RESUMO
INTRODUCTION: Gestational 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 METHODS: Data 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. RESULTS: Nineteen 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. CONCLUSIONS: We 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.
Assuntos
Diabetes Gestacional , Gravidez , Humanos , Feminino , Diabetes Gestacional/diagnóstico , Diabetes Gestacional/epidemiologia , Diabetes Gestacional/genética , México/epidemiologia , Teste de Tolerância a Glucose , Glucose , GenótipoRESUMO
Obesity, parental history (PH) of type 2 diabetes (T2D), and genes play an important role in T2D development. However, the influence of each factor on T2D variability is unclear. This study aimed to investigate the influence of obesity (body mass index [BMI], waist/hip ratio), PH, and 16 single-nucleotide polymorphisms (SNPs) associated with T2D on T2D variability in Mexico, comparing 1234 non-diabetic controls and 1219 diabetic patients. To replicate the data, a case-control (n = 2904) and a cross-sectional (n = 1901) study were also included. In a multivariate logistic regression model, all factors accounted for only 27.3% of T2D variability: SNPs (8.4%); PH (11.8%) and obesity (7.1%). These factors contributed more in men (33.2%) than in women (25%), specifically when the disease was diagnosed before the age of 46 (46.7% vs. 30%). Genes played a substantially more important role in men than in women (14.9% vs. 5.5%), while obesity and PH played a similar role in both genders. Genes and PH appeared to play a greater role than obesity in T2D. However, obesity contribution was calculated at the time of recruitment and may be underestimated in patients because the BMI decreased linearly with the number of years with the disease. The data suggest that sexual hormones may play important roles in genes that are associated with T2D.