Long-Term Subclinical Hyperglycemia and Hypoglycemia as Independent Risk Factors for Mild Cognitive Impairment in Elderly People
Bibliographic record
Abstract
Dementia is one of the most common geriatric diseases, and mild cognitive impairment (MCI) is considered to be incipient dementia. MCI patients have elevated risk of progressing to dementia. Multiple metabolic abnormalities have an unconfirmed effect on MCI risk, and taking adequate measures against metabolic abnormalities might prevent the developing of MCI. Thus, the present study explored the association of MCI risk with common metabolic abnormalities, such as hyperglycemia, hypoglycemia, hyperlipidemia and hypouricemia, and to provide the basis for MCI prevention. A total of 1,262 elderly outpatients with normal cognitive function and without confirmed diabetes mellitus, hyperlipoidemia and gout were enrolled. During the five-year follow-up period, 142 subjects were diagnosed with MCI according to Mini Mental State Examination and Montreal Cognitive Assessment. Furthermore, annual blood glucose, glycated hemoglobin, lipids and uric acid values were obtained, and mean of each indicator was calculated. Only mean values were included in the study to reflect long-term effect of metabolic abnormalities on MCI risk. Thus, the increased risk of MCI was associated with the mean values of blood glucose < 4.7 mmol/L (RR: 1.57, 95% CI: 1.14-2.32), blood glucose ≥ 6.3 mmol/L (RR: 1.49, 95% CI: 1.03-2.39), glycated hemoglobin ≥ 5.9% (RR: 2.28, 95% CI: 1.59-3.91), triglycerides ≥ 2.0 mmol/L (RR: 2.79, 95% CI: 2.14-3.79), total cholesterol ≥ 5.5 mmol/L (RR: 2.37, 95% CI: 1.69-3.39) and uric acid ≤ 380 μmol/L (RR: 1.62, 95% CI: 1.08-2.51). In conclusion, long-term subclinical hyperglycemia, hypoglycemia, hyperlipidemia, and hypouricemia are independent risk factors for MCI in elderly people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".