Early Increase of Abdominal Adiposity in Patients with Spondyloarthritis Receiving Anti-tumor Necrosis Factor-α Treatment
Bibliographic record
Abstract
OBJECTIVE: Patients with spondyloarthritis (SpA) receiving anti-TNF-α treatment have an increase in fat mass. This may be relevant to cardiovascular risk. The aim of this study was to estimate visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) changes by dual-energy x-ray absorptiometry (DEXA) in patients with SpA under anti-TNF-α therapy. METHODS: We used an ancillary protocol to an open, prospective 2-year followup study of patients with SpA. Waist circumference (WC), body weight, body mass index, VAT, and SAT were measured at baseline, 6 months, and 1 and 2 years. Univariate and multivariate analyses were performed to assess variables associated with VAT and SAT changes. RESULTS: A total of 85 patients were analyzed. Patients were 39.3 ± 11.4 years old and mean baseline Bath Ankylosing Spondylitis Disease Activity Index was 55.0 ± 20.2. Treatment was effective according to clinical and biological variables, and body weight increased by 0.9 ± 1.7 kg over 2 years. There was a significant gain in VAT after 6 months (13.7 ± 20.6 cm(2), p < 0.0001), 1 year (21.0 ± 26.6 cm(2), p < 0.0001), and after 2 years (29.1 ± 33.4 cm(2), p < 0.0001); and in SAT after 6 months (12.5 ± 27.4 cm(2), p < 0.0001), 1 year (27.1 ± 38.2 cm(2), p < 0.0001), and after 2 years (31.9 ± 53.2 cm(2), p < 0.0001). We could not find any determinant of these changes by multivariate analysis. CONCLUSION: In patients with SpA receiving anti-TNF-α therapy, there is an early significant increase in abdominal obesity with significant increase in both VAT and SAT after 1 and 2 years of treatment. Prospective studies are required to investigate the relationship between these changes and cardiovascular risk.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".