Association of iron metabolism with cognitive function in elderly patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Objective To investigate the association of iron overload with metabolic changes in hippocampal tissues, and to explore the relationship between iron metabolism abnormality and cognitive function in elderly patients with type 2 diabetes mellitus(T2DM). Methods A total of 97 elderly inpatients with T2DM were enrolled. According to the Mini-mental state examination (MMSE) score, the type 2 diabetic patients were divided into mild cognitive impairment (MCI) and non-mild cognitive impairment (Non-MCI) groups. A retrospective analysis was performed for their clinical data and laboratory parameters, including serum ferritin, MMSE, Montreal cognitive assessment (MoCA), carotid intima-media thickness, ankle brachial index, and the ELISA method was used to detect soluble transferring receptor (sTfR). Proton MR spectroscopy(1H-MRS)was performed in the hippocampus of 26 patients. Results Compared with Non-MCI group, MCI group revealed higher age(P<0.01), higher incidence of carotid plaque (P<0.01), decreased sTfR(P=0.049) and left hippocampal height(P=0.034). Age, sTfR, and carotid plaque were independent risk factors for MCI in elderly patients with T2DM. Conclusion The abnormal iron metabolism may contribute to the occurrence of MCI in the elderly patients with T2DM. Key words: Diabetes mellitus, type 2; Mild cognitive impairment; Soluble transferring receptor; Proton MR spectroscopy
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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 | 0.001 |
| 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".