Elevated placental growth factor concentrations at 11–14weeks of gestation to predict gestational diabetes mellitus

Eleftheriades, Makarios; Papastefanou, Ioannis; Lambrinoudaki, Ιrene; Kappou, Dimitra; Lavranos, Demetrios; Akalestos, Athanassios; Souka, Athena P.; Pervanidou, Panagiota; Hassiakos, Dimitrios; Chrousos, George P.

doi:10.1016/j.metabol.2014.07.016

Cited by 58 publications

(61 citation statements)

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“…Early detection of possible pregnancy complications (diabetes mellitus, pre-eclampsia, fetal macrosomia, FGR, etc.) (37)(38)(39)(40) and appropriate follow-up, in addition to nutritional interventions in early infancy (breastfeeding, avoidance of overeating in order to catch-up), may program adult metabolic and bone health and ameliorate diseases with developmental origins, such as the metabolic syndrome and osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

Assessment of body composition in Wistar rat offspring by DXA in relation to prenatal and postnatal nutritional manipulation

et al. 2016

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Section: Discussionmentioning

confidence: 99%

Assessment of body composition in Wistar rat offspring by DXA in relation to prenatal and postnatal nutritional manipulation

et al. 2016

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“…29 Thus, a total of 12 prognostic models remained for external validation in the current study. [30][31][32][33][34][35][36][37][38][39][40][41] Supplementary appendix C shows the equations of these 12 prognostic models as applied in our cohort. Table 1 30-41 also summarises predictors that were included in the prognostic models.…”

Section: Outcome Assessmentmentioning

confidence: 99%

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

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Kwee

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et al. 2016

BMJ

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ObjeCtiveTo perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. DesignExternal validation of all published prognostic models in large scale, prospective, multicentre cohort study. setting 31 independent midwifery practices and six hospitals in the Netherlands. PartiCiPantsWomen recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded. Main OutCOMe MeasuresDiscrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots. results 3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated.The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit. COnClusiOnsIn this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. IntroductionIn the field of obstetrics, the number of publications on prognostic models has more than tripled in the past decade, 1 which reflects an increasing interest in risk based medicine. Risk based medicine aims to provide the most appropriate care to each patient, often guided by outcome risk estimates based on individual patient characteristics, test results, or even genetic information. 2 As a result of the obesity pandemic, the incidence of gestational diabetes mellitus, notably occurring in the second or third trimester, is rising and is increasingly contributing to perinatal complications such as macrosomia, shoulder dystocia, caesarean section, and neonatal hypoglycaemia. 3 4 Moreover, long term sequelae of gestational diabetes mellitus are type 2 diabetes in mothers and obesity in their offspring. 5 6 Early diagnosis and treatment of gestational diabetes mellitus have been proven to improve pregnancy outcomes. 7 8 Some guidelines propose a population strategy for diagnosing the disorder [9][10][11][12] (that is, an oral glucose tolerance test) in all pregnant women, whereas others opt for a high ri...

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“…Reference cross-checking of the selected papers yielded no additional studies. Thus, a total of 17 studies on firsttrimester prediction modeling for GDM were identified for inclusion in this review [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Fourteen studies were development studies and another three studies were external validation studies.…”

Section: Study Selectionmentioning

confidence: 99%

Prediction models for the risk of gestational diabetes: a systematic review

et al. 2017

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Background: Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality. Methods: MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible. Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias. Results: Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis. Conclusions: Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

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Elevated placental growth factor concentrations at 11–14weeks of gestation to predict gestational diabetes mellitus

Cited by 58 publications

References 36 publications

Assessment of body composition in Wistar rat offspring by DXA in relation to prenatal and postnatal nutritional manipulation

Assessment of body composition in Wistar rat offspring by DXA in relation to prenatal and postnatal nutritional manipulation

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

Prediction models for the risk of gestational diabetes: a systematic review

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