Diagnostic Performance of HbA1c Compared With OGTT for Detecting Gestational Diabetes and Associated Maternal Risk Factors in Ghana

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Mensah, Victor O. Mensah, Priscilla H. Mensah, Lamisi J. Akoloba, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9329607/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The oral glucose tolerance test (OGTT), the reference standard for diagnosing gestational diabetes mellitus (GDM), is often challenging to implement in routine antenatal care. Glycated hemoglobin (HbA1c) is a simpler alternative, but evidence of its diagnostic performance during pregnancy is limited in sub-Saharan Africa. This study evaluated GDM prevalence, agreement between HbA1c and OGTT, and maternal factors associated with OGTT-diagnosed GDM in Ghana. Methods A cross-sectional study was conducted among 138 pregnant women attending antenatal care in Ghana. Participants underwent a 75-g OGTT and HbA1c testing between 24 and 28 weeks of gestation. Diagnostic performance of HbA1c relative to OGTT was assessed using sensitivity, specificity, predictive values, Cohen’s kappa statistic, and receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression was used to identify maternal factors associated with GDM. Results The prevalence of GDM based on OGTT was 47.8% in this clinic-based sample. HbA1c ≥ 5.7% identified fewer cases (16.7%) and demonstrated low sensitivity (42.4%) but high specificity (100.0%) for detecting OGTT-defined GDM. Agreement between HbA1c and OGTT was moderate (κ = 0.44). ROC analysis showed discrimination (AUC = 0.862); however, the clinically applied threshold demonstrated limited sensitivity. Higher maternal body mass index and family history of diabetes were associated with increased odds of GDM. Conclusion The prevalence of OGTT-diagnosed GDM was high in this population. Although HbA1c demonstrated high specificity, its low sensitivity indicates that it cannot replace OGTT as a standalone screening test. These findings support the continued use of OGTT for GDM diagnosis, particularly in higher-risk populations. Maternal & Fetal Medicine Gestational diabetes mellitus Oral glucose tolerance test Glycated hemoglobin (HbA1c) Diagnostic agreement Pregnancy Ghana Figures Figure 1 Figure 2 Introduction Gestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy and is defined as glucose intolerance with onset or first recognition during pregnancy [ 1 ]. The global burden of GDM has increased substantially over the past two decades, largely driven by rising maternal age, increasing prevalence of obesity, and shifts in lifestyle patterns [ 2 – 4 ]. Current estimates indicate that hyperglycaemia in pregnancy affects approximately one in six pregnancies worldwide, with GDM accounting for the majority of cases [ 4 ]. GDM is associated with a range of adverse maternal and neonatal outcomes, including preeclampsia, cesarean delivery, macrosomia, and neonatal hypoglycaemia, and is also linked to an increased long-term risk of type 2 diabetes for both mothers and their offspring [ 5 , 6 ]. Early detection and appropriate management of GDM are therefore critical components of antenatal care. The oral glucose tolerance test (OGTT) is widely regarded as the reference standard for diagnosing GDM and is recommended by several international organizations, including the World Health Organization (WHO) and the American Diabetes Association (ADA) [ 1 , 7 ]. The test measures plasma glucose responses following a standardized glucose load and allows detection of abnormalities in glucose metabolism during pregnancy. Despite its diagnostic accuracy, the OGTT presents practical challenges in routine clinical settings. The procedure requires overnight fasting, multiple blood samples over a two-hour period, and prolonged waiting times in health facilities [ 8 ]. These logistical demands may limit compliance among pregnant women and complicate universal screening in busy antenatal clinics, particularly in low- and middle-income countries where healthcare resources are constrained [ 9 , 10 ]. Glycated hemoglobin (HbA1c) has been proposed as a simpler alternative for assessing glycaemic status during pregnancy because it does not require fasting and reflects average blood glucose levels over the preceding two to three months [ 1 , 11 ]. These features make HbA1c particularly appealing in busy antenatal settings where streamlined testing approaches are needed [ 8 , 12 ]. However, physiological changes during pregnancy, including altered red blood cell turnover and hemodilution, may influence HbA1c concentrations and affect its diagnostic performance [ 13 , 14 ]. In addition, conditions such as anemia and hemoglobinopathies, which are relatively common in many African populations, may further impact HbA1c interpretation [ 16 ]. Despite these limitations, HbA1c remains a potentially useful tool in settings where implementation of OGTT is challenging. Evidence regarding its diagnostic performance during pregnancy is limited and inconsistent, particularly in low- and middle-income settings, and population-specific thresholds have not been well established [ 15 , 16 ]. This gap is especially relevant in sub-Saharan Africa, where healthcare systems face constraints related to laboratory capacity, resource availability, and high patient volumes in antenatal clinics. In such contexts, evaluating simpler and more feasible screening approaches is critical to improving early detection and management of GDM. Therefore, this study aimed to evaluate the diagnostic performance of HbA1c relative to OGTT for detecting GDM and to examine maternal factors associated with OGTT-diagnosed GDM. By generating context-specific evidence, this study seeks to inform more practical and feasible approaches to GDM screening in resource-limited antenatal care settings. Methods Study Design and Setting This study employed a cross-sectional analytical design to estimate the prevalence of GDM, evaluate the diagnostic performance of HbA1c relative to the OGTT, and identify maternal factors associated with GDM among pregnant women. The study was conducted at Abenkyiman Hospital in Bekwai Municipality, Ashanti Region, Ghana. The hospital provides antenatal care services and serves as a referral center for surrounding health facilities within the municipality. Its laboratory performs biochemical investigations, including oral glucose tolerance testing and glycated haemoglobin analysis. Data collection was conducted between February and November 2025. Study Population and Eligibility Criteria Pregnant women were eligible for inclusion if they were aged 18 years or older, had a singleton pregnancy, and were between 24 and 28 weeks of gestation at the time of recruitment. Participants were required to undergo both a 75 g OGTT and HbA1c testing and to provide written informed consent prior to participation. Women were excluded if they had a known history of pre-existing diabetes mellitus diagnosed prior to pregnancy, as the study aimed to evaluate gestational diabetes rather than overt diabetes. Additional exclusion criteria included multiple gestation, severe illness at the time of recruitment, or use of medications known to significantly affect glucose metabolism, such as systemic corticosteroids. Women with conditions that could influence HbA1c measurements, including known hemoglobinopathies (such as sickle cell disease), severe anemia, or recent blood transfusion, were excluded. Participants with incomplete laboratory data for either OGTT or HbA1c were also excluded from the final analysis. Sample Size Determination and Sampling Procedure The sample size was calculated using Cochran’s formula for estimating proportions in cross-sectional studies [ 17 ]: where n represents the required sample size, Z corresponds to the standard normal deviate at a 95% confidence level (1.96), p represents the estimated prevalence of GDM, and d represents the desired margin of error. An estimated prevalence of 10% was used based on prior literature indicating that GDM affects approximately 1–14% of pregnancies globally, depending on the population and diagnostic criteria applied [ 3 ]. Using a precision level of 5%, the minimum required sample size was calculated to be 138 participants. To account for potential ineligibility and nonresponse during recruitment, the initial number of women assessed for eligibility was increased. A total of 160 pregnant women were screened, of whom 138 met the inclusion criteria and were enrolled in the study. All enrolled participants completed both oral glucose tolerance testing and HbA1c assessment and were included in the final analysis. Participants were recruited using a consecutive sampling approach, whereby all eligible pregnant women attending antenatal clinics during the study period were invited to participate until the required sample size was achieved. Data Collection Maternal sociodemographic, anthropometric, obstetric, and clinical information was obtained using a structured data extraction form developed from antenatal records and laboratory registers. Variables collected included maternal age, weight, height, body mass index (BMI), parity, gestational age, family history of diabetes, and laboratory test results. Sickle cell status was determined using documented laboratory results from participants’ medical records. