ArticleNutrients2023
Prediction of Gestational Diabetes Mellitus in the First Trimester of Pregnancy Based on Maternal Variables and Pregnancy Biomarkers.
Article in Nutrients, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis.BJOG : an international journal of obstetrics and gynaecology · 2025Pooled it
- Diagnostic Performance of Serum Xenopsin-Related Peptide-1 in Gestational Diabetes Mellitus: A Prospective Observational Study.Metabolites · 2026Article
- Early Sonographic Markers of Gestational Diabetes Mellitus: A Narrative Review of Placental, Fetal and Uterine Artery Findings.Biomedicines · 2026Review
- Managing Gestational Diabetes Complexity with Continuous Glucose Monitoring: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- A Machine Learning Model Based on First-Trimester Lipidomic Signatures for Predicting Metabolic Pregnancy Complications.International journal of molecular sciences · 2025Article
- First-trimester biomarkers of gestational diabetes mellitus: A scoping review.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Lipidomic Signature of Pregnant and Postpartum Females by Longitudinal and Transversal Evaluation: Putative Biomarkers Determined by UHPLC-QTOF-ESIMetabolites · 2025Article
- Prediction of gestational diabetes mellitus using early-pregnancy data: a secondary analysis from a prospective cohort study in Iran.BMC pregnancy and childbirth · 2024Article
- The exploration of optimal gestational weight gain after oral glucose tolerance test for Chinese women with gestational diabetes mellitus.Scientific reports · 2024Article
- Development and validation of a risk prediction model for spontaneous preterm birth.American journal of translational research · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gestational diabetes mellitus (GDM) is a significant health concern with adverse outcomes for both pregnant women and their offspring. Recognizing the need for early intervention, this study aimed to develop an early prediction model for GDM risk assessment during the first trimester. Utilizing a prospective cohort of 4917 pregnant women from the Third Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece, the study sought to combine maternal characteristics, obstetric and medical history, and early pregnancy-specific biomarker concentrations into a predictive tool. The primary objective was to create a series of predictive models that could accurately identify women at high risk for developing GDM, thereby facilitating early and targeted interventions. To this end, maternal age, body mass index (BMI), obstetric and medical history, and biomarker concentrations were analyzed and incorporated into five distinct prediction models. The study's findings revealed that the models varied in effectiveness, with the most comprehensive model combining maternal characteristics, obstetric and medical history, and biomarkers showing the highest potential for early GDM prediction. The current research provides a foundation for future studies to refine and expand upon the predictive models, aiming for even earlier and more accurate detection methods.
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