Impact of Setmelanotide on Metabolic Syndrome Risk in Patients With Bardet-Biedl Syndrome
Bibliographic record
Abstract
CONTEXT: Bardet-Biedl syndrome (BBS) is a rare genetic disease associated with disruptions in melanocortin-4 receptor pathway signaling that can contribute to increased risk for metabolic syndrome and obesity-related comorbidities. OBJECTIVE: Here, MetS-Z-BMI scores, a continuous measure based on body mass index (BMI), were calculated to determine metabolic syndrome severity and response to treatment with the melanocortin-4 receptor agonist setmelanotide in BBS. METHODS: All patients from a phase 3 study (NCT03746522) of setmelanotide with data required for the calculation of MetS-Z-BMI scores were included. Mean MetS-Z-BMI score was determined at baseline and Week 52; subgroup analyses were conducted by sex, age, genotype, and response to setmelanotide. RESULTS: MetS-Z-BMI scores were evaluable for 22 of 32 patients enrolled. Baseline mean (SD) MetS-Z-BMI score across patients was 1.1 (0.5); baseline mean (SD) odds ratios of future cardiovascular disease or type 2 diabetes were 3.1 (1.5) and 3.7 (1.7), respectively, for adults and 10.2 (4.7) and 2.8 (1.3), respectively, for pediatric patients. Overall, mean (SD) MetS-Z-BMI score at Week 52 was reduced by 0.34 (0.62). Mean (SD) MetS-Z-BMI scores significantly differed depending on achievement of predetermined weight-based thresholds of ≥10% weight loss (patients aged ≥18 years) or ≥0.3-point reduction in BMI Z score (patients aged <18 years) at Week 52 (achievers, -0.64 [0.54]; nonachievers, 0.08 [0.47]; P = .0043). No significant difference was observed with other subgroup comparisons. CONCLUSION: MetS-Z-BMI score reductions were observed after 52 weeks of treatment, suggesting that setmelanotide may decrease metabolic syndrome severity and risk of future obesity-related comorbidities in those with BBS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".