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²) and categorized according to WHO criteria as underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥ 30.0 kg/m²)[ 18 ]. Laboratory Measurements Participants underwent a 75-g OGTT between 24 and 28 weeks of gestation following an overnight fast of 10–12 hours, consistent with recommendations from the WHO and the International Association of Diabetes and Pregnancy Study Groups [ 1 , 7 ]. Venous blood samples were collected at fasting and at 1 and 2 hours after ingestion of 75 g of anhydrous glucose dissolved in approximately 300 mL of water. Plasma glucose concentrations were measured using an enzymatic method on a fully automated clinical chemistry analyzer (e.g., Roche Cobas c311, Roche Diagnostics, Germany), consistent with standard laboratory practices for glucose assessment [ 1 ]. HbA1c was measured from EDTA whole blood using an immunoturbidimetric assay on an automated platform (e.g., Siemens DCA Vantage Analyzer, Siemens Healthcare Diagnostics, Germany), aligned with the National Glycohemoglobin Standardization Program (NGSP) and the International Federation of Clinical Chemistry (IFCC) reference systems. The analyzer was calibrated according to manufacturer guidelines, and internal quality control materials were run daily to ensure analytical accuracy and precision. Conditions known to affect HbA1c measurement, including disorders of erythrocyte turnover, hemoglobinopathies, severe anemia, and recent blood transfusion, were addressed during participant selection through the application of predefined exclusion criteria and were considered in the interpretation of study findings. Laboratory personnel performing the HbA1c and OGTT analyses were blinded to the results of the alternate test to minimize measurement and classification bias. Definition of Gestational Diabetes Mellitus GDM was diagnosed using a 75-g OGTT, which served as the reference standard in this study. Diagnosis was based on the WHO 2013 criteria, defined as the presence of one or more abnormal plasma glucose values following a 75-g glucose load. Specifically, GDM was diagnosed when fasting plasma glucose was ≥ 5.1 mmol/L, 1-hour plasma glucose was ≥ 10.0 mmol/L, or 2-hour plasma glucose was ≥ 8.5 mmol/L [ 7 ]. Women who did not meet these thresholds were classified as non-GDM. HbA1c levels were measured concurrently and evaluated for their diagnostic performance relative to OGTT-defined GDM. HbA1c values were categorized using ADA non-pregnant reference cut points as < 5.7%, 5.7–6.4%, and ≥ 6.5% [ 1 ]. For comparative analysis, an HbA1c threshold of ≥ 5.7% was examined as a reference cutoff based on nonpregnant criteria for increased diabetes risk. This threshold was not considered a validated pregnancy-specific diagnostic cutoff for gestational diabetes mellitus, but rather a reference point to assess the screening performance of HbA1c relative to OGTT. Statistical Analysis All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Descriptive statistics were used to summarize participant characteristics. Continuous variables were presented as means and standard deviations, while categorical variables were summarized using frequencies and percentages. The prevalence of GDM was estimated using OGTT diagnostic criteria, and the prevalence of elevated HbA1c levels was also calculated. The diagnostic performance of HbA1c relative to OGTT-defined GDM was evaluated by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Agreement between the diagnostic methods was assessed using Cohen’s kappa statistic. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the discriminatory ability of HbA1c for identifying OGTT-defined GDM, and the area under the curve (AUC) was calculated. To identify maternal factors associated with GDM, multivariable logistic regression analysis was performed. Variables considered in the model included maternal age, body mass index (BMI), parity, family history of diabetes, and maternal education. Multicollinearity among predictor variables was assessed using variance inflation factors (VIF); all VIF values were < 2.0, indicating no evidence of problematic multicollinearity. A sensitivity analysis was conducted by adding previous GDM/high blood sugar as a covariate to the multivariable logistic regression model to assess robustness of associations. Adjusted odds ratios (AORs) and 95% confidence intervals (CI) were reported. A two-sided p-value < 0.05 was considered statistically significant. Ethical Considerations Ethical approval for the study was obtained from the Research and Ethics Committee of the College of Health, Yamfo. Permission to conduct the study was also obtained from the administration of Abenkyiman Hospital. Participants were informed about the purpose and procedures of the study and provided written informed consent prior to participation. Confidentiality was maintained by assigning unique identification codes to participants and restricting access to study data. The study adhered to the ethical principles of respect for persons, beneficence, and justice. Results Table 1 summarizes the sociodemographic, clinical, and biochemical characteristics of the participants (n = 138). The mean age was 35.49 ± 3.49 years, with most women aged 35–39 years (51.5%). The mean BMI was 29.82 ± 5.68 kg/m², with 47.1% obese and 30.4% overweight. The mean gestational age at testing was 25.54 ± 1.70 weeks, and the mean parity was 2.64 ± 1.28, with most women having 2–3 previous births (63.8%). Nearly half of the participants had senior high school education (42.8%), while 24.6% had tertiary education. A family history of diabetes was reported by 11.6%, and 8.0% reported previous GDM or elevated blood glucose during pregnancy. Mean fasting glucose, 1-hour OGTT, and 2-hour OGTT levels were 5.61 ± 1.26 mmol/L, 9.27 ± 2.17 mmol/L, and 7.84 ± 1.92 mmol/L, respectively, while the mean HbA1c level was 4.83 ± 1.36%. Table 1 Sociodemographic, Clinical, and Biochemical Characteristics of Participants (n = 138) Variable Category n (%) or Mean ± SD Age (years) Mean ± SD 35.49 ± 3.49 30–34 53 (38.41) 35–39 71 (51.45) ≥ 40 14 (10.14) Body mass index (kg/m²) Mean ± SD 29.82 ± 5.68 Normal weight 31 (22.46) Overweight 42 (30.43) Obese 65 (47.10) Gestational age at testing (weeks) Mean ± SD 25.54 ± 1.70 Parity Mean ± SD 2.64 ± 1.28 0–1 18 (13.04) 2–3 88 (63.77) ≥ 4 32 (23.19) Marital status Married 138 (100.0) Educational level No formal education 4 (2.90) Primary 16 (11.59) Junior high 25 (18.12) Senior high 59 (42.75) Tertiary 34 (24.64) Family history of diabetes Yes 16 (11.59) No 122 (88.41) Previous GDM / high blood sugar in pregnancy Yes 11 (7.97) No 127 (92.03) Laboratory measurements Fasting glucose (mmol/L) Mean ± SD 5.61 ± 1.26 1-hour OGTT glucose (mmol/L) Mean ± SD 9.27 ± 2.17 2-hour OGTT glucose (mmol/L) Mean ± SD 7.84 ± 1.92 HbA1c (%) Mean ± SD 4.83 ± 1.36 Values are presented as mean ± standard deviation (SD) for continuous variables and frequency (percentage) for categorical variables. Table 2 presents the distribution of glycaemic status according to diagnostic method. Based on the OGTT using WHO 2013 criteria, 66 participants (47.8%) were classified as having GDM, while 72 (52.2%) were classified as non-GDM. Examination of OGTT diagnostic components showed that the fasting glucose threshold (≥ 5.1 mmol/L) was the most frequently met abnormal value in this cohort, with smaller proportions meeting the 1-hour (≥ 10.0 mmol/L) and 2-hour (≥ 8.5 mmol/L) thresholds (Supplementary Table S1). Using HbA1c categories based on ADA non-pregnant reference cut points, 110 participants (79.7%) had HbA1c values < 5.7%, 23 (16.7%) had HbA1c values between 5.7% and 6.4%, and 5 (3.6%) had HbA1c values ≥ 6.5%. Table 2 Distribution of OGTT-defined GDM and HbA1c categories among study participants Variable n (%) OGTT classification Non-GDM 72 (52.2) GDM 66 (47.8) HbA1c categories Normal (< 5.7%) 110 (79.7) Prediabetes-range (5.7–6.4%) 23 (16.7) Diabetes-range (≥ 6.5%) 5 (3.6) Values are presented as frequency (percentage). OGTT = oral glucose tolerance test; HbA1c = glycated hemoglobin; GDM = gestational diabetes mellitus. GDM was defined according to WHO 2013 criteria as ≥ 1 abnormal value on a 75-g OGTT (fasting ≥ 5.1 mmol/L, 1-hour ≥ 10.0 mmol/L, or 2-hour ≥ 8.5 mmol/L). HbA1c categories were based on American Diabetes Association non-pregnant reference cut points (< 5.7%, 5.7–6.4%, ≥ 6.5%) and were used for descriptive comparison with OGTT results. *Based on ADA non-pregnant criteria. Table 3 presents the cross-tabulation of OGTT glycemic status and HbA1c categories among the study participants (n = 138). Among participants classified as non-GDM by OGTT (n = 72), all had HbA1c values < 5.7%. Among participants diagnosed with GDM by OGTT (n = 66), 38 (57.6%) had HbA1c values < 5.7%, 23 (34.8%) had HbA1c values between 5.7% and 6.4%, and 5 (7.6%) had HbA1c values ≥ 6.5%. Overall, the distribution of HbA1c categories differed significantly according to OGTT classification (χ² = 38.32, p < 0.001). Table 3 Cross-tabulation of OGTT Glycemic Status and HbA1c Categories OGTT Status HbA1c < 5.7% HbA1c 5.7–6.4% HbA1c ≥ 6.5% Total Non-GDM 72 (100.0) 0 (0.0) 0 (0.0) 72 GDM 38 (57.6) 23 (34.8) 5 (7.6) 66 Total 110 23 5 138 Values are presented as frequency (percentage) within OGTT categories. OGTT = oral glucose tolerance test; HbA1c = glycated hemoglobin; GDM = gestational diabetes mellitus. Association between OGTT classification and HbA1c categories was evaluated using the Pearson chi-square test (χ² = 38.32, p < 0.001). The diagnostic performance of HbA1c for identifying OGTT-defined GDM is presented in Table 4 and Fig. 1 . Using OGTT as the reference standard, HbA1c ≥ 5.7% demonstrated a sensitivity of 42.4% (95% CI: 30.4–54.4) and a specificity of 100.0% (95% CI: 95.0–100.0). The positive predictive value was 100.0% (95% CI: 87.7–100.0) and the negative predictive value was 65.5% (95% CI: 56.6–74.4). Overall agreement between HbA1c and OGTT classifications was 72.5%, with moderate agreement observed between the two diagnostic methods (Cohen’s κ = 0.44; 95% CI: 0.30–0.57). McNemar’s test indicated a significant difference in classification between HbA1c and OGTT (χ² = 38.0, p < 0.001), suggesting substantial discordance between the two approaches. ROC curve analysis was performed to evaluate the discriminatory ability of HbA1c for predicting OGTT-defined GDM. The analysis yielded an area under the curve (AUC) of 0.862 (95% CI: 0.799–0.924), indicating good overall discriminatory performance (Fig. 1 ). Despite this favorable overall discrimination across thresholds, the diagnostic performance of HbA1c at the reference threshold of 5.7% demonstrated limited sensitivity, identifying fewer than half of women with OGTT-defined GDM, while maintaining high specificity Table 4 Diagnostic performance of HbA1c (≥ 5.7%) for detecting OGTT-defined GDM Measure Estimate (%) 95% CI Sensitivity 42.4 30.4–54.4 Specificity 100.0 95.0–100.0 Positive Predictive Value 100.0 87.7–100.0 Negative Predictive Value 65.5 56.6–74.4 Overall agreement 72.5 — Cohen’s κ 0.44 0.30–0.57 McNemar’s χ² 38.0 p < 0.001 OGTT = oral glucose tolerance test; HbA1c = glycated hemoglobin; GDM = gestational diabetes mellitus. Sensitivity, specificity, predictive values, and their 95% confidence intervals were calculated using OGTT-defined GDM as the reference standard. Cohen’s κ statistic quantifies agreement beyond chance between HbA1c and OGTT. McNemar’s test assesses differences in paired classification between the two diagnostic methods. Table 5 presents the results of the multivariable logistic regression analysis examining maternal