Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
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
_version_ 1783577187657449472
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
work_keys_str_mv AT pinaanaf metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT patarraoritas metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT ribeirorogeriot metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT penhagoncalvescarlos metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT raposojoaof metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT gardetecorreialuis metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT duarterui metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT mboavidajose metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT lmedinajose metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT henriquesroberto metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia
AT macedomariap metabolicfootprinttowardsunderstandingtype2diabetesbeyondglycemia