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INTRODUCTION: Biological 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. METHODS: We 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. RESULTS: Increased 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.
Landau et al. (Tue,) studied this question.