Finding studies
Finding studies
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Teresa Mezza, MD, PhD
CONTACT
Lead
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Type 2 diabetes mellitus is a heterogeneous disease characterized by substantial variability in pathophysiological mechanisms, disease progression, and clinical outcomes. Current clinical classifications do not fully capture the biological complexity underlying metabolic dysfunction. The EXPAND (Exocrine-Endocrine Pancreatic Axis in Diabetes) study is a prospective observational study aimed at identifying and characterizing metabolic endotypes through the integration of clinical, metabolic, imaging, genetic, microbiome, and multi-omic data. The study is based on the hypothesis that interactions among the exocrine pancreas, endocrine pancreas, and adipose tissue contribute to beta-cell dysfunction and metabolic heterogeneity. Approximately 440 participants will be enrolled across five predefined cohorts: patients undergoing pancreatic resection for pancreatic diseases, patients with chronic or previous pancreatitis, individuals at increased risk of type 2 diabetes, and subjects with newly diagnosed type 2 diabetes. Participants will undergo detailed metabolic phenotyping including oral glucose tolerance tests, hyperglycemic/euglycemic clamp studies, laboratory assessments, magnetic resonance imaging-based body fat quantification, dietary assessment, genetic analyses, microbiome profiling, and biomarker measurements. For surgical cohorts, pancreatic and adipose tissue samples obtained during clinically indicated surgery will also be analyzed. Unsupervised clustering and archetype analysis approaches will be used to identify metabolic endotypes. Associations between endotypes and clinical, metabolic, imaging, histopathological, genetic, and molecular characteristics will be explored. Longitudinal analyses in surgical cohorts will evaluate metabolic changes following pancreatic resection. The results are expected to improve the understanding of diabetes pathophysiology and facilitate the development of biomarker-driven precision medicine strategies.
Age
20–78
Sex
ALL
Healthy volunteers
Not accepted
