ref (1) | – | Ref (1) | – |
| Yes | 1 (0.49–1.88) | 1 | 0.88 (0.43–1.67) | 0.713 |
The distribution of risk levels within the GDM groups is shown (Fig. 2). Among women without GDM, the majority fell into the No Risk (37.3%) and Low Risk (35.0%) categories, while only a small proportion were classified as High Risk (7.2%) or Very High Risk (2.1%). Conversely, among women with GDM, the distribution shifted toward a higher risk level. 20.5% are at the No Risk level. While 28.2% remained at the low-risk level and 25.6% were in the intermediate-risk category, 25.7% were classified as high-risk or very high-risk.
Figure 2.
Distribution of risk levels by gestational diabetes status.
Discussion
The prevalence of GDM in this study was 20.2%, which is a low rate, according to a regional and global study by the International Association of Diabetes in Pregnancy Study group, which also found that our location is located in the areas with the highest prevalence of GDM, which was 27.6% (26.9-28.4%)[5]. The prevalence was slightly lower in several neighborhood surveys in Turkey; for example, a systematic review and meta-analysis revealed that the highest frequency was 17.6% in the Black Sea region and the lowest was 5.1% in the Central Anatolia region[17]. According to a study conducted in Jordan in 2019 by Basha et al, the prevalence was 13.5%[17], however, a study carried out in Kuwait by Zainab Groof et al produced notably different findings, reaching 12.6%[18]. However, studies have shown a higher rate than what the current investigation revealed; for example, a cross-sectional study of 13,627 pregnant women in Saudi Arabia found that the percentage of GDM was 36.6%[19]. According to a systematic review and meta-analysis of studies on the prevalence of GDM in the Middle East conducted in 2002[20], 36% of pregnant Iranian women are at risk of developing GDM. The presence of risk factors in the study area or the diagnostic criteria used to diagnose the condition may have caused this extreme variance in prevalence.
In the current study, age >35 years was one of the most important risk factors for GDM (strong association). After completing a systematic review and meta-analysis of maternal age and risk of GDM, which included more than 120 million participants, Yueyil et al discovered that advanced maternal age was an independent risk factor for developing GDM[21]. Gran multipara was found not associated with GDM in the current study. This result was supported by Nuriye et al’s study titled Frequency of GDM and Associated Risk Factors, which found a significant correlation between advanced maternal age, family history, and body mass index but not with the number of pregnancies or parity[22]. Babies with macrosomia have a significant association with GDM. According to the current study, this finding was also made by Reema et al in their study conducted in Jordan[23]. A study by Lee discovered a substantial correlation between GDM and previous macrosomia[24]. The current study found that prenatal obesity with a BMI of 30 or greater had a statistically significant but weak association. This finding was also supported by Zhang et al’s study on the relationship between gestational body mass index changes and the risk of GDM, which found that early pregnancy BMI was a risk factor for GDM[25].
Contrary to several other studies, we found no significant link between a prior history of PCOS, gestational diabetes, abnormal baby thyroid disease, and first-degree relative family history of type 2 diabetes and the risk of developing GDM. For example, PCOS and GDM were found to be significantly correlated in a meta-analysis by Qiu et al[26]. Although the prevalence of PCOS is high, especially in the Middle East, where it is estimated to be approximately 21%[27], this discrepancy from what the present study revealed may be caused by the fact that the history of PCOS was only taken into account in this study if the women had pre-pregnancy documents, such as ultrasound and laboratory investigations, confirming that they had PCOS. No significant amount of PCOS was detected because only 8% of the patients in this study had PCOS, whereas previous studies have shown that past GDM increases the future risk of GDM[28]. However, the current study did not find a link. This may be because GDM screening is only performed for high-risk groups in our location; therefore, it may be due to previously undiagnosed GDM. Additionally, we were unable to identify any associations with previously abnormal babies. This may be because all types of fetal malformations were included in the study. Yuxiao Wu et al discovered that GDM has a strong association mainly with congenital heart disease babies and have investigated this association[29]. Similar to the current study, an investigation by Mahmood Moosazadeh on the relationship between hypothyroidism and GDM revealed that the prevalence is higher in women with this condition, although it was not statistically significant (P = 0.7)[30]. A Chinese researcher named Geng-dong Chen also found no connection between GDM and thyroid function tests[31], however, Yasemin’s findings, which suggested a significant link between hypothyroidism and GDM[32], contradicted this finding. Women in the “No Risk” category were not expected to be at risk for GDM based on the conventional risk assessment criteria. However, the present study highlights that GDM can occur even in the absence of traditional risk factors; therefore, focusing only on traditional risk factors may lead to underdiagnosis or delayed diagnosis of GDM among women who do not fit the conventional “at-risk” profile.
