Takawy, Marina

Background: Increasing maternal body mass index (BMI) represents a risk factor for Gestational Diabetes Mellitus (GDM) and adverse obstetrical and perinatal outcomes. Objective: To stratify clinical outcomes for pregnancies affected by GDM according to maternal BMI. Methods: Retrospective cohort study including individuals ≥18 years of age who were diagnosed with GDM from 2018 to 2022. Universal GDM screening was employed with a 50 g oral glucose challenge test ± a 100 g oral glucose tolerance test. Maternal demographics, preexisting medical conditions, and selected obstetric and neonatal morbidities were evaluated. Results: A total of 2193 pregnancies in 2110 women affected by GDM were identified. This included 506 (23.0%) with normal baseline maternal BMI, 596 (27.2%) with overweight, and 1091 (49.7%) with obese BMI. Adverse maternal outcomes were more frequent in the obese compared to overweight or normal BMI categories (cesarean delivery: normal 26.9% vs. overweight 28.5% vs. obese 40.9%; p < 0.001; hypertensive disorders of pregnancy: normal 8.7% vs. overweight 12.1% vs. obese 16.8%; p < 0.001). Postpartum glucose intolerance was higher in women with obesity (normal 7.3% vs. overweight 5.9% vs. obese 14.9%; p < 0.001). Infants born to mothers with obesity had higher birthweights (normal 3.3 kg vs. overweight 3.4 kg vs. obese 3.5 kg; p < 0.001), were more likely to have neonatal hypoglycemia (normal 29.4% vs. overweight 24.3% vs. obese 41.9%; p < 0.001) and require intensive care unit admission (normal 8.1% vs. overweight 5.9% vs. obese 11.9%; p < 0.001). Conclusions: Patients with GDM and baseline BMI in the obese range experienced the highest rate of adverse outcomes, while those with overweight BMI had similar outcomes to individuals who had normal BMI at baseline.

INTRODUCTION: The recent US Food and Drug Administration approval of resmetirom for treating metabolic dysfunction-associated steatohepatitis in patients necessitates patient selection for significant fibrosis or higher (≥F2). No existing vibration-controlled transient elastography (VCTE) algorithm targets ≥F2. METHODS: The mAchine Learning ADvanceD fibrosis and rIsk metabolic dysfunction-associated steatohepatitis Novel predictor (ALADDIN) study addressed this gap by introducing a machine-learning-based web calculator that estimates the likelihood of significant fibrosis using routine laboratory parameters with and without VCTE. Our study included a training set of 827 patients, a testing set of 504 patients with biopsy-confirmed metabolic dysfunction-associated steatotic liver disease from 6 centers, and an external validation set of 1,299 patients from 9 centers. Five algorithms were compared using area under the curve (AUC) in the test set: ElasticNet, random forest, gradient boosting machines, XGBoost, and neural networks. The top 3 (random forest, gradient boosting machines, and XGBoost) formed an ensemble model. RESULTS: In the external validation set, the ALADDIN-F2-VCTE model, using routine laboratory parameters with VCTE (AUC 0.791, 95% confidence interval [CI]: 0.764–0.819), outperformed VCTE alone (0.745, 95% CI 0.717–0.772, P < 0.0001), FibroScan-aspartate aminotransferase (0.710, 0.679–0.748, P < 0.0001), and Agile-3 model (0.740, 0.710–0.770, P < 0.0001) regarding the AUC, decision curve analysis, and calibration. The ALADDIN-F2-Lab model, using routine laboratory parameters without VCTE, achieved an AUC of 0.706 (95% CI: 0.668–0.749) and outperformed Fibrosis-4, steatosis-associated fibrosis estimator, and LiverRisk scores. DISCUSSION: Along with the steatosis-associated fibrosis estimator model developed to target significant fibrosis or higher, ALADDIN-F2-VCTE (https://aihepatology.shinyapps.io/ALADDIN1) uniquely supports a refined noninvasive approach to patient selection for resmetirom without the need for liver biopsy. In addition, ALADDIN-F2-Lab (https://aihepatology.shinyapps.io/ALADDIN2) offers an effective alternative when VCTE is unavailable.

Abstract Context The risk of gestational diabetes mellitus (GDM) in twin pregnancies is more than double that of singleton pregnancies. Although twin pregnancies present unique challenges for fetal growth and prenatal management, the approach to GDM diagnosis and treatment is the same regardless of plurality. Data on pregnancy outcomes for individuals with GDM and a twin pregnancy are limited and conflicting. Objective To describe the maternal characteristics associated with GDM in twin pregnancies and to assess the associated pregnancy outcomes compared to twin pregnancies unaffected by GDM. Methods A retrospective cohort study was conducted at Mayo Clinic, Rochester, Minnesota, USA, and included predominantly Causasian women aged 18 to 45 years who received prenatal care for a twin pregnancy from 2017-2022. Maternal characteristics and a broad spectrum of pregnancy outcomes were evaluated. Universal GDM screening involved a 50 g oral glucose challenge test +/− a 100 g oral glucose tolerance test. Results GDM was diagnosed in 23% pregnancies (n = 104/452). Compared to those without, women with GDM had known risk factors including a higher prepregnancy body mass index (31.1vs 26.3 kg/m2; P &lt; .01) and a prior history of GDM (21.7 vs 5.9%; P &lt; .01). There were no differences in maternal pregnancy complications or neonatal outcomes between groups. Attendance at postpartum glucose testing among women with GDM was poor at 27.9% (29/104). Conclusion These data suggest that women with twin pregnancies share a similar GDM risk profile to those with singleton pregnancies and provide reassuring evidence that current management for GDM twin pregnancies produces similar outcomes to twin pregnancies without GDM.