42 research outputs found

    No association between fear of hypoglycemia and blood glucose variability in type 1 diabetes: The cross-sectional VARDIA study

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    AIMS: In type 1 diabetes (T1D), treatment efficacy is limited by the unpredictability of blood glucose results and glycemic variability (GV). Fear of Hypoglycemia (FOH) remains a major brake for insulin treatment optimization. We aimed to assess the association of GV with FOH in participants with T1D in an observational cross-sectional study performed in 9 French Diabetes Centres (NCT02790060). METHODS: Participants were T1D for ≥5 years, aged 18-75 years, on stable insulin therapy for ≥3 months. The coefficient of variation (CV) of blood glucose and mean amplitude of glycemic excursions (MAGE) were used to assess GV from 7-point self-monitoring of blood glucose (SMBG). FOH was assessed using the validated French version of the Hypoglycemia Fear Survey-II (HFS-II) questionnaire. RESULTS: Among a total of 570 recruited participants, 298 were suitable for analysis: 46% women, 58% on continuous subcutaneous insulin infusion [CSII], mean age 49 ± 16 years, HbA1c 7.5 ± 0.9%, HFS-II score 67 ± 18 and 12% with recent history of severe hypoglycemia during the previous 6 months, mean CV 39.8 ± 9.7% and MAGE 119 ± 42 mg/dL. CV and MAGE did not significantly correlate with HFS-II score (R = -0.05;P = 0.457 and R = 0.08;P = 0.170). Participants with severe hypoglycemia in the previous 6 months had higher HFS scores. Participants with higher HFS scores presented more hypoglycemias during follow-up. CONCLUSIONS: FOH as determined using the HFS-II questionnaire was not associated with 7-point SMBG variability in participants with T1D, but was associated with a positive history of severe hypoglycemia. Higher FOH was associated with higher frequency of hypoglycemia during follow-up

    The Type and the Position of HNF1A Mutation Modulate Age at Diagnosis of Diabetes in Patients with Maturity-Onset Diabetes of the Young (MODY)-3

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    OBJECTIVE—The clinical expression of maturity-onset diabetes of the young (MODY)-3 is highly variable. This may be due to environmental and/or genetic factors, including molecular characteristics of the hepatocyte nuclear factor 1-α (HNF1A) gene mutation. RESEARCH DESIGN AND METHODS—We analyzed the mutations identified in 356 unrelated MODY3 patients, including 118 novel mutations, and searched for correlations between the genotype and age at diagnosis of diabetes. RESULTS—Missense mutations prevailed in the dimerization and DNA-binding domains (74%), while truncating mutations were predominant in the transactivation domain (62%). The majority (83%) of the mutations were located in exons 1- 6, thus affecting the three HNF1A isoforms. Age at diagnosis of diabetes was lower in patients with truncating mutations than in those with missense mutations (18 vs. 22 years, P = 0.005). Missense mutations affecting the dimerization/DNA-binding domains were associated with a lower age at diagnosis than those affecting the transactivation domain (20 vs. 30 years, P = 10−4). Patients with missense mutations affecting the three isoforms were younger at diagnosis than those with missense mutations involving one or two isoforms (P = 0.03). CONCLUSIONS—These data show that part of the variability of the clinical expression in MODY3 patients may be explained by the type and the location of HNF1A mutations. These findings should be considered in studies for the search of additional modifier genetic factors

    Clinical Characteristics and Diagnostic Criteria of Maturity-Onset Diabetes Of The Young (MODY) due to Molecular Anomalies of the HNF1A Gene

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    Context: The diagnosis of maturity-onset diabetes of the young type 3 (MODY3), associated with HNF1A molecular abnormalities, is often missed.Objective: The objective of the study was to describe the phenotypes of a large series of MODY3 patients and to reassess parameters that may improve its diagnosis. Design, Setting, and Patients: This retrospective multicenter study included 487 unrelated patients referred because of suspicion of MODY3. Genetic analysis identified 196 MODY3 and 283 non-MODY3 cases. Criteria associated with MODY3 were assessed by multivariate analysis. The capacity of the model to predict MODY3 diagnosis was assessed by the area under the receiver-operating characteristic curve and was further validated in an independent sample of 851 patients (165 MODY3 and 686 non-MODY3). Results: In the MODY3 patients, diabetes was revealed by clinical symptoms in 25% of the cases and was diagnosed by screening in the others. Age at diagnosis of diabetes was more than 25 yr in 40% of the MODY3 patients. There was considerable variability and overlap of all assessed parameters in MODY3 and non-MODY3 patients. The best predictive model was based on criteria available at diagnosis of diabetes, including age, body mass index, number of affected generations, presence of diabetes symptoms, and geographical origin. The area under the curve of the receiver-operating characteristic analysis was 0.81. When sensitivity was set to 90%, specificity was 49%. Conclusions: Differential diagnosis between MODY3 and early-onset type 2 diabetes remains difficult. Whether the proposed model will improve the pick-up rate of MODY3 diagnosis needs to be confirmed in independent populations

    Type 1 diabetes: translating mechanistic observations into effective clinical outcomes

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    Type 1 diabetes remains an important health problem, particularly in Western countries where the incidence has been increasing in younger children(1). In 1986, Eisenbarth described Type 1 diabetes as a chronic autoimmune disease. Work over the past 3 ½ decades has identified many of the genetic, immunologic, and environmental factors that are involved in the disease and have led to hypotheses concerning its pathogenesis. Based on these findings, clinical trials have been conducted to test these hypotheses but have had mixed results. In this review, we discuss the findings that have led to current concepts of the disease mechanisms, how this understanding has prompted clinical studies, and the results of these studies. The findings from preclinical and clinical studies support the original proposed model for how type 1 diabetes develops, but have also suggested that this disease is more complex than originally thought and will require broader treatment approaches

    Clinical and metabolic characteristics of patients with latent autoimmune diabetes in adults (LADA): absence of rapid beta-cell loss in patients with tight metabolic control.

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    International audienceAIM AND METHODS: The present study compared the clinical and metabolic characteristics of latent autoimmune diabetes in adults (LADA) with type 2 diabetes, as well as the residual beta-cell function and progression to insulin treatment, over a 2-year follow-up period, of antibody (Ab)-positive and Ab-negative patients who achieved tight glycaemic control (HbA(1c) 7.0+/-0.8% and 6.5+/-0.9%, respectively, at the time of entry into the study). RESULTS: Glutamic acid decarboxylase antibodies (GADA) and/or islet cell antibodies (ICA) were detected in 10% of patients presenting with non-insulin-dependent diabetes. Around half of Ab-positive patients required insulin treatment during the follow-up. Ab-positive patients displayed lower stimulated C-peptide levels both at entry and during the follow-up compared with Ab-negative patients, although no significant decline in C-peptide levels was observed in either subgroup over two years. Nevertheless, Ab-positive patients progressed more frequently to insulin treatment, and stimulated C-peptide tended to decrease in LADA patients who subsequently required insulin, whereas it remained stable in those who were non-insulin-dependent. In those who progressed, the trend towards C-peptide decline persisted even after starting insulin treatment. CONCLUSION: LADA patients demonstrate lower residual beta-cell function than do type 2 diabetes patients. However, those who achieve tight metabolic control do not present with a rapid decline in beta-cell function. Further studies are needed to determine the optimal treatment strategy in such patients
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