Abstract
Background
The prevalence of gestational diabetes mellitus (GDM) is increasing worldwide in parallel with obesity, maternal age, improved screening methods and wider screening coverage. The objective of our study was to determine the prevalence of GDM in Lebanon and to evaluate its risk factors before and during the COVID-19 pandemic.
Methods
Records of 2595 pregnant women who gave birth at the Hôtel-Dieu de France Hospital in Beirut, were collected retrospectively from 2018 to 2022. GDM was determined by documentation in the maternity fileward. A univariate analysis was performed to assess the factors affecting GDM. The χ² test, Fisher’s exact test, independent samples T-test, and ANOVA were used for this purpose.
Results
The mean age was 32.03 years (± 4.75). The mean BMI at the beginning of pregnancy was 24.86 kg/m² (± 8.99), and the mean gestational weight gain (GWG) was 12.17 kg (± 5.06). The overall prevalence of GDM was 4.1%, increasing significantly from 2.9% to 6.4% between 2018 and 2022 (p value = 0.007). The peak in 2020 was temporally associated with the onset of the COVID-19 pandemic and the compounding socioeconomic crises. The risk factors associated with GDM included older age (p = 0.003) and obesity (p = 0.004).
Conclusion
The increasing trend of GDM incidence emphasizes the importance of implementing evidence-based prevention, diagnostic, and treatment strategies.
Keywords: Prevalence, Gestational Diabetes, Lebanon, COVID-19, Pregnancy
Background
Gestational diabetes mellitus (GDM) is a type of hyperglycemia that manifests for the first time during pregnancy and resolves after delivery [1]. It is the most common metabolic-associated complication of pregnancy; it increases the risk of neonatal and maternal morbidity and is highly predictive of the subsequent development of type 2 diabetes in affected women [2, 3]. Moreover, numerous studies indicate that hyperglycemia during pregnancy influences the long-term metabolic health of offspring [4]. The global epidemiology of GDM and secular trends over recent decades remain unclear due to the lack of consensus on diagnostic criteria [5]. In fact, the rates of GDM vary widely from approximately 1% to > 20% [6, 7]. Interestingly, a systematic review of 31 cohorts and cross-sectional studies with 136 705 women revealed that adopting the International Association of Diabetes in Pregnancy Study Group (IADPSG) criteria resulted in an overall 75% increase in GDM incidence compared with previous local criteria (relative risk, 1.75; 95% CI, 1.53–2.01) [8]. The major risk factors that contribute to the increasing prevalence of GDM include increased maternal obesity and advanced maternal age [6, 7]. Although there is still no consensus on the best way to screen for GDM, there is broad agreement on the importance of early diagnosis and identification of women at risk to optimize glycemic control and reduce pregnancy-related complications and associated healthcare costs [9]. Understanding population-specific healthcare needs over time is essential, and prevalence estimates are useful for this purpose [10].
The prevalence of GDM in Lebanon remains largely unknown, and the few available studies date back to 2016 [10]. In 2019, the country was plagued by one of the worst economic crises since the mid-20th century, followed by the COVID-19 pandemic, and culminated with the Beirut port explosion on August 4th, 2020 [11]. Several prospective studies have demonstrated that exposure to stressful events predisposes individuals to hyperglycemia, metabolic syndrome, and type 2 diabetes (T2D) [12, 13]. Nevertheless, the literature examining the relationship between life stressors and GDM remains limited [14]. To our knowledge, no such study has been conducted in Lebanon. Therefore, we aimed to determine the annual prevalence of GDM in a single tertiary care referral center in Lebanon from 2018 to 2022. We also examined the associated risk factors for GDM, such as maternal age, obesity, and gestational weight gain (GWG). Finally, we evaluated the impact of the COVID-19 pandemic and the economic crisis period on the incidence of GDM.
Methods
Study design
A retrospective observational study was conducted at Hôtel-Dieu de France (HDF) Hospital in Beirut, Lebanon. All pregnant Lebanese women admitted to the gynecology and obstetrics department for delivery between September 10, 2018, and December 31, 2022, were included. Data were extracted from the hospital’s electronic medical record system (DX Care). Prior to September 2018, the files were not yet electronic. Women of non-Lebanese nationality were excluded, as were those with pregestational type 1 or type 2 diabetes. The following variables were retrieved from the records: location of residence, average monthly income (extracted as nominal USD equivalent values at the time of admission), education level, and lifestyle habits such as smoking and a sedentary lifestyle. March 14, 2020, marked the beginning of the first total lockdown in Lebanon. Maternal age was stratified into the following categories: <20, 20–24, 25–29, 30–34, 35–39, and > 40 years. Height (m), prepregnancy weight (kg), and prepregnancy body mass index (BMI) (kg/m2) were noted. Underweight women had a BMI < 18.5, normal weight women had a BMI between 18.5 and 24.9, overweight women had a BMI between 25 and 29.9, and obese women had a BMI > 30. Moreover, we divided our population into 2 groups according to BMI: <25 kg/m² and ≥ 25 kg/m². GWG in kg was also recorded and then stratified according to Institute of Medicine (IOM) recommendations for each BMI category, with a total weight gain of 12.5–18 kg for underweight individuals (< 18.5 kg/m2), 11.5–16 kg (0.35–0.50 kg/m2) for normal weight individuals (18.5–24.9 kg/m2), 7–11.5 kg for overweight individuals (25–29.9 kg/m2) and 5–9 kg for obese individuals (≥ 30 kg/m2) [15]. Other variables were also extracted, such as GDM treatment (diet, metformin, or insulin), smoking during pregnancy, COVID-19 pneumonia during pregnancy, gestational hypertension (GH), preeclampsia, eclampsia, preterm premature rupture of membranes (PPROM), mode of delivery (vaginal delivery, instrumental vaginal delivery or cesarean section) and neonatal complications such as macrosomia (with infant birth weight > 4000 g), intrauterine growth restriction and prematurity.
