Metabolic Syndrome and The Likelihood of Knee Pain and Functional Disability: Evidence from a Large Middle Eastern Population-Based Study
Bibliographic record
Abstract
Abstract Objectives Metabolic Syndrome (MetS) has been associated with knee osteoarthritis (KOA) in animal studies, but epidemiologic evidence of the association remains controversial. We investigated the association between MetS and knee pain and functional disability, the hallmarks of KOA, in a Middle Eastern population with high MetS rates. Methods Between 01/2016 and 03/2019, a population-based sample of adult individuals was recruited prospectively. Data collected included age, sex, blood pressure, body mass index (BMI), waist circumference (WC), and comprehensive metabolic panel blood tests. Knee symptoms were assessed using The Western Ontario and McMaster Arthritis index (WOMAC) The Adult Treatment Panel III criteria was applied to determine if participants had MetS. Multivariable regression was used to determine the association of MetS, and its components, with the WOMAC total and subscale scores. Results A total of 6,000 participants were enrolled, and 15.5% had MetS. Multivariate regression demonstrated that participants with MetS had significantly higher WOMAC total and subscale scores after adjusting for demographic variables; however, these associations were not significant after adjusting for BMI. Multivariate regression examining the association between MetS components and the WOMAC scores showed that increased WC and decreased high-density lipoprotein (HDL) were significantly associated with worse WOMAC total and subscale scores even after adjusting for BMI. Conclusions This study demonstrated that the proposed effect fo MetS on knee symptoms may be mainly mechanically induced by obesity. However, the association of lower HDL with worse knee scores may suggest a metabolic pathway as well.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".