Prevalence and Characteristics of Metabolic Syndrome Differ in Men and Women with Early Rheumatoid Arthritis
Bibliographic record
Abstract
Objective Metabolic syndrome (MetS) prevalence in early rheumatoid arthritis (ERA) is conflicting. The impact of sex, including menopause, has not been described. We estimated the prevalence and factors associated with MetS in men and women with ERA. Methods A cross‐sectional study of the Canadian Early Arthritis Cohort (CATCH) was performed. Participants with baseline data to estimate key MetS components were included. Sex‐stratified logistic regression identified baseline variables associated with MetS. Results The sample included 1543 participants; 71% were female and the mean age was 54 (SD 15) years. MetS prevalence was higher in men 188 (42%) than women 288 (26%, P < 0.0001) and increased with age. Frequent MetS components in men were hypertension (62%), impaired glucose tolerance (IGT, 40%), obesity (36%), and low high‐density lipoprotein cholesterol (36%). Postmenopausal women had greater frequency of hypertension (65%), IGT (32%), and high triglycerides (21%) compared with premenopausal women (P < 0.001). In multivariate analysis, MetS was negatively associated with seropositivity and pulmonary disease in men. Increasing age was associated with MetS in women. In postmenopausal women, corticosteroid use was associated with MetS. Psychiatric comorbidity was associated with MetS in premenopausal women. MetS status was not explained by disease activity or core RA measures. Conclusion The characteristics and associations of MetS differed in men and women with ERA. Sex differences, including postmenopausal status, should be considered in comorbidity screening. With this knowledge, the interplay of MetS, sex, and RA therapeutic response on cardiovascular outcomes should be investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".