Screening and diagnostic methods for GDM at Hôtel-Dieu de France
At HDF Hospital, all obstetricians used the same method of screening for GDM at 24–26 weeks of gestation for all pregnant women. The test consisted of a modified version of the oral glucose tolerance test (OGTT), which measures fasting blood glucose and only 2 h after a 75 g oral glucose load. If the blood glucose level exceeded the established threshold (≥ 92 mg/dL at fasting or ≥ 153 mg/dL 2 h after the glucose load), the woman was considered to have GDM and was referred to an endocrinologist for further management. For high-risk patients, a fasting blood glucose test was performed early in pregnancy, coupled with an HbA1c. Notably, there was no change in the screening policy between 2018 and 2022.
Ethical considerations
The study received approval from the Ethics Committee of Hôtel-Dieu de France (Reference: Tfem/2023/72). Informed consent was not needed, as the data collection was anonymous and retrospective.
Statistical analysis
The data were analyzed via SPSS version 26. Continuous variables were expressed as means and percentages, whereas categorical variables were expressed as frequencies and percentages. Due to the truncated nature of the 2018 dataset (capturing only the post-electronic medical record implementation period from September to December), sensitivity analyses for temporal trends were conducted both including and excluding 2018 data to ensure sample size imbalances did not distort statistical validity. A univariate analysis was performed to assess the factors affecting GDM. The χ² test, Fisher’s exact test, independent samples t test, and ANOVA were used for univariate analyses. The statistical significance threshold was set at p ≤ 0.05.
In addition, to evaluate the independent risk factors associated with gestational diabetes mellitus (GDM), a multivariable binary logistic regression analysis was performed. Adjusted Odds Ratios (OR) and their corresponding 95% Confidence Intervals (CI) were calculated to assess the strength and direction of the associations.
Results
Social and demographic characteristics of the population
A total of 2595 pregnant women who gave birth at HDF between 2018 and 2022 were included in the cohort (Fig. 1). It should be noted that the number of deliveries reported for 2018 includes only those occurring between September and December 2018. Most women resided near the capital (Mount Lebanon 75.5% and Beirut 9.7%) (Table 1). Since 2020, self-reported nominal USD equivalent income significantly declined (p < 0.001) over the subsequent years, acting as a direct surrogate marker for the severe local currency devaluation and loss of purchasing power. Indeed, there was a shift from a majority with an income above $3000 (58.8% in 2018 and 56.1% in 2019) to a majority with an income lower than $1000 (46.8% in 2021 and 49.9% in 2022). Most of the women in our cohort (88.4%) had a university diploma (Table 1). The overall population was physically active (86.6%), but a significant increase in sedentary lifestyles was observed across the years (5% in 2018, 9.4% in 2019, 6.9% in 2020, 12.7% in 2021, and 14.1% in 2022). Moreover, 99% of women did not smoke during pregnancy, with no significant change from 2018 to 2022 (Table 1). Most pregnancies were carried at term (78.7%) and were singletons (95.6%) (Table 2). The mean age of the population was 32.03 years (± 4.75), with no significant difference across the study period. Most women became pregnant between 30 and 34 years of age (40%). Moreover, women 35 years and older represented a considerable proportion of our cohort (31.3%) (Table 2). The mean BMI at the beginning of pregnancy was 24.86 kg/m² (± 8.99), and the mean GWG was 12.17 kg (± 5.06), with no notable difference across the study period (Table 2). In fact, most women had a GWG in accordance with the IOM recommendations (38%) or even below these recommendations (32.7%) (Table 2).
Fig. 1.
Flowchart describing the selection of pregnant women. GDM: Gestational diabetes mellitus
Table 1.
Social and demographic characteristics of pregnant women from 2018–2022
| Year | Total | |||||||
|---|---|---|---|---|---|---|---|---|
| End of 2018 | 2019 | 2020 | 2021 | 2022 | ||||
| No (%) | No (%) | No (%) | No (%) | No (%) | p value | No (%) | ||
| Social characteristics | ||||||||
| Governorate of residence | Akkar | 1 (1.1%) | 13 (2.6%) | 5 (1.2%) | 5 (0.9%) | 7 (1.3%) | 0.062 | 31(1.5%) |
| Baalbeck Hermel | 1 (1.1%) | 10 (2.0%) | 4 (1.0%) | 3 (0.6%) | 7 (1.3%) | 25(1.2%) | ||
