Li Wu, Gaojie Fan, Li Zhang, Jianing Bi, Jing Peng, Qing Liu, Qing Fang, Lulu Song, Lijuan Zheng, Guocheng Liu, Youjie Wang
{"title":"Previous oral glucose tolerance test results as predictors of gestational diabetes mellitus in subsequent pregnancies: a retrospective cohort study.","authors":"Li Wu, Gaojie Fan, Li Zhang, Jianing Bi, Jing Peng, Qing Liu, Qing Fang, Lulu Song, Lijuan Zheng, Guocheng Liu, Youjie Wang","doi":"10.1186/s12986-026-01109-0","DOIUrl":null,"url":null,"abstract":"<p><p>BACKGROUND: Although the oral glucose tolerance test (OGTT) is the gold standard for diagnosing gestational diabetes mellitus (GDM), its potential to predict GDM recurrence or occurrence in subsequent pregnancies remains insufficiently explored. This study aimed to determine whether OGTT results from a previous pregnancy could predict GDM risk in a subsequent pregnancy. METHODS: We conducted a retrospective cohort study using data from 11,397 women with two consecutive singleton pregnancies between January 2015 and July 2024, obtained from the Guangdong Women and Children Hospital. A 75-g OGTT was performed at 24–28 gestational weeks, with GDM diagnosed according to the International Association of Diabetes and Pregnancy Study Groups criteria. Logistic regression and restricted cubic spline models were used to assess associations between prior OGTT values (fasting, 1-hour, and 2-hour glucose levels) and subsequent GDM risk. Predictive performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Elevated OGTT values (fasting, 1-hour, and 2-hour glucose levels) in the previous pregnancy were significantly associated with increased GDM risk in the subsequent pregnancy. Women with prior GDM had a markedly higher GDM incidence rate (51.6%) than those without prior GDM (10.6%). Combined hyperglycemia (elevated fasting and postprandial glucose) conferred the highest risk (adjusted OR: 16.21, 95% CI: 11.17–23.93), followed by isolated postprandial hyperglycemia (adjusted OR: 7.83, 95% CI: 6.85–8.98) and isolated fasting hyperglycemia (adjusted OR = 6.36, 95% CI: 4.64–8.68). OGTT values exhibited a J-shaped relationship with GDM risk. The model incorporating all three OGTT values demonstrated improved predictive accuracy (AUC: 0.82, 95% CI: 0.81–0.83). CONCLUSIONS: OGTT results from a previous pregnancy are strong predictors of GDM risk in subsequent pregnancies. These findings support the clinical use of routine OGTT data to identify high-risk women for targeted interventions, potentially reducing GDM recurrence and improving maternal-neonatal outcomes. </p>","PeriodicalId":19196,"journal":{"name":"Nutrition & Metabolism","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107726/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nutrition & Metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12986-026-01109-0","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
BACKGROUND: Although the oral glucose tolerance test (OGTT) is the gold standard for diagnosing gestational diabetes mellitus (GDM), its potential to predict GDM recurrence or occurrence in subsequent pregnancies remains insufficiently explored. This study aimed to determine whether OGTT results from a previous pregnancy could predict GDM risk in a subsequent pregnancy. METHODS: We conducted a retrospective cohort study using data from 11,397 women with two consecutive singleton pregnancies between January 2015 and July 2024, obtained from the Guangdong Women and Children Hospital. A 75-g OGTT was performed at 24–28 gestational weeks, with GDM diagnosed according to the International Association of Diabetes and Pregnancy Study Groups criteria. Logistic regression and restricted cubic spline models were used to assess associations between prior OGTT values (fasting, 1-hour, and 2-hour glucose levels) and subsequent GDM risk. Predictive performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Elevated OGTT values (fasting, 1-hour, and 2-hour glucose levels) in the previous pregnancy were significantly associated with increased GDM risk in the subsequent pregnancy. Women with prior GDM had a markedly higher GDM incidence rate (51.6%) than those without prior GDM (10.6%). Combined hyperglycemia (elevated fasting and postprandial glucose) conferred the highest risk (adjusted OR: 16.21, 95% CI: 11.17–23.93), followed by isolated postprandial hyperglycemia (adjusted OR: 7.83, 95% CI: 6.85–8.98) and isolated fasting hyperglycemia (adjusted OR = 6.36, 95% CI: 4.64–8.68). OGTT values exhibited a J-shaped relationship with GDM risk. The model incorporating all three OGTT values demonstrated improved predictive accuracy (AUC: 0.82, 95% CI: 0.81–0.83). CONCLUSIONS: OGTT results from a previous pregnancy are strong predictors of GDM risk in subsequent pregnancies. These findings support the clinical use of routine OGTT data to identify high-risk women for targeted interventions, potentially reducing GDM recurrence and improving maternal-neonatal outcomes.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.