Association of β-cell function and cognitive impairment in patients with abnormal glucose metabolism
Bibliographic record
Abstract
BACKGROUND: Insulin has been demonstrated to play an important role in the occurrence and development of Alzheimer's disease, especially in those with diabetes. β cells are important insulin-producing cells in human pancreas. This study aimed to investigate the association between β-cell dysfunction and cognitive impairment among patients over 40-year-old with abnormal glucose metabolism in Chinese rural communities. METHODS: A sample of 592 participants aged 40 years or older from the China National Stroke Prevention Project (CSPP) between 2015 and 2017 were enrolled in this study. Abnormal glucose metabolism was defined when hemoglobin Alc ≥ 5.7%. Cognitive function was assessed by the Beijing edition of the Montreal Cognitive Assessment scale. Homeostasis assessment of β-cell function was performed and classified into 4 groups according to the quartiles. A lower value of HOMA-β indicated a worse condition of β-cell function. Multivariate logistic regression was used to analyze the association between β-cell function and cognitive impairment. RESULTS: In a total of 592 patients with abnormal glucose metabolism, the average age was 60.20 ± 7.63 years and 60.1% patients had cognitive impairment. After adjusting for all potential risk factors, we found the first quartile of β-cell function was significantly associated with cognitive impairment (OR: 2.27, 95%CI: 1.32-3.92), especially at the domains of language (OR: 1.64, 95%CI: 1.01-2.65) and abstraction (OR: 2.29, 95%CI: 1.46-3.58). CONCLUSIONS: Our study showed that worse β-cell function is associated with cognitive impairment of people over 40-year-old with abnormal glucose metabolism in Chinese rural communities, especially in the cognitive domains of abstraction and language.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".