Predictive Value of First-Trimester Glycosylated Hemoglobin Levels in Gestational Diabetes Mellitus: A Chinese Population Cohort Study

This study was aimed at exploring the predictive value of first-trimester glycosylated hemoglobin (HbA1c) levels in the diagnosis of gestational diabetes mellitus (GDM). A total of 744 pregnant women registered at the Peking University International Hospital between March 2017 and March 2019 were included in this study. Data on personal characteristics and biochemical indicators of the pregnant women were collected during the first trimester. The International Association of Diabetes and Pregnancy Study Groups has adopted specific diagnostic criteria as the gold standard for the diagnosis of GDM. Receiver operating characteristic (ROC) curve statistics were used to assess the predictive value of first-trimester HbA1c levels in the diagnosis of GDM. HbA1c levels in the first trimester were significantly higher in the GDM group than in the non-GDM group ( 5.23 % ± 0.29 % vs. 5.06 ± 0.28 % , P < 0.05 ). The first-trimester HbA1c level was an independent risk factor for gestational diabetes. The area under the ROC curve (AUC) of HbA1c for GDM was 0.655 (95% confidence interval 0.620-0.689, P < 0.001 ). The positive likelihood ratio was the highest at HbA 1 c = 5.9 % , sensitivity was 2.78, and specificity was 99.83%. There was no statistical difference in AUC between fasting blood glucose and HbA1c ( P = 0.407 ). First-trimester HbA1c levels can be used to predict GDM. The risk of GDM was significantly increased in pregnant women with first ‐ trimester HbA 1 c levels > 5.9 % . There was no statistical difference between first-trimester HbA1c and fasting blood glucose levels in predicting GDM.

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The joint prediction model of pBMI and eFBG in predicting gestational diabetes mellitus

Journal of International Medical Research ◽

10.1177/0300060519889199 ◽

2019 ◽

Vol 48 (4) ◽

pp. 030006051988919

Author(s):

Ying Pan ◽

Ji Hu ◽

Shao Zhong

Keyword(s):

Diabetes Mellitus ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Roc Curve ◽

Predictive Value ◽

Fasting Blood Glucose ◽

First Trimester ◽

Credibility Interval ◽

Roc Curve Analysis

Objective To explore the predictive value of prepregnancy body mass index (pBMI) and early gestational fasting blood glucose (eFBG) in gestational diabetes mellitus (GDM). Methods This case–control study enrolled pregnant women at 6 to 16 weeks of gestation. The pBMI, eFBG and glycosylated haemoglobin (HbA1c) was recorded in the first trimester of pregnancy. Receiver-operating characteristic (ROC) curve analysis was used to measure the efficacy of factors that predict GDM. Results A total of 2119 pregnant women were enrolled in this study. Of these, 386 were diagnosed with GDM and 1733 did not have GDM. The age (odds ratio [OR] 1.16; 95% confidence interval [CI] 1.13, 1.20), pBMI (OR 1.12; 95% CI 1.07, 1.17) and eFBG (OR 5.37; 95% CI 3.93, 7.34) were independent risk factors for GDM occurrence. The areas under the ROC curve of eFBG, pBMI and eFBG + pBMI were 0.68 (95% credibility interval 0.65, 0.71), 0.66 (95% credibility interval 0.63, 0.69) and 0.71 (95% credibility interval 0.69, 0.74), respectively. The area under the curve of eFBG + pBMI was significantly higher than that of eFBG or pBMI alone. Conclusion The combination of eFBG and pBMI had a high predictive value for GDM.

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Spontaneous Abortions and Gestational Diabetes Mellitus- A Study from Pakistan

Journal of Shalamar Medical & Dental College - JSHMDC ◽

10.53685/jshmdc.v2i2.75 ◽

2021 ◽

Vol 2 (2) ◽

pp. 58-63

Author(s):

Aasia Kanwal ◽

Asma Salam ◽

Aisha Bashir

Keyword(s):

Diabetes Mellitus ◽

Blood Glucose ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Fasting Blood Glucose ◽

