Effects of Changes in Metabolic Syndrome Status on Cognitive Function: A 10‐Year Study in a Middle‐Aged Population
Bibliographic record
Abstract
The long-term cumulative impact of metabolic syndrome (MS) on cognitive decline remains uncertain. This study investigated how changes in MS status over 10 years relate to cognition and whether sex modifies this relationship. A total of 766 participants (mean baseline age: 54 years) from the Kaohsiung Atherosclerosis Longitudinal Study were enrolled. MS was defined using the modified National Cholesterol Education Program Adult Treatment Panel III criteria for Asian populations. Participants were categorized into four groups based on changes in MS status over the 10-year follow-up: never MS, ever MS, new MS, and persistent MS. Cognition was assessed using the Chinese version of the Montreal Cognitive Assessment (MoCA). Multivariate regression models were adjusted for age, sex, education, smoking status, physical activity, alcohol consumption, anxiety and depression. The results showed that participants with persistent MS had lower MoCA scores (β = -0.08, adjusted p = 0.020) compared to those who never had MS, with impairments primarily in the memory and language domains. This adverse effect was observed only in women (β = -0.12, adjusted p = 0.004), while no significant association was found in men (β = -0.03, adjusted p = 0.628). Individuals with nonpersistent MS (either ever or new MS) did not show significant cognitive decline compared to those who never had MS. This study demonstrates that persistent MS over a decade is linked to cognitive decline, with a more pronounced effect in women. These findings highlight the importance of early MS intervention in midlife, particularly for women, to reduce the risk of cognitive deterioration later in life.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".