Depression in Diabetic Patients: What Is the Link With Eating Disorders? Results of a Study in a Representative Sample of Patients With Type 1 Diabetes
Bibliographic record
Abstract
Background and Purpose: Comorbidity between diabetes and depression, and diabetes and eating disorders (ED) conveys significant diagnostic, clinical and therapeutic implications. The present study was conducted on a sample of adult outpatients affected by Type 1 Diabetes (T1DM) to assess lifetime prevalence of ED; current prevalence of depression and Disturbed Eating Behaviors (DEB) and their impact on glycemic control. We hypothesized that patients with depression would have higher rates of lifetime ED and current DEB. We hypothesized a significant and independent association between DEB and the prevalence of depression. Materials and Methods: The study was carried out using a cross-sectional design in a sample of 172 diabetic patients with T1DM aged from 17 to 55 years. Lifetime prevalence of ED according to DSM-5 criteria was assessed by means of the Module H modified of the Structured Clinical Interview for DSM-IV Axis I Disorder (SCID-I). The following questionnaires were used: Beck Depression Inventory-IA version (BDI-IA) and Diabetes Eating Problems Survey-Revised (DEPS-R), to assess respectively the current presence of depression and DEB. Socio-demographic, clinical, and laboratory data were also collected. Results: = 0.003). The presence of DEB was associated with poor glycemic control. On the other hand, no association was found between depression and metabolic compensation. Conclusion: Adult patients with T1DM and depression should be screened for ED and DEB. Treating DEB could positively impact both mood and glycemic control in this population. Further studies should be carried out on a larger patient population using a longitudinal design and an accurate method of evaluation to explore the complex relationship between diabetes, depression, ED, and DEB. Future research should investigate treatment strategies for DEB in T1DM patients and their impact on both psychopathological and metabolic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".