2025

UCLA Fielding School of Public Health-led team awarded $2.1 million NIH grant for diabetes research


$2.1 million NIH grant for diabetes research

Photo illustration_doctor and patient

A team led by UCLA Fielding School of Public Health (UCLA Fielding) researchers has received a $2.1 million grant from the National Institutes of Health (NIH) to use artificial intelligence (AI) to improve risk assessment for those suffering from diabetes and related complications. 

The research team, made up of scholars from UCLA, the Los Alamos National Laboratory (LANL), and the Department of Veterans Affairs (VA), was recognized by the National Institute of Diabetes and Digestive and Kidney Diseases, part of the NIH, as an example of bringing together scientists from health care providers, the U.S. national lab system, and academia. 

“This research is a testament to the power of interdisciplinary collaboration," said Dr. Michael Ong, professor-in-residence of medicine and Health Policy and Management at UCLA Fielding and associate chief of staff for research and development at VA Greater Los Angeles Healthcare System. "Combining AI, data science, and clinical expertise, we are building next-generation tools to transform complex disease management and personalized medicine.” 

Diabetes is a chronic condition that reduces the body’s ability to process glucose (blood sugar). Adverse health effects include damage to the circulatory system, vision problems, nerve damage, stomach or intestinal problems, slow healing, kidney disease, and the risk of heart disease, stroke, and cancer.  

The 4-year-long research project is led by Dr. Jin Zhou, associate professor-in-residence in UCLA Fielding’s Department of Biostatistics and a researcher at the Veteran Affairs Greater Los Angeles system. Zhou, who works at the interface of statistics, genetics, and biomedical data, earned her doctorate at UCLA. She is joined by principal investigators Dr. Gang Li and Dr. Hua Zhou, both professors in UCLA Fielding’s Department of Biostatistics. 

“We’re focused on developing AI-powered prediction models to track disease progression in diverse populations, improve risk assessment for diabetes complications, and address challenges in analyzing real-world data,” Jin Zhou said. “Our goal is to create real-world, scalable solutions that can be applied across healthcare systems.” 

The project brings together a distinguished team of collaborators, including Dr. Xiaowu Dai (UCLA), Dr. Peter Reaven (VA), and Dr. Benjamin McMahon (LANL). 

 

Dr. Jin Zhou
Jin Zhou
Biostatistics
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Michael Ong
Michael Ong
Health Policy and Management
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Feng Gao
Feng Gao
Environmental Health Sciences
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Yoshira Ornelas Van Horne
Environmental Health Sciences
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Sudipto Banerjee
Sudipto Banerjee
Biostatistics
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Rosenstock
Linda Rosenstock
Environmental Health Sciences Health Policy and Management
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Zoller_Joseph
Joseph Zoller
Biostatistics
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Jian Li
Jian Li
Environmental Health Sciences Epidemiology
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Michael Jerrett
Michael Jerrett
Environmental Health Sciences
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Li, Jingyi Jessica
Jingyi Jessica Li
Biostatistics
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Damla Senturk
Biostatistics
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Dr. Candace Tsai
Candace Tsai

Associate Professor for Industrial Hygiene and Environmental Health Sciences

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Dr. Yifang Zhu
Yifang Zhu
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Miriam Marlier
Miriam Marlier
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Richard Ambrose
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Hua Zhou
Hua Zhou
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Gang Li
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Beate Ritz
Environmental Health Sciences Epidemiology
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Jason Xu
Biostatistics
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Jason H. Moore, Ph.D.
Jason H. Moore

Automated and accessible artificial intelligence methods and software for biomedical data science.

Biostatistics
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Dr. Christina Ramirez
Christina Ramirez
Biostatistics
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Dr. Shane Que Hee
Shane Que Hee

Industrial Hygiene & Analytical Chemistry

Environmental Health Sciences
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Dr. Donatello Telesca
Donatello Telesca
Biostatistics
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Thomas Belin
Thomas R. Belin
Biostatistics
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Andrew Holbrook
Andrew Holbrook
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Xiaowu Dai headshot
Xiaowu Dai
Biostatistics
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Falco Joannes Bargagli Stoffi
Falco J. Bargagli Stoffi
Biostatistics
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