Dataset Information

First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus.

ABSTRACT:

Aims

Our objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.

Methods

We quantified miRNA using next-generation sequencing in discovery (Gen3G: n = 443/GDM = 56) and replication (3D: n = 139/GDM = 76) cohorts. We have diagnosed GDM using a 75-g oral glucose tolerance test and the IADPSG criteria. We applied stepwise logistic regression analysis among replicated miRNAs to build prediction models.

Results

We identified 17 miRNAs associated with GDM development in both cohorts. The prediction performance of hsa-miR-517a-3p|hsa-miR-517b-3p, hsa-miR-218-5p, and hsa-let7a-3p was slightly better than GDM classic risk factors (age, BMI, familial history of type 2 diabetes, history of GDM or macrosomia, and HbA1c) (AUC 0.78 vs. 0.75). MiRNAs and GDM classic risk factors together further improved the prediction values [AUC 0.84 (95% CI 0.73-0.94)]. These results were replicated in 3D, although weaker predictive values were obtained. We suggest very low and higher risk GDM thresholds, which could be used to identify women who could do without a diagnostic test for GDM and women most likely to benefit from an early GDM prevention program.

Conclusions

In summary, three miRNAs combined with classic GDM risk factors provide excellent prediction values, potentially strong enough to improve early detection and prevention of GDM.

SUBMITTER: Legare C

PROVIDER: S-EPMC9693764 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus.

Légaré Cécilia C Desgagné Véronique V Thibeault Kathrine K White Frédérique F Clément Andrée-Anne AA Poirier Cédrik C Luo Zhong Cheng ZC Scott Michelle S MS Jacques Pierre-Étienne PÉ Perron Patrice P Guérin Renée R Hivert Marie-France MF Bouchard Luigi L

Frontiers in endocrinology 20221111

<h4>Aims</h4>Our objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.<h4>Methods</h4>We quantified miRNA using next-generation sequencing in discovery (Gen3G: n = 443/GDM = 56) and replication (3D: n = 139/GDM = 76) cohorts. We have diagnosed GDM using a 75-g oral glucose tolerance test and the IADPSG criteria. We applied stepwise logistic regression analysis among replicated miRNAs to build prediction models.<h4>Results</h4>We identified 17 miRNAs ass ...[more]

PMID: 36440215

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Project description:PurposeTo investigate and identify first-trimester fasting plasma glucose (FPG) is related to gestational diabetes mellitus (GDM) and other adverse pregnancy outcomes in Shenzhen population.MethodsWe used data of 48,444 pregnant women that had been retrospectively collected between 2017 and 2019. Logistic regression analysis was used to evaluated the associations between first-trimester FPG and GDM and adverse pregnancy outcomes, and used to construct a nomogram model for predicting the risk of GDM. The performance of the nomogram was evaluated by using ROC and calibration curves. Decision curve analysis (DCA) was used to determine the clinical usefulness of the first-trimester FPG by quantifying the net benefits at different threshold probabilities.ResultsThe mean first-trimester FPG was 4.62 ± 0.42 mmol/L. A total of 6998 (14.4%) pregnancies developed GDM.489(1.01%) pregnancies developed polyhydramnios, the prevalence rates of gestational hypertensive disorder (GHD), cesarean section, primary cesarean section, preterm delivery before 37 weeks (PD) and dystocia was 1130 (2.33%), 20,426 (42.16%), 7237 (14.94%), 2386 (4.93%), and 1865 (3.85%), respectively. 4233 (8.74%) of the newborns were LGA, and the number of macrosomia was 2272 (4.69%), LBW was 1701 (3.51%) and 5084 (10.49%) newborns had admission to the ICU, which all showed significances between GDM and non-GDM groups (all P < 0.05). The univariate analysis showed that first-trimester FPG was strongly associated with risks of outcomes including GDM, cesarean section, macrosomia, GHD, primary cesarean section, and LGA (all OR > 1, all P < 0.05), furthermore, the risks of GDM, primary cesarean section, and LGA was increasing with first-trimester FPG as early as it was at 4.19-4.63 mmol/L. The multivariable analysis showed that the risks of GDM (ORs for FPG 4.19-4.63, 4.63-5.11 and 5.11-7.0 mmol/L were 1.137, 1.592, and 4.031, respectively, all P < 0.05) increased as early as first-trimester FPG was at 4.19-4.63 mmol/L, and first-trimester FPG which was also associated with the risks of cesarean section, macrosomia and LGA (OR for FPG 5.11-7.0 mmol/L of cesarean section: 1.128; OR for FPG 5.11-7.0 mmol/L of macrosomia: 1.561; OR for FPG 4.63-5.11 and 5.11-7.0 mmol/L of LGA: 1.149 and 1.426, respectively, all P < 0.05) and with its increasing, the risks of LGA increased. Furthermore, the nomogram had a C-indices 0.771(95% CI: 0.763~0.779) and 0.770(95% CI:0.758~0.781) in training and testing validation respectively, which showed an acceptable consistency between the observed, validation and nomogram-predicted probabilities, the DAC curve analysis indicated that the nomogram had important clinical application value for GDM risk prediction.ConclusionsFPG in the first trimester was an independent risk factor for GDM which can be used as a screening test for identifying pregnancies at risk of GDM and adverse pregnancy outcomes.

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Project description:BackgroundImprovement in the accuracy of identifying women who are at risk to develop gestational diabetes mellitus (GDM) is warranted, since timely diagnosis and treatment improves the outcomes of this common pregnancy disorder. Although prognostic models for GDM are externally validated and outperform current risk factor based selective approaches, there is little known about the impact of such models in day-to-day obstetric care.MethodsA prognostic model was implemented as a directive clinical prediction rule, classifying women as low- or high-risk for GDM, with subsequent distinctive care pathways including selective midpregnancy testing for GDM in high-risk women in a prospective multicenter birth cohort comprising 1073 pregnant women without pre-existing diabetes and 60 obstetric healthcare professionals included in nine independent midwifery practices and three hospitals in the Netherlands (effectiveness-implementation hybrid type 2 study). Model performance (c-statistic) and implementation outcomes (acceptability, adoption, appropriateness, feasibility, fidelity, penetration, sustainability) were evaluated after 6 months by indicators and implementation instruments (NoMAD; MIDI).ResultsThe adherence to the prognostic model (c-statistic 0.85 (95%CI 0.81-0.90)) was 95% (n = 1021). Healthcare professionals scored 3.7 (IQR 3.3-4.0) on implementation instruments on a 5-point Likert scale. Important facilitators were knowledge, willingness and confidence to use the model, client cooperation and opportunities for reconfiguration. Identified barriers mostly related to operational and organizational issues. Regardless of risk-status, pregnant women appreciated first-trimester information on GDM risk-status and lifestyle advice to achieve risk reduction, respectively 89% (n = 556) and 90% (n = 564)).ConclusionsThe prognostic model was successfully implemented and well received by healthcare professionals and pregnant women. Prognostic models should be recommended for adoption in guidelines.

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Dataset Information

First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus.

Aims

Methods

Results

Conclusions

Publications

First Trimester Plasma MicroRNA Levels Predict Risk of Developing Gestational Diabetes Mellitus.

Similar Datasets

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