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

Abstract

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.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

“…Even though multi-block analysis is often associated with an increased predictive power compared to single-block analysis, this is not always the case [ 42 ]. One strategy that can improve predictive performance is to apply variable selection [ 38 , 40 , 43 ], however, in this study it did not have that effect. Another strategy is to use multi-block techniques of a higher data fusion level [ 42 ].…”

“…For the best models, the area under the receiver operating characteristic curve (AUROC) was also determined. The mathematical definition of these parameters can be found elsewhere [ 39 , 40 ]. In general terms, the specificity and the sensitivity denote the ability to correctly classify NGT and GDM subjects, respectively.…”

Near-Infrared Spectroscopy-Based Machine Learning Models as a Simple and Fast Tool for the Prediction of Gestational Diabetes Mellitus at Different Stages of Pregnancy

“…Even though multi-block analysis is often associated with an increased predictive power compared to single-block analysis, this is not always the case [29]. One strategy that can improve predictive performance is to apply variable selection [24,26,30], however in this study it did not have that effect. Another strategy is to use multi-block techniques of a higher data fusion level [29].…”

“…For the best models, the area under the receiver operating characteristic curve (AUROC) was also determined. The mathematical definition of these parameters can be found elsewhere [25,26]. In general terms, the specificity and the sensitivity denote the ability to correctly classify NGT and GDM subjects, respectively; the NER reflects the ability to correctly classify both NGT and GDM subjects; and the AUROC represents the overall predictive performance of the model in a graphical manner.…”

Generación de un modelo clinico-genético durante el embarazo para la predicción temprana de glicemia alterada.

“…Un estudio similar ha sido presentado porMennickent et al (2023), en el cual nuevamente mediante PCA se logró evidenciar que ciertas variables, de manera conjunta, permitían la separación parcial de las pacientes según su diagnóstico de DMG. En dicho estudio se presentan ciertas variables que contribuyen en mayor medida en la separación y agrupación de las pacientes según su diagnóstico.…”