factors associated with OGTT-defined GDM. Higher body mass index was significantly associated with increased odds of GDM (adjusted OR = 1.08, 95% CI: 1.01–1.15, p = 0.029). Similarly, participants with a family history of diabetes had significantly higher odds of GDM compared with those without such history (adjusted OR = 3.29, 95% CI: 1.03–10.50, p = 0.045). Age (adjusted OR = 0.97, 95% CI: 0.87–1.08, p = 0.56), parity (adjusted OR = 0.93, 95% CI: 0.69–1.26, p = 0.64), and educational level were not significantly associated with OGTT-diagnosed GDM. When previous GDM or high blood sugar was added to the multivariable model, it emerged as the strongest independent predictor of current GDM (AOR = 11.55, 95% CI: 1.38–96.72, p = 0.024). The associations for BMI (AOR = 1.07, 95% CI: 0.998–1.143, p = 0.057) and family history of diabetes (AOR = 2.96, 95% CI: 0.87–10.04, p = 0.083) were attenuated and no longer statistically significant (Supplementary Table S2) . Table 5 Maternal Factors Associated With OGTT-Defined GDM Variable Adjusted OR (95% CI) p-value Age (years) 0.97 (0.87–1.08) 0.56 Body mass index (kg/m²) 1.08 (1.01–1.15) 0.029 Parity 0.93 (0.69–1.26) 0.64 Junior high vs tertiary 0.71 (0.24–2.12) 0.54 No formal education vs tertiary 1.00 (0.11–9.46) 0.998 Primary vs tertiary 1.42 (0.40–5.04) 0.58 Senior high vs tertiary 1.07 (0.44–2.60) 0.89 Family history of diabetes (Yes vs No) 3.29 (1.03–10.50) 0.045 Adjusted odds ratios were obtained from multivariable logistic regression analysis with OGTT-defined gestational diabetes mellitus as the dependent variable. Reference category for educational level: tertiary education. CI = confidence interval; OR = odds ratio; OGTT = oral glucose tolerance test; GDM = gestational diabetes mellitus. Discussion This study examined the prevalence of GDM, evaluated the diagnostic performance of HbA1c relative to the OGTT, and identified maternal factors associated with OGTT-diagnosed GDM among pregnant women in Ghana. The findings indicate a relatively high prevalence of GDM in this population. HbA1c identified substantially fewer cases of hyperglycemia compared with OGTT, reflecting limited sensitivity when used alone for screening during pregnancy. Although HbA1c demonstrated very high specificity and positive predictive value, many women with OGTT-diagnosed GDM had HbA1c values within the normal range. In addition, higher maternal body mass index and family history of diabetes were associated with increased odds of GDM. Together, these findings suggest that HbA1c alone is insufficient for detecting GDM and support the continued use of OGTT as the primary diagnostic method in antenatal care. The prevalence of OGTT-defined GDM in this study was higher than estimates reported in many population-based studies. Several factors may explain this finding. First, the study population consisted of women aged ≥ 30 years, an age group known to have a higher risk of GDM. Second, a large proportion of participants were overweight or obese, which is a well-established risk factor for impaired glucose metabolism during pregnancy. In addition, participants were identified from women who had undergone both HbA1c and OGTT testing as part of routine clinical care. Because OGTT is often performed when there is clinical suspicion or elevated risk for GDM, the study sample may have been enriched with higher-risk pregnancies rather than representing a universally screened antenatal population. Consequently, the prevalence observed in this study should not be interpreted as a population estimate but rather as a reflection of the higher-risk clinical population included in the analysis. A substantial discrepancy was observed between hyperglycemia detected by OGTT and that detected by HbA1c. While nearly half of the participants met the OGTT diagnostic criteria for GDM, only a small proportion had HbA1c values in the diabetes range. This finding highlights the limited sensitivity of HbA1c for detecting gestational glucose abnormalities and suggests that HbA1c may underestimate the true burden of hyperglycemia during pregnancy. Similar discrepancies between HbA1c and OGTT have been reported in previous studies evaluating its diagnostic performance during pregnancy [ 8 , 19 ]. Consistent with these findings, HbA1c demonstrated high specificity but relatively low sensitivity for identifying GDM [ 5 , 19 ]. However, the observed high specificity should be interpreted with caution given the relatively small sample size and distribution of HbA1c values in this cohort. The low sensitivity indicates that many women with OGTT-diagnosed GDM had HbA1c levels within the normal range. The reduced sensitivity of HbA1c during pregnancy may be explained by physiological changes that occur during gestation. Increased plasma volume, accelerated red blood cell turnover, and shortened erythrocyte lifespan may influence HbA1c measurements and lead to underestimation of glycemic exposure [ 13 , 19 ]. In addition, iron deficiency—commonly observed during pregnancy, particularly in low- and middle-income countries—may further affect HbA1c values and contribute to variability in its diagnostic performance [ 14 ]. Beyond diagnostic evaluation, this study identified maternal factors associated with OGTT-diagnosed GDM. Higher maternal body mass index and a family history of diabetes were associated with GDM in this sample, consistent with existing evidence linking maternal adiposity and inherited metabolic risk to increased insulin resistance during pregnancy [ 20 , 21 ]. However, these findings should be interpreted with caution, as the regression analysis was exploratory and intended to provide contextual insight rather than definitive risk factor identification. When previous GDM or elevated blood glucose in pregnancy was included in the model, it emerged as the strongest independent predictor of current GDM, reflecting the well-documented recurrence of glucose intolerance across pregnancies [ 22 – 24 ]. The inclusion of this variable attenuated the associations of BMI and family history of diabetes, likely due to shared metabolic pathways and overlapping risk profiles involving insulin resistance and β-cell dysfunction [ 24 ]. These findings highlight the importance of early screening among women with a history of glucose dysregulation during pregnancy, as recommended by current clinical guidelines [ 1 ]. The findings of this study have important clinical and public health implications. The high prevalence of OGTT-diagnosed GDM observed in this population underscores the need for effective screening strategies within antenatal care services in Ghana. Although HbA1c demonstrated high specificity, its relatively low sensitivity indicates that it may not be adequate as a standalone screening test for GDM. Reliance solely on HbA1c could therefore result in missed diagnoses and delayed management of glucose intolerance during pregnancy. These findings support current recommendations that emphasize OGTT as the primary diagnostic test for GDM [ 1 ]. In settings where universal OGTT screening may be difficult to implement, risk based screening strategies targeting women with known risk factors such as elevated body mass index, family history of diabetes, and advanced maternal age are recommended and may facilitate earlier detection [ 25 ]. Evidence indicates that early identification and treatment of gestational diabetes mellitus can reduce adverse maternal and neonatal outcomes [ 26 ]. In addition, public health interventions aimed at preventing maternal overweight and obesity, including nutrition education and lifestyle modification during antenatal care, may help reduce the risk of gestational diabetes and its associated complications [ 1 , 5 ]. Several strengths and limitations should be considered when interpreting these findings. A key strength of this study is the use of the OGTT, which remains the reference standard for diagnosing gestational diabetes mellitus and allows accurate classification of glucose tolerance status. In addition, the study evaluated both the diagnostic performance of HbA1c and maternal factors associated with OGTT-defined GDM within the same population. The use of ROC analysis further enabled assessment of the overall discriminatory ability of HbA1c across a range of thresholds. However, several limitations should be acknowledged. The sample size was relatively modest, and participants were recruited from antenatal clinics within a single municipality, which may limit generalizability to other regions or populations. Another limitation relates to the interpretation of HbA1c during pregnancy, as physiological changes such as increased red blood cell turnover and hemodilution may influence HbA1c levels and reduce concordance with OGTT-based diagnosis. Furthermore, the HbA1c threshold of 5.7% used in this study was derived from nonpregnant populations and is not a validated pregnancy-specific diagnostic cutoff for GDM, which may limit its clinical applicability in this context. In addition, the study did not evaluate alternative HbA1c thresholds or conduct threshold optimization analyses, and therefore conclusions regarding optimal cutoff values cannot be drawn. Finally, the cross-sectional design limits the ability to infer causal relationships between maternal factors and the development of GDM. In conclusion, this study identified a relatively high prevalence of GDM based on OGTT among pregnant women in this population. HbA1c demonstrated high specificity but limited sensitivity for detecting OGTT-diagnosed GDM, indicating that it is not suitable as a standalone screening test during pregnancy. Maternal body mass index and family history of diabetes were important predictors of GDM. These findings highlight the importance of appropriate screening strategies and targeted interventions addressing modifiable risk factors such as maternal overweight and obesity. Further research involving larger and more diverse populations is needed to better understand the role of alternative screening approaches and improve early detection of GDM in similar settings. Declarations Ethics Approval and Consent to Participate Ethical approval for this study was obtained from the Research and Ethics Committee of the College of Health, Yamfo, Ghana. Written informed consent was obtained from all participants prior to enrollment in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and/or analyzed during the current study are not publicly available due to ongoing analyses but are available from the corresponding author on reasonable request, subject to institutional and ethical approval where applicable. Competing Interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors’ Contributions D.O.M. conceived and designed the study, performed the statistical analysis, and drafted the manuscript. V.O.M. contributed to the study design, supervised data collection, and critically revised the manuscript. P.H.M. assisted with data collection and contributed to drafting the manuscript. L.J.A. and O.C.A. supported data collection and data management. G.A. provided clinical oversight and contributed to interpretation of the results. E.B. assisted with data management and manuscript revision. