Higher C‐peptide levels are associated with regional cortical thinning in 1093 cognitively normal subjects
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent studies have demonstrated an association between increased insulin secretion and cognitive impairment. However, there is no previous study that directly evaluates the association between increased insulin secretion and cortical thickness to our knowledge. Therefore, our aim was to evaluate the effect of hyperinsulinemia, as measured by C-peptide level, on cortical thickness in a large sample of cognitively normal individuals. METHODS: Cortical thickness was measured in 1093 patients who visited the Samsung Medical Health Promotion Center and underwent brain magnetic resonance imaging (MRI) and a blood test to measure C-peptide concentration. Automated surface-based analyses of the MRI data were used to measure cortical thickness. C-peptide levels were divided into quartiles for comparison. Patients in the first to third quartiles were used as the reference category. RESULTS: Patients in the highest quartile group (Q4) of C-peptide levels showed cortical thinning, predominantly in both medial temporal lobes, the right inferior temporal gyrus, both medial prefrontal lobes and the right superior parietal lobule, compared with the lower quartile groups (Q1-Q3) after controlling for age, gender, body mass index, history of hypertension, hyperlipidemia, previous stroke, cardiovascular disease and fasting glucose level. CONCLUSIONS: A higher C-peptide level is associated with regional cortical thinning, even in cognitively normal individuals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".