ReviewBiomedicines2025
Employing an Artificial Intelligence Platform to Enhance Treatment Responses to GLP-1 Agonists by Utilizing Metabolic Variability Signatures Based on the Constrained Disorder Principle.
Review in Biomedicines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A feasibility open-label clinical trial utilizing second-generation artificial intelligence based on the constrained-disorder principle in patients with Parkinson's disease.IBRO neuroscience reports · 2026Article
- Biological Systems Depend on Communication Over Distances: A Review of the Fundamental Mechanisms, Associated challenges, and the Potential Effect of the Constrained Disorder Principle.Acta biotheoretica · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionBiological systems inherently exhibit metabolic variability that functions within optimal ranges, as described by the Constrained Disorder Principle (CDP). Deviations from these ranges, whether excessive or insufficient, are linked to adverse health outcomes. This review examines how signatures of metabolic variability can enhance GLP-1 receptor agonist therapy using artificial intelligence platforms.
methodsWe conducted a comprehensive literature review examining metabolic variability across various parameters, including heart rate, blood pressure, lipid levels, glucose control, body weight, and metabolic rate. We focused on studies investigating the relationship between variability patterns and treatment responses, particularly in the context of GLP-1 receptor agonist therapy and the use of CDP-based AI systems.
resultsIncreased variability in metabolic parameters consistently predicts adverse outcomes, such as cardiovascular events, mortality, and disease progression. Heart rate variability shows a U-shaped association with outcomes, while blood pressure, lipid, and glucose variability demonstrate predominantly linear relationships with risk. Body weight variability is associated with cognitive decline and an increased risk of cardiovascular complications. Additionally, genetic polymorphisms and baseline metabolic profiles can influence responses to GLP-1 receptor agonists. CDP-based AI platforms have successfully enhanced therapeutic outcomes in conditions like heart failure, cancer, and multiple sclerosis by leveraging biological variability rather than suppressing it. SUMMARY: The identification of metabolic variability signatures offers valuable predictive insights for personalizing therapy with GLP-1 receptor agonists. Artificial intelligence systems based on clinical data patterns that include these variabilities represent a significant shift toward dynamic and individualized treatment approaches. This can enhance therapeutic efficacy and help counteract drug resistance in chronic metabolic disorders, potentially improving the response to GLP-1-based therapies.
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Registered trials
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 OpenQuestion graph.