Abdominal Obesity in Comparison with General Obesity and Risk of Developing Rheumatoid Arthritis in Women
Bibliographic record
Abstract
Objective. Being overweight or obese increases rheumatoid arthritis (RA) risk among women, particularly among those diagnosed at a younger age. Abdominal obesity may contribute to systemic inflammation more than general obesity; thus, we investigated whether abdominal obesity, compared to general obesity, predicted RA risk in 2 prospective cohorts: the Nurses’ Health Study (NHS) and NHS II. Methods. We followed 50,682 women (1986–2014) in NHS and 47,597 women (1993–2015) in NHS II, without RA at baseline. Waist circumference (WC), BMI, health outcomes, and covariate data were collected through biennial questionnaires. Incident RA cases and serologic status were identified by chart review. We examined the associations of WC and BMI with RA risk using time-varying Cox proportional hazards models. We repeated analyses restricted to age ≤ 55 years. Results. During 28 years of follow-up, we identified 844 incident RA cases (527 NHS, 317 NHS II). Women with WC > 88 cm (35 in) had increased RA risk (HR 1.22, 95% CI 1.06–1.41). A similar association was observed for seropositive RA, which was stronger among young and middle-aged women. Further adjustment for BMI attenuated the association to null. In contrast, BMI was associated with RA (HRBMI ≥ 30 vs < 25 1.33, 95% CI 1.05–1.68) and seropositive RA, even after adjusting for WC, and, as in WC analyses, this association was stronger among young and middle-aged women. Conclusion. Abdominal obesity was associated with increased RA risk, particularly for seropositive RA, among young and middle-aged women; however, it did not independently contribute to RA risk beyond general obesity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".