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Showing 4 results for Gestational Diabetes Mellitus
Evaluation of selective screening for diagnosis of gestational diabetes mellitus
Garshasbi A, Faghihzadeh S, Falah N, Khosniat M, Torkestani F, Ghavam M, Abasian M,
Volume 67, Issue 4 (7-2009)
Abstract

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 !mso]> ject classid="clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D" id=ieooui> Background: Gestational diabetes mellitus is diagnosed as carbohydrate in tolerance demonstrated for the first time in the course of pregnancy. The aim of this study was to evaluate the selective screening method for gestational diabetes mellitus (GDM) based on: 1- recommendation of the fourth workshop- conference on GDM 2- evaluation of risk factors
Methods: A case- control study was performed on 370 pregnancies inflicted by GDM in Hazrat Zaynab Hospital, Shahed University. The maternal and perinatal outcomes and prevalence of risk factors based on recommendation of the fourth workshop- conference on GDM in these women with GDM were compared with the same data and risk factors of randomly selected 600 pregnant women at the same time and in the same hospital, they all underwent universal testing for GDM, and their OGTT were normal.
Results: The prevalence of all risk factors was significantly higher in the group with GDM, but 45 of these women (12%) had no risk factors. 107 women (29%) with GDM were at low risk and would remain undiagnosed if selective screening method was used. The main neonatal complications in the low- risk group did not differ from the complications in other women with GDM.
Conclusions: The universal screening of all pregnant women seems to justified whereas the recommendations for not screening low- risk group are doubtful and require further examination.


T-helper cytokine patterns and related antibodies in patients with gestational diabetes mellitus
Etaati Z, Moazzami Godarzi R, Kalhori F, Sobhani Sa, Solati M, Alavi A, Tashnizi Sh, Naderi N,
Volume 70, Issue 1 (4-2012)
Abstract

Background: Diabetes mellitus (DM) is a group of metabolic disorders such as DM I, DM II, secondary causes of DM and gestational diabetes mellitus characterized by hyperglycemic phonotype. The etiology of gestational diabetes mellitus is unknown. Recent studies address the chronic activity of immune system against infections (not autoimmunity) as an important cause of gestational diabetes mellitus. This study aimed to compare T-helper cells 1 and 2 cytokines and associated antibodies in patients with gestational diabetes mellitus and normal pregnant women.

Methods: This cross-sectional study was performed on 45 female patients with GDM and 45 healthy pregnant women in Bandar Abbas, Iran, from 2008- 2009. The exclusion criteria were presence of any infectious diseases or autoimmune disorders such as SLE or RA. Present and past medical histories were taken from the participants thorough physical examination. Blood samples (10 mL) were drawn and sent to laboratory for measuring serum IgE, IgG1, IgG2, IgG3, IgG4, interleukin-10 (IL-10), interleukin-12 (IL-12), transforming growth factor-beta (TGF1), and interferon-gamma (IFN) measurements. T-test and Kolmogorov-Smirnov test were used for data analysis.

Results: The mean age of the patients with GDM and healthy pregnant women was 32.5 and 27.9 yrs, respectively. T-helper 1 and 2 associated antibodies and cytokines had no significant differences between the case and control groups.

Conclusion: The changes in T-helper 1 and 2 associated antibodies and cytokines are not associated with gestational diabetes mellitus and could not be considered as a predictor for gestational diabetes mellitus.


The effect of maternal gestational diabetes and HbA1c on hypertrophic cardiomyopathy of infants
Hoda Rezaie , Ahmad Naghibzadeh-Tahami, Mohammad Mehdi Bagheri ,
Volume 75, Issue 6 (9-2017)
Abstract
Background: The prevalence of gestational diabetes is increasing among pregnant women. It is associated with an increased risk of congenital heart disease, including hypertrophic cardiomyopathy. The aim of this study was to evaluate the effect of maternal diabetes control (based on HbA1c) on their hypertrophic cardiomyopathy in newborns.
Methods: This case-control study was performed on 60 neonates born in Afzalipour Hospital (Kerman University of Medical Sciences) from May to November 2014 in two groups of eligible infants using the convenience sampling method. Information about the age, sex, weight, gestational age, maternal age, obstetric history, gestational diabetes through the checklist were collected. Then Doppler echocardiography, M- Mode, Doppler tissue was conducted on two groups. Echocardiographic criteria including ventricular septal thickness and blood HbA1c mothers in both groups were compared. To compare quantitative and qualitative variables between the two groups’ Independent samples t‐test and Chi-square test was used. A significant level of 0.05 was considered in all of the statistical samples and SPSS software, ver. 20 (IBM, Armonk, NY, USA) was used to analyze the data.
Results: In this study, the birth weight of infants and the age of mothers did not differ between two groups (Respectively P=0.56, P=0.08) However, HbA1c was significantly higher in the infants of mothers with impaired glucose tolerance test (GTT) (P<0.001). In infants of mothers with impaired GTT, ventricular septal thickness was significantly higher than the healthy controls (P=0.03), Also there was a significant difference between two groups in tissue Doppler criteria (Ea) (P=0.04), In other echocardiographic criteria, no significant differences were reported (The LA/AO, LVPWT, LVEF, LVEF, LVFS, LVFS, LVEDd, LVESd, Sa and Aa, All P-values were ≥ 0.05).
Conclusion: Diabetes mellitus of mothers causes several complications in their infants. The prevalence of cardiomyopathy hypertrophy is higher in babies whose mothers have higher levels of HbA1c and a sign of poor control of blodd glucose level during pregnancy.

Comparison of gestational diabetes prediction with artificial neural network and decision tree models
Mansour Rezaei, Negin Fakhri , Fateme Rajati , Soodeh Shahsavari ,
Volume 77, Issue 6 (9-2019)
Abstract
Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural network (ANN) and decision tree and also comparing these models in the diagnosis of GDM.
Methods: In this modeling study, among the cases of pregnant women who were monitored by health care centers of Kermanshah City, Iran, from 2010 to 2012, four hundred cases were selected, therefore the information in these cases was analyzed in this study. Demographic information, mother's maternal pregnancy rating, having diabetes at the beginning of pregnancy, fertility parameters and biochemical test results of mothers was collected from their records. Perceptron ANN and decision tree with CART algorithm models were fitted to the data and those performances were compared. According to the accuracy, sensitivity, specificity criteria and surface under the receiver operating characteristic (ROC) curve (AUC), the superior model was introduced.
Results: Following the fitting of an artificial neural network and decision tree models to data set, the following results were obtained. The accuracy, sensitivity, specificity and area under the ROC curve were calculated for both models. All of these values were more in the neural network model than the decision tree model. The accuracy criterion for these models was 0.83, 0.77, the sensitivity 0.62, 0.56 and specificity 0.95, 0.87, respectively. The surface under the ROC curve in ANN model was significantly higher than decision tree (0.79, 0.74, P=0.03).
Conclusion: In predicting and categorizing the presence and absence of gestational diabetes mellitus, the artificial neural network model had a higher accuracy, sensitivity, specificity, and surface under the receiver operating characteristic curve than the decision tree model. It can be concluded that the perceptron artificial neural network model has better predictions and closer to reality than the decision tree model.

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