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the staff of Abenkyiman Hospital, particularly the laboratory personnel, for their support during data collection and biochemical analyses. We are also grateful to all the pregnant women who participated in this study. References ElSayed NA et al (2022) 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023. Diabetes Care 46(Supplement1):S19–S40 Ferrara A (2007) Increasing Prevalence of Gestational Diabetes Mellitus: A public health perspective. Diabetes Care 30(Supplement2):S141–S146 Guariguata L et al (2014) Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract 103(2):176–185 Duncan BB, Magliano DJ, Boyko EJ (2026) IDF diabetes atlas 11th edition 2025: global prevalence and projections for 2050. Oxford University Press, pp 7–9 Plows JF et al (2018) The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci 19(11):3342 Wendland EM et al (2012) Gestational diabetes and pregnancy outcomes-a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria. BMC Pregnancy Childbirth 12(1):23 López Stewart G (2014) Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: A World Health Organization Guideline. Hughes RCE et al (2014) An Early Pregnancy HbA1c ≥ 5.9% (41 mmol/mol) Is Optimal for Detecting Diabetes and Identifies Women at Increased Risk of Adverse Pregnancy Outcomes. Diabetes Care 37(11):2953–2959 Macaulay S, Dunger DB, Norris SA (2014) Gestational diabetes mellitus in Africa: a systematic review. PLoS ONE 9(6):e97871 Mwanri AW et al (2015) Gestational diabetes mellitus in sub-Saharan Africa: systematic review and metaregression on prevalence and risk factors. Tropical Med Int Health 20(8):983–1002 World Health Organization (2011) Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. WHO, Geneva Renz PB et al (2015) HbA1c test as a tool in the diagnosis of gestational diabetes mellitus. PLoS ONE 10(8):e0135989 Nielsen LR et al (2004) HbA1c levels are significantly lower in early and late pregnancy. Diabetes Care 27(5):1200–1201 Sacks DB (2011) A1C versus glucose testing: a comparison. Diabetes Care 34(2):518 Sweeting AN et al (2019) A novel early pregnancy risk prediction model for gestational diabetes mellitus. Fetal Diagn Ther 45(2):76–84 Fong A et al (2014) Use of hemoglobin A1c as an early predictor of gestational diabetes mellitus. Am J Obstet Gynecol 211(6):641e1-641. e7 Cochran WG (1977) Sampling techniques. Wiley World Health Organization (2000) Obesity: Preventing and managing the global epidemic. WHO, Geneva Siricharoenthai P, Phupong V (2020) Diagnostic accuracy of HbA1c in detecting gestational diabetes mellitus. J Maternal-Fetal Neonatal Med 33(20):3497–3500 You H et al (2021) Risk of type 2 diabetes mellitus after gestational diabetes mellitus: A systematic review & meta-analysis. Indian J Med Res 154(1):62–77 Torloni M et al (2009) Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev 10(2):194–203 Moses RG (1996) The recurrence rate of gestational diabetes in subsequent pregnancies. Diabetes Care 19(12):1348–1350 Getahun D, Fassett MJ, Jacobsen SJ (2010) Gestational diabetes: risk of recurrence in subsequent pregnancies. Am J Obstet Gynecol 203(5):467e1-467. e6 Buchanan TA, Xiang AH (2005) Gestational diabetes mellitus. J Clin Investig 115(3):485–491 Gupta Y et al (2015) Updated guidelines on screening for gestational diabetes. Int J women's health, : p. 539–550 Pillay J et al (2021) Screening for gestational diabetes: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 326(6):539–562 Additional Declarations The authors declare no competing interests. Supplementary Files VictorSupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9329607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617988727,"identity":"bd81e618-13b1-4e78-94fe-bc8cbc10d0a1","order_by":0,"name":"Daniel O. Mensah","email":"","orcid":"","institution":"Sampa Government Hospital, Bono Region, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"O.","lastName":"Mensah","suffix":""},{"id":617988728,"identity":"18e4c433-f36b-4974-9804-a7f9ed3e63d3","order_by":1,"name":"Victor O. Mensah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDACZgSTjflPhYQciHXgAX4tjA0wLQw8Z2yMwVoS8NuDpIW3JS0RzMOnxeA48/MHH/dskzPnP/zsgWTD4fT5YYcfAm2xk9NtwKHlMJth44xnt40tZ6SZGxjuOJy78XaaAVBLsrHZAexaJJt5GJt5DtxO3HCDwUwi8QxQy+wEkJYDidsIaKnfcP74N4mDbYfTDWenf8CrhZ8ZogVoco6ZZGNbWoK8dA5+W/iZ2Qxnzjhw23DDjZwyaYYzNoYbpHMKDiQY4PYLG//hBx8+HLgtb3D++DZphgoJefnZ6Zs/fKiwk8OlBRMYgFUaEKscBOQbSFE9CkbBKBgFIwEAACIdZk5Qb4Y2AAAAAElFTkSuQmCC","orcid":"","institution":"College of Health, Yamfo, Ahafo Region, Ghana; Abenkyiman Hospital, Ghana","correspondingAuthor":true,"prefix":"","firstName":"Victor","middleName":"O.","lastName":"Mensah","suffix":""},{"id":617988729,"identity":"b8157b6d-5dd1-4950-b4f8-0437f3277c5a","order_by":2,"name":"Priscilla H. Mensah","email":"","orcid":"","institution":"Sampa Government Hospital, Bono Region, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"H.","lastName":"Mensah","suffix":""},{"id":617988730,"identity":"6398b6b1-3ca5-4ee4-859b-53f30f84e210","order_by":3,"name":"Lamisi J. Akoloba","email":"","orcid":"","institution":"College of Health, Yamfo, Ahafo Region, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Lamisi","middleName":"J.","lastName":"Akoloba","suffix":""},{"id":617988731,"identity":"90144ce2-e790-4b01-a18c-46f9a0809c2b","order_by":4,"name":"Osei C. Amponsah","email":"","orcid":"","institution":"College of Health, Yamfo, Ahafo Region, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Osei","middleName":"C.","lastName":"Amponsah","suffix":""},{"id":617988733,"identity":"e9348044-1caa-4094-8518-2e6a207fec0f","order_by":5,"name":"Gideon Asare","email":"","orcid":"","institution":"Abenkyiman Hospital, Ghana; Garden City University College, Kumasi, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Gideon","middleName":"","lastName":"Asare","suffix":""},{"id":617988732,"identity":"7304d482-8734-4b3b-997a-ba827263abc9","order_by":6,"name":"Elvis Batuu","email":"","orcid":"","institution":"College of Health, Yamfo, Ahafo Region, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Elvis","middleName":"","lastName":"Batuu","suffix":""}],"badges":[],"createdAt":"2026-04-06 04:11:41","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9329607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9329607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106348889,"identity":"ca776292-52b0-48b0-ba0e-2c92718fa77c","added_by":"auto","created_at":"2026-04-07 16:50:44","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant flow diagram.\u003c/strong\u003e\u003cbr\u003e\nFlow diagram illustrating the recruitment and inclusion of study participants. Of the 160 pregnant women assessed for eligibility, 22 were excluded due to not meeting gestational age criteria, declining participation, or other exclusion criteria. A total of 138 eligible participants were enrolled, all of whom completed OGTT and HbA1c assessment and were included in the final analysis.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9329607/v1/9265d7974b6deb92c8e1d2d1.jpeg"},{"id":106348891,"identity":"fc942850-32fb-4c30-8628-e12957a175be","added_by":"auto","created_at":"2026-04-07 16:50:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58979,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve evaluating the performance of HbA1c for predicting OGTT-diagnosed gestational diabetes mellitus. The area under the curve (AUC) was 0.862 (95% CI: 0.799–0.924), indicating good discriminatory ability.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9329607/v1/633dc09d8e6f7ad068184caf.png"},{"id":106724687,"identity":"14bcf72b-e974-4730-883d-3536f321f99b","added_by":"auto","created_at":"2026-04-12 18:29:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9329607/v1/061ee85a-5227-479a-8ba8-653588e9f989.pdf"},{"id":106404002,"identity":"05ab7e08-99f5-4cc5-a3ad-8f4de44e9a21","added_by":"auto","created_at":"2026-04-08 09:15:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29928,"visible":true,"origin":"","legend":"","description":"","filename":"VictorSupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9329607/v1/f8d7a2429633d4bfdd4cd264.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDiagnostic Performance of HbA1c Compared With OGTT for Detecting Gestational Diabetes and Associated Maternal Risk Factors in Ghana\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy and is defined as glucose intolerance with onset or first recognition during pregnancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The global burden of GDM has increased substantially over the past two decades, largely driven by rising maternal age, increasing prevalence of obesity, and shifts in lifestyle patterns [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current estimates indicate that hyperglycaemia in pregnancy affects approximately one in six pregnancies worldwide, with GDM accounting for the majority of cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. GDM is associated with a range of adverse maternal and neonatal outcomes, including preeclampsia, cesarean delivery, macrosomia, and neonatal hypoglycaemia, and is also linked to an increased long-term risk of type 2 diabetes for both mothers and their offspring [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Early detection and appropriate management of GDM are therefore critical components of antenatal care.