| Bekaa | 0 (0.0%) | 16 (3.2%) | 15 (3.6%) | 11 (2.1%) | 10 (1.8%) | 52 (2.5%) | ||
| Beirut | 7 (7.9%) | 56 (11.3%) | 36 (8.6%) | 46 (8.7%) | 58 (10.4%) | 203 (9.7%) | ||
| Kesrouan - Jbeil Mount Lebanon | 70 (78.7%) | 344 (69.2%) | 307 (73.4%) | 428 (80.6%) | 431 (77.2%) | 1581 (75.5%) | ||
| North Lebanon | 2 (2.2%) | 17 (3.4%) | 20 (4.8%) | 12 (2.3%) | 13 (2.3%) | 64 (3.1%) | ||
| South Lebanon | 5 (5.6%) | 25 (5.0%) | 22 (5.3%) | 16 (3.0%) | 17 (3.0%) | 85 (4.1%) | ||
| Nabatiyeh | 3 (3.4%) | 16 (3.2%) | 9 (2.2%) | 10 (1.9%) | 15 (2.7%) | 53 (2.5%) | ||
| Income, US$ | < 1000 | 0 (0.0%) | 10 (2.0%) | 26 (5.9%) | 192 (46.8%) | 195 (49.9%) | < 0.001 | 423 (23.8%) |
| 1000 - 3000 | 21 (41.2%) | 204 (41.8%) | 201 (45.6%) | 164 (40.0%) | 173 (44.2%) | 763 (42.8%) | ||
| > 3000 | 30 (58.8%) | 274 (56.1%) | 214 (48.5%) | 54 (13.2%) | 23 (5.9%) | 595 (33.4%) | ||
| Level of education | Uneducated | 0 (0.0%) | 0 (0.0%) | 1 (0.3%) | 4 (0.8%) | 5 (0.9%) | 0.008* | 10 (0.5%) |
| Primary school | 1 (2.2%) | 17 (4.2%) | 15 (4.1%) | 16 (3.2%) | 6 (1.1%) | 55(3%) | ||
| High School | 6 (13.0%) | 47 (11.5%) | 28 (7.6%) | 37 (7.5%) | 33 (6.1%) | 151 (8.1%) | ||
| University degree | 39 (84.8%) | 344 (84.3%) | 326 (88.1%) | 439 (88.5%) | 496 (91.9%) | 1644 (88.4%) | ||
| Lifestyle habits | Sedentary | 2 (5.0%) | 38 (9.4%) | 21 (6.9%) | 54 (12.7%) | 67 (14.1%) | 0.009* | 182(11%) |
| Active | 36 (90.0%) | 350 (86.8%) | 277 (91.1%) | 367 (86.2%) | 398 (83.6%) | 1428 (86.6%) | ||
| Very Active | 2 (5.0%) | 15 (3.7%) | 6 (2.0%) | 5 (1.2%) | 11 (2.3%) | 39 (2.4%) | ||
| Tobacco use during pregnancy | 1 (0.7%) | 4 (0.62%) | 6 (1.10%) | 5 (0.79%) | 9 (1.41%) | 0.645 | 25 (1.0%) | |
The χ² test was used to compare categorical variables. (*) Fisher's test, p values in bold are statistically significant
Table 2.
Anthropometric characteristics of pregnancies from 2018–2022
| Year | Total | |||||||
|---|---|---|---|---|---|---|---|---|
| End of 2018 | 2019 | 2020 | 2021 | 2022 | ||||
| No (%) | No (%) | No (%) | No (%) | No (%) | p value | No (%) | ||
| Pregnancy characteristics | ||||||||
| Term | Term | 82 (84.5%) | 379 (73.4%) | 336 (77.4%) | 44080.6% | 499 (81.5%) | 0.005 | 1736 (78.7%) |
| Preterm a | 15 (15.5%) | 137 (26.6%) | 98 (22.6%) | 10619.4% | 113 (18.5%) | 469 (21.3%) | ||
| Parity | Singleton | 94 (96.9%) | 487 (95.1%) | 420 (95.7%) | 51794.2% | 593 (96.9%) | 0.466* | 2111 (95.6%) |
| Twin | 3 (3.1%) | 23 (4.5%) | 18 (4.1%) | 31 (5.6%) | 17 (2.8%) | 92 (4.2%) | ||
| Triplet | 0 (0.0%) | 2 (0.4%) | 1 (0.2%) | 0 (0.0%) | 2 (0.3%) | 5 (0.2%) | ||
| Quadruplet | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (0.2%) | 0 (0.0%) | 1 (0.0%) | ||
| Anthropometric characteristics | ||||||||
| Maternal age, in years (mean ± SD) | 31.8 ±5.34 | 31.71±4.97 | 32±4.63 | 32.3±4.64 | 32.17±4.58 | 0.36** | 32.03 ± 4.75 | |
| Maternal age categories, in years. | <20 | 4 (2.9%) | 4 (0.6%) | 1 (0.2%) | 0 (0.0%) | 00.0% | < 0.001 | 9 (0.3%) |
| 20 - 24 | 8 (5.9%) | 48 (7.4%) | 20 (3.7%) | 26 (4.2%) | 26 (4.0%) | 129 (5%) | ||
| 25 - 29 | 30 (22.1%) | 155 (24.0%) | 154 (28.3%) | 152 (24.3%) | 153 (23.8%) | 644 (24.8%) | ||
| 30 - 34 | 53 (39.0%) | 253 (39.2%) | 203 (37.2%) | 252 (40.3%) | 279 (43.5%) | 1040 (40.1%) | ||
| 35 - 39 | 32 (23.5%) | 150 (23.3%) | 140 (25.7%) | 162 (25.9%) | 144 (22.4%) | 628 (24.2%) | ||
| > 40 | 9 (6.6%) | 35 (5.4%) | 27 (5.0%) | 34 (5.4%) | 40 (6.2%) | 145 (5.6%) | ||
| BMI at the beginning of pregnancy, in kg/m² (mean ± SD) | 24.01 ± 4.28 | 24.73±10.8 | 24.99±6.92 | 24.80±12.01 | 24.39±4.92 | 0.27 | 24.86± 8.99** | |
| BMIbCategories | Underweight | 3 (2.2%) | 22 (3.4%) | 12 (2.2%) | 24 (3.8%) | 33 (5.1%) | 0.109 | 94 (3.6%) |
| Normal weight | 86 (63.7%) | 394 (61.1%) | 319 (58.5%) | 371 (59.3%) | 381 (59.3%) | 1551 (59.8%) | ||
| Overweight | 30 (22.2%) | 161 (25.0%) | 129 (23.7%) | 149 (23.8%) | 134 (20.9%) | 603 (23.3%) | ||
| Obesity | 16 (11.9%) | 68 (10.5%) | 85 (15.6%) | 82 (13.1%) | 94 (14.6%) | 345 (13.3%) | ||
| GWG in kg (mean ±SD) | 11.98 ± 4.52 | 12.03±5.14 | 12.15±5.28 | 12.02±5.07 | 12.49±4.98 | 0.15 | 12.17 ±5.06** | |
| GWG% IOM | Below the recommendations | 31 (36.0%) | 182 (34.6%) | 138 (29.8%) | 203 (34.5%) | 195 (31.2%) | 0.428 | 749 (32.7%) |
| In accordance with the recommendations | 35 (40.7%) | 200 (38.0%) | 173 (37.4%) | 223 (37.9%) | 238 (38.1%) | 869 (38.0%) | ||
| Above the recommendations | 20 (23.3%) | 144 (27.4%) | 152 (32.8%) | 162 (27.6%) | 192 (30.7%) | 670 (29.3%) | ||
