A review of electroencephalographic changes in diabetes mellitus in relation to major depressive disorder
Bibliographic record
Abstract
A bidirectional relationship exists between diabetes mellitus (DM) and major depressive disorder (MDD), with depression commonly reported in both type 1 DM (T1DM) and type 2 DM (T2DM), and depressive symptoms associated with a higher incidence of diabetes. However, how the two conditions are pathologically connected is not completely understood. Similar neurophysiological abnormalities have been reported in both DM and MDD, including elevated electroencephalographic (EEG) activity in low-frequency slow waves and increased latency and/or reduced amplitude of event-related potentials. It is possible that this association reflects some common underlying pathology, and it has been proposed that diabetes may place patients at risk for depression through a biological mechanism linking the metabolic changes of DM to changes in the central nervous system. In this review we will discuss EEG abnormalities in DM, as well as the biological mechanisms underlying various EEG parameters, in order to evaluate whether or not a common EEG biosignature exists between DM and MDD. Identifying such commonalities could significantly inform the current understanding of the mechanisms that subserve the development of the two conditions. Moreover, this new insight may provide the basis for informing new drug discovery capable of mitigating and possibly even preventing both conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".