Arthritis prevalence is associated with metabolic syndrome risk factors but not with physical activity in middle-aged and older patients - a cross-sectional study
Bibliographic record
Abstract
Abstract Background In light of the aging population, increasingly suffering from the metabolic syndrome (MS), strategies need to be developed to address global public health challenges known to be associated with MS such as arthritis. As physical activity (PA) may play a crucial role in tackling those challenges, this study aimed to determine the association between the number of MS risk factors, PA and arthritis in people ≥ 50 years old. Methods Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) were used to estimate the prevalence of arthritis and MS risk factors in the European population ≥ 50 years and to evaluate the associations between MS risk factors, PA and arthritis. Binary logistic regression was performed to calculate the odds ratio of different factors. Results 73,125 participants were included in the analysis. 55.75% of patients stated at least one of the three MS risk factors. The prevalence of rheumatoid arthritis (RA) and osteoarthritis (OA)/other rheumatism among ≥ 50 years population was 10.19% and 19.32% respectively. Females showed a higher prevalence of arthritis than males. Prevalence did not differ between groups with different levels of PA. Arthritis prevalence was positively correlated with the number of MS risk factors (P < 0.01) but not with PA (P > 0.05). Conclusion Middle-aged and older Europeans with multiple comorbidities suffered from RA, OA or other rheumatism more frequently than participants with fewer comorbidities, while the level of physical activity was not associated with the number of metabolic risk factors in patients with RA and OA/other rheumatism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.002 | 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".