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Record W4399517572 · doi:10.4239/wjd.v15.i6.1374

Reconsidering the role of depression and common psychiatric disorders as partners in the type 2 diabetes epidemic

2024· article· en· W4399517572 on OpenAlexaff
Angelo Emilio Claro, Clelia Palanza, Marianna Mazza, Alessandro Rizzi, Andrea Corsello, Linda Tartaglione, Giuseppe Marano, Giovanna Elsa Ute Muti Schuenemann, Marta Rigoni, Alfredo Pontecorvi, Luigi Janiri, Paola Muti, Dario Pitocco

Bibliographic record

VenueWorld Journal of Diabetes · 2024
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineDepression (economics)Type 2 diabetesOverweightPsychiatryAddictionObesityPopulationBupropionMechanism (biology)Diabetes mellitusInternal medicineEndocrinologySmoking cessation

Abstract

fetched live from OpenAlex

Common psychiatric disorders (CPDs) and depression contribute significantly to the global epidemic of type 2 diabetes (T2D). We postulated a possible pathophysiological mechanism that through Bridge-Symptoms present in depression and CPDs, promotes the establishment of emotional eating, activation of the reward system, onset of overweight and obesity and, ultimately the increased risk of developing T2D. The plausibility of the proposed pathophysiological mechanism is supported by the mechanism of action of drugs such as naltrexone-bupropion currently approved for the treatment of both obesity/overweight with T2D and as separate active pharmaceutical ingredients in drug addiction, but also from initial evidence that is emerging regarding glucagon-like peptide 1 receptor agonists that appear to be effective in the treatment of drug addiction. We hope that our hypothesis may be useful in interpreting the higher prevalence of CPDs and depression in patients with T2D compared with the general population and may help refine the integrated psychiatric-diabetic therapy approach to improve the treatment and or remission of T2D.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.335
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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