Conclusions and recommendations
The prevalence of GDM in this study was 21%, and the most significant risk variables were advanced age, multigravida, macrosomia, overweight, and obesity. These findings challenge certain established perceptions and underscore the need for localized research to inform healthcare policies accurately. The occurrence of GDM in the “No Risk” group reveals a critical gap in the current understanding and prediction of GDM. This underscores the importance of universal screening, a broadened focus on potential hidden risk factors, and the need for equitable care for all pregnant women, regardless of their assessed risk level. We recommend that every pregnant woman should be tested for GDM to prevent adverse fetal and maternal outcomes because 20.5% of patients with GDM have no risk factors, the prevalence of GDM is significant in our area, and it can be present in those without risk factors. Future research should focus on longitudinal outcomes to better understand the long-term implications of GDM and refine screening processes to improve maternal and fetal health outcomes.
Acknowledgements
The authors extend their heartfelt gratitude to all participants who willingly contributed to this study. The authors also express our sincere appreciation to Baxshin Hospital and the Department of Obstetrics and Gynecology for their invaluable support and cooperation in facilitating this research.
Footnotes
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Published online 2 September 2025
Contributor Information
Nasreen Ghafor Majeed, Email: nasreengafor392@gmail.com.
Shnow Hussain Abdullah, Email: Shnowabdullah1971@gmail.com.
Shokh Mukhtar Ahmad, Email: shokh.mukhtar@komar.edu.iq.
Rebwar Hasan Khdir, Email: rebwarh27@gmail.com.
Jeza Muhamad Abdul Aziz, Email: jeza1981@gmail.com.
Lava Abdullah, Email: lava7abdullah@gmail.com.
Ethical approval
The Ethics Committee of the Baxshin Research Centre approved this study (reference number: BRC110022/2023). The study was carried out accordance with Declaration of Helsinki.
Consent
The study subjects were enrolled only after obtaining informed written consent for both participation and dissemination of results.
Sources of funding
Not applicable.
Author contributions
N.G.M., P.H.M., S.H.A., and L.A.: conceptualization, data acquisition, data interpretation, making the first draft, critically revising the manuscript; R.H.K.,J.M.A., and S.M.A.: data analysis, data interpretation, making the first draft. All the authors approved the final version of the manuscript and are accountable for all aspects of the work.
Conflicts of interest disclosure
The authors declare no conflicts of interest.
Guarantor
Lava Abdullah.
Research registration unique identifying number (UIN)
Not applicable.
Provenance and peer review
Not commissioned, externally peer-reviewed.
Data availability statement
Data are available on request.
References
- [1].Johns EC, Denison FC, Norman JE, et al. Gestational diabetes mellitus: mechanisms, treatment, and complications. Trends Endocrinol Meta 2018;29:743–54. [Google Scholar]
- [2].Wu L, Cui L, Tam WH, et al. Genetic variants associated with gestational diabetes mellitus: a meta-analysis and subgroup analysis. Sci Rep 2016;6:30539. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Chanda S, Dogra V, Hazarika N, et al. Prevalence and predictors of gestational diabetes mellitus in rural Assam: a cross-sectional study using mobile medical units. BMJ Open 2020;10:e037836. [Google Scholar]
- [4].Murray SR, Reynolds RM. Short- and long-term outcomes of gestational diabetes and its treatment on fetal development. Prenat Diagn 2020;40:1085–91. [DOI] [PubMed] [Google Scholar]
- [5].Armengaud JB, Ma RCW, Siddeek B, et al. Offspring of mothers with hyperglycaemia in pregnancy: the short term and long-term impact. What is new? Diabetes Res Clin Pract 2018;145:155–66. [DOI] [PubMed] [Google Scholar]
- [6].Yefet E, Schwartz N, Sliman B, et al. Good glycemic control of gestational diabetes mellitus is associated with the attenuation of future maternal cardiovascular risk: a retrospective cohort study. Cardiovasc Diabetol 2019;18:1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Hauspurg A, Ying W, Hubel CA, et al. Adverse pregnancy outcomes and future maternal cardiovascular disease. Clin Cardiol 2018;41:239–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Kim KS. The importance of treating mild hyperglycemia in pregnant women with diabetes. Korean J Intern Med 2018;33:1079–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Cosson E, Vicaut E, Sandre-Banon D, et al. Early screening for gestational diabetes mellitus is not associated with improved pregnancy outcomes: an observational study including 9795 women. Diabetes Metab 2019;45:465–72. [DOI] [PubMed] [Google Scholar]
