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Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia
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 s...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463676/ https://www.ncbi.nlm.nih.gov/pubmed/32785111 http://dx.doi.org/10.3390/jcm9082588 |
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author | Pina, Ana F. Patarrão, Rita S. Ribeiro, Rogério T. Penha-Gonçalves, Carlos Raposo, João F. Gardete-Correia, Luís Duarte, Rui M. Boavida, José L. Medina, José Henriques, Roberto Macedo, Maria P. |
author_facet | Pina, Ana F. Patarrão, Rita S. Ribeiro, Rogério T. Penha-Gonçalves, Carlos Raposo, João F. Gardete-Correia, Luís Duarte, Rui M. Boavida, José L. Medina, José Henriques, Roberto Macedo, Maria P. |
author_sort | Pina, Ana F. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7463676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74636762020-09-02 Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia Pina, Ana F. Patarrão, Rita S. Ribeiro, Rogério T. Penha-Gonçalves, Carlos Raposo, João F. Gardete-Correia, Luís Duarte, Rui M. Boavida, José L. Medina, José Henriques, Roberto Macedo, Maria P. J Clin Med Article 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. MDPI 2020-08-10 /pmc/articles/PMC7463676/ /pubmed/32785111 http://dx.doi.org/10.3390/jcm9082588 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pina, Ana F. Patarrão, Rita S. Ribeiro, Rogério T. Penha-Gonçalves, Carlos Raposo, João F. Gardete-Correia, Luís Duarte, Rui M. Boavida, José L. Medina, José Henriques, Roberto Macedo, Maria P. Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title | Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title_full | Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title_fullStr | Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title_full_unstemmed | Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title_short | Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia |
title_sort | metabolic footprint, towards understanding type 2 diabetes beyond glycemia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463676/ https://www.ncbi.nlm.nih.gov/pubmed/32785111 http://dx.doi.org/10.3390/jcm9082588 |
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