Maternal thyroid profile in first and second trimester of pregnancy is correlated with gestational diabetes mellitus through machine learning

Araya, Juan; Rodriguez, Antonio B.; Lagos-SanMartin, Karin; Mennickent, Daniela; Gutiérrez-Vega, Sebastián; Ortega-Contreras, Bernel; Valderrama-Gutiérrez, Barbara; González, Marcelo; Farías-Jofré, Marcelo; Guzmán-Gutiérrez, Enrique

doi:10.1016/j.placenta.2020.10.015

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Araya et al used PCA to explore clinical and biochemical maternal data from NGT and GDM pregnancies. After removing the OGTT post load glycemia from the analysis, the separation between two groups was mostly attributable to first trimester clinical variables and second trimester thyroid parameters, with a minor but noticeable contribution of first trimester thyroid data [22]. These observations are also in agreement with other authors, who described that different second trimester thyroid markers are more strongly associated with the risk of GDM, than their first trimester counterparts [14,16,[43][44][45].…”

Section: Exploration Of Maternal Data By Pcasupporting

confidence: 88%

“…Fasting and post load plasma glucose were measured using the hexokinase method [21]. Thyroid stimulating hormone (TSH), total triiodothyronine (TT3), total thyroxine (TT4), free T4 (FT4), thyroglobulin (TG), TG antibody (aTG), thyroid peroxidase antibody (aTPO), TSH receptor antibody (TRAb) and fat mass and obesity-associated (FTO) rs9939609 genotype were determined as described by Araya et al [22]. Briefly, serum thyroid hormones and antibodies were measured by different immunoassays.…”

Section: Biochemical Determinationsmentioning

confidence: 99%

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Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

et al. 2023

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Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24–28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

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Section: Exploration Of Maternal Data By Pcasupporting

confidence: 88%

Section: Biochemical Determinationsmentioning

confidence: 99%

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

et al. 2023

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“… Study Machine learning model Prediction Performance Key findings Early prediction of gestational diabetes Artzi et al, (2020) Gradient boosted model, 2355 features. Electronic data for 588,622 pregnancies, 0–20 weeks gestational age Early prediction (<20 weeks gestational age) of gestational diabetes auROC = 0.85 Large, granular dataset, model out-performed current baseline risk scores (auROC = 0.68), retrospective data, no external validation, model bias not assessed Simpler model based on 9 feature selection auROC = 0.80 Araya et al, 2021 Primary component analysis, 29 features. Electronic data for 39 pregnancies, 12–28 weeks gestational age Maternal thyroid profiles classification with gestational diabetes Thyroid profiles separated distribution of patients Differences in gestational diabetes and normal glucose tolerance.…”

Section: Resultsmentioning

confidence: 99%

“…Another study considered the potential for thyroid function tests to be used as a secondary tool for diagnosing GDM in borderline cases. Using a principal component analysis in a small population they determined that thyroid patterns correlated with the development of gestational diabetes ( Araya et al, 2021 ). Though this provided novel insights, an objective assessment of the model performance and risk of bias was not performed and results will need to be replicated in a larger, more diverse dataset.…”

Section: Resultsmentioning

confidence: 99%

Machine learning and disease prediction in obstetrics

Arain

Iliodromiti

Slabaugh

et al. 2023

Current Research in Physiology

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“…Type 2 is more dangerous than type 1 and can cause death. In this case, the body either does not produce insulin or becomes insulin resistant ( Araya et al, 2021 ). Gestational DM arises only during pregnancy because of hormonal changes in the body.…”

Section: Introductionmentioning

confidence: 99%

A novel multistage ensemble approach for prediction and classification of diabetes

et al. 2022

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Diabetes mellitus is a metabolic syndrome affecting millions of people worldwide. Every year, the rate of occurrence rises drastically. Diabetes-related problems across several vital organs of the body can be fatal if left untreated. Diabetes must be detected early to receive proper treatment, preventing the condition from escalating to severe problems. Tremendous health sciences and biotechnology advancements have resulted in massive data that generated massive Electronic Health Records and clinical information. The exponential increase of electronically gathered information has resulted in more complicated, accurate prediction models that can be updated continuously using machine learning techniques. This research mainly emphasizes discovering the best ensemble model for predicting diabetes. A new multistage ensemble model is proposed for diabetes prediction. In this model, accuracy is predicated on the Pima Indian Diabetes dataset. The accuracy of the proposed ensemble model is compared with the existing machine learning model, and the experimental results demonstrate the performance of the proposed model in terms of higher Precision, f-measure, Recall, and area under the curve.

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Maternal thyroid profile in first and second trimester of pregnancy is correlated with gestational diabetes mellitus through machine learning

Cited by 10 publications

References 7 publications

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Machine learning and disease prediction in obstetrics

A novel multistage ensemble approach for prediction and classification of diabetes

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