Prefrontal cortex brain damage and glycemic control in patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Background This study examined brain tissue integrity in sites that controls cognition (prefrontal cortices; PFC) and its relationships to glycemic outcomes in adults with type 2 diabetes mellitus (T2DM). Methods We examined 28 T2DM patients (median age 57.1 years; median body mass index [BMI] 30.6 kg/m2;11 males) and 47 healthy controls (median age 55.0 years; median BMI 25.8 kg/m2; 29 males) for cognition (Montreal Cognitive Assessment [MoCA]), glycemic control (hemoglobin A1c [HbA1c]), and PFC tissue status via brain magnetic resonance imaging (MRI). High‐resolution T1‐weighted images were collected using a 3.0‐Tesla MRI scanner, and PFC tissue changes (tissue density) were examined with voxel‐based morphometry procedures. Results Reduced PFC density values were observed in T2DM patients compared to controls (left, 0.41 ± 0.02 mm3/voxel vs 0.44 ± 0.02 mm3/voxel, P < 0.001; right, 0.41 ± 0.03 mm3/voxel vs 0.45 ± 0.02 mm3/voxel, P < 0.001). PFC density values were positively correlated with cognition; left PFC region (r = 0.53, P = 0.005) and right PFC region (r = 0.56, P = 0.003), with age and sex as covariates. Significant negative correlations were found between PFC densities and HbA1c values; left PFC region (r = −0.39, P = 0.049) and right PFC region (r = −0.48, P = 0.01), with age and sex as covariates. Conclusions T2DM patients showed PFC brain tissue damage, which is associated with cognitive deficits and poor glycemic control. Further research is needed to identify causal relationships between HbA1c, cognition, and brain changes in T2DM and to evaluate the impact of interventions to prevent brain tissue injury or neuroregeneration in this high‐risk patient population, to eventually preserve or enhance cognition and improve glucose outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".