Categorical Variables ◽

Spontaneous Abortions ◽

Rural And Urban ◽

Increased Risk

Background: Gestational diabetes mellitus leads to adverse pregnancy outcomes. Objectives: The objective of the study was to explore the relationship of spontaneous abortions with gestational diabetes mellitus in pregnant women from rural and urban Lahore. Methods: This cross-sectional study was conducted at University of Health Sciences, Lahore in 2019. Among 60 pregnant women sampled, 30 had gestational diabetes mellitus (GDM) and 30 were normal pregnant controls. Pregnant women were sampled from different hospitals of rural and urban areas of Lahore. Independent sample t-test was applied for analyzing the data. Chi- square test was used to analyze the categorical variables. Association of fasting blood glucose (FBG) and abortions was checked. Odd ratio and relative risk were calculated. Results: Mean fasting blood sugar levels were significantly higher in GDM group (105 mg/dL) as compared to non-GDM group (80.50 mg/dL) at p<0.001. The proportions of the women with increased number of abortions had significantly higher blood glucose levels (OR 5.091, 95% CI, RR 1.27). Conclusions: Gestational diabetes mellitus is associated with an increased risk of spontaneous abortions.

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Association of WISP1/CCN4 with Risk of Overweight and Gestational Diabetes Mellitus in Chinese Pregnant Women

Disease Markers ◽

10.1155/2020/4934206 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Lei Liu ◽

Jiajin Hu ◽

Liu Yang ◽

Ningning Wang ◽

Yang Liu ◽

...

Keyword(s):

Diabetes Mellitus ◽

Blood Glucose ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Fasting Blood Glucose ◽

Adverse Outcomes ◽

Obese Women ◽

Complement C1q

Background. Obese women with gestational diabetes mellitus (GDM) have a higher risk of adverse outcomes than women with obesity or GDM alone. Our study is aimed at investigating the discriminatory power of circulatory Wnt1-inducible signaling pathway protein-1 (WISP1), a novel adipocytokine, on the copresence of prepregnancy overweight/obesity and GDM and at clarifying the relationship between the WISP1 level and clinical cardiometabolic parameters. Methods. A total of 313 participants were screened from a multicenter prospective prebirth cohort: Born in Shenyang Cohort Study (BISCS). Subjects were examined with a 2×2 factorial design for body mass index BMI≥24 and GDM. Between 24 and 28 weeks of pregnancy, follow-up individuals underwent an OGTT and blood sampling for cardiometabolic characterization. Results. We observed that the WISP1 levels were elevated in prepregnancy overweight/obesity patients with GDM, compared with nonoverweight subjects with normal blood glucose (3.45±0.89 vs. 2.91±0.75 ng/mL). Multilogistic regression analyses after adjustments for potential confounding factors revealed that WISP1 was a strong and independent risk factor for prepregnancy overweight/obesity with GDM (all ORs>1). In addition, the results of the ROC analysis indicated that WISP1 exhibited the capability to identify individuals with prepregnancy overweight/obesity and GDM (all AUC>0.5). Finally, univariate and multivariate linear regression showed that WISP1 level was positively and independently correlated with fasting blood glucose, systolic blood pressure, and aspartate aminotransferase and was negatively correlated with HDL-C and complement C1q. Conclusions. WISP1 may be critical for the prediction, diagnosis, and therapeutic strategies against obesity and GDM in pregnant women.

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Higher mean amplitude of glycaemic excursion in the second trimester of pregnancy is associated with the subsequent development of gestational diabetes mellitus: an observational study

10.22541/au.163843479.99686909/v1 ◽

2021 ◽

Author(s):

Phaik Ling Quah ◽

Kok Hian Tan ◽

Nurul Razali ◽

Nurul Sakinah Razali

Keyword(s):

Diabetes Mellitus ◽

Blood Glucose ◽

Gestational Diabetes ◽

Observational Study ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Subsequent Development ◽