BMI Body mass index in kg/m², GWG Gestational weight gain in kg, GWG %IOM Gestational weight gain relative to the IOM (Institute of Medicine) recommendations. (a) Preterm <37 weeks of gestation (SA). (b) BMI categories: see methods section. The χ² test was used to compare categorical variables. (*) Fisher's test. (**) ANOVA Mean ± standard deviation. p values in bold are statistically significant
Prevalence of GDM at HDF
The overall GDM prevalence was 4.1%. This rate varied significantly over the years (3.1% in 2019, 2.9% in 2021, 3.6% in 2022, p value of 0.007) and peaked in 2020 (6.4%). The majority of GDM cases (79.4%) were mild and managed with lifestyle and dietary measures (Table 3). When stratified according to BMI, GDM prevalence was 4.46% and 10.54% in women with a BMI < 25 kg/m² and those with a BMI ≥ 25 kg/m², respectively. The GDM incidence according to each BMI category followed the same trends over the years, as shown in Fig. 2 (p = 0.498). On the other hand, the mean maternal age remained the same across the years (p = 0.36), with most individuals aged between 30 and 34 years (40.1%) (Table 2). Similarly, no significant fluctuations in GWG were observed from 2018 to 2022 (p = 0.15). (Table 2)
Table 3.
Prevalence of gestational diabetes mellitus and treatment modalities from 2018–2022
| Year | Total | |||||||
|---|---|---|---|---|---|---|---|---|
| End of 2018 | 2019 | 2020 | 2021 | 2022 | ||||
| No (%) | No (%) | No (%) | No (%) | No (%) | p value | No (%) | ||
| GDM | No | 127 (93.4%) | 624 (96.9%) | 510 (93.6%) | 607 (97.1%) | 617 (96.4%) | 0.007 | 2486 (95.9%) |
| Yes | 9 (6.6%) | 20 (3.1%) | 35 (6.4%) | 18 (2.9%) | 23 (3.6%) | 105 (4.1%) | ||
| Treatment of GDM | Diet | 7 (87.5%) | 14 (73.7%) | 26 (74.3%) | 15 (83.3%) | 19 (86.4%) | 0.808* | 81 (79.4%) |
| Oral hypoglycemic agents | 0 (0.0%) | 3 (15.8%) | 5 (14.3%) | 2 (11.1%) | 3 (13.6%) | 13 (12.7%) | ||
| Insulin | 1 (12.5%) | 2 (10.5%) | 4 (11. 4%) | 1 (5.6%) | 0 (0%) | 8 (7.8%) | ||
GDM Gestational Diabetes Mellitus Statistical Tests: χ² test to compare categorical variables. (*) Fisher’s test. p values in bold are statistically significant
Fig. 2.
Prevalence of gestational diabetes mellitus stratified by body mass index from 2019–2022; p value 0.498. BMI: Body mass index in kg/m². GDM: gestational diabetes
Other maternal complications during pregnancy
A significant increase in the rate of gestational hypertension was noted in 2020 and 2022 (1.4% in 2018, 1.5% in 2019, 2.56% in 2020, 1.43% in 2021, and 4.04% in 2022) (p value 0.013) (Table 4). However, no concomitant increase in the rates of preeclampsia, eclampsia, or premature rupture of membranes was observed (Table 4). A total of 177 COVID-19 cases were reported in our cohort. Additionally, a marked increase in the rate of cesarean sections was observed (34.37% in 2018, 55.73% in 2019, 55.56% in 2020, 53.67% in 2021, and 57.29% in 2022, with a p value < 0.001) (Table 4).
Table 4.
Maternal complications during pregnancy from 2018–2022
| Year | Total | |||||||
|---|---|---|---|---|---|---|---|---|
| End of 2018 | 2019 | 2020 | 2021 | 2022 | ||||
| No (%) | No (%) | No (%) | No (%) | No (%) | p value | No (%) | ||
| GH | 2 (1.4%) | 10 (1. 5%) | 14 (2.56%) | 9 (1.43%) | 26 (4.04%) | 0.013 | 61 (2.4%) | |
| Preeclampsia | 0 (0%) | 7 (1.08%) | 4 (0.73%) | 6 (0.95%) | 3 (0.46%) | 0.564* | 20 (0.8%) | |
| Eclampsia | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.16%) | 0 (0%) | 0.534* | 1 (0%) | |
| PPROM | 0 (0%) | 8 (1.24%) | 5 (0.91%) | 2 (0.32%) | 6 (0.93%) | 0.322* | 21 (0.8%) | |
| COVID-19 during pregnancy | 177 (6.8%) | |||||||
| Mode of Delivery | Cesarean Section | 22 (34.37%) | 253 (55.73%) | 235 (55.56%) | 263 (53.67%) | 330 (57.29%) | < 0.001 | 1103 (55%) |
| Spontaneous VD | 21 (32.81%) | 89 (19.60%) | 87 (20.56%) | 77 (15.71%) | 77 (13.36%) | 351 (17.5%) | ||
| VD with instrumentation | 21 (32.81%) | 112 (24.67%) | 101 (23.87%) | 150 (30.61%) | 169 (29.34%) | 553 (27.6%) | ||
GH Gestational hypertension, VD Vaginal delivery, PPROM Preterm premature rupture of membranes. Statistical tests: Chi-square tests (χ²) were used to compare categorical variables. (*) Fisher’s exact test. p values in bold are statistically significant
Neonatal complications
The mean birth weight of newborns was 3181.47 g ± 609.145, with no significant variation over the years. The highest rate of macrosomia was recorded in 2020 (4.17%), although this difference was not statistically significant (p = 0.07) (Table 5). In parallel, excluding 2018, the incidence of intrauterine growth restriction (IUGR) was comparable between 2019 and 2022, with an overall incidence of 20 cases recorded (0.8%) (p = 0.3) (Table 5).
