What Clinicians Need to Know About Glucagon-like Peptide 1 Agonists
Bibliographic record
Abstract
The interplay between metabolic health and autoimmune diseases such as psoriasis (PsO) and psoriatic arthritis (PsA) has garnered increasing attention. Obesity, a key feature of metabolic syndrome, exacerbates disease severity in these conditions, prompting the exploration of treatments addressing both the immune system and metabolism. Glucagon-like peptide 1 receptor agonists (GLP-1RAs), primarily used for type 2 diabetes mellitus, have demonstrated benefits beyond glycemic control, including promoting weight loss, improving metabolic health, and potentially modulating immune responses. There is also a dual GLP-1 and glucose-dependent insulinotropic polypeptide receptor agonist with similar and potentially superior capabilities; throughout this manuscript these will be collectively known as GLP-1RA. Recent studies also suggest that GLP-1RAs may help manage PsO and PsA in patients with obesity. These medications may offer dual benefits by reducing inflammation and addressing metabolic abnormalities like insulin resistance and hyperlipidemia. This article reports on a presentation given at the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting underscoring the potential of GLP-1RAs as a therapeutic option, particularly for obese patients with PsO and PsA. Although promising, the evidence supporting GLP-1RAs for treating PsO and PsA remains limited, necessitating further clinical research to evaluate their safety and efficacy.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".