- [10].Curtis AM, Farmer AJ, Roberts NW, et al. Performance of guidelines for the screening and diagnosis of gestational diabetes mellitus during the COVID-19 pandemic: a scoping review of the guidelines and diagnostic studies evaluating the recommended testing strategies. Diabet Epidemiol Manag 2021;3:100023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Yaktine AL, Rasmussen KM, Bodnar LM. Weight gain during pregnancy: reexamining the guidelines. Obstet Gynecol 2010;116:1191–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Rashid R, Sohrabi C, Kerwan A, et al. The STROCSS 2024 guideline: strengthening the reporting of cohort, cross-sectional, and case-control studies in surgery. Int J Surg 2024;110:3151–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].O’Malley EG, Reynolds CME, O’Kelly R, et al. The diagnosis of gestational diabetes mellitus (GDM) using a 75 g oral glucose tolerance test: a prospective observational study. Diabetes Res Clin Pract 2020;163:108144. [DOI] [PubMed] [Google Scholar]
- [14].Association AD. Gestational diabetes mellitus. Diabetes Care 2004;27:s88–s90. [DOI] [PubMed] [Google Scholar]
- [15].National Institute for Health and Care Excellence (NICE). Diabetes in pregnancy: management from preconception to the postnatal period. Accessed 16 December 2020. https://www.nice.org.uk/guidance/ng3
- [16].Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health 2015;36:89–108. [DOI] [PubMed] [Google Scholar]
- [17].Basha A, Fram K, Thekrallah F, et al. Prevalence of gestational diabetes and contributing factors among pregnant Jordanian women attending Jordan University Hospital. Int J Diabetes Dev Countries 2018;39:132–8. [Google Scholar]
- [18].Groof Z, Garashi G, Husain H, et al. Prevalence, risk factors, and fetomaternal outcomes of gestational diabetes mellitus in Kuwait: a cross-sectional study. J Diabetes Res 2019;2019:9136250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Alharthi AS, Althobaiti KA, Alswat KA. Gestational diabetes mellitus knowledge assessment among Saudi women. Open Access Maced J Med Sci 2018;6:1522–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Al-Rifai RH, Abdo NM, Paulo MS, et al. Prevalence of gestational diabetes mellitus in the Midsdle East and North Africa, 2000-2019: a systematic review, meta-analysis, and meta-regression. Front Endocrinol (Lausanne) 2021;12:668447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Li Y, Ren X, He L, et al. Maternal age and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of over 120 million participants. Diabetes Res Clin Pract 2020;162:108044. [DOI] [PubMed] [Google Scholar]
- [22].Duman NB. Frequency of gestational diabetes mellitus and the associated risk factors. Pak J Med Sci 2015;31:194–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Karasneh RA, Migdady FH, Alzoubi KH, et al. Trends in maternal characteristics, and maternal and neonatal outcomes of women with gestational diabetes: a study from Jordan. Ann Med Surg (Lond) 2021;67:102469. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Lee KW, Ching SM, Ramachandran V, et al. Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis. BMC Pregnancy Childbirth 2018;18:494. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Zhang S, Liu H, Li N, et al. Relationship between gestational body mass index change and the risk of gestational diabetes mellitus: a community-based retrospective study of 41,845 pregnant women. BMC Pregnancy Childbirth 2022;22:336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Qiu Y, Zhang X, Ni Y. Association between polycystic ovarian syndrome and risk of gestational diabetes mellitus: a meta-analysis. Gynecol Obstet Invest 2022;87:150–58. [DOI] [PubMed] [Google Scholar]
- [27].Deswal R, Narwal V, Dang A, et al. The prevalence of polycystic ovary syndrome: a brief systematic review. J Hum Reprod Sci 2020;13:261–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].McIntyre HD, Catalano P, Zhang C, et al. Gestational diabetes mellitus. Nat Rev Dis Primers 2019;5:47. [DOI] [PubMed] [Google Scholar]
- [29].Wu Y, Liu B, Sun Y, et al. Association of maternal prepregnancy diabetes and gestational diabetes mellitus with congenital anomalies of the newborn. Diabetes Care 2020;43:2983–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Moosazadeh M, Bahar A, Sarhangi R, et al. Relationship between hypothyroidism and gestational diabetes mellitus: a retrospective cohort study. J Endocrinol Meta Diabetes South Africa 2022;28:1–5. [Google Scholar]
- [31].Chen GD, Gou XY, Pang TT, et al. Associations between thyroid function and gestational diabetes mellitus in Chinese pregnant women: a retrospective cohort study. BMC Endocr Disord 2022;22:44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Sert UY, Buyuk GN, Engin Ustun Y, et al. Is there any relationship between thyroid function abnormalities, thyroid antibodies and development of gestational diabetes mellitus (GDM) in pregnant women? Medeni Med J 2020;35:195–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available on request.