Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05476861 |
| Other study ID # |
2023-3876, 22239 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 1, 2022 |
| Est. completion date |
May 10, 2023 |
Study information
| Verified date |
May 2023 |
| Source |
Laval University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Type 1 Diabetes management is requiring and implies numerous lifestyle modifications. Insulin
restriction to control weight is a frequent phenomenon, affecting up to 40% of PWT1D.
Broadly, purging or binge eating behaviors are also frequently disordered eating behaviors
(DEB) in people living with a Type 1 Diabetes (associated or not with restrictive eating
behaviors). In a study on adolescents with T1D, the prevalence of moderate or high level of
DEB ranged from 21% to 32%. Moreover, the presence of binge eating behavior seems to be
associated with higher anxiety and depression levels.
Omitting insulin for weight control has been associated with the highest rates of retinopathy
and nephropathy when compared to other weight control behaviors and to increase the risk of
mortality by 3.2 times and decrease life spans from an average of 58 to 44 years at 11-year
follow-up. Moreover, insulin misuse may be much more complex behavior than just the need for
weight control. These behaviors may also involve increased distress, loss of control, and
feelings of regret, guilt, and shame.
Interestingly, most studies of eating disorders and type 1 diabetes use question regarding
insulin omission as a surrogate marker for eating disorders and disordered eating. For
instance, the question used in the BETTER registry are: "In the past 12 months, did you
intentionally omit insulin injections with the objective of losing weight?" or "In a typical
week, how often do you miss an insulin dose?". However, the validity and robustness of such a
marker have not been specifically investigated yet.
Our study objectives are : 1) To confirm that participants who reported intentionally
omitting insulin had significantly more disordered eating behavior (based on the review of
food records available); 2) To compare the prevalence and the severity of physical and mental
health comorbidities (e.g., diabetes micro and macrovascular complications, glycated
hemoglobin levels, current and past psychiatric disorders, distress related to diabetes) in
people living with diabetes having or not declared to intentionally omit insulin; 3) To
establish, using machine learning techniques, the main factors associated with intentional
insulin omission behavior, taking into account biological, anthropometric and psychometric
factors.
Description:
Our main hypotheses:
1. that people living with Type 1 diabetes who report intentional insulin omission will
have a higher risk of disordered eating behaviors and diabetes-related comorbidities;
2. that it will be possible to establish different predictors of intentional insulin
omissions behaviors by using machine learning techniques.
Statistical Analysis:
The normality of the data distribution will be checked for each value using a graphical
analysis of the distribution and a Kolmogorov-Smirnov test before parametric tests are
performed. A description of the characteristics of the participants included in this study
will be performed. Continuous variables will be presented by the mean and standard deviation
for normal distributions, median and range for others. Categorical variables will be
presented by the number and percentage of each modality. The level of significance of the
tests must be equal or lower than 0.05. The risk of alpha error is set at 0.05, with
Bonferroni correction if necessary.
- Objective 1 - 24-hour dietary recall: Macronutrients composition and repartition (in the
day), as well as the serving sizes of the food intake will be calculated using the
Canadian Food Guide 2007 or the Canadian Nutrient File.
- Objective 2 - Prevalence and severity of diabetes and its comorbidities: Both groups,
having or not intentionally omit insulin, will be compared using conventional
statistical analyses.
- Objective 3 - Predictive modeling: Based on a previous published methodology (Iceta et
al., 2021), the first step will be to reduce entropy in the dataset using a ranking
procedure (the Fast Correlation-Based Filter, FCBF) to identify the most discriminating
predictor between the two class-labeled datasets (with and without insulin omission).
Only items with an FCBF score > 0.1 will be retained for the subsequent steps of the
data-mining analysis. The second step of the data-mining analysis will aim at selecting
the most relevant predictive algorithm for intentional insulin omission. Performances of
different predictive algorithms will be tested and compared: logistic regression,
artificial neural networks, naive Bayes classification, decision trees, AdaBoost
meta-algorithm, CN2 rule inducer algorithm, SVM algorithm, k-nearest neighbors'
algorithm and stochastic gradient algorithm. These artificial intelligence algorithms
will be cross-validated (ten times in a row) with a randomized learning sample, renewed
ten times and representing 66% of the study population. The validation sample will be
represented by the other 33% of the population. The predictive algorithm with the best
precision and F1 score will be considered as the best valuable algorithm.
