Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Aims/hypothesis This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes. Methods CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c. Results There was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week. Conclusions/interpretation The HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care

Severe hypoglycemia and diabetic ketoacidosis among youth with type 1 diabetes in the T1D Exchange clinic registry

OBJECTIVE: Severe hypoglycemia (SH) and diabetic ketoacidosis (DKA) are common serious acute complications of type 1 diabetes (T1D). The aim of this study was to determine the frequency of SH and DKA and identify factors related to their occurrence in the T1D Exchange pediatric and young adult cohort. RESEARCH DESIGN AND METHODS: The analysis included 13,487 participants in the T1D Exchange clinic registry aged 2-<26 years with T1D ≥2 years. Separate logistic regression models were used to evaluate the association of baseline demographic and clinical factors with the occurrence of SH or DKA in the prior 12 months. RESULTS: Non-White race, no private health insurance and lower household income were associated with higher frequencies of both SH and DKA (p<0.001). SH frequency was highest in children <6 years old (p=0.005), but across the age range, SH was not associated with HbA1c levels after controlling for other factors (p=0.72). DKA frequency was highest in adolescents (p<0.001) and associated with higher HbA1c (p<0.001). CONCLUSIONS: Our data show that poor glycemic control increases the risk of DKA but does not protect against severe hypoglycemia in youth and young adults with type 1 diabetes. The high frequencies of SH and DKA observed in disadvantaged minorities with T1D highlight the need for targeted interventions and new treatment paradigms for patients in these high risk groups

Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT,), change of HR, and change of QT, are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (199 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification.Department of Electronic and Information Engineerin

Family Therapy for Adolescents with Poorly Controlled Diabetes: Initial Test of Clinical Significance

Objective We examined a structured family therapy approach in promoting clinically meaningful improvements in parent–adolescent conflict in adolescents with poorly controlled diabetes. Method Eighteen adolescents with poorly controlled diabetes and their parent(s) participated in 10 sessions of home-based Behavioral Family Systems Therapy (BFST). Outcome comparisons were made using a sample of adolescents with poorly controlled diabetes (n = 40) from a previous study. Clinically significant improvements were determined by calculating SD differences between treatment and comparison groups on measures of diabetes-related and general parent–adolescent conflict. Results Home-based BFST produced change in diabetes-related family conflict ranging from 1/3 to 1/2 SD and general family conflict ranging from 1/3 to 3/4 SD. Conclusions BFST produced change in family conflict, a variable shown through previous research to relate to treatment adherence in adolescents with diabetes. The test of clinical significance represents an example of a method useful for pediatric research