Association between elevated glycosylated hemoglobin and cognitive impairment in older Korean adults: 2009–2010 Ansan cohort of the Korean genome and epidemiology study
Bibliographic record
Abstract
Objective: Cohort studies on the risk of cognitive impairment in the older population of S. Korea based on glycosylated hemoglobin (HbA1c) levels are exceedingly rare. This study aimed to analyze the association between HbA1c levels and cognitive impairment in older Korean adults without dementia. Methods: We conducted a cross-sectional study using data from a community-based Ansan cohort (2009-2010), which was part of the Korea Genome Epidemiology Study. The study included 853 cohort participants aged ≥59 years living in Ansan city. Cognitive function was evaluated using the Korean version of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The MMSE and MoCA scores were categorized into normal cognition (≥24 and ≥ 23, respectively) and cognitive impairment (≤23 and ≤ 22, respectively). Multiple logistic regression analysis was used to estimate the association between HbA1c levels and cognitive impairment, with adjustments for covariates. Results: The mean age of the participants was 66 years, and 433 (50.8%) were female. Cognitive impairment was observed in 12.5 and 44.3% of participants, based on the MMSE and MoCA, respectively. Regarding the MMSE scores, HbA1c level was a risk factor for cognitive impairment in women. Compared to normal HbA1c (≤5.6%) levels, adjusted odds ratios of MMSE decline for HbA1c 5.7-6.4% and HbA1c ≥6.5% were high: 2.16 (95% confidence interval [CI] 1.04-4.49) and 2.96 (95% CI, 1.04-8.39), respectively. Conclusion: By improving glycemic control, the risk of cognitive impairment in the older population can be reduced. Further research on the role of sex differences in cognitive impairment is needed.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".