Weight gain secondary to the use of oral Janus kinase inhibitors: A systematic review and meta-analysis
Bibliographic record
Abstract
Oral Janus kinase inhibitors (JAKi) are increasingly used in dermatology, rheumatology, gastroenterology, and hematology. While effective, they can cause adverse effects such as acne, nausea, cytopenia, dyslipidemia, and Herpes zoster. Recent reports have linked JAKi usage to weight changes, particularly weight gain, which can significantly impact patients' quality of life. This study aimed to describe the incidence and characteristics of weight changes associated with the use of JAKi. Ovid MEDLINE, Embase, Web of Science, and Clinicaltrials.gov were searched up to April 2024. From 1080 initial articles, 90 studies covering 16,000 patients were selected. Our analysis found a notable incidence of weight gain with JAKi usage. Overall, 5.9% (947/16,000) of patients reported weight again. In randomized control trials, weight gain was observed in 7% (95% CI: 0.04; 0.09) of patients, while weight loss was observed in 1% (95% CI: 0.00; 0.03). Patients with dermatologic indications had lower weight gain rates (4%, 95% CI: 0.01; 0.06) than those with nondermatological indications (7%, 95% CI: 0.04; 0.10). Overall, JAKi therapy is associated with weight changes, particularly weight gain, underscoring the importance of appropriate counseling and weight monitoring. Further long-term studies are needed to better understand the mechanisms and management of JAKi-related weight changes.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".