Mean Amplitude ◽

First Trimester ◽

Second Trimester

Objective: To examine glycaemic variability (GV) and glycaemic control (GC) parameters in early pregnancy with subsequent development of gestational diabetes mellitus (GDM). Design: Longitudinal observational study. Setting: Pregnant women from KK Women and Children’s Hospital in Singapore Participants: 51 study participants in the first trimester (9-13 weeks’ gestational), and 44 participants (18-23 weeks’ gestation) in the second trimester of pregnancy. Methods: Independent t-tests were used to examine the differences in the parameters between participants who developed GDM and those who did not. Main outcome measure: GDM was determined at 24-30 weeks’ gestation using oral glucose tolerance test (OGTT). GV parameters examined were, mean amplitude of glycaemic excursion (MAGE), standard deviation of blood glucose (SDBG) and mean of daily continuous 24 h blood glucose (MBG) and coefficient of variation (CV). GC parameters measured were, J-Index and % time spent in glucose target ranges. Results: In the second trimester of pregnancy, mean amplitude of glycaemic excursions (MAGE) was significantly higher in participants who subsequently developed GDM, compared to those who did not (mean (SD): 3.18(0.68) vs 2.60(0.53), p=0.02). Other study parameters measured in the second trimester of pregnancy were not significantly different between groups. There were no significant associations between all the GV and GC parameters determined from the CGM in the first trimester with subsequent development of GDM (p>0.05). Conclusion: MAGE is an important GV parameter associated to the development of subsequent GDM in pregnant women. The findings highlight the potential value of CGM in gestational glycaemic profiling.

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An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Journal of Medical Internet Research ◽

10.2196/21573 ◽

2020 ◽

Vol 22 (9) ◽

pp. e21573 ◽

Cited By ~ 1

Author(s):

Jiayi Shen ◽

Jiebin Chen ◽

Zequan Zheng ◽

Jiabin Zheng ◽

Zherui Liu ◽

...

Keyword(s):

Artificial Intelligence ◽

Diabetes Mellitus ◽

Blood Glucose ◽

Gestational Diabetes ◽

Pregnant Women ◽

Fasting Blood Glucose ◽

External Validation ◽

Validation Dataset ◽

Gradient Boosting ◽

Local Hospital

Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

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Serum cobalamin status of pregnant women suffering from gestational diabetes mellitus.

The Professional Medical Journal ◽

10.29309/tpmj/2020.27.05.4076 ◽

2020 ◽

Vol 27 (05) ◽

pp. 1004-1010

Author(s):

Saher Fatima ◽

Sadia Saeed ◽

Syeda Fariha Hasnny ◽

Nathumal Maheshwari ◽

Urooj Tabassum ◽

...

Keyword(s):

Diabetes Mellitus ◽

Blood Glucose ◽

Glycemic Control ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Fasting Blood Glucose ◽

Inverse Correlation ◽

Cobalamin Deficiency ◽

Assay Method

Objectives: Determining serum cobalamin levels in Pregnant Women suffering from Gestational Diabetes mellitus (GDM) presenting at our tertiary care hospital. Study Design: Case control study. Setting: Department of Gynecology and Medicine, SMBB Medical College Layari General Hospital Karachi. Period: January 2016 to April 2017. Material & Methods: Sample of 100 pregnant women in 2nd and 3rd trimester was selected into; 50 controls and 50 GDM cases through convenient sampling. GDM was defined as pregnant women with fasting blood sugar ≥100 mg/dL. 5 ml blood was collected; 3 ml put into EDTA tubes for complete blood counts and 2 ml for sera. Blood glucose was estimated by hexokinase method, HbA1c by colorimetric method and cobalamin by ECLIA assay method. SPSS software 21.0 (IBM, Inc USA) was used for data analysis using Student t-test and Chi-square test (P ≤ 0.05). Results: Age of control was 30.43±1.49 years and 29.95±1.27 years in cases. Gestational age was 33.67±2.69 weeks in controls and 34.75±2.53 weeks in cases. Control and cases shows serum cobalamin levels of 316.34± 113.77 pg/ml and 253.5±121.32 pg/ml respectively (P=0.009). Serum cobalamin deficiency was noted in 68% of cases and 40% of controls (P<0.05). Glycemic control was bad in majority of cases. Serum cobalamin shows inverse correlation with random blood glucose, fasting blood glucose and Glycated HbA1. Conclusion: We found low serum cobalamin levels in pregnant women suffering from gestational diabetes mellitus that showed inverse correlation with random and fasting blood glucose and glycemic control.

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Predictive significance of first trimester serum uric acid as risk factor for the gestational diabetes mellitus.

The Professional Medical Journal ◽

10.29309/tpmj/2021.28.07.4075 ◽

2021 ◽

Vol 28 (7) ◽

pp. 967-972

Author(s):

Anjum Rehman ◽

◽

Sadia Saeed ◽

Syeda Fariha Hasny ◽

Nathumal Maheshwari ◽

...

