Relation of Poor Nutritional Status to Mild Cognitive Impairment in Patients with Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVES: Nutritional status affects cerebral circulation and cognitive function. More attention needs to be paid to nutritional status in coronary artery disease (CAD) patients, yet the relation between nutritional status or dietary intake (DI) and cognitive function or mild cognitive impairment (MCI) in CAD patients remain unclear. Thus, we examined the following relations: 1) that between nutritional status and cognitive function, and MCI and 2) that between DI and cognitive function, and MCI. DESIGN, SETTING, AND PARTICIPANTS: We conducted a cross-sectional study of 208 patients with CAD but without dementia. MEASUREMENTS: MCI was estimated with the Japanese version of the Montreal Cognitive Assessment (MoCA-J). Nutritional status was assessed by the Geriatric Nutritional Risk Index (GNRI), and DI was assessed by total energy intake per day. We investigated the relation between nutritional status or DI and cognitive function by Pearson correlation analysis, and that between nutritional status or DI and MCI by multivariable logistic regression analysis. RESULTS: The GNRI and DI were positively associated with the MoCA-J score (r = 0.23, p < 0.001, and r = 0.24, p < 0.001, respectively), and both were independently associated with MCI in the multivariable logistic regression analysis (odds ratio, 0.96; p = 0.045, and odds ratio, 0.998; p = 0.020, respectively). CONCLUSIONS: Poor nutritional status and low DI were found to be significantly associated with cognitive function and MCI in CAD patients. Our findings regarding nutritional status and DI might be useful for clinicians to prevent or intervene in the early cognitive decline of inpatients 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".