Accelerating Breakthroughs in Diabetes Research

Empowering researchers with comprehensive data integration and analysis tools.

Vision & Mission

Global Challenge of Diabetes

Millions worldwide live with diabetes, facing significant health risks and financial burdens. Despite ongoing research, the need for breakthroughs in prevention, management, and potential cures remains dire.

Diabetes Research Hub Vision

The DRH envisions a future free from diabetes, where both Type 1 and Type 2 diabetes are effectively prevented, managed, and ultimately cured.

Diabetes Research Hub Mission

The DRH is a comprehensive data ecosystem designed to accelerate breakthroughs in diabetes research. We empower researchers by unlocking the potential of data trapped in proprietary medical devices and health systems. By creating a secure and user-friendly platform for data collection, analysis, and collaboration, the DRH fuels next-generation research using AI and ML to develop personalized and effective solutions for diabetes.

Why is DRH Important to the Diabetes Ecosystem?

The DRH is committed to a long-term strategy focused on three key pillars.

Data Liberation

Partnering with research institutions, NIH, health systems, device manufacturers, and patients to integrate data from diverse sources, including CGM, clinical records, genetic data, patient-reported outcomes, and device usage data.

Empowering Researchers

Providing a user-friendly platform with advanced analytics tools and fostering collaboration among researchers around the globe.

Innovation Catalyst

Acting as the lynchpin for AI and ML advancements in diabetes research, leading to the development of personalized prevention, management, and potential cures.

Why does DRH Matter to Stakeholders?

Research Institutions

Gain access to a vast, standardized data repository to accelerate research efforts.

National Institutes of Health (NIH)

Facilitate groundbreaking discoveries and fulfill the mandate for a secure data storage bank for NIH-funded researchers.

Health Systems

Contribute to improved patient outcomes by fostering research that leads to more effective treatments and management strategies.


Gain access to insights from anonymized data analysis, potentially leading to improved self-management and personalized care plans.

Medical Device Manufacturers

Contribute to product development and innovation by understanding real-world device usage patterns and patient needs.

Telecom Vendors

Develop integrated solutions for data transmission and secure storage within the DRH framework.

Who does DRH want to Collaborate with?

The DRH invites collaboration from a wide range of stakeholders

Research institutions

Join us in shaping research priorities and leveraging the DRH platform to accelerate groundbreaking discoveries.

National Institutes of Health (NIH)

Partner with us to fulfill the mandate for a secure data storage bank and accelerate NIH-funded research efforts.

Health Systems

Contribute patient data and clinical expertise to inform research that translates into improved patient care.


Become active participants in research by contributing your anonymized data to the DRH and shaping the future of diabetes care.

Medical Device Manufacturers

Work with us to integrate your devices with the DRH platform and unlock the potential of real-world device usage data.

Telecom Vendors

Collaborate on developing secure and efficient data transmission solutions for the DRH.

Why is DRH needed for CGM Data

The Diabetes Research Hub (DRH) addresses a growing need for a centralized platform to manage and analyze continuous glucose monitor (CGM) data. Clinicians and engineers are increasingly using this real-world data for two key purposes

Identifying Effects of Diet, Behavior, and Sleep

In people with diabetes, prediabetes, obesity, and no diabetes at all, glycemic patterns from CGM data might provide insights for understanding selected populations.

Predicting and Preventing Complications

Population-level CGM data holds immense potential in predicting, preventing, and treating diabetes complications. This aligns with the growing focus on population health management.

Glycemic Patterns to Guide Treatment

The effects of treatments directed at fasting, postprandial, and nocturnal glycemia can be determined for individuals and populations with CGM data applied to pharmacotherapy and behavioral therapy.

Developing Improved Closed-Loop Systems

CGM data plays a vital role in constructing better algorithms for automated insulin delivery (closed-loop) systems. This not only improves glycemic control for patients but also allows researchers to understand how these systems perform under different glycemic conditions and rates of change.

DRH Starts with CGM but goes beyond

While anchored by a secure CGM data repository, the DRH goes beyond. It's designed to be a comprehensive research hub, integrating data from various sources

Healthcare organizations

Research institutions

Medical device companies

Even patient-generated data (with proper consent)

This creates a de-identified data lake that serves as a springboard for researchers developing solutions for diabetes management.

Continuous glucose monitoring (CGM) technology has revolutionized diabetes management, providing clinicians and patients with a continuous stream of real-world data on blood sugar levels. This data holds immense potential for advancing our understanding of diabetes and developing better solutions. However, maximizing this potential requires a comprehensive approach that goes beyond CGM data alone.

The Limitations of CGM Data in Isolation

While CGM data is a critical anchor point, it represents only one piece of the puzzle. Relying solely on CGM data paints an incomplete picture of a patient's health and the complex factors influencing diabetes. To truly understand and combat this disease, we need a broader perspective.