Keyword(s):

Diabetes Mellitus ◽

Uric Acid ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Serum Uric Acid ◽

Fasting Blood Glucose ◽

First Trimester ◽

Gynecology And Obstetrics ◽

Diagnostic Threshold

Objective: Determining the predictive significance of first trimester serum uric acid for the development of gestational diabetes mellitus (GDM) in pregnant women. Study Design: Case Control study. Setting: Department Gynecology and Obstetrics, Shaheed Muhtrama Benazir Bhutto Medical College Layari General Hospital Karachi. Period: March 2017 to December 2018. Material & Methods: Sample of 172 pregnant women in first trimester (<14 weeks gestation) were divided into; 72 controls and 72 cases through purposive sampling. Pregnant women with fasting blood glucose (FBG) ≥100 mg/dl were defined as GDM. FBG was estimated by hexokinase and uric acid by enzymatic method (uricase) using commercial colorimetric assay (Nikken Seal Co., Ltd, Japan). Data was analyzed on SPSS software 21.0 (IBM, Inc USA) at 95% CI. Results: Maternal age of control and cases was noted 30.23±1.47 and 30.14±1.41 years. Gestational age in controls was 9.80±2.23 weeks compared to 10.37±2.34 weeks in cases. Serum Uric acid in control was 3.19±0.49 mg/dl compared to 3.73±0.43 mg/dl in cases (P=0.0001). Logistic regression analysis model generated ROC curve shows excellent area under the curve (AUC) of 0.92 [95% CI (0.87-0.97)] with a diagnostic threshold of 3.91 mg/dl for uric acid. At this Uric acid threshold, the specificity and sensitivity was 96.4% and 69.7% respectively (P=0.0001). Conclusion: It is concluded first trimester serum uric acid may be used for predicting the future development of gestational diabetes mellitus.

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Early diagnosis of gestational diabetes mellitus using circulating microRNAs

Acta Endocrinologica ◽

10.1530/eje-19-0206 ◽

2019 ◽

Vol 181 (5) ◽

pp. 565-577 ◽

Cited By ~ 13

Author(s):

Liron Yoffe ◽

Avital Polsky ◽

Avital Gilam ◽

Chen Raff ◽

Federico Mecacci ◽

...

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Pregnant Women ◽

Predictive Value ◽

First Trimester ◽

Learning Models ◽

Circulating Micrornas ◽

Machine Learning Models

Design Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and its prevalence is constantly rising worldwide. Diagnosis is commonly in the late second or early third trimester of pregnancy, though the development of GDM starts early; hence, first-trimester diagnosis is feasible. Objective Our objective was to identify microRNAs that best distinguish GDM samples from those of healthy pregnant women and to evaluate the predictive value of microRNAs for GDM detection in the first trimester. Methods We investigated the abundance of circulating microRNAs in the plasma of pregnant women in their first trimester. Two populations were included in the study to enable population-specific as well as cross-population inspection of expression profiles. Each microRNA was tested for differential expression in GDM vs control samples, and their efficiency for GDM detection was evaluated using machine-learning models. Results Two upregulated microRNAs (miR-223 and miR-23a) were identified in GDM vs the control set, and validated on a new cohort of women. Using both microRNAs in a logistic-regression model, we achieved an AUC value of 0.91. We further demonstrated the overall predictive value of microRNAs using several types of multivariable machine-learning models that included the entire set of expressed microRNAs. All models achieved high accuracy when applied on the dataset (mean AUC = 0.77). The significance of the classification results was established via permutation tests. Conclusions Our findings suggest that circulating microRNAs are potential biomarkers for GDM in the first trimester. This warrants further examination and lays the foundation for producing a novel early non-invasive diagnostic tool for GDM.

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An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study (Preprint)

10.2196/preprints.21573 ◽

2020 ◽

Author(s):

Jiayi Shen ◽

Jiebin Chen ◽

Zequan Zheng ◽

Jiabin Zheng ◽

Zherui Liu ◽

...

Keyword(s):

Artificial Intelligence ◽

Diabetes Mellitus ◽

Blood Glucose ◽

Gestational Diabetes ◽

Pregnant Women ◽

Fasting Blood Glucose ◽

External Validation ◽

Validation Dataset ◽

Gradient Boosting ◽

Local Hospital

BACKGROUND Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. OBJECTIVE This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. METHODS An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. RESULTS The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. CONCLUSIONS Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

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