ArticleMetabolism open2025
Synthetic target trial emulation and predictive modeling of amylin-pathway therapies for obesity and type 2 diabetes.
Article in Metabolism open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Advanced Computational Modeling and Machine Learning for Risk Stratification, Treatment Optimization, and Prognostic Forecasting in Appendiceal Neoplasms.Healthcare (Basel, Switzerland) · 2025Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Amylin-pathway therapies represent a novel therapeutic class for obesity and type 2 diabetes, however head-to-head comparative data and long-term outcome predictions remain limited. We conducted target trial emulation and computational predictive modeling aiming to predict future trial outcomes and comparative effectiveness across the amylin-pathway development program. Methods: Following PRISMA 2020 and TARGET framework guidelines, we search in the current literature for eligible trials and extracted data from seven randomized controlled trials (N = 5,786 participants) of amylin-pathway therapies published up to September 2025. We reconstructed high-precision synthetic individual patient data (IPD) and developed computational models for virtual head-to-head comparisons, dose-response optimization, longitudinal trajectory prediction, and trial simulation. Network meta-analysis integrated evidence across CagriSema, cagrilintide, and amycretin formulations. Results: Synthetic IPD reconstruction achieved >99 % fidelity to source trials, validated through leave-trial-out cross-validation (efficacy RMSE: 2.9 % points, calibration slope: 0.61; discontinuation RMSE: 0.18, slope: 1.08). Virtual head-to-head modeling confirmed CagriSema superiority over amycretin subcutaneous at matched timepoints (posterior probability >0.95). Dose-response modeling identified optimal amycretin exposures (ED80: 8.88 mg subcutaneous, 95 % CI: 7.12-11.08), with benefit-risk frontier analysis delineating a therapeutic window at 10-20 mg balancing efficacy plateau against tolerability thresholds (GI-AE <75 %, discontinuation <20 %). Longitudinal kinetics showed plateau timing at 52-68 weeks for obesity outcomes and 24-32 weeks for glycemic endpoints. Heterogeneity analysis revealed complete resolution for GI adverse events (I Conclusions: Synthetic target trial emulation with structured validation (leave-trial-out, posterior predictive checks, simulation-based calibration) demonstrated promising evidence for amylin-pathway development optimization. Benefit-risk frontier analysis identified an optimal 10-20 mg subcutaneous therapeutic window, and heterogeneity quantification through maximum a posteriori (MAP) predictive interval provides design-ready estimates for confirmatory trials requiring around 800-1,200 participants per arm for 90 % power.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.