HOMA‐IR as a risk factor of gestational diabetes mellitus and a novel simple surrogate index in early pregnancy

Song, Shen; Zhang, Yuemei; Xiaolin, Qiao; Duo, Yanbei; Xu, Jiyu; Peng, Zhenyao; Zhang, Jing; Chen, Yan; Nie, Xiao-Rui; Sun, Qiujin; Yang, Xianchun; Lu, Zechun; Liu, Shixuan; Zhao, Tianyi; Yuan, Tao; Fu, Yong; Dong, Yingyue; Zhao, Weigang; Sun, Wei; Wang, Ailing

doi:10.1002/ijgo.13905

Cited by 23 publications

(19 citation statements)

References 26 publications

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“…In the study conducted by Song et al on 700 pregnant women in which they evaluated HOMA-IR as a risk factor for GDM in early pregnancy, 145 (20.7%) pregnant women were found to have GDM. They showed that HOMA-IR was higher in the GDM group than in the healthy group and was an individual risk factor for GDM (22). The study by Benhalima et al reported that high insulin resistance in pregnant women with GDM suggested a more unfavorable metabolic profile and pregnancy outcomes when compared to pregnant women with normal glucose tolerance (23).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of First Trimester Fasting Blood Glucose, HOMA-IR and HbA1c in Prediction of Gestational Diabetes Mellitus in Non-Obese Pregnant Women: A Retrospective Study

Kirlangic

Halıcı

2022

Turk J Diab Obes

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Aim: Gestational Diabetes Mellitus (GDM) is the most common endocrine complication in pregnancy with fetomaternal comorbidities. It is aimed to evaluate fasting blood glucose (FBG), homeostasis model assessment of insulin resistance (HOMA-IR), and hemoglobin A1c (HbA1c) values in the first trimester in non-obese pregnant women for early detection of GDM. Material and Methods: Pregnant women with first-trimester FBG, HOMA-IR, HbA1c values, and second-trimester OGTT results were scanned from the hospital database. First-trimester height, weight, age and gestational weeks were also recorded. The presence of Body Mass Index (BMI) over 30 kg/m2, chronic and systemic disease and history of G(DM) were not included in the study. ROC analysis was performed on FBG, HOMA-IR, and HbA1c. Results: This retrospective study was conducted between 01/01/2021, and 01/01/2022 in Tuzla State Hospital Gynecology and Obstetrics Clinic. 131 pregnant women who met the inclusion criteria were reached. Twenty of pregnant women were evaluated as GDM positive(+). Age, height, weight, BMI, gestational week, and nulliparity were observed to be similar between the groups. FBG, insulin, HOMA-IR, and HbA1c values were higher in the group with GDM. As a result of ROC analysis, those with an FBG value of 88,5 mg/dl (sensitivity 68.2%, specificity 68.2%), a HOMA-IR value of 2.24 (sensitivity 63.6%, specificity 64.5%), and a HbA1c (sensitivity 68.2%, specificity 66.7%) value of over 5.25% were observed to be at risk for GDM. Conclusion: It has shown that pregnant women with high first trimester FBG, HOMA-IR and HbA1c have a high risk for GDM and can be used as a predictor of GDM.

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Section: Discussionmentioning

confidence: 99%

Evaluation of First Trimester Fasting Blood Glucose, HOMA-IR and HbA1c in Prediction of Gestational Diabetes Mellitus in Non-Obese Pregnant Women: A Retrospective Study

Kirlangic

Halıcı

2022

Turk J Diab Obes

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“…All participants underwent a clinical investigation at the first prenatal visit in early pregnancy (6–12 weeks of gestation). 20 …”

Section: Methodsmentioning

confidence: 99%

ALT/AST as an Independent Risk Factor of Gestational Diabetes Mellitus Compared with TG/HDL-C

