ArticleScience translational medicine2015
Identification of type 2 diabetes subgroups through topological analysis of patient similarity.
Article in Science translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 232 papers, 3 of them syntheses that pooled it.
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Who cites it
232 citing papers in PubMed, 3 syntheses or guidelines pooled it, 566 citations in OpenAlex.
- Clinical Characterization of Copy Number Variants Associated With Neurodevelopmental Disorders in a Large-scale Multiancestry Biobank.JAMA psychiatry · 2022Pooled it
- Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent.BMJ open diabetes research & care · 2021Pooled it
- The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review.International journal of environmental research and public health · 2020Pooled it
- Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.BMC medical informatics and decision making · 2021Trial
- A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment.Diabetes care · 2021Trial
- Type 2 Diabetes Subgroups, Risk for Complications, and Differential Effects Due to an Intensive Lifestyle Intervention.Diabetes care · 2021Trial
- RNA-independent cis-autoregulatory circuits within bidirectional gene pairs control metabolism.Science advances · 2026Article
- Integration of the circulating miRNome and clinical information to predict 90-day mortality in elderly COVID-19 subphenotypes.iScience · 2026Article
- A Methodological Framework for Non-Invasive Body-Composition Phenotyping in Young Adults: Integrating Bioelectrical Impedance and Patient Similarity Networks.Life (Basel, Switzerland) · 2026Article
- Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026Article
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Inflammatory Proteomic Heterogeneity Beyond Glycemia Status in Severe Obesity.International journal of molecular sciences · 2026Article
- Representation learning to advance multi-institutional studies with electronic health record data from US and France.Nature communications · 2026Article
- Manifold-based learning for high-throughput single-peanut phenotyping.NPJ systems biology and applications · 2026Article
- Patient Similarity Networks for Irritable Bowel Syndrome: Revisiting Brain Morphometry and Cognitive Features.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Gene-environment-brain topology reveals clinical subtypes of depression in UK Biobank.Scientific reports · 2025Article
- Longitudinal Metabolic Trajectories in Diabetes Prevention Program Participants Reveal Subgroups With Varying Micro- and Macrovascular Complication Risks.Diabetes care · 2025Article
- Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes.Nature medicine · 2025Article
172 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.