\u003c/p\u003e \u003cp\u003eThe oral glucose tolerance test (OGTT) is widely regarded as the reference standard for diagnosing GDM and is recommended by several international organizations, including the World Health Organization (WHO) and the American Diabetes Association (ADA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The test measures plasma glucose responses following a standardized glucose load and allows detection of abnormalities in glucose metabolism during pregnancy. Despite its diagnostic accuracy, the OGTT presents practical challenges in routine clinical settings. The procedure requires overnight fasting, multiple blood samples over a two-hour period, and prolonged waiting times in health facilities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These logistical demands may limit compliance among pregnant women and complicate universal screening in busy antenatal clinics, particularly in low- and middle-income countries where healthcare resources are constrained [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlycated hemoglobin (HbA1c) has been proposed as a simpler alternative for assessing glycaemic status during pregnancy because it does not require fasting and reflects average blood glucose levels over the preceding two to three months [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These features make HbA1c particularly appealing in busy antenatal settings where streamlined testing approaches are needed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, physiological changes during pregnancy, including altered red blood cell turnover and hemodilution, may influence HbA1c concentrations and affect its diagnostic performance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, conditions such as anemia and hemoglobinopathies, which are relatively common in many African populations, may further impact HbA1c interpretation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these limitations, HbA1c remains a potentially useful tool in settings where implementation of OGTT is challenging. Evidence regarding its diagnostic performance during pregnancy is limited and inconsistent, particularly in low- and middle-income settings, and population-specific thresholds have not been well established [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This gap is especially relevant in sub-Saharan Africa, where healthcare systems face constraints related to laboratory capacity, resource availability, and high patient volumes in antenatal clinics. In such contexts, evaluating simpler and more feasible screening approaches is critical to improving early detection and management of GDM.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to evaluate the diagnostic performance of HbA1c relative to OGTT for detecting GDM and to examine maternal factors associated with OGTT-diagnosed GDM. By generating context-specific evidence, this study seeks to inform more practical and feasible approaches to GDM screening in resource-limited antenatal care settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis study employed a cross-sectional analytical design to estimate the prevalence of GDM, evaluate the diagnostic performance of HbA1c relative to the OGTT, and identify maternal factors associated with GDM among pregnant women. The study was conducted at Abenkyiman Hospital in Bekwai Municipality, Ashanti Region, Ghana. The hospital provides antenatal care services and serves as a referral center for surrounding health facilities within the municipality. Its laboratory performs biochemical investigations, including oral glucose tolerance testing and glycated haemoglobin analysis. Data collection was conducted between February and November 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Eligibility Criteria\u003c/h3\u003e\n\u003cp\u003ePregnant women were eligible for inclusion if they were aged 18 years or older, had a singleton pregnancy, and were between 24 and 28 weeks of gestation at the time of recruitment. Participants were required to undergo both a 75 g OGTT and HbA1c testing and to provide written informed consent prior to participation. Women were excluded if they had a known history of pre-existing diabetes mellitus diagnosed prior to pregnancy, as the study aimed to evaluate gestational diabetes rather than overt diabetes. Additional exclusion criteria included multiple gestation, severe illness at the time of recruitment, or use of medications known to significantly affect glucose metabolism, such as systemic corticosteroids. Women with conditions that could influence HbA1c measurements, including known hemoglobinopathies (such as sickle cell disease), severe anemia, or recent blood transfusion, were excluded. Participants with incomplete laboratory data for either OGTT or HbA1c were also excluded from the final analysis.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination and Sampling Procedure\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated using Cochran\u0026rsquo;s formula for estimating proportions in cross-sectional studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"141\" height=\"60\"\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e represents the required sample size, \u003cem\u003eZ\u003c/em\u003e corresponds to the standard normal deviate at a 95% confidence level (1.96), \u003cem\u003ep\u003c/em\u003e represents the estimated prevalence of GDM, and \u003cem\u003ed\u003c/em\u003e represents the desired margin of error. An estimated prevalence of 10% was used based on prior literature indicating that GDM affects approximately 1\u0026ndash;14% of pregnancies globally, depending on the population and diagnostic criteria applied [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using a precision level of 5%, the minimum required sample size was calculated to be 138 participants. To account for potential ineligibility and nonresponse during recruitment, the initial number of women assessed for eligibility was increased. A total of 160 pregnant women were screened, of whom 138 met the inclusion criteria and were enrolled in the study. All enrolled participants completed both oral glucose tolerance testing and HbA1c assessment and were included in the final analysis. Participants were recruited using a consecutive sampling approach, whereby all eligible pregnant women attending antenatal clinics during the study period were invited to participate until the required sample size was achieved.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eMaternal sociodemographic, anthropometric, obstetric, and clinical information was obtained using a structured data extraction form developed from antenatal records and laboratory registers. Variables collected included maternal age, weight, height, body mass index (BMI), parity, gestational age, family history of diabetes, and laboratory test results. Sickle cell status was determined using documented laboratory results from participants\u0026rsquo; medical records. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m\u0026sup2;) and categorized according to WHO criteria as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLaboratory Measurements\u003c/h3\u003e\n\u003cp\u003eParticipants underwent a 75-g OGTT between 24 and 28 weeks of gestation following an overnight fast of 10\u0026ndash;12 hours, consistent with recommendations from the WHO and the International Association of Diabetes and Pregnancy Study Groups [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Venous blood samples were collected at fasting and at 1 and 2 hours after ingestion of 75 g of anhydrous glucose dissolved in approximately 300 mL of water. Plasma glucose concentrations were measured using an enzymatic method on a fully automated clinical chemistry analyzer (e.g., Roche Cobas c311, Roche Diagnostics, Germany), consistent with standard laboratory practices for glucose assessment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. HbA1c was measured from EDTA whole blood using an immunoturbidimetric assay on an automated platform (e.g., Siemens DCA Vantage Analyzer, Siemens Healthcare Diagnostics, Germany), aligned with the National Glycohemoglobin Standardization Program (NGSP) and the International Federation of Clinical Chemistry (IFCC) reference systems. The analyzer was calibrated according to manufacturer guidelines, and internal quality control materials were run daily to ensure analytical accuracy and precision. Conditions known to affect HbA1c measurement, including disorders of erythrocyte turnover, hemoglobinopathies, severe anemia, and recent blood transfusion, were addressed during participant selection through the application of predefined exclusion criteria and were considered in the interpretation of study findings. Laboratory personnel performing the HbA1c and OGTT analyses were blinded to the results of the alternate test to minimize measurement and classification bias.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of Gestational Diabetes Mellitus\u003c/h2\u003e \u003cp\u003eGDM was diagnosed using a 75-g OGTT, which served as the reference standard in this study. Diagnosis was based on the WHO 2013 criteria, defined as the presence of one or more abnormal plasma glucose values following a 75-g glucose load. Specifically, GDM was diagnosed when fasting plasma glucose was \u0026ge;\u0026thinsp;5.1 mmol/L, 1-hour plasma glucose was \u0026ge;\u0026thinsp;10.0 mmol/L, or 2-hour plasma glucose was \u0026ge;\u0026thinsp;8.5 mmol/L [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Women who did not meet these thresholds were classified as non-GDM. HbA1c levels were measured concurrently and evaluated for their diagnostic performance relative to OGTT-defined GDM. HbA1c values were categorized using ADA non-pregnant reference cut points as \u0026lt;\u0026thinsp;5.7%, 5.7\u0026ndash;6.4%, and \u0026ge;\u0026thinsp;6.5% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For comparative analysis, an HbA1c threshold of \u0026ge;\u0026thinsp;5.7% was examined as a reference cutoff based on nonpregnant criteria for increased diabetes risk. This threshold was not considered a validated pregnancy-specific diagnostic cutoff for gestational diabetes mellitus, but rather a reference point to assess the screening performance of HbA1c relative to OGTT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Descriptive statistics were used to summarize participant characteristics. Continuous variables were presented as means and standard deviations, while categorical variables were summarized using frequencies and percentages. The prevalence of GDM was estimated using OGTT diagnostic criteria, and the prevalence of elevated HbA1c levels was also calculated. The diagnostic performance of HbA1c relative to OGTT-defined GDM was evaluated by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Agreement between the diagnostic methods was assessed using Cohen\u0026rsquo;s kappa statistic. