569-P: Prefrontal Cortex Brain Damage and Glycemic Control in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Cognitive impairments are common in patients with type 2 diabetes (T2DM) and adversely impact self-care and glycemic control. However, brain injury in a site that controls cognition (prefrontal cortices; PFC) and its relationship to glycemic outcomes in T2DM remains unclear. Methods: We examined 9 T2DM patients (age 58.2 ± 4.6 years; BMI 30.3 ± 5.0 kg/m2;3 males) and 47 healthy controls (age 55.7 ± 4.2 years; BMI 26.4 ± 3.0 kg/m2; 29 males) for cognition (Montreal Cognitive Assessment [MoCA]), glycemic control (hemoglobin A1c [A1C]), and PFC status via brain magnetic resonance imaging (MRI). Two high-resolution T1-weighted images were collected using a 3.0-Tesla MRI scanner to evaluate PFC status with voxel-based morphometry (VBM) procedures. Results: No significant differences in age and gender emerged between T2DM and control subjects. However, BMI values were significantly higher in T2DM subjects over controls. MoCA scores were significantly lower in T2DM patients compared to controls (24.3 ± 2.4 vs. 26.9 ± 2.3; p = 0.004). Mean A1C was 7.6±0.8% (51-68 mmol/mol) in T2DM patients. Reduced PFC volume was observed in T2DM patients compared to controls (left 0.35±0.02 vs. 0.41±0.03, p<0.001; right 0.31±0.02 vs. 0.38±0.02, p<0.001). PFC volume was positively correlated with cognition; left PFC regions (r= 0.94, p=0.002) and right PFC regions (r= 0.89, p=0.007) with age and gender as covariates. Significant negative correlations were found between PFC volume and A1C; left PFC (r= -0.92, p=0.003) and right PFC regions (r=-0.92, p=0.004) with age and gender as covariates. Conclusions: T2DM patients have PFC brain damage which is associated with cognitive deficits and poor glycemic control. Further research is needed to identify causal relationships between A1C and brain changes in T2DM and to examine interventions to enhance brain/neurogenesis and its impact on A1C. Disclosure S.E. Choi: None. B. Roy: None. R. Kumar: None. M. Freeby: None. R.S. Mullur: None. M.A. Woo: None. Funding National Institutes of Health (R01NR017190)
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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) | 0.002 | 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".