Association of Low-Density Lipoprotein Receptor-Related Protein 1 and Its rs1799986 Polymorphism With Mild Cognitive Impairment in Chinese Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: Low-density lipoprotein receptor associated protein 1 (LRP1) plays roles in cerebral glucose metabolism and clearance of amyloid-β. This study aims to investigate the pathogenetic roles of LRP1 and its rs1799986 polymorphism in mild cognitive impairment (MCI) among type 2 diabetes (T2DM) patients. Methods: 166 Chinese T2DM patients were enrolled and divided into two groups according to Montreal Cognitive Assessment score. Neuropsychological tests were performed, the soluble LRP1 (sLRP1) concentration was assessed using ELISA, genotype of LRP1 rs1799986 was detected using sequenom method. Results: 60 diabetic-MCI patients displayed significantly lower plasma sLRP1 levels (p=0.033) and poor glucose control (p=0.009) than their healthy-cognition controls (n=106), and plasma sLRP1 levels (OR=0.971, p=0.005) and HbA1c (OR=1.298, p=0.003) are both risk factors for diabetic-MCI patients in addition to use of insulin and hypertension, but association of plasma sLRP1 levels and HbA1c was not found. Their plasma sLRP1 concentrations negatively correlated with Stroop Color Word Test B Number (r=-0.335, p=0.011), which represents measure selective attention, cognitive flexibility and processing speed. Additionally, T allele carriers of LRP1 rs1799986 showed higher MMSE scores and AVLT delayed recall scores (p=0.025; p=0.025 respectively) in T2DM patients. Conclusion: In T2DM patients, lower plasma levels of sLRP1 portend the early cognitive deficits, especially attention dysfunction. Further study is needed to elucidated the roles of LRP1 in hyperglycemia induced cognitive declines. T allele of LRP1 rs1799986 probably decreases the MCI susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".