Table 5.
Neonatal Complications from 2018–2022
| Year | Total | ||||||
|---|---|---|---|---|---|---|---|
| End of 2018 | 2019 | 2020 | 2021 | 2022 | |||
| No (%) | No (%) | No (%) | No (%) | No (%) | p value | No (%) | |
| Baby’s weight in g (mean ±SD) | 3659.02 | 3070.36 ± 605.77 | 3039.53 ± 630.71 | 3036.12 ± 623.61 | 0.64** | 3181.47 ± 609.145 | |
| Macrosomiaa | 0 (0%) | 6 (1.38%) | 18 (4.17%) | 17 (3.16%) | 13 (2.29%) | 0.07* | 54 (2.7%) |
| IUGR | 0 (0%) | 3 (0.46%) | 3 (0.55%) | 8 (1.28%) | 6 (0.93%) | 0.34* | 20 (0,8%) |
(a) Macrosomia ≥ 4000 g. IUGR Intrauterine growth restriction. Statistical tests: (*) Fisher’s test. (**) ANOVA Mean ± standard deviation
Risk factors for GDM and associated complications
There was a significant association (p = 0.007) between the year of delivery and the presence of GDM, with 36.5% of women with GDM having delivered in 2020. A significant correlation between maternal age and GDM (p = 0.041) was observed, with higher rates of GDM among women aged 35–39 years and ≥ 40 years than among non-GDM individuals (35.4% vs. 23.8% and 9.0% vs. 5.4%, respectively) (Table 6). Similarly, patients with GDM were more overweight (35.4% vs. 22.8%) and obese (18.8% vs. 13.1%) than non-GDM individuals were (p = 0.004). Conversely, there was no significant association between GWG and GDM in the overall population (Table 6). Finally, women with GDM tended to have more gestational hypertension (p = 0.012) and an increased risk of fetal macrosomia (p < 0.001, Table 6).
Table 6.
Univariate analysis of risk factors and complications of GD
| Presence of gestational diabetes | p value | |||
|---|---|---|---|---|
| No | Yes | |||
| No (%) | No (%) | |||
| Sociodemographic factors: | ||||
| Governorate of residence | Akkar | 30 (1.6%) | 0 (0.0%) | 0.17* |
| Baalbeck Hermel | 23 (1.2%) | 1 (1.1%) | ||
| Bekaa | 46 (2.4%) | 6 (6.8%) | ||
| Beirut | 185 (9.7%) | 9 (10.2%) | ||
| Kesrouan-Jbeil- mont liban | 1448 (75.7%) | 61 (69.3%) | ||
| North Lebanon | 56 (2.9%) | 5 (5.7%) | ||
| South Lebanon | 76 (4.0%) | 4 (4.5%) | ||
| Nabatiyeh | 48 (2.5%) | 2 (2.3%) | ||
| Income ($) | < 1000 | 407 (24.8%) | 16 (18.8%) | 0.461 |
| 1000 - 3000 | 702 (42.7%) | 39 (45.9%) | ||
| > 3000 | 534 (32.5%) | 30 (35.3%) | ||
| Level of education | Not educated | 9 (0.5%) | 1 (1.3%) | 0.53* |
| Primary school | 50 (2.9%) | 4 (5.2%) | ||
| High school | 139 (8.0%) | 6 (7.8%) | ||
| University degree | 1537 (88.6%) | 66 (85.7%) | ||
| Lifestyle habits | Sedentary | 173 (11.2%) | 7 (10.1%) | 0.849 |
| Active | 1330 (86.4%) | 61 (88.4%) | ||
| Sporty | 36 (2.3%) | 1 (1.4%) | ||
| Temporal factor | ||||
| Year | End of 2018 | 0 (0.0%) | 0 (0.0%) | 0.007* |
| 2019 | 624 (26.5%) | 20 (20.8%) | ||
| 2020 | 510 (21.6%) | 35 (36.5%) | ||
| 2021 | 607 (25.7%) | 18 (18.8%) | ||
| 2022 | 617 (26.2%) | 23 (24.0%) | ||
| Period | Before the pandemic | 719 (37.0%) | 36 (41.4%) | 0.409 |
| During the pandemic | 1224 (63.0%) | 51 (58.6%) | ||
| Anthropometric factors: | ||||
| Parity | Single pregnancy | 1929 (95.7%) | 85 (92.4%) | 0.378* |
| Twin | 81 (4.0%) | 7 (7.6%) | ||
| Triplets | 5 (0.2%) | 0 (0.0%) | ||
| Quadruplets | 1 (0.0%) | 0 (0.0%) | ||
| Maternal age, years, mean ±SD | 31.99 ±4.71 | 33.46±4.64 | 0.003 | |
| Maternal age categories | <20 | 5 (0.2%) | 0 (0.0%) | 0.041* |
| 20 - 25 | 117 (5.0%) | 3 (3.1%) | ||
| 25 - 29 | 596 (25.3%) | 17 (17.7%) | ||
| 30 - 34 | 952 (40.4%) | 33 (34.4%) | ||
| 35 - 39 | 561 (23.8%) | 34 (35.4%) | ||
| > 40 | 127 (5.4%) | 9 (9.4%) | ||
| BMI in kg/m², mean ± SD | 24.64 ±9.29 | 26.35 ±5.51 | 0.26 | |
| BMI categories | Underweight | 88 (3.7%) | 3 (3.1%) | 0.004 |
| Normal weight | 1423 (60.3%) | 41 (42.7%) | ||
| Overweight | 538 (22.8%) | 34 (35.4%) | ||
| Obese | 309 (13.1%) | 18 (18.8%) | ||
| GWG% IOM | Below the recommendations | 680 (32.3%) | 37 (39.4%) | 0.352 |
| In line with the recommendations | 800 (38.0%) | 33 (35.1%) | ||
| Above the recommendations | 624 (29.7%) | 24 (25.5%) | ||
| Smoking during Pregnancy | 22 (0.9%) | 1 (1.0%) | 0.915* | |
