Gestational diabetes mellitus (GDM) is defined as glucose intolerance with onset or first recognition during pregnancy. It represents one of the most common medical complications of pregnancy and has emerged as a significant public health concern worldwide, particularly in low- and middle-income countries where the burden of non-communicable diseases is escalating rapidly. The prevalence of GDM has been rising globally in parallel with increasing rates of obesity, sedentary lifestyles, and advancing maternal age, posing substantial challenges to healthcare systems and threatening maternal and child health outcomes.

The global burden of hyperglycemia in pregnancy is staggering. In 2024, hyperglycemia during pregnancy was estimated to have affected 23.0 million (19.7%) live births worldwide, with GDM accounting for approximately 79.2% of these cases. The prevalence varies considerably across regions, influenced by genetic predisposition, lifestyle factors, dietary patterns, and diagnostic criteria employed. South Asian populations, including Indians, are known to have a particularly high susceptibility to GDM due to a combination of genetic factors, higher insulin resistance, and the phenomenon of "thin-fat" phenotype characterized by higher body fat at lower body mass indices.

India, with its vast and diverse population, bears a disproportionately high burden of GDM. A systematic review and meta-analysis estimated the pooled prevalence of GDM in India at 13% (95% CI: 9–16%). However, substantial regional variations exist, with southern states consistently reporting higher prevalence (15–22%) compared to northern (10–17%) and eastern regions (8–15%). The recently published ICMR-INDIAB national study, the first nationally representative study on GDM in India, reported a weighted national prevalence of 22.4% (95% CI: 16.7–28%), with Central India having the highest prevalence at 32.9% and West India the lowest at 16%. This study also highlighted the significant burden of early GDM, diagnosed before 20 weeks of gestation, with a prevalence of 19.2%.

The rising prevalence of GDM in India is attributable to multiple interrelated factors. Rapid urbanization has led to decreased levels of physical activity, changes in dietary patterns with increased consumption of processed and high-calorie foods, and increasing prevalence of obesity. Additionally, the epidemiological transition has resulted in women conceiving at older ages, further elevating the risk. The Government of India recognized the growing threat of GDM and mandated universal screening for all pregnant women as part of essential obstetric care within the Reproductive and Child Health programme in 2014, with revised guidelines issued in 2018. Despite these policy initiatives, implementation remains inconsistent across the country.

This was a hospital-based cross-sectional observational study conducted over a period of 18 months, at a tertiary care teaching hospital. The hospital serves as a major referral center for a large catchment area, providing comprehensive obstetric care to a diverse patient population comprising both urban and rural residents.

Study Population

The study population consisted of pregnant women attending the antenatal care (ANC) clinic of the hospital. Women were included if they were between 24 and 28 weeks of gestation, aged 18 years or older, had a singleton pregnancy, and provided written informed consent for participation. Women with pre-existing diabetes mellitus (type 1 or type 2), those diagnosed with overt diabetes in pregnancy, those with multiple gestations, and those with known chronic illnesses that could affect glucose metabolism were excluded from the study. A total of 650 pregnant women who met the inclusion criteria were enrolled consecutively.

Sample Size Calculation

The sample size was calculated based on an anticipated GDM prevalence of 14% (derived from previous Indian studies), with a 95% confidence level, 5% absolute precision, and accounting for a 10% non-response rate. The calculated sample size was 650 participants.

Data Collection

Data were collected using a pretested structured proforma. Information was obtained through face-to-face interviews, review of medical records, and physical examination. The proforma captured the following variables:

Socio-demographic characteristics: Age, education level, occupation, socio-economic status, and area of residence (urban/rural).

Obstetric history: Gravida, parity, gestational age, number of previous pregnancies, history of abortions, previous stillbirths, previous history of GDM, and history of previous macrosomic babies (birth weight ≥4 kg).

Medical history: Family history of diabetes mellitus (first-degree relatives), history of hypertension, history of PCOS, and history of thyroid disorders.