The DRH: A Hub for Integrated Real-World Evidence targeting Diabetes Research

The Diabetes Research Hub (DRH) addresses this challenge by creating a centralized platform specifically designed to integrate and analyze real-world evidence (RWE) from a multitude of sources.

Electronic Health Records (EHRs)

The DRH facilitates seamless integration with EHRs, leveraging the DTS iCODE standards to ensure consistent and interoperable data capture. This allows researchers to incorporate vital clinical information such as medications, diagnoses, and lab results alongside CGM data.

Wearable Devices

Data from wearable devices like fitness trackers and smartwatches can provide valuable insights into a patient's activity levels, sleep patterns, and physiological responses. The DRH can integrate this data to create a more holistic picture of a patient's health.

Patient-Reported Outcomes (PROs)

The DRH can incorporate patient-reported data such as diet, lifestyle habits, and quality-of-life measures. This subjective information provides crucial context for interpreting CGM data and understanding the true impact of diabetes on patients' lives.

Other Datasets

The DRH is flexible and can integrate a wide range of additional datasets relevant to diabetes research. This could include social determinants of health, environmental factors, or genetic data, depending on the specific research question.

The DRH goes beyond a simple CGM data repository. It represents a paradigm shift in how we approach diabetes research, fostering a data-driven ecosystem that holds the key to unlocking new solutions and ultimately, a future free from diabetes.

DRH Capabilities for Researchers

The DRH empowers researchers with a comprehensive suite of tools designed to streamline the data management and analysis process. Here's a closer look at its capabilities.

Data Ingestion and Preprocessing

Edge-based Cleansing and Enrichment

The DRH facilitates cleaning and enriching data at the source (edge) before transmission, improving data quality and efficiency.

Flexible Data Upload Options

Researchers can upload data from diverse sources in various formats, ensuring compatibility with the platform.

Automated Data Transformation

The DRH provides tools for automated Extract, Transform, and Load (ETL) processes, simplifying data preparation for analysis.

Data Cleaning and Analysis

Interactive Data Cleaning Tools

Researchers can leverage interactive tools to identify and address data inconsistencies or errors.

Advanced Statistical Analysis

The DRH integrates statistical analysis capabilities, allowing researchers to perform complex calculations and generate robust insights.

Machine Learning Integration

The platform can integrate with machine learning tools, enabling researchers to develop predictive models and uncover hidden patterns within the data.

Visualization and Exploration

Interactive Data Visualization Tools

The DRH offers a suite of interactive visualization tools for researchers to explore trends, identify correlations, and create compelling data presentations.

Customizable Dashboards

Researchers can create personalized dashboards for real-time data monitoring and progress tracking.

Collaboration and Knowledge Sharing

Research Notebooks

The platform integrates research notebooks, facilitating collaborative research and the sharing of methodologies and findings.

Version Control

Version control capabilities ensure researchers can track changes and revert to previous versions of their work if needed.

Secure Data Sharing

Researchers can securely share anonymized datasets with colleagues within the platform, fostering collaboration and accelerating research progress.

API Access for Custom Applications

Open APIs 

The DRH provides open APIs, allowing researchers to build custom applications tailored to their specific research needs.

Advanced Analytics Integration 

Researchers can integrate external analytical tools and services via APIs, enhancing the platform's analytical capabilities.

This rich functionality empowers researchers to focus on their core research questions, streamlining the data management process and accelerating scientific discovery.

DRH Targets Breakthrough Discoveries

By integrating CGM data with this broader range of real-world evidence, the DRH empowers researchers to

Develop More Effective Treatment Strategies

By understanding the interplay between CGM data and other health factors, researchers can develop personalized treatment plans that address the individual needs of each patient.

Identify New Treatment Targets

Analyzing integrated data sets may reveal hidden patterns and associations that point towards previously unknown factors influencing diabetes progression and complications.

Accelerate Development of New Therapies

The DRH can facilitate clinical trials by providing real-world data to assess the effectiveness and safety of new medications and devices in a real-world setting.

Improve Population Health Management

Aggregated and anonymized CGM data alongside other RWE can inform public health initiatives aimed at preventing diabetes and mitigating its complications.

The Future

Give funders a vision of the future

The Diabetes Research Hub (DRH) isn't just a data repository; it's a springboard for a revolution in diabetes care. Imagine a future where,

In Months

Personalized Diabetes Management

Clinicians leverage real-time CGM data integrated with EHRs and wearables to tailor treatment plans that optimize blood sugar control and patient well-being.

Accelerated Clinical Trials

Researchers leverage the DRH's rich datasets to design and conduct more efficient clinical trials, bringing life-saving therapies to market faster.

In Years

Predictive Analytics

Machine learning algorithms, trained on the DRH's vast data lake, identify patients at high risk for complications, enabling early intervention and prevention.

