A first trimester prediction model for gestational diabetes utilizing aneuploidy and pre-eclampsia screening markers

Sweeting, Arianne; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P.; Kouru, Heikki; Williams, Paul F.; Sairanen, Mikko; Hyett, Jon

doi:10.1080/14767058.2017.1336759

Cited by 36 publications

(44 citation statements)

References 29 publications

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“…The combined addition of routinely tested first-trimester aneuploidy and pre-eclampsia screening markers (MAP, UtA PI, PAPP-A, and free hCGβ) provided an AUC of 0.90 (95% CI 0.87–0.92) [7]. The present model performed at a similarly high AUC, but has several advantages.…”

Section: Discussionmentioning

confidence: 75%

“…Cohort ( n = 980) clinical characteristics and first-trimester aneuploidy and pre-eclampsia screening markers according to the presence or absence of GDM and timing of GDM diagnosis have been reported previously [6, 7]. Overall, women with GDM ( n = 248) were diagnosed at a mean of 22.7 ± 7.9 weeks’ gestation.…”

Section: Resultsmentioning

confidence: 99%

“…Maternal clinical parameters and first-trimester aneuploidy and pre-eclampsia screening markers were prospectively obtained at 11–13 + 6 weeks’ gestation, and methodology for data collection and measurement has been previously described [6, 7]. These included maternal body mass index (BMI), MAP, UtA PI, age, ethnicity, and parity (classified as multiparous or nulliparous if no previous pregnancies ≥24 weeks’ gestation), family history of diabetes (defined as type 2 diabetes in a first- or second-degree relative and/or sibling with GDM), previous history of GDM or macrosomia (defined as birth weight > 4,000 g) and polycystic ovarian syndrome.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, we evaluated low-cost, routinely collected biochemistry markers, including nonfasting maternal glucose, cholesterol, and triglycerides, in combination with a panel of novel biomarkers potentially implicated in the pathogenesis of GDM: the adipokines adiponectin, leptin, and lipocalin-2, and the placental coagulation factor plasminogen activator inhibitor-2 (PAI-2) [8, 9, 11-14]. We also evaluated the influence of ethnicity on marker predictive performance for GDM, based on our earlier findings that ethnicity modified aneuploidy and pre-eclampsia screening marker associations with GDM [7]. …”

Section: Introductionmentioning

confidence: 99%

“…Further, the addition of biomarkers routinely measured during first-trimester screening for aneuploidy and pre-eclampsia (i.e., pregnancy-associated plasma protein A [PAPP-A], free β-human chorionic gonadotropin [free hCGβ], mean arterial pressure (MAP), and uterine artery pulsatility index [UtA PI]) provided an AUC of 0.90 (95% CI 0.87–0.92) [7]. Importantly, this combined model best predicted early GDM (AUC 0.96, 95% CI 0.94–0.98), suggesting that the addition of biologically plausible biomarkers may potentially improve the early pregnancy prediction of GDM, particularly in higher-risk subcohorts.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

et al. 2018

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Introduction: Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Methods: Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11–13⁺⁶ weeks’ gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks’ gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Results: Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89–0.94) overall, and 0.93 (95% CI 0.89–0.96) for early GDM, in a combined multivariate prediction model. Conclusions: A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

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Section: Discussionmentioning

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

et al. 2018

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Risk Prediction Model of Gestational Diabetes Mellitus in a Chinese Population Based on a Risk Scoring System

Wang

Chen

et al. 2021

Diabetes Ther

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Introduction: Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system. Methods: This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively. Results: In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916). Conclusion:The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.Yanmei Wang, Zhijuan Ge and Lei Chen equally contributed to the manuscript.

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