Anthropometric measurements: Pre-pregnancy weight (self-reported or documented in antenatal records), height, pre-pregnancy body mass index (BMI) calculated as weight in kilograms divided by height in meters squared, and weight gain during pregnancy up to the time of enrollment. BMI was categorized using Asian cutoffs: underweight (<18.5 kg/m²), normal (18.5–22.9 kg/m²), overweight (23–24.9 kg/m²), and obese (≥25 kg/m²).

Screening and Diagnosis of GDM

Universal screening for GDM was performed for all enrolled women between 24 and 28 weeks of gestation. A 75-g oral glucose tolerance test (OGTT) was administered following an overnight fast of at least 8 hours. Venous blood samples were collected at fasting, 1-hour, and 2-hour post-glucose load. Plasma glucose was estimated using the glucose oxidase-peroxidase method on an automated analyzer.

GDM was diagnosed using the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria, which are also endorsed by the World Health Organization. According to these criteria, GDM was diagnosed if one or more of the following plasma glucose values were met or exceeded:

• Fasting plasma glucose: ≥92 mg/dL (5.1 mmol/L)
• 1-hour plasma glucose: ≥180 mg/dL (10.0 mmol/L)
• 2-hour plasma glucose: ≥153 mg/dL (8.5 mmol/L)

Women diagnosed with GDM were categorized into three groups based on their management: (1) medical nutrition therapy (MNT) alone, (2) MNT plus metformin, and (3) MNT plus insulin.

Maternal and Neonatal Outcome Assessment

All participants were followed up until delivery. Maternal outcomes recorded included: development of hypertensive disorders of pregnancy (gestational hypertension and preeclampsia), mode of delivery (vaginal, instrumental, or cesarean section), preterm delivery (delivery before 37 completed weeks of gestation), postpartum hemorrhage, and urinary tract infections.

Neonatal outcomes recorded included: birth weight (macrosomia defined as birth weight ≥4 kg, and low birth weight defined as <2.5 kg), Apgar scores at 1 and 5 minutes, neonatal hypoglycemia (blood glucose <45 mg/dL within the first 24 hours of life), neonatal hyperbilirubinemia requiring phototherapy, respiratory distress syndrome, and admission to the neonatal intensive care unit (NICU).

Statistical Analysis

Data were entered into Microsoft Excel and analyzed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were expressed as frequencies and percentages for categorical variables, and mean ± standard deviation (SD) or median with interquartile range for continuous variables, as appropriate. The normality of continuous data was assessed using the Kolmogorov-Smirnov test.

For comparison between groups (GDM vs. non-GDM), the chi-square test or Fisher's exact test was used for categorical variables, and the independent t-test or Mann-Whitney U test was used for continuous variables, as appropriate. Univariate logistic regression analysis was performed to identify potential risk factors associated with GDM. Variables found to be significant at p<0.10 in univariate analysis were entered into a multivariate logistic regression model using the forward stepwise method to identify independent predictors of GDM. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were calculated. A p-value of <0.05 was considered statistically significant.

Baseline Characteristics of the Study Population

A total of 650 pregnant women between 24 and 28 weeks of gestation were enrolled in the study. The mean age of the participants was 27.4 ± 4.8 years (range: 18–42 years). The majority of women (68.9%) were in the age group of 21–30 years, while 21.4% were aged 31 years or older, and 9.7% were aged 20 years or younger. Regarding parity, 58.5% of women were primigravida, 32.3% were multigravida (2–3 pregnancies), and 9.2% were grand multigravida (≥4 pregnancies). Urban residents constituted 61.8% of the study population, while 38.2% were from rural areas. The mean pre-pregnancy BMI was 23.6 ± 4.2 kg/m², with 36.9% of women having a normal BMI, 28.6% being overweight, 19.8% being obese, and 14.6% being underweight.

Prevalence of GDM

Of the 650 pregnant women screened, 105 were diagnosed with GDM based on the IADPSG criteria, yielding an overall prevalence of 16.2% (95% CI: 13.4–19.0%). Among the 105 women with GDM, the distribution of abnormal glucose values was as follows: isolated elevated fasting glucose in 38 (36.2%), isolated elevated 1-hour glucose in 22 (21.0%), isolated elevated 2-hour glucose in 15 (14.3%), and two or more abnormal values in 30 (28.6%).

