scholarly journals Early diagnosis of gestational diabetes mellitus using circulating microRNAs

2019 ◽  
Vol 181 (5) ◽  
pp. 565-577 ◽  
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.

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

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.

86-LB: Elevated First-Trimester Neutrophil Count Is Associated with the Development of Gestational Diabetes Mellitus in Pregnant Women

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 86-LB
Author(s):  
TIANGE SUN ◽  
FANHUA MENG ◽  
RUI ZHANG ◽  
ZHIYAN YU ◽  
SHUFEI ZANG ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Neutrophil Count ◽  
First Trimester

scholarly journals Leptin, resistin and visfatin as useful predictors of gestational diabetes mellitus

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Ahmed Tijani Bawah ◽  
Mohammed Mustapha Seini ◽  
Albert Abaka-Yawason ◽  
Huseini Alidu ◽  
Salifu Nanga
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Operating Characteristic ◽  
Case Control Study ◽  
First Trimester ◽  
Anthropometric Parameters ◽  
Receiver Operating Characteristic Curves ◽  
Control Study

Abstract Background Lipids and adipokines including leptin, resistin and visfatin play various roles in the pathophysiology of Gestational Diabetes Mellitus (GDM). This study was aimed at determining whether serum leptin, resistin and visfatin are significantly altered during the first trimester of pregnancies that subsequently develop GDM and whether such changes are useful in predicting the disease. Methods This was a case-case control study which compared first trimester biochemical and anthropometric parameters in 70 pregnant women who subsequently developed GDM and 70 pregnant women without GDM at the Volta Regional Hospital, Ho, Ghana. Lipid profile and some selected adipokines were analyzed and first trimester body mass index (BMI) was determined. Results There were significant differences (p < 0.05) in leptin, resistin, and visfatin as well as significant dyslipidemia among those with GDM compared to those without GDM. Furthermore, the area under the Receiver Operating Characteristic Curves (AUCs) for leptin, resistin and visfatin were; 0.812, 0.836 and 0.799 respectively. Increased first trimester leptin (OR = 1.166; CI = 1.104–1.233; p < 0.0001), resistin (p < 0.0001) and visfatin (p < 0.0001) were associated with GDM. Conclusion Hyperleptinemia, hyperesistinemia and hypervisfatinemia precede GDM and can serve as good predictive indices for gestational diabetes mellitus.

scholarly journals Correlation between first trimester uric acid level and subsequent development of gestational diabetes mellitus

2017 ◽  
Vol 6 (2) ◽  
pp. 606
Author(s):  
Amudha P. ◽  
Nithya D. ◽  
Pradeeba S. ◽  
Manochithra B.
Keyword(s):  
Diabetes Mellitus ◽  
Uric Acid ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Serum Uric Acid ◽  
Uric Acid Level ◽  
Acid Level ◽  
Subsequent Development ◽  
First Trimester

Background: The aim of the study was to correlate between first trimester uric acid level and its association with subsequent development of gestational diabetes mellitus.Methods: This is a prospective study conducted at Govt. Raja Mirasudar Hospital attached to Thanjavur Medical College, Thanjavur over a period of one year from September 2015. A total of one hundred and eighty seven ante natal women less than 14 weeks of gestational age who attended the outpatient antenatal department were included in this study. Serum uric acid estimation was done in women with <14 weeks of gestation and they were subsequently screened for GDM between 24 to 28 weeks by oral glucose tolerance test (OGTT) with 75 gms glucose according to IADPSG criteria.Results: In our study, among 178 antenatal pregnant women 13 with uric acid >3.6 mg/dl and 2 with serum uric acid <3.6 mg/dl developed GDM. This shows development of GDM increases with increase in uric acid concentration.Conclusions: Though our study results suggest that serum uric acid level estimation in first trimester can be used as a marker to predict GDM in pregnant women, large scale studies are required before it can be recommended as a routine first trimester screening test for prediction of gestational diabetes mellitus.

scholarly journals Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning

2020 ◽  
Author(s):  
Yan-Ting Wu ◽  
Chen-Jie Zhang ◽  
Ben Willem Mol ◽  
Andrew Kawai ◽  
Cheng Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Chinese Population ◽  
Feature Selection Method ◽  
Area Under The Curve ◽  
First Trimester ◽  
Record System ◽  
Increased Risk

