Cognitive decline and neuroimaging correlates in comorbid type 2 diabetes and depression: A systematic review
Bibliographic record
Abstract
Background: The comorbid state of diabetes and depression increases the risk of developing Alzheimer's disease compared to a single disease, but the relationship between cognitive decline and brain imaging changes in this state remains unclear. Objective: To systematically review the association between brain imaging changes and mild cognitive impairment or cognitive decline in this comorbid state. Methods: We searched four databases until April 15, 2025, combining keyword searches with MeSH terms and free words, and supplemented relevant studies with reference back. Observational studies were included to assess the relationship between brain imaging changes and cognitive decline in diabetes and depression comorbid states. The Newcastle-Ottawa Scale was used to assess the quality of the included observational studies. Results: Thirteen studies with a total of 1509 participants were finally included. Imaging methods included structural MRI, magnetization transport imaging, diffusion tensor imaging, and functional MRI. Frequently reported affected brain regions included the frontal lobe, limbic system, and basal ganglia regions. Compared to type 2 diabetes mellitus patients, the comorbid state showed more alterations in brain structure and function, and these were associated with executive function and attentional impairment in cognitive decline. The brain functional connectivity findings were inconsistent. Conclusions: The comorbid state exhibits characteristic brain imaging alterations and is associated with cognitive impairment, indicating potential relevance to the risk of developing mild cognitive impairment and dementia. Further longitudinal and multimodal studies with more rigorous and standardized experimental designs are warranted to validate and extend these findings.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".