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Sci Transl Med. 2015 Oct 28;7(311):311ra174. doi: 10.1126/scitranslmed.aaa9364.

Identification of type 2 diabetes subgroups through topological analysis of patient similarity.

Author information

1
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 700 Lexington Ave., New York, NY 10065, USA.
2
Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
3
Division of Endocrinology, Diabetes, and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
4
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 700 Lexington Ave., New York, NY 10065, USA. Department of Health Policy and Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA. joel.dudley@mssm.edu.

Abstract

Type 2 diabetes (T2D) is a heterogeneous complex disease affecting more than 29 million Americans alone with a rising prevalence trending toward steady increases in the coming decades. Thus, there is a pressing clinical need to improve early prevention and clinical management of T2D and its complications. Clinicians have understood that patients who carry the T2D diagnosis have a variety of phenotypes and susceptibilities to diabetes-related complications. We used a precision medicine approach to characterize the complexity of T2D patient populations based on high-dimensional electronic medical records (EMRs) and genotype data from 11,210 individuals. We successfully identified three distinct subgroups of T2D from topology-based patient-patient networks. Subtype 1 was characterized by T2D complications diabetic nephropathy and diabetic retinopathy; subtype 2 was enriched for cancer malignancy and cardiovascular diseases; and subtype 3 was associated most strongly with cardiovascular diseases, neurological diseases, allergies, and HIV infections. We performed a genetic association analysis of the emergent T2D subtypes to identify subtype-specific genetic markers and identified 1279, 1227, and 1338 single-nucleotide polymorphisms (SNPs) that mapped to 425, 322, and 437 unique genes specific to subtypes 1, 2, and 3, respectively. By assessing the human disease-SNP association for each subtype, the enriched phenotypes and biological functions at the gene level for each subtype matched with the disease comorbidities and clinical differences that we identified through EMRs. Our approach demonstrates the utility of applying the precision medicine paradigm in T2D and the promise of extending the approach to the study of other complex, multifactorial diseases.

PMID:
26511511
PMCID:
PMC4780757
DOI:
10.1126/scitranslmed.aaa9364
[Indexed for MEDLINE]
Free PMC Article

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