Association between extrapolated time in range and large for gestational age infants in pregnant women with type 1 diabetes.

AIMS: To evaluate the association between extrapolated time in range (eTIR), measured by self-monitoring of blood glucose (SMBG), and large-for-gestational-age (LGA) infants in pregnancies with type 1 diabetes (T1D). METHODS: Retrospective cohort analysis including singleton pregnancies with T1D who started antenatal care before 20 gestational weeks and delivered live newborns at a Brazilian hospital between 2010 and 2019, with LGA fetuses as the main outcome. Glycemic records acquired using SMBG were categorized as eTIR, extrapolated time below range (eTBR), and extrapolated time above range (eTAR). Women were divided into two groups (LGA and adequate for gestational age [AGA]) and compared regarding clinical characteristics, obstetric outcomes, and frequencies of eTIR, eTBR, and eTAR. Logistic regression analysis verified the independent predictive variables for LGA infants. RESULTS: Data from 125 pregnancies were analyzed. For the first, second and third trimesters, each 1 % increase in eTIR was associated with a decreased risk of LGA by 2.9 % (OR: 0.971; 95%CI: 0.945-0.998), 2.5 % (OR: 0.975; 95%CI: 0.951-0.999) and 2.3 % (OR: 0.977; 95%CI: 0.955-0.998) and each 1 % increase in eTAR was associated with an increased risk of LGA by 2.7 % (OR: 1.027; 95%CI: 1.005-1.050), 3.9 % (OR: 1.039; 95%CI: 1.014-1.063) and 4.6 % (OR: 1.046; 95%CI: 1.018-1.075), respectively. CONCLUSION: The concept of TIR can be extrapolated to patients undergoing SMBG to assess the risk of LGA infants in pregnant women with T1D.

Predicting insulin use among women with gestational diabetes diagnosed in oral glucose tolerance test.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common complications affecting pregnant women. While most women will achieve adequate glycemic levels with diet and exercise, some will require pharmacological treatment to reach and maintain glucose levels between the desired thresholds. Identifying these patients early in pregnancy could help direct resources and interventions. METHODS: This retrospective cohort of women with GDM diagnosed with an abnormal 75g-OGTT presents data from 869 patients (724 in the diet group and 145 in the insulin group). Univariate logistic regression was used to compare the groups, and multivariable logistic regression was used to identify independent factors associated with the need for insulin. A log-linear function was used to estimate the probability of requiring pharmacological treatment. RESULTS: Women in the insulin group had higher pre-pregnancy BMI index (29.8 vs 27.8 kg/m2, odds ratio [OR] 1.06, 95% confidence interval [CI] 1.03-1.09), more frequent history of previous GDM (19.4% vs. 7.8%, OR 2.84, 95% CI 1.59-5.05), were more likely to have chronic hypertension (31.7% vs. 23.2%, OR 1.54, 95% CI 1.04-2.27), and had higher glucose levels at all three OGTT points. Multivariable logistic regression final model included age, BMI, previous GDM status, and the three OGTT values as predictors of insulin requirement. CONCLUSIONS: We can use regularly collected data from patients (age, BMI, previous GDM status, and the three OGTT values) to calculate the risk of a woman with GDM diagnosed in OGTT needing insulin. Identifying patients with a greater risk of requiring pharmacological treatment could help healthcare services to better allocate resources and offer closer follow-up to high-risk patients.

WHO criteria for diabetes in pregnancy: a retrospective cohort.

BACKGROUND: Recognizing that hyperglycemia in pregnancy can impact both individually a patient's health and collectively the healthcare system and that different levels of hyperglycemia incur different consequences, we aimed to evaluate the differences and similarities between patients who met the diagnostic criteria for gestational diabetes mellitus (GDM) or diabetes in pregnancy (DIP) according to the World Health Organization diagnostic criteria based on the 75 g oral glucose tolerance test (OGTT). METHODS: This retrospective study included a cohort of 1064 women followed-up at the Gestational Diabetes Unit of Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo (Sao Paulo, Brazil). Patients were classified into GDM and DIP groups, according to their OGTT results. Their electronic charts were reviewed to obtain clinical and laboratory data for all participants. RESULTS: Women in the DIP group had a higher pre-pregnancy body mass index (30.5 vs 28.1 kg/m2, odds ratio [OR] 1.07, 95% confidence interval [CI] 1.02-1.11), more frequently experienced GDM in a previous pregnancy (25% vs. 11%, OR 2.71, 95% CI 1.17-6.27), and were more likely to have chronic hypertension (43.1% vs. 23.5%, OR 2.46, 95% CI 1.47-4.11), a current twin pregnancy (10.8% vs. 2.9%, OR 4.04, 95% CI 1.70-9.61), or require insulin (46.1% vs. 14.3%, OR 5.14, 95% CI 3.06-8.65) than those in the GDM group. Patients in the DIP group also had a higher frequency of large-for-gestational-age infants (12.3% vs. 5.1%, OR 2.78, 95% CI 1.23-6.27) and abnormal postpartum OGTT (45.9% vs. 12.6%, OR 5.91, 95% CI 2.93-11.90) than those in the GDM group. Nevertheless, in more than half of the DIP patients, glucose levels returned to normal after birth. CONCLUSIONS: Diabetes in pregnancy is associated with an increased risk of adverse perinatal outcomes but does not equate to a diagnosis of diabetes post-pregnancy. It is necessary to identify and monitor these women more closely during and after pregnancy. Keeping patients with hyperglycemia in pregnancy engaged in healthcare is essential for accurate diagnosis and prevention of complications related to abnormal glucose metabolism.

Can we stratify the risk for insulin need in women diagnosed early with gestational diabetes by fasting blood glucose?

OBJECTIVE: The objective of this study is to evaluate risk factors and propose a model for the prediction of insulin requirement during the treatment of early-diagnosed gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS: Retrospective cohort analysis of all pregnant women who were diagnosed with GDM by abnormal fasting blood glucose at the first prenatal visit. According to the requirement for insulin therapy to achieve good glycemic control (insulin or diet group), women were compared regarding clinical and laboratory variables. The performance of these variables in predicting insulin need for GDM treatment was identified by a logistic regression model, and a nomogram was created based on the model to facilitate clinical interpretation. RESULTS: In total, 408 women were included for analysis. Among them, 135 (33%) needed insulin therapy. In the logistic regression model, maternal age, prepregnancy body mass index, fasting blood glucose (FBG) value, prior GDM, and family history of diabetes were significant independent variables for the prediction of insulin need. CONCLUSIONS: The need for insulin therapy in women with early diagnosis of GDM can be predicted by a logistic regression model, which can be converted to a clinically usable nomogram that could help to properly address follow-up strategies for GDM treatment in regions where health resources are limited.