| Comorbidities during Pregnancy: | ||||
| COVID-19 during pregnancy | 168 (7.1%) | 7 (7.3%) | 0.95 | |
| GH | 53 (2.2%) | 6 (6.3%) | 0.012 | |
| IUGR | 19 (0.8%) | 1 (1.0%) | 0.801* | |
| Mode of delivery | Cesarean Section | 1017 (54.9%) | 61 (70.9%) | 0.013 |
| SVD | 320 (17.3%) | 10 (11.6%) | ||
| Vaginal Delivery with Instrumentation | 517 (27.9%) | 15 (17.4%) | ||
| Macrosomia | 42 (2.2%) | 12 (14.6%) | < 0.001 | |
Abbreviations: BMI Body Mass Index in kg/m², GWG Gestational Weight Gain in kg, GWG %IOM Gestational Weight Gain relative to IOM (Institute of Medicine) recommendations, GH Gestational Hypertension, VD Vaginal Delivery, PROM Premature Rupture of Membranes, IUGR Intrauterine Growth Restriction, CS Cesarean Section χ² test to compare categorical variables. (*) Fisher's test. (**) ANOVA. Mean ± standard deviation
The results of the multivariable binary logistic regression model predicting GDM development are summarized in (Table 7). Only GWG, fetal macrosomia and the year emerged as a statistically significant, independent risk factor for GDM (p < 0.001). Conversely, maternal age, BMI and the year did not retain independent statistical significance within the multivariable model (Table 7).
Table 7.
Multivariate logistic regression model
| B | S.E. | Wald | df | Sig. | Exp(B) | 95% C.I.for EXP(B) | 95% C.I.for EXP(B) | ||
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Step 1a | Maternal Age | 0.035 | 0.025 | 2.015 | 1 | 0.156 | 1.036 | 0.987 | 1.087 |
| BMI | 0.008 | 0.007 | 1.333 | 1 | 0.248 | 1.008 | 0.994 | 1.023 | |
| GWG (Kg) | -0.086 | 0.025 | 11.850 | 1 | 0.001 | 0.918 | 0.874 | 0.964 | |
| Macrosomia | -2.237 | 0.370 | 36.639 | 1 | 0.000 | 0.107 | 0.052 | 0.220 | |
| Year | -0.053 | 0.106 | 0.249 | 1 | 0.618 | 0.948 | 0.770 | 1.168 | |
| Constant | -1.288 | 1.031 | 1.561 | 1 | 0.212 | 0.276 |
a. Variable(s) entered on step 1: Maternal Age, BMI, GWG, Macrosomia, Year
Discussion
Our study revealed that the overall prevalence of GDM among pregnant women followed at HDF was 4.1%. This rate is slightly lower than the global prevalence of GDM, estimated at 6.6% by the latest edition of the International Diabetes Federation. It is also lower than the prevalence in Europe (7.0%) and North America (6.0%). Although our center is located in the Middle East and North Africa (MENA) region, its prevalence is notably lower than the estimated prevalence in this region, which is 30.2% [16]. This difference is even more evident when comparing our population’s GDM rate to the prevalence of GDM according to the IADPSG criteria, which is 14.2% globally, 12.3% in Europe, 11.7% in North America, 14.2% in South America and Africa, 23.3% in Southeast Asia, and 30% in the MENA region [16]. According to the same study, our population’s GDM rate is closer to the prevalence reported in high-income countries (6.6%) than in middle- or low-income countries (9.9% and 11.7%, respectively) [16]. A meta-analysis of the prevalence of GDM in the MENA region between 2000 and 2019 revealed that it is the most affected region in the world, with a weighted overall GDM prevalence of 13%: Qatar (20.7%, 95% CI, 15.2–26.7%; 19 studies), Saudi Arabia (15%, 95% CI, 12.6–18.8%; 48 studies), and the UAE (13.4%, 95% CI, 9.4–18.0%; 14 studies) have the highest GDM prevalence, whereas Jordan has the lowest prevalence, with 4.7% (95% CI, 3.0–6.7%; six studies) [10]. The same study places Lebanon in the middle, with a prevalence of 10.1% (6.7–15.1), but with an unpredictable distribution, as this estimate was based on only two small studies [17, 18] involving approximately one hundred patients, without clear diagnostic criteria for GDM [10]. Thus, our reported prevalence is closest to that reported in Jordan and seems to be lower than that reported in all other regional countries. This result is consistent with Lebanon having one of the lowest rates of T2DM among women of childbearing age in MENA (7%, 95% CI 5.9, 9.3) from 2000 to 2018 [19].
