Regional Brain Gray Matter Changes in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Patients with Type 2 diabetes mellitus (T2DM) show cognitive and mood impairment, indicating potential for brain injury in regions that control these functions. However, brain tissue integrity in cognition, anxiety, and depression regulatory sites, and their associations with these functional deficits in T2DM subjects remain unclear. We examined gray matter (GM) changes in 34 T2DM and 88 control subjects using high-resolution T1-weighted images, collected from a 3.0-Tesla magnetic resonance imaging scanner, and assessed anxiety [Beck Anxiety Inventory], depressive symptoms [Beck Depression Inventory-II], and cognition [Montreal Cognitive Assessment]. We also investigated relationships between GM status of cognitive and mood control sites and these scores in T2DM. Significantly increased anxiety (p = 0.003) and depression (p = 0.001), and reduced cognition (p = 0.002) appeared in T2DM over controls. Decreased GM volumes appeared in several regions in T2DM patients, including the prefrontal, hippocampus, amygdala, insular, cingulate, cerebellum, caudate, basal-forebrain, and thalamus areas (p < 0.01). GM volumes were significantly associated with anxiety (r = -0.456,p = 0.009), depression (r = -0.465,p = 0.01), and cognition (r = 0.455,p = 0.009) scores in regions associated with those regulations (prefrontal cortices, hippocampus, para hippocampus, amygdala, insula, cingulate, caudate, thalamus, and cerebellum) in T2DM patients. Patients with T2DM show brain damage in regions that are involved in cognition, anxiety, and depression control, and these tissue alterations are associated with functional deficits. The findings indicate that mood and cognitive deficits in T2DM patients has brain structural basis in the condition.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".