Association between Metabolic Syndrome and Cognitive Impairment after Acute Ischemic Stroke: A Cross-Sectional Study in a Chinese Population
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Metabolic syndrome (MetS), a risk factor for many vascular conditions, is associated with vascular cognitive disorders. The objective of the present study was to explore the associations of MetS and its individual components with the risks of cognitive impairment and neurological dysfunction in patients after acute stroke. METHODS: This cross-sectional study enrolled 840 patients ranging in age from 53 to 89 years from the Tianjin area of North China. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination. Neuropsychiatric behavior was assessed using the Neuropsychiatric Inventory Questionnaire. Emotional state was examined according to the Hamilton Depression Rating Scale, and neuromotor function was evaluated using the National Institutes of Health Stroke Scale, Barthel index, and the Activity of Daily Living test. After overnight fasting, blood samples were obtained to measure biochemistry indicators. RESULTS: MetS and its individual components were closely correlated with MoCA score. MetS patients had high levels of inflammation and a 3.542-fold increased odds ratio (OR) for cognitive impairment [95% confidence interval (CI): 1.972-6.361]. Of the individual MetS components, central obesity (OR 3.039; 95% CI: 1.839-5.023), high fasting plasma glucose (OR 1.915; 95% CI: 1.016-3.607), and type 2 diabetes (OR 2.241; 95% CI: 1.630-3.081) were associated with an increased incidence of cognitive impairment. Consistent and significant worsening in different neurological domains was observed with greater numbers of MetS components. CONCLUSIONS: MetS was associated with worse cognitive function, neuromotor dysfunction, and neuropsychological symptoms among Chinese acute stroke patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".