Memory Dysfunction in Type 2 Diabetes Mellitus Correlates with Reduced Hippocampal CA1 and Subiculum Volumes
Bibliographic record
Abstract
BACKGROUND: Little attention has been paid to the role of subcortical deep gray matter (SDGM) structures in type 2 diabetes mellitus (T2DM)-induced cognitive impairment, especially hippocampal subfields. Our aims were to assess the in vivo volumes of SDGM structures and hippocampal subfields using magnetic resonance imaging (MRI) and to test their associations with cognitive performance in T2DM. METHODS: A total of 80 T2DM patients and 80 neurologically unimpaired healthy controls matched by age, sex and education level was enrolled in this study. We assessed the volumes of the SDGM structures and seven hippocampal subfields on MRI using a novel technique that enabled automated volumetry. We used Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores as measures of cognitive performance. The association of glycosylated hemoglobin (HbA1c) with SDGM structures and neuropsychological tests and correlations between hippocampal subfields and neuropsychological tests were assessed by partial correlation analysis in T2DM. RESULTS: Bilaterally, the hippocampal volumes were smaller in T2DM patients, mainly in the CA1 and subiculum subfields. Partial correlation analysis showed that the MoCA scores, particularly those regarding delayed memory, were significantly positively correlated with reduced hippocampal CA1 and subiculum volumes in T2DM patients. Additionally, higher HbA1c levels were significantly associated with poor memory performance and hippocampal atrophy among T2DM patients. CONCLUSIONS: These data indicate that the hippocampus might be the main affected region among the SDGM structures in T2DM. These structural changes in the hippocampal CA1 and subiculum areas might be at the core of underlying neurobiological mechanisms of hippocampal dysfunction, suggesting that degeneration in these regions could be responsible for memory impairments in T2DM patients.
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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 | 0.001 | 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".