Abstract Context Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking. Objectives Establishing effective models to predict early GDM. Setting Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Main measures Based on a machine learning (ML) driven feature selection method, 17 variables were selected for early GDM prediction. In order to facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable dataset and the 73-variable dataset to build models predicting early GDM for different situations respectively. Results 16,819 and 14,992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low BMI (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = 0.0935). TT3 and TT4 were superior to FT3 and FT4 in predicting GDM. Lipoprotein (a) was demonstrated a promising predictive value (AUC = 0.66). Conclusions We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.

scholarly journals Cost effectiveness of different screening strategies for gestational diabetes mellitus screening: study protocol of a randomized community non-inferiority trial

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Fahimeh Ramezani Tehrani ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Health Care ◽  
Cost Effectiveness ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Standard Care ◽  
First Trimester ◽  
Trial Registration ◽  
Second Trimester

Abstract Background There is lack of ideal and comprehensive economic evaluations of various GDM strategies. The aim of this study is to the compare efficacy and cost-effectiveness of five different methods of screening for gestational diabetes mellitus (GDM). Methods This study is a randomized community non-inferiority trial among 30,000 pregnant women in five different geographic regions of Iran, who were randomly assigned to one of the five GDM screening methods. All first trimester pregnant women, seeking prenatal care in governmental health care systems, who met our eligibility criteria were enrolled. The criteria suggested by the International-Association-of-Diabetes-in-Pregnancy-Study-Group, the most intensive approach, were used as reference. We used the non-inferiority approach to compare less intensive strategies to the reference one. Along with routine prenatal standard care, all participants were scheduled to have two phases of GDM screening in first and second-trimester of pregnancy, based on five different pre-specified protocols. The screening protocol included fasting plasma glucose in the first trimester and either a one step or a two-step screening method in the second trimester of pregnancy. Pregnant women were classified in three groups based on the results: diagnosed with preexisting pre-gestational overt diabetes; gestational diabetes and non-GDM women. Each group received packages for standard-care and all participants were followed till delivery; pregnancy outcomes, quality of life and cost of health care were recorded in detail using specific standardized questionnaires. Primary outcomes were defined as % birth-weight > 90th percentile and primary cesarean section. In addition, we assessed the direct health care direct and indirect costs. Results This study will enable us to compare the cost effectiveness of different GDM screening protocols and intervention intensity (low versus high). Conclusion Results which if needed, will also enable policy makers to optimize the national GMD strategy as a resource for enhancing GDM guidelines. Trial registration Name of the registry: Iranian Registry of Clinical Trials. Trial registration number: IRCT138707081281N1. Date of registration: 2017-02-15. URL of trial registry record: https://www.irct.ir/trial/518

scholarly journals 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

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.

scholarly journals Performance and the Optimal Cut-Off Value of First-Trimester Fasting Plasma Glucose To Identify Gestational Diabetes Mellitus: A Retrospective Study From Southern China

2021 ◽  
Author(s):  
Jia-Ning Tong ◽  
Lin-Lin Wu ◽  
Yi-Xuan Chen ◽  
Xiao-Nian Guan ◽  
Kan Liu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Retrospective Study ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Maternal Age ◽  
First Trimester ◽  
Fasting Plasma

Abstract Purpose Previous studies have suggested that first-trimester fasting plasma glucose (FPG) is associated with gestational diabetes mellitus (GDM) and is a predictor of GDM. The aim of the present study was to explore whether first-trimester FPG levels can be used as a screening and diagnostic test for GDM in pregnant women. Methods This retrospective study included pregnant women who had their first-trimester FPG recorded at 9-13+6 weeks and underwent screening for GDM using the 2-hour 75 g oral glucose tolerance test (OGTT) between 24th and 28th gestational weeks. The cut-off values were calculated using a receiver operating characteristic (ROC) curve. Results The medical records of 28,030 pregnant women were analysed, and 4,669 (16.66%) of them were diagnosed with GDM. The mean first-trimester FPG was 4.62 ± 0.37 mmol/L. The total trend in the optimal cut-off value of first-trimester FPG in pregnant women was 4.735 mmol/L, with a sensitivity of 49%, a specificity of 67.6% and AUC of 0.608 (95% CI: 0.598-0.617, p༜0.001). Moreover, as the maternal age increased, the optimal cut-off values increased, respectively. The results suggest that first-trimester FPG can be considered a marker for identifying pregnant women with GDM. Conclusion The level of first-trimester FPG increased slightly with maternal age and, as maternal age increased, the optimal cut-off values increased, especially after age 30. The first-trimester FPG should be considered a screening marker when diagnosing GDM in pregnant women.

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