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the discriminatory ability of HbA1c for identifying OGTT-defined GDM, and the area under the curve (AUC) was calculated. To identify maternal factors associated with GDM, multivariable logistic regression analysis was performed. Variables considered in the model included maternal age, body mass index (BMI), parity, family history of diabetes, and maternal education. Multicollinearity among predictor variables was assessed using variance inflation factors (VIF); all VIF values were \u0026lt;\u0026thinsp;2.0, indicating no evidence of problematic multicollinearity. A sensitivity analysis was conducted by adding previous GDM/high blood sugar as a covariate to the multivariable logistic regression model to assess robustness of associations. Adjusted odds ratios (AORs) and 95% confidence intervals (CI) were reported. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Research and Ethics Committee of the College of Health, Yamfo. Permission to conduct the study was also obtained from the administration of Abenkyiman Hospital. Participants were informed about the purpose and procedures of the study and provided written informed consent prior to participation. Confidentiality was maintained by assigning unique identification codes to participants and restricting access to study data. The study adhered to the ethical principles of respect for persons, beneficence, and justice.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the sociodemographic, clinical, and biochemical characteristics of the participants (n\u0026thinsp;=\u0026thinsp;138). The mean age was 35.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49 years, with most women aged 35\u0026ndash;39 years (51.5%). The mean BMI was 29.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68 kg/m\u0026sup2;, with 47.1% obese and 30.4% overweight. The mean gestational age at testing was 25.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 weeks, and the mean parity was 2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28, with most women having 2\u0026ndash;3 previous births (63.8%). Nearly half of the participants had senior high school education (42.8%), while 24.6% had tertiary education. A family history of diabetes was reported by 11.6%, and 8.0% reported previous GDM or elevated blood glucose during pregnancy. Mean fasting glucose, 1-hour OGTT, and 2-hour OGTT levels were 5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 mmol/L, 9.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17 mmol/L, and 7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92 mmol/L, respectively, while the mean HbA1c level was 4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic, Clinical, and Biochemical Characteristics of Participants (n\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (38.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (51.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (10.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (22.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (30.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (47.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational age at testing (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (13.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (63.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (23.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (18.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (42.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (24.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (88.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious GDM / high blood sugar in pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (7.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (92.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory measurements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-hour OGTT glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-hour OGTT glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and frequency (percentage) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of glycaemic status according to diagnostic method. Based on the OGTT using WHO 2013 criteria, 66 participants (47.8%) were classified as having GDM, while 72 (52.2%) were classified as non-GDM. Examination of OGTT diagnostic components showed that the fasting glucose threshold (\u0026ge;\u0026thinsp;5.1 mmol/L) was the most frequently met abnormal value in this cohort, with smaller proportions meeting the 1-hour (\u0026ge;\u0026thinsp;10.0 mmol/L) and 2-hour (\u0026ge;\u0026thinsp;8.5 mmol/L) thresholds (Supplementary Table S1). Using HbA1c categories based on ADA non-pregnant reference cut points, 110 participants (79.7%) had HbA1c values\u0026thinsp;\u0026lt;\u0026thinsp;5.7%, 23 (16.7%) had HbA1c values between 5.7% and 6.4%, and 5 (3.6%) had HbA1c values\u0026thinsp;\u0026ge;\u0026thinsp;6.5%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of OGTT-defined GDM and HbA1c categories among study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-GDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72 (52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (47.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c categories\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (\u0026lt;\u0026thinsp;5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (79.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrediabetes-range (5.7\u0026ndash;6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes-range (\u0026ge;\u0026thinsp;6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eValues are presented as frequency (percentage).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eOGTT\u0026thinsp;=\u0026thinsp;oral glucose tolerance test; HbA1c\u0026thinsp;=\u0026thinsp;glycated hemoglobin; GDM\u0026thinsp;=\u0026thinsp;gestational diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eGDM was defined according to WHO 2013 criteria as \u0026ge;\u0026thinsp;1 abnormal value on a 75-g OGTT (fasting\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L, 1-hour\u0026thinsp;\u0026ge;\u0026thinsp;10.0 mmol/L, or 2-hour\u0026thinsp;\u0026ge;\u0026thinsp;8.5 mmol/L). HbA1c categories were based on American Diabetes Association non-pregnant reference cut points (\u0026lt;\u0026thinsp;5.7%, 5.7\u0026ndash;6.4%, \u0026ge;\u0026thinsp;6.5%) and were used for descriptive comparison with OGTT results. *Based on ADA non-pregnant criteria.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the cross-tabulation of OGTT glycemic status and HbA1c categories among the study participants (n\u0026thinsp;=\u0026thinsp;138). Among participants classified as non-GDM by OGTT (n\u0026thinsp;=\u0026thinsp;72), all had HbA1c values\u0026thinsp;\u0026lt;\u0026thinsp;5.7%. Among participants diagnosed with GDM by OGTT (n\u0026thinsp;=\u0026thinsp;66), 38 (57.6%) had HbA1c values\u0026thinsp;\u0026lt;\u0026thinsp;5.7%, 23 (34.8%) had HbA1c values between 5.7% and 6.4%, and 5 (7.6%) had HbA1c values\u0026thinsp;\u0026ge;\u0026thinsp;6.5%. Overall, the distribution of HbA1c categories differed significantly according to OGTT classification (χ\u0026sup2; = 38.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-tabulation of OGTT Glycemic Status and HbA1c Categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGTT Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHbA1c\u0026thinsp;\u0026lt;\u0026thinsp;5.7%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHbA1c 5.7\u0026ndash;6.4%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-GDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (100.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (57.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (34.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (7.6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e110\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e138\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are presented as frequency (percentage) within OGTT categories.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOGTT\u0026thinsp;=\u0026thinsp;oral glucose tolerance test; HbA1c\u0026thinsp;=\u0026thinsp;glycated hemoglobin; GDM\u0026thinsp;=\u0026thinsp;gestational diabetes mellitus. Association between OGTT classification and HbA1c categories was evaluated using the Pearson chi-square test \u003cb\u003e(χ\u0026sup2; = 38.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe diagnostic performance of HbA1c for identifying OGTT-defined GDM is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Using OGTT as the reference standard, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;5.7% demonstrated a sensitivity of 42.4% (95% CI: 30.4\u0026ndash;54.4) and a specificity of 100.0% (95% CI: 95.0\u0026ndash;100.0). The positive predictive value was 100.0% (95% CI: 87.7\u0026ndash;100.0) and the negative predictive value was 65.5% (95% CI: 56.6\u0026ndash;74.4). Overall agreement between HbA1c and OGTT classifications was 72.5%, with moderate agreement observed between the two diagnostic methods (Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.44; 95% CI: 0.30\u0026ndash;0.57). McNemar\u0026rsquo;s test indicated a significant difference in classification between HbA1c and OGTT (χ\u0026sup2; = 38.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting substantial discordance between the two approaches. ROC curve analysis was performed to evaluate the discriminatory ability of HbA1c for predicting OGTT-defined GDM. The analysis yielded an area under the curve (AUC) of 0.862 (95% CI: 0.799\u0026ndash;0.924), indicating good overall discriminatory performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite this favorable overall discrimination across thresholds, the diagnostic performance of HbA1c at the reference threshold of 5.7% demonstrated limited sensitivity, identifying fewer than half of women with OGTT-defined GDM, while maintaining high specificity\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance of HbA1c (\u0026ge;\u0026thinsp;5.7%) for detecting OGTT-defined GDM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u0026ndash;54.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.0\u0026ndash;100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Predictive Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.7\u0026ndash;100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Predictive Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6\u0026ndash;74.