This low GDM prevalence reported in our study, despite the rather advanced average maternal age (mean age 32 years), may be related to the socioeconomic profile of our population, as the majority of women belong to middle- and high-income groups. In addition, 88.5% of the women in our study had a university degree and lived in Beirut and its suburbs. A higher educational level may be associated with greater awareness of healthy dietary practices and better access to nutritious foods. Several studies have reported an inverse association between higher educational levels and the risk of GDM [20], as well as between higher socioeconomic status and GDM [21, 22]. Individuals with a higher educational level and better socioeconomic status may have a better understanding of health risk factors and better access to food and quality healthcare, which could reduce the risk of GDM [22]. Our Lebanese cuisine, which is part of the Mediterranean diet, is protective against GDM, with an RR of 0.67; 95% CI 0.58–0.78 [23]. Our population also appears to be mostly physically active, with a sedentary rate not exceeding 11%, while it reached 62.6% in Kuwait in 2014, 32.1% in Egypt in 2011, and 47% in Iraq in 2015 [24]. Physical activity has also been shown to reduce the incidence of GDM [25]. The obesity prevalence in our center was low (13.3%), markedly below the prevalence reported among women in the Middle East, which ranges between 40% and 50% [26]. Finally, the adoption of a modified version of the OGTT by our gynecologists (which consists of only two blood glucose measurements at fasting and 2 h after a 75 g oral glucose load) might have led to a lower estimation of GDM prevalence. Thus, it is difficult to compare prevalence rates on the basis of different criteria, especially since the IADPSG criteria are known to increase prevalence rates by 1.75 times [16].
Our study also demonstrated an evident peak in the prevalence of GDM in 2020, at 6.4%, compared with less than 4% in the other studied years. The year 2020 marked the beginning of the COVID-19 pandemic, with two total lockdown periods (from March 14, 2020, to June 21, 2020, and then from November 14–30, 2020) [27]. This same period coincided with a dramatic decrease in income due to the worst economic crisis in the country’s history. Indeed, the Lebanese currency lost 90% of its value by the end of 2019, severely limiting the purchasing power of the Lebanese people and their access to necessities. The country then suffered from shortages of medication, fuel, and electricity [28]. Finally, the explosion at Beirut port on August 4, 2020, described as the most powerful nonnuclear explosion of the 21st century, plunged the country into total disarray [29]. Several studies have investigated the effect of the pandemic period on the prevalence of GDM and confirmed our findings. Zanardo et al. reported a significant increase in the prevalence of GDM in pregnant women during the COVID-19 pandemic in Italy, which was related mainly to the lockdown during the first trimester of pregnancy [30]. Zheng et al. reported similar findings in China, indicating an association between exposure to lockdown and the risk of GDM when it occurs during the first four months of pregnancy, especially during the Level I lockdown, with cumulative exposures further increasing this risk [31]. Ruiz-Roso et al. highlighted an increase in the consumption of sweet foods and snacks associated with a high rate of physical inactivity during the lockdown [32]. However, our study does not show a decrease in the rate of physical activity in 2020. Indeed, during the early stages of the pandemic, some adaptive behaviors, such as engaging in self-care practices, exercising, and preparing healthy meals, were noted during the first lockdowns [33]. La Verde et al. linked the increase in GDM diagnoses in Italy during the lockdown period and the months following it to increased weight gain during pregnancy, resulting in a higher BMI at delivery [34]. In fact, Italy’s national policy to restrict COVID-19 infection was rigorous, and outdoor physical activity was strictly limited [34]. However, Zanardo et al. reported that the increase in GDM incidence was independent of maternal age, parity, socioeconomic status, maternal BMI before and after pregnancy, and GWG [30]. These findings are consistent with our study, which did not show a significant increase in BMI before pregnancy or in GWG in 2020. The less stringent lockdown regulations in Lebanon than in China or Italy could be a contributing factor. Another interesting study evaluated the overall prevalence of GDM in Romania (5.78%), which increased from 4.06% to 8.2% before and during the COVID-19 pandemic, respectively [35]. Thus, this increase in the GDM rate during the pandemic, without changes in BMI or physical activity, could result from exposure to stress factors, such as anxiety generated by the severity of the COVID-19 virus, uncertainty surrounding vaccines, or the search for effective therapeutic agents [36]. Prospective studies have shown that elevated levels of inflammatory markers predict the risk of developing T2DM [37]. Since GDM and T2DM share similar etiologies [38], it is therefore possible that exposure to stressful events during pregnancy triggers chronic inflammation and thus increases the risk of GDM. This hypothesis is supported by the fact that pregnancy itself is a very stressful period in a woman’s life and is considered one of the most stressful conditions under normal circumstances [39]. However, the causal relationship between early gestational stress and GDM remains unclear and requires more studies based on biochemical markers from the first trimester related to glucose, inflammation, insulin resistance, adipocytes, and the placenta [40]. Lebanon experienced several political, economic, and security crises in 2020, layered on top of the health crisis. The phenomenon of causality between catastrophes and T2DM or pregnancy complications was well studied before the COVID-19 pandemic. For example, a study conducted in New York reported an increased risk of GDM following widespread power outages during Hurricane Sandy [41]. A study following the 2011 earthquake in eastern Japan revealed a 5% increase in the prevalence of GDM among residents most affected by the disaster compared with those who were not affected [42]. Importantly, following the year 2020, the rate of GDM decreased to 2.9% in 2021. The univariate analysis considering the entire pandemic period (2020–2022) does not reveal a statistically significant relationship between giving birth during this period and the development of GDM. It is plausible that this is due to the resilience of the Lebanese population and the numerous adaptive mechanisms in response to the obstacles hindering their daily lives. Importantly, following the year 2020, the rate of GDM decreased to 2.9% in 2021. While the economic crisis in Lebanon persisted into 2021, the acute, cumulative shock characterizing 2020—marked by sudden national lockdowns, an unprecedented 90% currency crash, and the severe collective psychological trauma of the Beirut port explosion—gave way to a phase of chronic adaptation. Sociological and disaster-response frameworks demonstrate that populations exposed to protracted crises develop behavioral coping mechanisms and community-based support systems over time. Physiologically, this shift from an acute, hyper-adrenergic stress response to a stabilized chronic allostatic load may explain the normalization of glycemic trends, as cortisol levels and systemic inflammatory surges adapt [43]. Furthermore, this peak in GDM in 2020 was not accompanied by a significant increase in insulin or oral hypoglycemic medication requirements within our cohort. This stability in management modalities suggests that the crisis-induced surge was predominantly driven by mild-to-moderate hyperglycemia. Biologically, acute psychosocial stress and environmental trauma induce transient rises in counter-regulatory hormones (such as cortisol and catecholamines) and pro-inflammatory cytokines, which exacerbate peripheral insulin resistance. In a borderline-tolerant pregnant population, this stress-induced shift can push a significant number of women just past the diagnostic glycemic thresholds, without causing the severe pancreatic beta-cell exhaustion that would mandate advanced insulin therapy [44, 45]. These results contradict those of a Swiss study, which revealed increased needs for antidiabetic medications in women with GDM exposed to the pandemic compared with those not exposed to the pandemic [46]. There was also a significant increase in the rate of gestational hypertension in 2020, without a significant increase in other maternal complications of GDM, such as preeclampsia, eclampsia, or premature rupture of membranes (PROM). Furthermore, no rise in macrosomia rates was observed despite the higher prevalence of GDM in 2020. The same Swiss study noted no differences regarding other cardiovascular-metabolic, psychological, obstetrical, or neonatal complications [46]. This is likely a reflection of the continuity of proper medical follow-up for these women during this crisis period.
