The nonlinear association between HbA1c and cognitive impairment in patients with alcohol use disorder
Bibliographic record
Abstract
Background: Extensive research has indicated that higher levels of glycated hemoglobin (HbA1c) are associated with poor cognitive performance regardless of the presence of diabetes. To our knowledge, the association between HbA1c levels and cognitive decline in patients with alcohol use disorder is not well understood. This study aimed to investigate whether HbA1c was associated with cognitive impairment in patients with alcohol use disorder. Methods: Patients admitted to the Psychiatry Department of the Third Hospital of Quzhou with a confirmed diagnosis of alcohol-related cognitive impairment were recruited between January 2019 and February 2022. Their HbA1c levels were measured, and they completed the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) after at least one week of monitored abstinence from alcohol. Univariate linear regression, multivariate linear regression and generalized additive models (GAMs) were used to investigate the association of HbA1c with MMSE and MoCA scores. Results: In total, 227 patients were included. Univariate and multivariate regression analyses suggested that HbA1c was negatively associated with MMSE and MoCA scores after adjustment for potential confounders (P < 0.05). The GAM analysis revealed that the relationships between the HbA1c level and the MMSE and MoCA scores were bell-shaped. The inflection points were 5.3% and 5.2% for the MMSE and MoCA respectively. Conclusion: The results of this study suggest that HbA1c levels are significantly related to cognitive impairment in patients with alcohol use disorder. Future studies are required to determine the effects of optimal glucose control in people with alcohol use disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".