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall agreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohen\u0026rsquo;s κ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026ndash;0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcNemar\u0026rsquo;s χ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eOGTT\u0026thinsp;=\u0026thinsp;oral glucose tolerance test; HbA1c\u0026thinsp;=\u0026thinsp;glycated hemoglobin; GDM\u0026thinsp;=\u0026thinsp;gestational diabetes mellitus. Sensitivity, specificity, predictive values, and their 95% confidence intervals were calculated using OGTT-defined GDM as the reference standard. Cohen\u0026rsquo;s κ statistic quantifies agreement beyond chance between HbA1c and OGTT. McNemar\u0026rsquo;s test assesses differences in paired classification between the two diagnostic methods.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the multivariable logistic regression analysis examining maternal factors associated with OGTT-defined GDM. Higher body mass index was significantly associated with increased odds of GDM (adjusted OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.01\u0026ndash;1.15, p\u0026thinsp;=\u0026thinsp;0.029). Similarly, participants with a family history of diabetes had significantly higher odds of GDM compared with those without such history (adjusted OR\u0026thinsp;=\u0026thinsp;3.29, 95% CI: 1.03\u0026ndash;10.50, p\u0026thinsp;=\u0026thinsp;0.045). Age (adjusted OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.87\u0026ndash;1.08, p\u0026thinsp;=\u0026thinsp;0.56), parity (adjusted OR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.69\u0026ndash;1.26, p\u0026thinsp;=\u0026thinsp;0.64), and educational level were not significantly associated with OGTT-diagnosed GDM. When previous GDM or high blood sugar was added to the multivariable model, it emerged as the strongest independent predictor of current GDM (AOR\u0026thinsp;=\u0026thinsp;11.55, 95% CI: 1.38\u0026ndash;96.72, p\u0026thinsp;=\u0026thinsp;0.024). The associations for BMI (AOR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.998\u0026ndash;1.143, p\u0026thinsp;=\u0026thinsp;0.057) and family history of diabetes (AOR\u0026thinsp;=\u0026thinsp;2.96, 95% CI: 0.87\u0026ndash;10.04, p\u0026thinsp;=\u0026thinsp;0.083) were attenuated and no longer statistically significant (Supplementary Table S2) .\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaternal Factors Associated With OGTT-Defined GDM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.87\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.08 (1.01\u0026ndash;1.15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.69\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high vs tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.24\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education vs tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.11\u0026ndash;9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary vs tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.40\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high vs tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.44\u0026ndash;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.29 (1.03\u0026ndash;10.50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted odds ratios were obtained from multivariable logistic regression analysis with OGTT-defined gestational diabetes mellitus as the dependent variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eReference category for educational level: tertiary education.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCI\u0026thinsp;=\u0026thinsp;confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio; OGTT\u0026thinsp;=\u0026thinsp;oral glucose tolerance test; GDM\u0026thinsp;=\u0026thinsp;gestational diabetes mellitus.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the prevalence of GDM, evaluated the diagnostic performance of HbA1c relative to the OGTT, and identified maternal factors associated with OGTT-diagnosed GDM among pregnant women in Ghana. The findings indicate a relatively high prevalence of GDM in this population. HbA1c identified substantially fewer cases of hyperglycemia compared with OGTT, reflecting limited sensitivity when used alone for screening during pregnancy. Although HbA1c demonstrated very high specificity and positive predictive value, many women with OGTT-diagnosed GDM had HbA1c values within the normal range. In addition, higher maternal body mass index and family history of diabetes were associated with increased odds of GDM. Together, these findings suggest that HbA1c alone is insufficient for detecting GDM and support the continued use of OGTT as the primary diagnostic method in antenatal care.\u003c/p\u003e \u003cp\u003eThe prevalence of OGTT-defined GDM in this study was higher than estimates reported in many population-based studies. Several factors may explain this finding. First, the study population consisted of women aged\u0026thinsp;\u0026ge;\u0026thinsp;30 years, an age group known to have a higher risk of GDM. Second, a large proportion of participants were overweight or obese, which is a well-established risk factor for impaired glucose metabolism during pregnancy. In addition, participants were identified from women who had undergone both HbA1c and OGTT testing as part of routine clinical care. Because OGTT is often performed when there is clinical suspicion or elevated risk for GDM, the study sample may have been enriched with higher-risk pregnancies rather than representing a universally screened antenatal population. Consequently, the prevalence observed in this study should not be interpreted as a population estimate but rather as a reflection of the higher-risk clinical population included in the analysis.\u003c/p\u003e \u003cp\u003eA substantial discrepancy was observed between hyperglycemia detected by OGTT and that detected by HbA1c. While nearly half of the participants met the OGTT diagnostic criteria for GDM, only a small proportion had HbA1c values in the diabetes range. This finding highlights the limited sensitivity of HbA1c for detecting gestational glucose abnormalities and suggests that HbA1c may underestimate the true burden of hyperglycemia during pregnancy. Similar discrepancies between HbA1c and OGTT have been reported in previous studies evaluating its diagnostic performance during pregnancy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consistent with these findings, HbA1c demonstrated high specificity but relatively low sensitivity for identifying GDM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the observed high specificity should be interpreted with caution given the relatively small sample size and distribution of HbA1c values in this cohort. The low sensitivity indicates that many women with OGTT-diagnosed GDM had HbA1c levels within the normal range. The reduced sensitivity of HbA1c during pregnancy may be explained by physiological changes that occur during gestation. Increased plasma volume, accelerated red blood cell turnover, and shortened erythrocyte lifespan may influence HbA1c measurements and lead to underestimation of glycemic exposure [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, iron deficiency\u0026mdash;commonly observed during pregnancy, particularly in low- and middle-income countries\u0026mdash;may further affect HbA1c values and contribute to variability in its diagnostic performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond diagnostic evaluation, this study identified maternal factors associated with OGTT-diagnosed GDM. Higher maternal body mass index and a family history of diabetes were associated with GDM in this sample, consistent with existing evidence linking maternal adiposity and inherited metabolic risk to increased insulin resistance during pregnancy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, these findings should be interpreted with caution, as the regression analysis was exploratory and intended to provide contextual insight rather than definitive risk factor identification. When previous GDM or elevated blood glucose in pregnancy was included in the model, it emerged as the strongest independent predictor of current GDM, reflecting the well-documented recurrence of glucose intolerance across pregnancies [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The inclusion of this variable attenuated the associations of BMI and family history of diabetes, likely due to shared metabolic pathways and overlapping risk profiles involving insulin resistance and β-cell dysfunction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings highlight the importance of early screening among women with a history of glucose dysregulation during pregnancy, as recommended by current clinical guidelines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of this study have important clinical and public health implications. The high prevalence of OGTT-diagnosed GDM observed in this population underscores the need for effective screening strategies within antenatal care services in Ghana. Although HbA1c demonstrated high specificity, its relatively low sensitivity indicates that it may not be adequate as a standalone screening test for GDM. Reliance solely on HbA1c could therefore result in missed diagnoses and delayed management of glucose intolerance during pregnancy. These findings support current recommendations that emphasize OGTT as the primary diagnostic test for GDM [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In settings where universal OGTT screening may be difficult to implement, risk based screening strategies targeting women with known risk factors such as elevated body mass index, family history of diabetes, and advanced maternal age are recommended and may facilitate earlier detection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Evidence indicates that early identification and treatment of gestational diabetes mellitus can reduce adverse maternal and neonatal outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In addition, public health interventions aimed at preventing maternal overweight and obesity, including nutrition education and lifestyle modification during antenatal care, may help reduce the risk of gestational diabetes and its associated complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral strengths and limitations should be considered when interpreting these findings. A key strength of this study is the use of the OGTT, which remains the reference standard for diagnosing gestational diabetes mellitus and allows accurate classification of glucose tolerance status. In addition, the study evaluated both the diagnostic performance of HbA1c and maternal factors associated with OGTT-defined GDM within the same population. The use of ROC analysis further enabled assessment of the overall discriminatory ability of HbA1c across a range of thresholds. However, several limitations should be acknowledged. The sample size was relatively modest, and participants were recruited from antenatal clinics within a single municipality, which may limit generalizability to other regions or populations. Another limitation relates to the interpretation of HbA1c during pregnancy, as physiological changes such as increased red blood cell turnover and hemodilution may influence HbA1c levels and reduce concordance with OGTT-based diagnosis. Furthermore, the HbA1c threshold of 5.7% used in this study was derived from nonpregnant populations and is not a validated pregnancy-specific diagnostic cutoff for GDM, which may limit its clinical applicability in this context. In addition, the study did not evaluate alternative HbA1c thresholds or conduct threshold optimization analyses, and therefore conclusions regarding optimal cutoff values cannot be drawn. Finally, the cross-sectional design limits the ability to infer causal relationships between maternal factors and the development of GDM.