Exploring the impact of conventional and targeted DMARDs on body weight in patients with PsA
Bibliographic record
Abstract
OBJECTIVES: To evaluate change in body weight with DMARDs in PsA. METHODS: We analysed data from a large cohort of PsA patients with at least two weight measurements over follow-up. The absolute weight difference at one year from drug initiation was evaluated across-no medications or NSAIDs, conventional synthetic (cs) DMARDs, tumour necrosis factor inhibitors (TNFi), IL-12/23 inhibitor (i), IL-17i, IL-23i, Janus kinase inhibitors (JAKis) and apremilast classes. Two separate linear mixed models examined trends in weight change before and after treatment initiation with change-point modelling and factors affecting weight over follow-up. RESULTS: Of 1754 patients, 473 were on NSAIDs or no medications, 571 on csDMARDs, 702 on bDMARDs, 42 on JAKi and 70 on apremilast. Compared with weight at drug initiation, TNFi was associated with a weight gain at one year (1.54 kg, P < 0.01), whereas no significant change was observed with other drug classes. On change-point modelling, a significant decrease in the rate of weight change was observed following IL-17i (slope 0.63 ± 0.20 vs -0.26 ± 0.25, P < 0.01), IL-23i (0.82 ± 0.34 vs -0.56 ± 0.56, P < 0.01) and csDMARDs (0.43 ± 0.24 vs -0.20 ± 0.26, P < 0.01) compared with before treatment initiation. Factors associated with weight gain over follow-up included TNFi, IL12/23i use, male sex, follow-up duration and higher baseline weight, while with weight loss were apremilast, older age and diabetes mellitus. CONCLUSION: The rate of weight gain significantly decreases following the initiation of IL-17i, IL-23i and csDMARDs in PsA. Overall, TNFi and IL12/23i use is associated with weight gain, while apremilast with weight loss.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".