Metabolic Syndrome and Cognitive Performance Among Chinese ≥50 Years: A Cross-Sectional Study with 3988 Participants
Bibliographic record
Abstract
BACKGROUND: To date, the relationship between metabolic syndrome (MetS) and cognitive performance has not been well defined. This study aimed to explore the relationship between MetS and cognitive performance among Chinese elderly population. METHODS: A cross-sectional study was performed, with data collected in seven clinical centers from five provinces of Northern China. All recruited participants were ≥50 years of age and complained with cognitive impairment or were reported with cognitive impairment by his/her caregiver(s). MetS was diagnosed according to the criteria issued by Chinese Medical Association Diabetes Association. Cognitive function was scored by Montreal Cognitive Assessment (MoCA). RESULTS: Three thousand nine hundred eighty-eight participants (in an average of 66.4 ± 8.8 years of age, male 53.1%) were included in the analysis. Six hundred seventy-three (16.9%) participants were diagnosed with MetS, and 3013 (75.6%) participants had mild cognitive impairment (MCI) (MoCA score <26). There was no statistically significant difference in the MoCA scores between participants with MetS (21.0 ± 5.4) and without MetS (21.3 ± 5.3). In the logistic regression, after adjusting factors of age, education, marital status, smoking, and physical activity, diabetes and dyslipidemia were associated with MCI, whereas hypertension and overweight or obesity were not. Participants with diabetes had a higher risk of MCI (OR = 1.24, 95% CI: 1.03-1.50). Participants with dyslipidemia had a lower risk of MCI (OR = 0.81, 95% CI: 0.68-0.97). CONCLUSION: In our study, MetS is not associated with cognitive performance in elderly Chinese population. However, elderly Chinese with diabetes would have lower cognition function, and the dyslipidemia might be reversely associated with the cognitive function.
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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 | 0.000 |
| 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 | 0.000 | 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".