Suna Onengut-Gumuscu

About

Suna Onengut-Gumuscu is an Associate Professor in the Department of Genome Sciences at the University of Virginia School of Medicine. Her research focuses on understanding the genetic causes of complex diseases, with a particular interest in the genetics of Type 1 Diabetes (T1D). Her laboratory aims to identify genetic variants that influence the development of T1D, a disorder characterized by autoimmune destruction of insulin-secreting beta cells in the pancreas, leading to lifelong dependence on exogenous insulin. The research involves using a variety of genomic tools, high throughput technology, and analysis methods to uncover the molecular mechanisms underlying genetic risk factors in T1D. The ultimate goal of her work is to understand the mechanisms that lead to autoimmunity in T1D and to aid in the development of preventive and therapeutic treatments.

Research topics

• Medicine
• Political Science
• Immunology
• Genetics
• Internal medicine
• Endocrinology
• Biology
• Intensive care medicine
• Pathology
• Cancer research

Selected publications

• Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy

  Nature · 2021 · 396 citations

  DOI

• Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

  Nature Genetics · 2021 · 293 citations

  DOI

• Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

  Nature Medicine · 2023 · 194 citations

  Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common…

  PublisherOA PDFDOI

• A combined risk score enhances prediction of type 1 diabetes among susceptible children

  Nature Medicine · 2020 · 142 citations

  DOI

• Precision subclassification of type 2 diabetes: a systematic review

  Communications Medicine · 2023 · 92 citations

  BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical chara…

  PublisherOA PDFDOI

Frequent coauthors

• Stephen S. Rich

  146 shared

• Wei‐Min Chen

  University of Virginia

  71 shared

• Andrea K. Steck

  62 shared

• Patrick Concannon

  56 shared

• Norbert Stefan

  Deutsches Diabetes-Zentrum e.V.

  55 shared

• Cécile Saint‐Martin

  Assistance Publique – Hôpitaux de Paris

  54 shared

• Feifei Cheng

  Dalian Medical University

  54 shared

• Róbert Wágner

  Heinrich Heine University Düsseldorf

  53 shared

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