RESUMO
OBJECTIVES: To compare the ability of three fetal growth charts (Fetal Medicine Foundation (FMF), Hadlock and National Institutes of Child Health and Human Development (NICHD) race/ethnicity-specific) to predict large-for-gestational age (LGA) at birth in pregnant individuals with pregestational diabetes, and to determine whether inclusion of hemoglobin A1c (HbA1c) level improves the predictive performance of the growth charts. METHODS: This was a retrospective analysis of individuals with Type-1 or Type-2 diabetes with a singleton pregnancy that resulted in a non-anomalous live birth. Fetal biometry was performed between 28 + 0 and 36 + 6 weeks of gestation. The primary exposure was suspected LGA, defined as estimated fetal weight ≥ 90th percentile using the Hadlock (Formula C), FMF and NICHD growth charts. The primary outcome was LGA at birth, defined as birth weight ≥ 90th percentile, using 2017 USA natality reference data. The performance of the three growth charts to predict LGA at birth, alone and in combination with HbA1c as a continuous measure, was assessed using the area under the receiver-operating-characteristics curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: Of 358 assessed pregnant individuals with pregestational diabetes (34% with Type 1 and 66% with Type 2), 147 (41%) had a LGA infant at birth. Suspected LGA was identified in 123 (34.4%) by the Hadlock, 152 (42.5%) by the FMF and 152 (42.5%) by the NICHD growth chart. The FMF growth chart had the highest sensitivity (77% vs 69% (NICHD) vs 63% (Hadlock)) and the Hadlock growth chart had the highest specificity (86% vs 76% (NICHD) and 82% (FMF)) for predicting LGA at birth. The FMF growth chart had a significantly higher AUC (0.79 (95% CI, 0.74-0.84)) for LGA at birth compared with the NICHD (AUC, 0.72 (95% CI, 0.68-0.77); P < 0.001) and Hadlock (AUC, 0.75 (95% CI, 0.70-0.79); P < 0.01) growth charts. Prediction of LGA improved for all three growth charts with the inclusion of HbA1c measurement in comparison to each growth chart alone (P < 0.001 for all); the FMF growth chart remained more predictive of LGA at birth (AUC, 0.85 (95% CI, 0.81-0.90)) compared with the NICHD (AUC, 0.79 (95% CI, 0.73-0.84)) and Hadlock (AUC, 0.81 (95% CI, 0.76-0.86)) growth charts. CONCLUSIONS: The FMF fetal growth chart had the best predictive performance for LGA at birth in comparison with the Hadlock and NICHD race/ethnicity-specific growth charts in pregnant individuals with pregestational diabetes. Inclusion of HbA1c improved further the prediction of LGA for all three charts. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Assuntos
Diabetes Mellitus , Doenças do Recém-Nascido , Gravidez , Recém-Nascido , Feminino , Criança , Humanos , Gráficos de Crescimento , Idade Gestacional , Hemoglobinas Glicadas , Estudos Retrospectivos , Recém-Nascido Pequeno para a Idade Gestacional , Retardo do Crescimento Fetal/diagnóstico , Ultrassonografia Pré-Natal/métodos , Terceiro Trimestre da Gravidez , Peso Fetal , Desenvolvimento Fetal , Peso ao Nascer , Macrossomia Fetal/diagnóstico por imagemRESUMO
A number of mouse models have been identified and are being used for aging and age-associated disease research. However, the use of the genetically manipulated mouse model is still a relatively untapped resource for the study of the biology of aging. Genetically altered mice can be powerful tools for biology of aging research because gene expression can be controlled and correlated with established biomarkers. Standard transgene overexpression and gene targeting techniques were modified and used to generate 30 mouse lines during a 4-year period. These lines include models of Werner's syndrome (premature aging or progeria), Alzheimer's disease, other neurodegenerative condition, atherosclerosis, diabetes, immune dysfunction, musculoskeletal disorders, and oxidative stress. These new mouse models are providing additional insights into aging processes and will be useful for developing intervention strategies and collaborative interactions.