Risk Factors for GDM

The comparison of baseline characteristics between women with GDM and those without GDM is presented in Table 1.

Table 1: Comparison of baseline characteristics between GDM and non-GDM groups

*Independent t-test; †Chi-square test; SD: standard deviation; GDM: gestational diabetes mellitus; BMI: body mass index; PCOS: polycystic ovarian syndrome.

Univariate logistic regression analysis identified several significant risk factors for GDM, including maternal age ≥30 years, pre-pregnancy BMI ≥25 kg/m², family history of diabetes, previous history of GDM, previous macrosomia, PCOS, and history of hypertension. Multivariate logistic regression analysis was performed to identify independent predictors of GDM, and the results are presented in Table 2.

Table 2: Multivariate logistic regression analysis of risk factors for GDM

Maternal Outcomes

Maternal outcomes in the GDM and non-GDM groups are compared in Table 3.

Table 3: Maternal outcomes in GDM and non-GDM groups

†Chi-square test

Women with GDM had significantly higher rates of preeclampsia (18.1% vs. 7.0%, p=0.001), cesarean section (48.6% vs. 28.3%, p<0.001), preterm delivery (15.2% vs. 7.2%, p=0.008), postpartum hemorrhage (12.4% vs. 5.3%, p=0.009), urinary tract infections (14.3% vs. 6.6%, p=0.008), and induction of labor (26.7% vs. 15.4%, p=0.005) compared to the non-GDM group.

Neonatal Outcomes

Neonatal outcomes in the two groups are presented in Table 4.

Table 4: Neonatal outcomes in GDM and non-GDM groups

*Independent t-test; †Chi-square test; SD: standard deviation; NICU: neonatal intensive care unit

Neonates born to mothers with GDM had significantly higher mean birth weights (3.12 ± 0.58 kg vs. 2.89 ± 0.49 kg, p<0.001) and higher rates of macrosomia (14.3% vs. 4.2%, p<0.001), neonatal hypoglycemia (11.4% vs. 3.1%, p<0.001), neonatal hyperbilirubinemia (17.1% vs. 8.8%, p=0.011), and NICU admissions (19.0% vs. 8.4%, p=0.002) compared to the non-GDM group. There were no significant differences in the rates of low birth weight, Apgar scores <7 at 5 minutes, or respiratory distress syndrome between the two groups.

Management of GDM

Among the 105 women diagnosed with GDM, the distribution of management modalities was as follows: 72 women (68.6%) were managed with medical nutrition therapy (MNT) alone, 24 women (22.9%) required MNT plus metformin, and 9 women (8.6%) required MNT plus insulin. The decision to initiate pharmacological therapy was based on failure to achieve glycemic targets (fasting plasma glucose <95 mg/dL and 2-hour postprandial glucose <120 mg/dL) after 1–2 weeks of dietary and lifestyle modification. Among women requiring insulin, the mean daily insulin dose was 28.4 ± 12.6 units. All women received dietary counseling from a qualified nutritionist and were advised on self-monitoring of blood glucose.

The present study, conducted in a tertiary care hospital, revealed a GDM prevalence of 16.2% using the IADPSG criteria among pregnant women between 24 and 28 weeks of gestation. This finding is consistent with several recent Indian studies. A prospective observational study from a tertiary hospital in South India reported a GDM incidence of 4.7%, while a study from Haryana using ADA criteria reported a prevalence of 7.1%. A study from West Bengal using the non-fasting DIPSI criteria reported a higher prevalence of 14.9%. The ICMR-INDIAB national study, which is the most comprehensive nationally representative study on GDM in India, reported a weighted national prevalence of 22.4% using NICE criteria. The variation in prevalence estimates across studies can be attributed to differences in diagnostic criteria, study populations, geographic regions, and socio-demographic characteristics of the study participants. The prevalence of 16.2% observed in our study falls within the range reported in the systematic review and meta-analysis by Mantri et al., which estimated the pooled prevalence of GDM in India at 13% (95% CI: 9–16%).

The rising prevalence of GDM in India is a matter of significant public health concern. The ICMR-INDIAB study highlighted that nearly one in four pregnant women in India have GDM, with substantial regional variability. Central India had the highest prevalence at 32.9%, while West India had the lowest at 16%. Our finding of 16.2% is comparable to the prevalence reported from West India, suggesting that regional variations persist and that localized data are essential for healthcare planning.

