Association Between Lipid Accumulation Product and Mild Cognitive Impairment in Patients with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Diabetes may increase the risk of conversion of mild cognitive impairment (MCI) to dementia. Lipid accumulation product (LAP), an index of visceral obesity, has been shown to be a powerful predictor of insulin resistance and type 2 diabetes (T2D). However, little attention has been paid to the relationship between LAP and MCI in T2D. OBJECTIVE: We aimed to investigate the association between the LAP index and MCI in patients with T2D. METHODS: In total, 220 hospitalized patients with T2D, including 113 MCI patients and 107 patients with normal cognition, were enrolled in this cross-sectional study. We collected demographic, anthropometric, and biochemical data on each subject. The LAP index was calculated according to the following formulas: [waist circumference (WC) (cm) - 65]×triglyceride (TG) (mmol/L) for males and [WC (cm) - 58] ×TG (mmol/L) for females. RESULTS: Compared with patients with normal cognition, MCI patients were older and had a higher LAP index, WC, body mass index, and glycosylated hemoglobin A1c level, as well as a lower Montreal Cognitive Assessment score and education level (p < 0.05). After adjusting for confounding factors, LAP index was associated with MCI (OR = 1.047, 95% CI = 1.031-1.063, p < 0.01). The area under the ROC curve (AUC) for the LAP index was higher than that for WC and BMI. CONCLUSION: A high LAP index is associated with an increased risk of MCI in T2D patients. The LAP index appears to be a good indicator of risk of MCI in patients with T2D.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".