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Metabolic Footprint, towards Understanding Type 2 Diabetes beyond Glycemia.
Ana F Pina1,2, Rita S Patarrão1,3, Rogério T Ribeiro4
1CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, 1150-082 Lisboa, Portugal.
Journal of Clinical Medicine
|August 14, 2020
Summary
Metabolic profiling reveals significant heterogeneity in type 2 diabetes (T2D). Cluster analysis identified distinct patient subgroups based on metabolic factors, paving the way for personalized T2D treatment strategies.
Area of Science:
- Metabolic profiling
- Endocrinology
- Personalized medicine
Background:
- Type 2 diabetes (T2D) exhibits significant heterogeneity impacting complications and treatment response.
- Understanding this heterogeneity is crucial for developing effective, individualized therapies.
Purpose of the Study:
- To stratify glycemia within metabolic multidimensionality using cluster analysis.
- To extract pathophysiological insights from metabolic profiling in individuals with varying glucose levels.
Main Methods:
- Cluster analysis of 974 subjects (PREVADIAB2 cohort) with normoglycemia, prediabetes, or untreated diabetes.
- Algorithm informed by age, anthropometry, metabolic milieu (glucose, insulin, C-peptide, free fatty acid), and oral glucose tolerance test (OGTT) data.
- Profiling included metabolic mechanisms (insulin resistance, clearance, secretion), non-alcoholic fatty liver disease (NAFLD), and glomerular filtration rate (GFR).
Main Results:
- Two optimal clusters identified: Cluster-I (normometabolism) and Cluster-II (insulin resistance and NAFLD).
- Sub-clusters revealed heterogeneity within Cluster-II based on glycemia, FFA, and GFR despite similar NAFLD prevalence.
- Sub-clusters within Cluster-I showed varying insulin clearance and secretion despite similar glycemia and FFA.
Conclusions:
- T2D heterogeneity is effectively captured by a comprehensive "metabolic footprint" analysis.
- Deeper phenotyping and pathophysiological understanding can enable precise T2D progression prediction and treatment.
- This approach supports the advancement of precision medicine for type 2 diabetes.
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