Several risk factors were identified as independent predictors of GDM in our study. Advanced maternal age (≥30 years) was associated with a nearly three-fold increased risk (AOR: 2.84, 95% CI: 1.72–4.69). This finding is consistent with numerous studies from India and globally. A study from Delhi reported that 52% of women with GDM were aged ≥30 years compared to 16% in the non-GDM group (p<0.001). The association between advancing maternal age and GDM risk is thought to be mediated by age-related decline in pancreatic β-cell function and increased insulin resistance.

Pre-pregnancy overweight/obesity (BMI ≥25 kg/m²) emerged as the strongest independent predictor of GDM in our study, with an AOR of 3.12 (95% CI: 1.89–5.14). This finding aligns with the Chennai study, which identified pre-pregnancy overweight/obesity as the most reliable independent predictor of GDM (AmOR: 7.01; 95% CI: 2.96–16.64). A study from Bhubaneswar also reported a significantly higher risk of GDM among women with higher BMI (AOR: 3.85, 95% CI: 1.31–11.11). The rising prevalence of obesity among women of reproductive age in India is a major contributor to the increasing burden of GDM. The "thin-fat" phenotype common among Asian Indians, characterized by higher body fat percentage at lower BMI, further amplifies this risk.

Family history of diabetes was associated with a 2.56-fold increased risk of GDM in our study (AOR: 2.56, 95% CI: 1.58–4.15). This finding is consistent with the Kolkata study, which reported an OR of 1.86 (95% CI: 1.25–2.78) for family history of diabetes. The ICMR-INDIAB study also identified family history of diabetes as an independent risk factor for GDM. The strong familial aggregation of GDM suggests a significant genetic component, with shared genetic susceptibility to insulin resistance and β-cell dysfunction.

Previous history of GDM was the strongest predictor in our study, with an AOR of 4.87 (95% CI: 2.34–10.12). This is consistent with the Kolkata study, which reported an OR of 20.19 (95% CI: 5.64–72.21) for previous GDM. The STRiDE study from South India also found that previous history of GDM independently predicted early GDM. A systematic review and meta-analysis reported a pooled recurrence rate of GDM of 50.7% (95% CI: 46.6–55.2%), underscoring the importance of identifying women with a history of GDM for early intervention and intensive monitoring in subsequent pregnancies.

Previous macrosomia, a history of delivering a baby weighing ≥4 kg, was also identified as an independent predictor of GDM (AOR: 3.45, 95% CI: 1.67–7.12). A study from West Bengal also reported a statistically significant association between previous macrosomia and GDM. This association likely reflects shared underlying metabolic abnormalities, including maternal hyperglycemia during previous pregnancies that may have gone undetected.

PCOS and pre-existing hypertension were also significant independent predictors in our study, with AORs of 2.18 (95% CI: 1.08–4.41) and 2.34 (95% CI: 1.24–4.42), respectively. These findings are consistent with the Kolkata study, which reported an OR of 5.53 for PCOS, and with systematic review findings identifying PCOS and hypertensive disorders as key risk factors for GDM in low- and middle-income countries.

The maternal and neonatal outcomes observed in our study underscore the clinical significance of GDM. Women with GDM had significantly higher rates of preeclampsia (18.1% vs. 7.0%), cesarean section (48.6% vs. 28.3%), preterm delivery (15.2% vs. 7.2%), postpartum hemorrhage (12.4% vs. 5.3%), and urinary tract infections (14.3% vs. 6.6%). These findings are consistent with a prospective observational study from South India, which reported significantly higher rates of preeclampsia (p=0.020), urinary tract infection (p=0.008), preterm labor (p=0.001), and postpartum hemorrhage (p=0.015) in GDM pregnancies. Another study reported that GDM pregnancies had higher rates of preeclampsia (28% vs. 10%) and cesarean delivery (55% vs. 30%) compared to non-GDM pregnancies. The association between GDM and preeclampsia is biologically plausible, as both conditions share common pathogenic mechanisms involving insulin resistance, endothelial dysfunction, and inflammation.

