Association of metabolic syndrome with mobility in the older adults: a Korean nationwide representative cross-sectional study
Bibliographic record
Abstract
We aimed to examine whether metabolic syndrome (MetS) is associated with mobility in the older adults, using the timed up and go (TUG) test which is one of the most widely used tests for evaluating mobility. This is population-based study with the National Health Insurance Service-National Health Screening Cohort database of National Health Information Database. Participants included were those who completed the TUG as part of the National Screening Program for Transitional Ages. An abnormal TUG result was defined as a time ≥ 10 s. Multiple logistic regression models were used to assess the associations between MetS and TUG results. We constructed three models with different levels of adjustment. Furthermore, we conducted a stratified analysis according to the risk. Among the 40,767 participants included, 19,831 (48.6%) were women. Mean TUG value was 8.34 ± 3.07 s, and abnormal TUG test results were observed in 4,391 (10.8%) participants; 6,888 (16.9%) participants were categorised to have MetS. The worst TUG test results were obtained in participants with three or four MetS features, and a J-shaped relationship of each MetS feature, except triglyceride (TG) and high-density lipoprotein-cholesterol (HDL-C), with TUG test was found. Participants with MetS had 18% higher likelihood of showing abnormal TUG test results in a fully adjusted model (adjusted odds ratio 1.183, 95% confidence interval 1.115-1.254). The stratified analysis revealed that participants with central obesity, high blood pressure, and normal HDL-C and TG were more likely to have abnormal TUG times. Participants with MetS had a higher risk of exhibiting abnormal TUG results, and except for HDL-C and TG, all other MetS features had a J-shaped relationship with TUG. Preventive lifestyle such as lower carbohydrate and higher protein intake, and endurance exercise is needed.
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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) | 0.001 | 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".