Precision Medicine for Diabetes

Researchers unlock the secrets hidden within CGM data and other real-world evidence, leading to the development of targeted therapies based on an individual's unique biology.

In the Long Run

A Cure for Diabetes

The DRH empowers researchers to uncover the root causes of diabetes, paving the way for a future free from this debilitating disease.

Revolutionizing Population Health

Aggregated and anonymized data from the DRH informs public health policy, leading to preventative measures that significantly reduce the prevalence of diabetes.

This ambitious vision is not a pipe dream. The DRH's existing capabilities, coupled with the expertise of the world's leading diabetes researchers, create a powerful platform for transformative change. However, unlocking its full potential requires the collective support of the diabetes ecosystem.
By investing in the DRH, you're not just funding a platform, you're investing in a future free from diabetes. Imagine the countless lives saved, the economic burden lifted, and the healthier future we can create together. The power to turn this vision into reality lies within our grasp. Join us in making it a reality.


Medical Director

David Klonoff, MD

David Klonoff, MD is an endocrinologist specializing in the development and use of diabetes technology. He is Medical Director of the Dorothy L. and James E. Frank Diabetes Research Institute of Mills-Peninsula Medical Center in San Mateo, California and a Clinical Professor of Medicine at UCSF. He is developing new ways of applying data from devices to improve outcomes. Dr. Klonoff is Editor of Journal of Diabetes Science and Technology and Chair of the Scientific Advisory Board of the Texas A&M/UCLA/FIU/Rice PATHS-UP Engineering Research Center. Dr. Klonoff received an FDA Director’s Special Citation Award in 2010 for outstanding contributions related to diabetes technology, and he received the American Diabetes Association’s 2019 Outstanding Physician Clinician Award.

Scientific Director

Juan Espinoza, MD

Juan Espinoza, MD is the Chief Research Informatics Officer at Lurie Children's Hospital and Associate Director of the Center for Biomedical Informatics and Data Science at Northwestern University. He is also the Director and Principal Investigator of the Consortium for Technical & Innovation in Pediatrics, an FDA funded pediatric medical device accelerator. His research focuses on identifying, refining, and innovating approaches to using data, media, and technology to improve health outcomes and narrow the health gap faced by marginalized communities in the US and abroad. His work on diabetes device data and data integration standards is rooted in collaboration with industry, regulatory agencies, researchers, and healthcare organizations, and has been critical to the advancement of the field.

Technical Director

Shahid Shah

Shahid Shah is a company building engineer who loves making products that require complex engineering skills but need to be easy to deploy and use. His passion is innovation that improves people’s lives in measurable ways. His experience has been in regulated, security-conscious, safety-critical industries such as Med Devices, Digital Health (health IT), and Gov 2.0 because they’re usually creating the most demanding products and services. He has years of leadership experience as a CTO helping transform products from marginal to high performance. He has personally written code for or led the teams behind products used by millions. Shahid is Chair of the Board of Netspective.


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Riccardo Bellazzi, PhD

University of Pavia, Pavia, Italy

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Andjela Drincic, MD

Division of Diabetes, Endocrinology & Metabolism, University of Nebraska Medical Center, Omaha, Nebraska, USA

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Chiara Fabris, PhD

Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA

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Eileen R. Faulds PhD, MS, RN, FNP-BC, CDCES

The Ohio State University College of Nursing, Columbus, Ohio

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Korey Hood, PhD

Stanford University School of Medicine, Stanford, California, USA

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Boris Kovatchev, PhD

Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA

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Anne L. Peters, MD

Keck School of Medicine of University of Southern California, Los Angeles, California, USA

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Priya Prahalad, MD, PhD

Division of Pediatric Endocrinology & Diabetes, Stanford University School of Medicine, Stanford, California, USA

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Martina Rothenbühler, PhD

Diabetes Center Berne, Bern, Switzerland

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Viral N. Shah, MD

IU Center for Diabetes and Metabolic Diseases Indiana University School of Medicine Indianapolis, Indiana, USA

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Elias Spanakis, MD

University of Maryland, School of Medicine, Baltimore, Maryland, USA

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Guillermo E. Umpierrez, MD, CDCES, FACE, MACP

Emory University School of Medicine, Atlanta, Georgia, USA

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Alaina P. Vidmar, MD

Children's Hospital of Los Angeles and University of Southern California, Los Angeles, California

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Kayo Waki, MD, MPH, PhD

Department of Biomedical Informatics and Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

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Jill Weissberg-Benchell, Ph.D, CDCES

Robert H. Lurie Children’s Hospital of Chicago and Northwestern University, Chicago, Illinois

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Dessi Zaharieva, PhD, CEP, CDCES

Division of Endocrinology & Diabetes, Stanford University School of Medicine, Stanford, California, USA

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Yaguang Zheng, PhD, RN

New York University Rory Meyers College of Nursing, New York, New York, USA