Song¹,

Zhang²,

Xiaolin³

et al. 2022

IJGM

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Objective: This study aimed to find the association between alanine transaminase-toaspartate aminotransferase ratio (ALT/AST) and the incidence of gestational diabetes mellitus (GDM). Methods: A total of 1128 pregnant women were included in this prospective, double-center, observational cohort study. ALT, AST and total bilirubin (TBil) were tested during 6-12 weeks of gestation and 75-g oral glucose tolerance test (OGTT) was conducted during 24-28 weeks of gestation to screen GDM. The association between ALT/AST and glucose concentration during OGTT was analyzed by linear regression model. The OR with 95% CI for incidence of GDM associated with ALT/AST was estimated by binary logistic regression. The discriminatory values of ALT/AST and triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) for GDM were calculated by the area under the receiver operating characteristic curve (ROC-AUC). Results: The incidence of GDM was 22.07% (249/1128). ALT/AST was higher in GDM group than in NGT group (0.92 [0.75, 1.18] vs 0.80[0.65, 1.02], P <0.001). ALT/AST had positive correlations with fasting blood glucose, 1-hour and 2-hour blood glucose concentration during OGTT (0.089 [95% CI: 0.034, 0.163], 0.176 [95% CI: 0.052, 0.104], and 0.115 [95% CI: 0.199, 0.609], respectively). The OR of ALT/AST for incidence of GDM was 1.603 (95% CI:1.097, 2.344). The ROC-AUC of ALT/AST and TG/HDL-C reached 0.615 (95% CI: 0.575, 0.655) and 0.619 (95% CI: 0.580, 0.659), respectively. Conclusion: ALT/AST in early pregnancy was an independent risk factor of GDM. The predictive ability of ALT/AST was similar to TG/HDL-C for GDM.

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“…Factor individu seperti beban latihan fisik dan menstruasi. Sedangkan factor tak terduga antara lain stress, penyakit bawaan dan infeksi [8]. Variasi normal penyebab gula darah disebabkan oleh factor-faktor umum dan individu tersebut, sedangkan penyebab gula darah karena factor khusus seperti hipoglikemia dan hiperglikemia tidak dapat diprediksi dan faktor kurangnya pemahaman pasien yang sering menyuntikkan insulin untuk menurunkan kadar gula darah [9].…”

Section: Pendahuluanunclassified

Metode Bayesian dan Multilayer Percepton dalam Mengklasifikasi Diabetes Mellitus

Rasna

Matdoan

2022

jsisfotek

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Diabetes mellitus is a chronic metabolic disorder that causes glucose regulation in the blood. Blood sugar anomalies can be defined as unwanted readings either due to normal causes or reasons unknown to the patient. Machine learning applications have been widely introduced in diabetes research and blood sugar anomaly detection. However, modeling options and strategies for classification in diabetes mellitus are needed. This study aims to classify the data as diabetic or non-diabetic and improve classification accuracy. Classification accuracy is improved by using many of the data sets as training data and test data. Classification accuracy is improved by using multiple of the data set as data. In the test, the C4.5 and RF hybrid methods, as well as the MLP and Net Bayes hybrid classification methods were developed for the classification of diabetes. In the case of C4.5 + RF it provides an accuracy of 79.31% which is higher than the individual models. Similarly, MLP + Net Bayes, provides an 81.89% higher accuracy than the individual models. In the second case the 85-15% ensemble model training and test partitions have an important role for the classification of diabetes data. The proposed MLP + Net Bayes provides 81.89% accuracy as a robust model for data classification. So that the proposed model achieves the highest accuracy of 81.89% with 6 features and reaches the highest sensitivity of 64.10% and the highest specificity of 90.90%.

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HOMA‐IR as a risk factor of gestational diabetes mellitus and a novel simple surrogate index in early pregnancy

Cited by 23 publications

References 26 publications

Evaluation of First Trimester Fasting Blood Glucose, HOMA-IR and HbA1c in Prediction of Gestational Diabetes Mellitus in Non-Obese Pregnant Women: A Retrospective Study

Evaluation of First Trimester Fasting Blood Glucose, HOMA-IR and HbA1c in Prediction of Gestational Diabetes Mellitus in Non-Obese Pregnant Women: A Retrospective Study

ALT/AST as an Independent Risk Factor of Gestational Diabetes Mellitus Compared with TG/HDL-C

Metode Bayesian dan Multilayer Percepton dalam Mengklasifikasi Diabetes Mellitus

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