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Uncovering the Heterogeneity of Type 2 Diabetes Progression Using Metabolomics: A Data-Driven Discovery of Seven Natural Pre-Diabetic Subgroups
34 Pages Posted: 19 Apr 2022
More...Abstract
Background: Currently affecting more than 1 in 3 Americans, pre-diabetic indication occurs when blood sugar levels are higher than normal, but lower than what would warrant a diagnosis of type 2 diabetes (T2D). Although metabolomic studies have discovered metabolites associated with T2D, to our knowledge these molecules have not been utilized to determine groups of pre-diabetics with varying disease outcomes.
Methods: Our analysis included 2,339 pre-diabetic patients with 168 metabolomic profiles from the UK Biobank. All patients were confidently identified as progressing or not progressing to T2D during the study. By applying hierarchical clustering to the metabolomic profiles, we identified seven groups of pre-diabetic patients.
Findings: These groups are characterized by unique metabolomic abundance, associated traditional risk factors from blood assays such as glycated haemoglobin, different allele frequencies at single nucleotide polymorphisms (SNPs), varying T2D progression outcomes, and different distributions of diagnosed comorbidities. Patients in the group with the least T2D progression have an over-abundance of sphingomyelins (< 0·001) and docosahexaenoic acid (< 0·001), and an under-abundance of alanine (< 0·001), leucine (< 0·001), isoleucine (< 0·001), phenylalanine (< 0·001), and tyrosine (0·003). Pre-diabetics in the group with the most T2D progression have an over-abundance of glucose (< 0·001) and valine (< 0·001), and under-abundance of glycine (< 0·001). We created a classifier that identifies a new patient into one of the seven groups with an accuracy of 82%.
Interpretation: Our results provide insight into metabolites whose abundances influence different disease progression outcomes for different sets of pre-diabetics, allowing for a new understanding of the metabolomic heterogeneity encompassing pre-diabetes.
Funding Information: OE is supported by UL1TR002384, UG3CA244697, R01CA194547, P01CA214274 LLS SCOR grants 180078-02, 7021-20, 180078-01. JK is supported by the National Institute of Aging of the National Institutes of Health under award 1U19AG063744.
Declaration of Interests: None to declare.
Keywords: pre-diabetes, machine learning, subgroups, metabolites, genetics, T2D progression, metabolomics, UK Biobank
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