Depression, Diabetes and Dementia
Bibliographic record
Abstract
Depression, diabetes and dementia are three disorders associated with staggering morbidity and mortality worldwide. The association between depression and diabetes has been well established. Furthermore, both depression and diabetes have been shown to increase the incidence of dementia individually and synergistically. The metabolic-brain axis appears to be a key mediator connecting depression, diabetes and dementia. Brain regions important for cognition and emotional regulation may be damaged by the effects of hyperglycemia and insulin resistance. Indeed, insulin resistance and decreased insulin in the central nervous system (CNS) results in decreased intracellular glucose levels in frontal and subcortical regions, neurotoxicity, decreased neuroplasticity, decreased signaling, decreased synaptic connectivity and disturbances in neural circuitry. The aforementioned changes may be attributable to brain bioenergetics wherein there is a bias toward energy conservation. The insulin pathway also has a bidirectional interaction with amyloid-β oligomer formation, one of the hallmarks of Alzheimer's disease. As well, depression may further facilitate neural circuit damage through the inflammatory pathway, hypothalamic-pituitary-adrenal axis dysregulation, monoamine changes and lowering of neurotrophic support to the CNS. Stress and psychosocial determinants of health may also be key mediators in how these systems interact. The involvement of several pathways may present new potential drug targets for the treatment and prevention of dementia using a lifetime approach. Systemic and intranasal insulin, oral diabetic medications, exercise, dietary changes, bariatric surgery and improved screening practices with early treatment of depression and diabetes all show promise in the treatment and prevention of comorbid depression, diabetes and dementia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".