Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms

Abstract

IntroductionGestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.Research design and methodsData from pregnant Mexican women enrolled in the ‘Cuido mi Embarazo’ (CME) cohort were used for development (107 cases, 469 controls) and data from the ‘Mónica Pretelini Sáenz’ Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24–28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.ResultsNineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.ConclusionsWe developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

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

“…Such methods are valid to predict GDM at any point of life. Their predictive power is moderate, e.g., with an NER of 0.531–0.552 [ 55 ] and an AUROC of 0.7694 [ 27 ]. Moreover, they are associated with long analysis times.…”

“…This review revealed that many of the predictive models for this pregnancy disease derive from data acquired by instrumental techniques, such as liquid or gas chromatography coupled to mass spectrometry (LC-MS or GC-MS, respectively), nuclear magnetic resonance (NMR) spectroscopy, and polymerase chain reaction (PCR), among others [ 17 ]. More recently, new articles have been published in the context of GDM diagnosis based on the same techniques, i.e., LC-MS [ 18 , 19 , 20 , 21 ], GC-MS [ 22 ], NMR spectroscopy [ 23 ], and PCR [ 24 , 25 , 26 , 27 ]. Methods based on these techniques are very time consuming, as they require tedious sample preparation procedures or prolonged instrumental runs.…”

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

“…Their predictive power is moderate, e.g. with an accuracy of 0.531-0.552 [46] and an AUROC of 0.7694 [47]. Moreover, they are associated with long analysis times.…”

“…The PCR-RFLP protocol alone takes more than 2 hours. Likewise, the method of Zulueta et al [47] consists of genomic DNA extraction, and its analysis by iPLEX-PCR, using the MassARRAY system from Agena Bioscience. According to the manufacturer, the entire workflow for iPLEX MassARRAY PCR takes 8 hours [48].…”

Gestational Diabetes Mellitus Subtypes Classified by Oral Glucose Tolerance Test and Maternal and Perinatal Outcomes: Results of a Mexican Multicenter Prospective Cohort Study “Cuido Mi Embarazo”

“…Other studies using the data from the CME cohort study have been published previously. [19][20][21][22][23][24] We analyzed the 2-h 75-g OGTT results of 2056 Mexican pregnant women between 24 and 28 weeks of gestation, without a prior diagnosis of type 2 diabetes, from the CME cohort study.…”