Neonatal outcomes were also significantly worse in the GDM group. Neonates born to mothers with GDM had higher mean birth weights, higher rates of macrosomia (14.3% vs. 4.2%), neonatal hypoglycemia (11.4% vs. 3.1%), neonatal hyperbilirubinemia (17.1% vs. 8.8%), and NICU admissions (19.0% vs. 8.4%). These findings are consistent with the South Indian study, which reported significantly higher mean birth weights (p=0.021) and higher rates of neonatal hyperbilirubinemia (p=0.02) in the GDM group. Fetal exposure to maternal hyperglycemia leads to fetal hyperinsulinemia, which in turn promotes excessive fetal growth (macrosomia) and increases the risk of neonatal hypoglycemia following delivery. The higher rates of NICU admissions reflect the increased need for monitoring and management of these complications.

Regarding management, the majority of GDM women (68.6%) in our study were managed with medical nutrition therapy alone, while 22.9% required metformin and 8.6% required insulin. This distribution is comparable to a study from Delhi, which reported that 79.41% of GDM patients could be controlled on diet alone, 12.35% required insulin, and 8.23% required oral hypoglycemic agents. Another study reported that 53.75% of GDM patients managed their condition through dietary modifications alone. These findings highlight that a substantial proportion of women with GDM can achieve glycemic control with lifestyle modifications alone, emphasizing the importance of early diagnosis and prompt initiation of dietary and lifestyle interventions. For women requiring pharmacological therapy, metformin is emerging as a safe and effective alternative to insulin, with studies showing comparable maternal and neonatal outcomes.

The findings of this study have several important implications for clinical practice and public health policy. First, the high prevalence of GDM (16.2%) and the identification of several modifiable risk factors (obesity, family history of diabetes, PCOS, hypertension) underscore the need for targeted preconception counseling and lifestyle interventions among women of reproductive age. Routine BMI screening and counseling on weight management should be integral components of preconception and early antenatal care. Second, the significantly higher rates of adverse maternal and neonatal outcomes in GDM pregnancies highlight the need for early screening, intensive antenatal surveillance, and timely intervention. The Government of India's mandate for universal GDM screening for all pregnant women is a step in the right direction, but implementation needs to be strengthened, particularly in resource-limited settings. Third, the finding that the majority of GDM women can be managed with MNT alone suggests that investment in nutrition counseling services and diabetes education programs could yield substantial benefits in reducing the need for pharmacological therapy and improving outcomes. Fourth, the association of GDM with long-term risks for both mothers (increased risk of type 2 diabetes, cardiovascular disease) and offspring (increased risk of obesity, glucose intolerance, metabolic disorders) underscores the need for postpartum follow-up and long-term surveillance.

This study demonstrates a substantial burden of gestational diabetes mellitus in a tertiary care hospital setting, with a prevalence of 16.2%. Advanced maternal age (≥30 years), pre-pregnancy overweight/obesity (BMI ≥25 kg/m²), family history of diabetes, previous history of GDM, previous macrosomia, PCOS, and pre-existing hypertension are independent predictors of GDM. The condition is associated with significantly increased maternal complications, including preeclampsia, cesarean section, preterm delivery, postpartum hemorrhage, and urinary tract infections, as well as neonatal complications, including macrosomia, neonatal hypoglycemia, hyperbilirubinemia, and NICU admissions. The majority of women with GDM can be managed with medical nutrition therapy alone, with a smaller proportion requiring metformin or insulin.

These findings underscore the urgent need for: (1) universal early screening for GDM as recommended by national guidelines; (2) targeted preconception counseling and lifestyle interventions focusing on weight management, particularly among women with identifiable risk factors; (3) strengthening of nutrition counseling and diabetes education services; (4) intensive antenatal surveillance and timely intervention in GDM pregnancies; and (5) postpartum follow-up and long-term surveillance for both mothers and offspring to mitigate the long-term metabolic consequences of GDM. Future research should focus on evaluating the cost-effectiveness of different screening strategies, assessing the long-term outcomes of GDM in the Indian context, and developing culturally appropriate lifestyle interventions for prevention and management.

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