Inflammatory Status Modifies the Association Between Glycemic Traits and Cognitive Impairment in Patients With Coronary Artery Disease
Bibliographic record
Abstract
Background The evidence regarding the modifying role of inflammation in the association between glycemic traits and cognitive impairment remains insufficient. This study aimed to explore the association between glycemic traits and mild cognitive impairment (MCI) in patients with coronary artery disease (CAD), with particular emphasis on whether this association is influenced by varying levels of inflammation. Methods This cross‐sectional study included 1437 patients with CAD who underwent cognitive function assessment during hospitalization. Cognitive function was assessed using the Mini‐Mental State Examination and the Montreal Cognitive Assessment to identify patients with MCI. The hsCRP (high‐sensitivity C‐reactive protein) level>3 mg/L was defined as high inflammatory risk. Multivariable logistic regression models were employed to estimate the associations of glycemic traits and inflammatory risk status with cognitive impairment. Results Elevated fasting plasma glucose, glycosylated hemoglobin, and diabetes were positively associated with an increased risk of MCI in patients with CAD, with odds ratios ranging from 1.18 (95% CI, 1.10–1.26) to 1.55 (95% CI, 1.09–2.20). A significant interaction was observed between glycosylated hemoglobin and high inflammatory status on the risk of MCI ( P for multiplicative interaction <0.001), with the effects of glycosylated hemoglobin on the risk of MCI being significant only in the group with high hsCRP. Additionally, there was evidence of significant additive interaction between high inflammatory risk and diabetes on the MCI risk in patients with CAD ( P for additive interaction <0.05). Conclusions Inflammatory status modifies the association between glycemic traits and the risk of cognitive impairment. This finding highlights the importance of incorporating inflammation into the management of blood glucose levels in patients with CAD.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".