Major Depressive Disorder and Diabetes: Does Serotonin Bridge the Gap?
Bibliographic record
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric illnesses worldwide, with reported prevalence rates ranging between 10% and 19%. Pharmacotherapy is a first-line option for the management of MDD and, as a result, the use of antidepressants has increased 4 fold in the last 20 years. Serotonin is the most commonly dysregulated neurotransmitter in the etiology of MDD and this system is the primary focus of most medications used in the treatment of illness. Although antidepressant use in adults increases the risk of developing new onset type 2 diabetes, the mechanisms underlying this association are poorly defined. This review will focus on 1) the evidence from human and animal studies suggesting a link between the use of antidepressants that target serotonin signaling (i.e., SSRIs, serotonin-norepinephrine reuptake inhibitors (SNRIs), serotonin antagonist and reuptake inhibitors (SARIs), and noradrenergic and specific serotonergic antidepressants (NaSSAs)) and increased risk of diabetes, and 2) the mechanisms by which alterations in serotonin signalling by antidepressants can affect glucose homeostasis. Keywords: Antidepressants, insulin, noradrenergic and specific serotonergic antidepressants selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, serotonin antagonist and reuptake inhibitors, type 2 diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".