Fat Mass Lowers the Response to Tumor Necrosis Factor-α Blockers in Patients with Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Our main objective was to assess the relationship between body composition (BC) and response to tumor necrosis factor-α (TNF-α) blocker treatment in patients with ankylosing spondylitis (AS). Our secondary objective was to evaluate the change of BC after treatment, accounting for sex and age. METHODS: All included patients fulfilled the modified New York criteria for AS and were naive to TNF-α blocker. They were followed for at least 6 months after the start of etanercept or adalimumab. The Ankylosing Spondylitis Disease Activity Score containing C-reactive protein (ASDAS-CRP) and the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) were reported. BC was assessed by whole body dual-energy X-ray absorptiometry. Body fat percentage (BF%), fat mass index (FMI), and fat free mass index (FFMI) were reported as absolute values and as percentiles. RESULTS: Forty-one patients were included (61% men). The median followup was 14.3 months (interquartile range 8.4-19.4). After multivariate regression analysis, more fat at baseline (BF%, FMI, or FMI percentile) was significantly related with a lower chance of achieving a clinically important improvement of the ASDAS-CRP or BASDAI after treatment. The body composition did not change significantly after treatment, but there was a trend toward muscle recovery in men (FFMI change from 34.0th to 37.4th percentile). CONCLUSION: Higher body fat content at baseline was independently associated with a worse response to treatment with TNF-α blockers, measured by ASDAS-CRP and BASDAI change, and might contribute to the lower response rates in female patients. Also, there is a trend toward muscle mass recovery in male patients after treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".