\u003c/p\u003e \u003cp\u003eIn conclusion, this study identified a relatively high prevalence of GDM based on OGTT among pregnant women in this population. HbA1c demonstrated high specificity but limited sensitivity for detecting OGTT-diagnosed GDM, indicating that it is not suitable as a standalone screening test during pregnancy. Maternal body mass index and family history of diabetes were important predictors of GDM. These findings highlight the importance of appropriate screening strategies and targeted interventions addressing modifiable risk factors such as maternal overweight and obesity. Further research involving larger and more diverse populations is needed to better understand the role of alternative screening approaches and improve early detection of GDM in similar settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Research and Ethics Committee of the College of Health, Yamfo, Ghana. Written informed consent was obtained from all participants prior to enrollment in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are not publicly available due to ongoing analyses but are available from the corresponding author on reasonable request, subject to institutional and ethical approval where applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.O.M. conceived and designed the study, performed the statistical analysis, and drafted the manuscript. V.O.M. contributed to the study design, supervised data collection, and critically revised the manuscript. P.H.M. assisted with data collection and contributed to drafting the manuscript. L.J.A. and O.C.A. supported data collection and data management. G.A. provided clinical oversight and contributed to interpretation of the results. E.B. assisted with data management and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of Abenkyiman Hospital, particularly the laboratory personnel, for their support during data collection and biochemical analyses. We are also grateful to all the pregnant women who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eElSayed NA et al (2022) 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes\u0026mdash;2023. Diabetes Care 46(Supplement1):S19\u0026ndash;S40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrara A (2007) Increasing Prevalence of Gestational Diabetes Mellitus: A public health perspective. Diabetes Care 30(Supplement2):S141\u0026ndash;S146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuariguata L et al (2014) Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract 103(2):176\u0026ndash;185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncan BB, Magliano DJ, Boyko EJ (2026) IDF diabetes atlas 11th edition 2025: global prevalence and projections for 2050. Oxford University Press, pp 7\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlows JF et al (2018) The Pathophysiology of Gestational Diabetes Mellitus. 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Diabetes Care 37(11):2953\u0026ndash;2959\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacaulay S, Dunger DB, Norris SA (2014) Gestational diabetes mellitus in Africa: a systematic review. PLoS ONE 9(6):e97871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMwanri AW et al (2015) Gestational diabetes mellitus in sub-Saharan Africa: systematic review and metaregression on prevalence and risk factors. Tropical Med Int Health 20(8):983\u0026ndash;1002\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2011) Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. WHO, Geneva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenz PB et al (2015) HbA1c test as a tool in the diagnosis of gestational diabetes mellitus. PLoS ONE 10(8):e0135989\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen LR et al (2004) HbA1c levels are significantly lower in early and late pregnancy. Diabetes Care 27(5):1200\u0026ndash;1201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks DB (2011) A1C versus glucose testing: a comparison. Diabetes Care 34(2):518\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweeting AN et al (2019) A novel early pregnancy risk prediction model for gestational diabetes mellitus. Fetal Diagn Ther 45(2):76\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFong A et al (2014) Use of hemoglobin A1c as an early predictor of gestational diabetes mellitus. Am J Obstet Gynecol 211(6):641e1-641. e7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCochran WG (1977) Sampling techniques. Wiley\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2000) Obesity: Preventing and managing the global epidemic. WHO, Geneva\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiricharoenthai P, Phupong V (2020) Diagnostic accuracy of HbA1c in detecting gestational diabetes mellitus. J Maternal-Fetal Neonatal Med 33(20):3497\u0026ndash;3500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou H et al (2021) Risk of type 2 diabetes mellitus after gestational diabetes mellitus: A systematic review \u0026amp; meta-analysis. Indian J Med Res 154(1):62\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorloni M et al (2009) Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev 10(2):194\u0026ndash;203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoses RG (1996) The recurrence rate of gestational diabetes in subsequent pregnancies. Diabetes Care 19(12):1348\u0026ndash;1350\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetahun D, Fassett MJ, Jacobsen SJ (2010) Gestational diabetes: risk of recurrence in subsequent pregnancies. Am J Obstet Gynecol 203(5):467e1-467. e6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuchanan TA, Xiang AH (2005) Gestational diabetes mellitus. J Clin Investig 115(3):485\u0026ndash;491\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta Y et al (2015) Updated guidelines on screening for gestational diabetes. Int J women's health, : p. 539\u0026ndash;550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePillay J et al (2021) Screening for gestational diabetes: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA 326(6):539\u0026ndash;562\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"College of health, Yamfo","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes mellitus, Oral glucose tolerance test, Glycated hemoglobin (HbA1c), Diagnostic agreement, Pregnancy, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-9329607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9329607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe oral glucose tolerance test (OGTT), the reference standard for diagnosing gestational diabetes mellitus (GDM), is often challenging to implement in routine antenatal care. Glycated hemoglobin (HbA1c) is a simpler alternative, but evidence of its diagnostic performance during pregnancy is limited in sub-Saharan Africa. This study evaluated GDM prevalence, agreement between HbA1c and OGTT, and maternal factors associated with OGTT-diagnosed GDM in Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 138 pregnant women attending antenatal care in Ghana. Participants underwent a 75-g OGTT and HbA1c testing between 24 and 28 weeks of gestation. Diagnostic performance of HbA1c relative to OGTT was assessed using sensitivity, specificity, predictive values, Cohen\u0026rsquo;s kappa statistic, and receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression was used to identify maternal factors associated with GDM.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of GDM based on OGTT was 47.8% in this clinic-based sample. HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;5.7% identified fewer cases (16.7%) and demonstrated low sensitivity (42.4%) but high specificity (100.0%) for detecting OGTT-defined GDM. Agreement between HbA1c and OGTT was moderate (κ\u0026thinsp;=\u0026thinsp;0.44). ROC analysis showed discrimination (AUC\u0026thinsp;=\u0026thinsp;0.862); however, the clinically applied threshold demonstrated limited sensitivity. Higher maternal body mass index and family history of diabetes were associated with increased odds of GDM.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe prevalence of OGTT-diagnosed GDM was high in this population. Although HbA1c demonstrated high specificity, its low sensitivity indicates that it cannot replace OGTT as a standalone screening test. These findings support the continued use of OGTT for GDM diagnosis, particularly in higher-risk populations.\u003c/p\u003e","manuscriptTitle":"Diagnostic Performance of HbA1c Compared With OGTT for Detecting Gestational Diabetes and Associated Maternal Risk Factors in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 16:50:40","doi":"10.21203/rs.3.rs-9329607/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af2cf5c0-b190-41d8-997a-3d83f27aa342","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65761534,"name":"Maternal \u0026 Fetal Medicine"}],"tags":[],"updatedAt":"2026-04-07T16:50:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 16:50:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9329607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9329607","identity":"rs-9329607","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}