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Record W4282978637 · doi:10.3389/fpsyt.2022.848031

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

2022· article· en· W4282978637 on OpenAlexaff
Federica Pinna, Federico Suprani, Valeria Deiana, Lorena Lai, Mirko Manchia, Pasquale Paribello, Giulia Somaini, Enrica Diana, Eraldo Francesco Nicotra, Fernando Farci, Mariangela Ghiani, Rossella Cau, Marta Tuveri, Efisio Cossu, Elena Loy, Andrea Crapanzano, Paola Grassi, Andrea Loviselli, Fernanda Velluzzi, Bernardo Carpiniello

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsDalhousie University
FundersRegione Autonoma della Sardegna
KeywordsDepression (economics)MedicineComorbidityEating disordersGlycemicDiabetes mellitusType 1 diabetesBeck Depression InventoryInternal medicineType 2 diabetesPsychiatryOdds ratioDisordered eatingEndocrinologyAnxiety

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.244
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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