A new review titled, "Integrating Precision Nutrition with GLP-1 Receptor Agonist Therapy: Mechanisms, Clinical Outcomes, and Pharmacoeconomic Implications¹," examines the biological mechanisms of nutrition, body composition, gut microbiome, inflammation, metabolism, and genetics to develop individualized recommendations for optimal treatment with GLP-1 drugs. Due to considerable variability in patients’ responses to these drugs, treatment tolerability remains a major challenge, so researchers are seeking answers for the approximately 1 in 7 people that do not get results. 

Published in the international peer-reviewed journal Pharmaceuticals, the review was developed by a multidisciplinary team of researchers representing leading academic and healthcare institutions, including the Local Health Authority of Foggia (ASL Foggia), the University Magna Graecia of Catanzaro, the University of Siena, the Anton Dohrn Zoological Station, the Sbarro Institute for Cancer Research and Molecular Medicine and the Sbarro Health Research Organization (SHRO), located at the Center for Biotechnology, College of Science and Technology, Temple University.

The review presents an innovative perspective on the management of obesity and type 2 diabetes mellitus, highlighting how the integration of glucagon-like peptide-1 receptor agonists like Wegovy and Ozempic, combined with precision nutrition, may represent a promising strategy to optimize therapeutic outcomes. Although GLP-1 receptor agonists have transformed the treatment of metabolic diseases through their remarkable ability to promote substantial weight loss and improve glycemic control, some patients experience unpleasant side effects and poor results. 

To find a solution for patients not getting results from GLP-1 drugs, the authors comprehensively assess how precision nutrition can serve as a fundamental adjunct to pharmacological therapy through personalized dietary interventions. By preserving lean body mass, ensuring adequate protein and micronutrient intake, favorably modulating the gut microbiota, and improving the gastrointestinal tolerability of GLP-1-based therapies, researchers theorize that patients may experience better outcomes and more effective results.

The review also explores the growing contribution of nutrigenomics, metabolomics, multi-omics technologies, artificial intelligence, and digital health in advancing personalized metabolic medicine, highlighting their potential to support increasingly individualized therapeutic strategies.

“It has the potential to integrate nutritional, biological, and metabolic knowledge and develop truly personalized therapies,” says Professor Antonio Giordano, M.D., Ph.D., President of SHRO and Director of the Sbarro Institute for Cancer Research and Molecular Medicine at Temple University.

Another key aspect addressed in the review concerns the pharmacoeconomic implications of integrating precision nutrition with pharmacological treatment. According to the authors, this combined approach may improve treatment adherence, reduce adverse events, minimize long-term clinical complications, and ultimately enhance the cost-effectiveness and sustainability of obesity and diabetes management within healthcare systems.

“GLP-1 receptor agonists have revolutionized the treatment of obesity and diabetes,” continues Giordano. “However, their full therapeutic potential will only be realized through a multidisciplinary approach involving clinicians, nutrition specialists, researchers, and healthcare professionals.”

The authors emphasize that prospective clinical trials should be specifically designed to evaluate the integration of precision nutrition with GLP-1 receptor agonist therapy. 

“This review represents an important step toward a new paradigm in metabolic medicine, in which pharmacology, precision nutrition, and innovative technologies such as artificial intelligence could work synergistically to improve clinical outcomes and patients' quality of life," concludes Giordano.

Reference:

1) https://www.mdpi.com/1424-8247/19/8/1230

(Newswise/HG)