The prevalence of eating disorders is particularly high among people with type 1 diabetes (T1D). These abnormalities, such as "diabulimia," are frequently responsible for poor insulin therapy management and, consequently, chronic glycemic imbalance, exposing them to an increased risk of complications. Their detection and management unfortunately remain insufficient in current practice. However, to date, no study has addressed the question of the prevalence and impact of eating disorders in this context. Our research hypotheses are therefore as follows: 1. The existence of an eating disorder is likely frequently overlooked and, therefore, not taken into account in patients with T1D initiating semi-automated insulin therapy with BF. 2. The existence of an eating disorder could impair the performance of the BF device in terms of improving glycemic control in patients with T1D initiating this treatment method. Patients clinical characteristics, glycemic monitoring parameters ad questionnaires answers will be recorded. The prevalence of eating disorders will be calculated, and the association between the presence of these abnormalities and baseline clinical characteristics and glycemic control parameters will be analyzed.
The prevalence of eating disorders (EDs) and problematic eating behaviors (PEBs) is particularly high among people with type 1 diabetes (T1D), affecting 20 to 50% of women and 5 to 25% of men among young patients. These abnormalities, such as "diabulimia," are frequently responsible for poor insulin therapy management and, consequently, chronic glycemic imbalance, exposing them to an increased risk of complications. Their detection and management unfortunately remain insufficient in current practice. The treatment of T1D is currently undergoing a veritable technological revolution linked to the arrival of the first semi-automated insulin therapy devices (or closed loop, LF). The use of these devices most often makes it possible to achieve recommended glycemic control targets (HbA1c, continuous glucose monitoring \[CGM\] data), reduce glycemic variability, and avoid hypoglycemia. Insulin administration is based on the data transmitted: food intake and quantity of carbohydrates consumed. The role of dietitians is therefore crucial in the therapeutic education of patients and the care pathway imposed by the initiation of this new treatment method. It is likely that many patients initiating automated insulin therapy with BF present an eating disorder or CAP that may interfere with the management of the device. However, to date, no study has addressed the question of the prevalence and impact of eating disorders and CAP in this context. In the absence of available data, national and international recommendation texts dedicated to insulin therapy with BF do not specify appropriate conduct for patients presenting with these eating disorders. Our research hypotheses are therefore as follows: 1. The existence of an eating disorder (ED and/or CAP) is likely frequently overlooked and, therefore, not taken into account in patients with T1D initiating semi-automated insulin therapy with BF. 2. The existence of an eating disorder (ED and/or CAP) could impair the performance of the BF device in terms of improving glycemic control in patients with T1D initiating this treatment method. Patients clinical characteristics and glycemic monitoring parameters (HbA1c and CGM data), available as part of routine care, will be recorded in an electronic case report (REDCap software). The mSCOFF and QACD questionnaires will be administered during the dietary interview preceding BF device implantation, and responses will be collected. The prevalence of eating disorders will be calculated, and the association between the presence of these abnormalities and baseline clinical characteristics and glycemic control parameters will be analyzed. The care pathway established for initiating BF treatment requires a follow-up medical consultation at 3 months, followed by a consultation at 6 months. As part of the study, visits at 3 months (12 ± 2 weeks) and 6 months (24 ± 4 weeks) will be scheduled and will include an interview with a dietitian, to collect glycemic control parameters (at 3 and 6 months), measure body weight (at 3 and 6 months), and repeat the mSCOFF and QACD questionnaires (at 6 months). Data on the evolution of glycemic control and variability parameters, weight and mSCOFF and QACD scores will be analyzed in the overall study population, then according to the existence or not of disturbances in eating behavior at inclusion.
Study Type
OBSERVATIONAL
Enrollment
100
Patients will be asked to complete the MScoff and QACD questionnaires during interviews with the dietitian at baseline, 3 months and 6 months.
Collection of HbA1c level and continuous glucose monitoring data
Diabetology, Metabolic Diseases and Nutrition Department Rangueil Hospital, Toulouse University Hospital 1 avenue du Pr Jean Poulhès
Toulouse, France
Prevalence of eating disorders
The primary outcome measure for establishing the prevalence of eating disorders is a composite outcome measure based on determining the existence of eating disorders \[ED\] by analyzing responses to the mSCOFF questionnaire (score ≥ 2) and/or problematic eating behaviors \[PEB\] by analyzing responses to the QACD questionnaire (score ≥ 20).
Time frame: 6 months after the inclusion
Prevalence of eating disorders (EDs)
Prevalence of EDs (mSCOFF score ≥ 2 and QACD ≥ 20)
Time frame: 6 months after the inclusion
Clinical characteristics at inclusion
Measurement of clinical characteristics at inclusion: age
Time frame: 6 months after the inclusion
Clinical characteristics at inclusion
Measurement of clinical characteristics at inclusion: sex
Time frame: 6 months after the inclusion
Clinical characteristics at inclusion
Measurement of clinical characteristics at inclusion: weight in kg and height in cm which will be combined to calculate the BMI (kg/m²)
Time frame: 6 months after the inclusion
Clinical characteristics at inclusion
Measurement of clinical characteristics at inclusion: medical history
Time frame: 6 months after the inclusion
Clinical characteristics at inclusion
Measurement of clinical characteristics at inclusion: treatments
Time frame: 6 months after the inclusion
Description of glycemic control at inclusion
Measurement of basic glycemic control : Hba1c average
Time frame: 6 months after the inclusion
Description of variability indicators at inclusion
Measurement of basic glycemic variability parameters : continuous glucose measurement data
Time frame: 6 months after the inclusion
Evolution of biological parameters of glycemic variability between the inclusion visit and the follow-up visits
Measurement of the variation in blood glycemic variability (continuous glucose measurement data) parameters between the inclusion visit and the different follow-up visits (3 months and 6 months)
Time frame: 6 months after the inclusion
Evolution of biological control parameters variability between the inclusion visit and the follow-up visits
Measurement of the variation in blood control parameters variability (HbA1c) between the inclusion visit and the different follow-up visits (3 months and 6 months)
Time frame: 6 months after the inclusion
Evolution of BMI between inclusion and follow-up visits
Change in BMI between the baseline visit and the 3-month and 6-month follow-up visits
Time frame: 6 months after the inclusion
Evolution of body weight between inclusion and follow-up visits
Change in body weight between the baseline visit and the 3-month and 6-month follow-up visits
Time frame: 6 months after the inclusion
Change in questionnaires scores between baseline and follow-up
Change in mSCOFF (Sick, control, one stone, fat, food) scores between the baseline visit and the 6-month follow-up visit
Time frame: 6 months after the inclusion
Change in questionnaires scores between baseline and follow-up
Change in QACD (questionnaires of attitudes and behaviors related to diabetes management) scores between the baseline visit and the 6-month follow-up visit
Time frame: 6 months after the inclusion
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