Inverted U-shaped correlation between serum low-density lipoprotein cholesterol levels and cognitive functions of patients with type 2 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) metabolic disorder is common in individuals with diabetes. The role of LDL-C in mild cognitive impairment (MCI) remains to be explored. We aim to investigate the associations between LDL-C at different levels and details of cognition decline in patients with type 2 diabetes mellitus (T2DM). METHODS: Patients with T2DM (n = 497) were recruited. Clinical parameters and neuropsychological tests were compared between patients with MCI and controls. Goodness of fit was assessed to determine the linear or U-shaped relationship between LDL-C and cognitive function. The cut-off point of LDL-C was calculated. Correlation and regression were carried out to explore the relationship between cognitive dysfunction and LDL-C levels above and below the cut-off point. RESULTS: Although no significant difference in LDL-C levels was detected in 235 patients with MCI, compared with 262 patients without MCI, inverted-U-shaped association was determined between LDL-C and Montreal Cognitive Assessment (MoCA). The cut-off point of LDL-C is 2.686 mmol/l. LDL-C (>2.686 mmol/l) is positively related to Trail Making Test B (TMTB) indicating executive function. LDL-C (<2.686 mmol/l) is positively associated with Clock Drawing Test (CDT) reflecting visual space function in patients with T2DM. CONCLUSION: Inverted U-shaped correlation was found between serum LDL-C and cognitive function in patients with T2DM. Despite that the mechanisms of different LDL-C levels involved in special cognitive dysfunctions remain incompletely clarified, excessive LDL-C damages executive function, while the deficient LDL-C impairs visual space function. TRIAL REGISTRATION: ChiCTR-OCC-15006060 .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".