Facultad de Farmacia, Departamento de Bioquímica Clínica e Inmunología, Universidad de Concepción, Concepción, Chile.
Facultad de Farmacia, Departamento de Análisis Instrumental, Universidad de Concepción, Concepción, Chile.
PLoS One. 2023 Jan 13;18(1):e0280513. doi: 10.1371/journal.pone.0280513. eCollection 2023.
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.
母体甲状腺功能改变与妊娠期糖尿病（GDM）的风险密切相关。本研究旨在：1）单独和联合非甲状腺数据测试第一和第二孕期完整的母体甲状腺功能以预测 GDM；2）通过评估不同母体变量对口服葡萄糖耐量试验（OGTT）后负荷血糖的预测效果，使该预测独立于诊断标准。在智利康塞普西翁招募了孕妇。GDM 诊断采用 OGTT 在 24-28 周妊娠时进行（糖耐量正常者 n=54，GDM 者 n=12）。在妊娠的第一和第二孕期记录了 75 项母体甲状腺和非甲状腺参数。通过不同的分类和回归机器学习技术评估了各种变量组合对 GDM 和后负荷血糖的预测。通过变量选择简化最佳预测模型。每个模型均进行了留一法交叉验证。我们的结果表明，甲状腺标志物对 GDM 和后负荷血糖的预测有用，尤其是在妊娠第二孕期。因此，它们可以作为 GDM 的替代筛查工具，独立于使用的诊断标准。最终分类模型在第一和第二孕期的验证曲线下面积分别为 0.867（p<0.001）和 0.920（p<0.001），预测 GDM 的准确性较高。最终回归模型在第一和第二孕期的验证 Spearman r 相关系数分别为 0.259（p=0.036）和 0.457（p<0.001），预测后负荷血糖的准确性较高。本研究首次尝试测试整个母体甲状腺功能谱对 GDM 和 OGTT 后负荷血糖预测的性能。需要进一步的外部验证研究在更大的队列和不同人群中证实这些发现。
