Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia

• Autor(a) principal: Pina, Ana Lúcia F.
• Data de Publicação: 2020
• Outros Autores: Patarrao, Rita S., Ribeiro, Rogerio T., Penha-Goncalves, Carlos, Raposo, Joao F., Gardete-Correia, Luis, Duarte, Rui, M. Boavida, Jose, L. Medina, Jose, Henriques, Roberto, Macedo, Maria P.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/105040
• topic: diabetes; heterogeneity; clustering; dysmetabolism; SDG 3 - Good Health and Well-being

Type 2 diabetes (T2D) heterogeneity is a major determinant of complications risk and treatment response. Using cluster analysis, we aimed to stratify glycemia within metabolic multidimensionality and extract pathophysiological insights out of metabolic profiling. We performed a cluster analysis to stratify 974 subjects (PREVADIAB2 cohort) with normoglycemia, prediabetes, or non-treated diabetes. The algorithm was informed by age, anthropometry, and metabolic milieu (glucose, insulin, C-peptide, and free fatty acid (FFA) levels during the oral glucose tolerance test OGTT). For cluster profiling, we additionally used indexes of metabolism mechanisms (e.g., tissue-specific insulin resistance, insulin clearance, and insulin secretion), non-alcoholic fatty liver disease (NAFLD), and glomerular filtration rate (GFR). We found prominent heterogeneity within two optimal clusters, mainly representing normometabolism (Cluster-I) or insulin resistance and NAFLD (Cluster-II), at higher granularity. This was illustrated by sub-clusters showing similar NAFLD prevalence but differentiated by glycemia, FFA, and GFR (Cluster-II). Sub-clusters with similar glycemia and FFA showed dissimilar insulin clearance and secretion (Cluster-I). This work reveals that T2D heterogeneity can be captured by a thorough metabolic milieu and mechanisms profiling-metabolic footprint. It is expected that deeper phenotyping and increased pathophysiology knowledge will allow to identify subject's multidimensional profile, predict their progression, and treat them towards precision medicine.