Previous oral glucose tolerance test results as predictors of gestational diabetes mellitus in subsequent pregnancies: a retrospective cohort study.

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS
Li Wu, Gaojie Fan, Li Zhang, Jianing Bi, Jing Peng, Qing Liu, Qing Fang, Lulu Song, Lijuan Zheng, Guocheng Liu, Youjie Wang
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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.

既往口服糖耐量试验结果作为后续妊娠妊娠糖尿病的预测因素:一项回顾性队列研究
背景:尽管口服糖耐量试验(OGTT)是诊断妊娠期糖尿病(GDM)的金标准,但其预测妊娠期糖尿病复发或发生的潜力仍未得到充分探讨。本研究旨在确定先前妊娠的OGTT结果是否可以预测随后妊娠的GDM风险。方法:我们对2015年1月至2024年7月在广东妇幼医院连续两次单胎妊娠的11397名妇女进行了回顾性队列研究。在妊娠24-28周进行75 g OGTT,根据国际糖尿病和妊娠研究小组的标准诊断为GDM。使用Logistic回归和受限三次样条模型来评估先前OGTT值(空腹、1小时和2小时血糖水平)与随后的GDM风险之间的关系。采用受试者工作特征曲线分析评估预测性能。结果:先前妊娠OGTT值升高(空腹、1小时和2小时血糖水平)与随后妊娠GDM风险增加显著相关。有GDM病史的女性GDM发病率(51.6%)明显高于无GDM病史的女性(10.6%)。合并高血糖(空腹和餐后血糖升高)的风险最高(调整后OR: 16.21, 95% CI: 11.17-23.93),其次是单独的餐后高血糖(调整后OR: 7.83, 95% CI: 6.85-8.98)和单独的空腹高血糖(调整后OR = 6.36, 95% CI: 4.64-8.68)。OGTT值与GDM风险呈j型关系。纳入所有三个OGTT值的模型显示出更高的预测准确性(AUC: 0.82, 95% CI: 0.81-0.83)。结论:既往妊娠的OGTT结果是后续妊娠GDM风险的有力预测因子。这些发现支持常规OGTT数据的临床应用,以确定高危妇女进行有针对性的干预,可能减少GDM复发并改善孕产妇-新生儿结局。
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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
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.
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