Our study has several strengths. This is the first study that provides the prevalence of GDM in Lebanon after 2016 and examines its trends from 2018 to 2022, a period marked by several crises and incidents. We acknowledge that the 2018 dataset represents a sample size imbalance. However, sensitivity analyses restricting the trend evaluation to full consecutive calendar years (2019 to 2022) successfully replicated the statistically significant peak in 2020, confirming that the observed temporal trends are robust and not an artifact of the 2018 sample size constraint. However, this study has several limitations. First, this was a retrospective study, and no direct causal relationships between GDM and risk factors could be established. Additionally, data were extracted retrospectively from electronic medical records; therefore, several individual covariates, such as alcohol consumption, dietary habits, the presence of PCOS, and previous history of GDM, were missing. Moreover, the main criterion for determining the presence of GDM was the inclusion of information in the medical records, decreasing the sensitivity of detection [47]. Third, the primary limitation of our study is the use of a modified version of the 75 g OGTT, measuring only fasting and 2-hour plasma glucose. By omitting the 1-hour measurement required by the strict IADPSG criteria, our protocol systematically underestimates the true prevalence of GDM in our cohort, as a significant proportion of women are known to exhibit isolated 1-hour hyperglycemia. Consequently, our overall reported prevalence of 4.1% should be interpreted as a conservative estimate. Nevertheless, because this screening policy remained strictly uniform and unchanged from 2018 to 2022, it provides a highly reliable framework for evaluating relative annual trends and temporal fluctuations within this specific population. Therefore, our study may underestimate the prevalence of GDM. Furthermore, this study was conducted in a single academic center, which limits the generalizability of the results to the entire Lebanese population. Our findings reflect the dynamics of a private tertiary care referral center and cannot be generalized to the broader, more vulnerable, or rural Lebanese populations who faced even harsher systemic barriers during the crisis. Importantly, several stressful events affected the country simultaneously, whereby the increase in GDM rates in 2020 could not be attributed to a single factor. In fact, the hypothesis that being exposed to stress could explain that the peak observed in 2020 was an assumption and not proven by a psychological evaluation or stress hormone measurements. Finally, our study did not analyze the relationship between the duration of exposure to the lockdown or its timing relative to the pregnancy term and the risk of developing GDM.
Conclusion
In conclusion, the prevalence of GDM among pregnant women followed at Hôtel Dieu de France from 2018 to 2022 was 4.1% (ranging from 2.9% to 6.4%). This rate, which is lower than that of other countries in the MENA region, reflects a high standard of the medical health system. Furthermore, we highlighted a significant increase in this rate in 2020, peaking at 6.4%. In the absence of variations in BMI, GWG, and physical activity, the severe acute psychosocial and environmental stressors prevailing in Lebanon during 2020 emerge as prominent factors associated with this temporary spike. Finally, it would be interesting to conduct a nationwide prospective study comparing the prevalence of GDM according to different screening methods and cutoffs.
Acknowledgements
Not applicable.
Abbreviations
- GDM
Gestational diabetes mellitus
- IADPSG
International Association of Diabetes in Pregnancy Study Group
- T2D
Type 2 Diabetes
- GWG
Gestational weight gain
- HDF
Hôtel Dieu de France
- BMI
Body Mass Index
- IOM
Institute of Medicine
- GH
Gestational hypertension
- PPROM
Preterm premature rupture of membranes
- OGTT
Oral glucose tolerance test
- IUGR
Intrauterine Growth Restriction
- WA
Weeks of Amenorrhea
- MENA
Middle East and North Africa
- COVID-19
Coronavirus SARS-CoV-2
Authors’ contributions
NY and NG contributed to the conception and design of the study, acquisition of the data and writing of the manuscript.GAT contributed to data acquisition.MBA and MHG contributed to the revision and writing of the manuscript.
Funding
Not applicable.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study received approval from the Ethics Committee of Hôtel-Dieu de France in accordance with the “Good Clinical Practice” guidelines described in the “Declaration of Helsinki” (October 2013 version) and the “International Ethical Guidelines for Biomedical Research Involving Human Subjects” of the Council for International Organizations of Medical Sciences (CIOMS), in collaboration with the World Health Organization (WHO). Informed consent was waived, as the data collection was anonymous and retrospective.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.