UAB study finds increase in severe diabetes subtypes after COVID-19 pandemic and leads to new clustering tool for clinicians

Anath Shalev, M.D., and Brian Lu, Ph.D.University of Alabama at Birmingham researchers have found that the proportion of patients with severe diabetes subtypes has increased in the years since the COVID-19 pandemic. 

While COVID-19 has been associated with increased risk for new-onset diabetes, how or if the pandemic affected subtype distribution remained unknown.

Diabetes has shown to cluster into five clinically distinct subtypes beyond Types 1 and 2, including two severe subtypes associated with Type 2 diabetes: severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD).

A precision diabetes approach

During the pandemic, Anath Shalev, M.D., director of the UAB Comprehensive Diabetes Center, and Matthew Might, Ph.D., director of the Hugh Kaul Precision Diabetes (PMI), formed the UAB Precision Diabetes Program to gain a better understanding of the nuances of the disease and find new ways to personalize patient care instead of using a one-size-fits-all approach.

The collaboration has resulted in three published research articles about COVID-19 and diabetes, including two on diabetes subtypes, which show key opportunities for better diabetes management strategies and outcomes for patients.

“Since severe subtypes of diabetes have an increased lifetime risk of diabetes complications, recognizing and adequately adjusting treatment for these diabetes subtypes should help improve the outcome, said Shalev, professor in the Division of Endocrinology, Diabetes, and Metabolism.

A Precision Diabetes Workgroup study published in the Journal of Clinical Endocrinology and Metabolism in 2025 revealed an increased risk of severe insulin-deficient diabetes in Black/African Americans between 2010 and 2019.

The group’s latest paper in the journal Metabolites (March 2026) evaluated diabetes subtype distributions before and after the COVID-19 pandemic among U.S. adults, leading to the observation of increased severe diabetes subtypes post-pandemic and a new tool to help clinicians identify diabetes subtypes in their patients.

Validation of a diabetes subtype classification model

Brian Lu, Ph.D., a research scientist in the Shalev Lab and the study’s first author, trained a computer classification model to identify diabetes subtypes in patients using established clustering parameters.

Using the trained model, Lu assigned subtypes to patients with diabetes from UAB between 2020 and 2024. He found the distribution of diabetes subtypes was significantly different between 2010–2019, (before the pandemic), and 2020–2024, (after the pandemic) in the UAB cohort, while cohort demographics did not change overall, and the proportions of males and Black/African Americans remained similar.

In particular, the proportion of patients with severe subtypes of diabetes (severe insulin-deficient diabetes and severe insulin-resistant diabetes) increased from 42% before the pandemic to 61% after, while the proportion of patients with mild subtypes of diabetes (mild obesity-related diabetes and mild age-related diabetes) decreased from 58% to 39%. The increase in severe diabetes subtypes remained significant even after adjusting for gender, race, age, and body mass index (BMI).

To validate their findings, researchers repeated the analysis using data from the 2015–2023 National Health and Nutrition Examination Surveys (NHANES). While NHANES also had similar proportions of males and Black/African American participants between 2015–2020 and 2021–2023, there was again a significant difference between the distribution of diabetes subtypes.

The proportion of survey participants with severe subtypes of diabetes increased from 31% before the pandemic to 40% after, while the proportion of participants with mild subtypes of diabetes decreased from 69% to 60%. The increase in severe diabetes subtypes remained similarly significant after adjusting for gender, race, age, and BMI. To the surprise of the researchers, only a small fraction of people assigned the severe insulin-deficient diabetes (SIDD) subtype were prescribed insulin in this cohort.

“Using this model, our studies identified an increase in the proportions of patients with severe subtypes of diabetes in the more recent years following the pandemic,” Lu said. “We also observed a surprisingly low rate of insulin usage in patients with SIDD in the NHANES cohort, suggesting that severe insulin deficiency in patients with diabetes may not be adequately recognized and managed, underlining the need for tools to facilitate better recognition and management.”

A practical tool for diabetes subtype identification and management

To facilitate the use of their model to help clinicians identify subtypes of adult-onset, non-gestational diabetes, the researchers created DiaClue, which can now be accessed as a free web application at diaclue.com, and as a downloadable application on mobile devices.

Clinicians can directly input the six clinical parameters required for cluster assignment and DiaClue will output the most likely subtype assignment. Recognizing that a person with diabetes may have clinical features that overlap with multiple subtypes, DiaClue also outputs a percentage score for each subtype, along with a score breakdown to aid users in making their independent and balanced subtype assignment.

“Our application can provide information relevant to pathophysiology and complication risks and thereby support clinicians in their tailoring of diabetes management strategies,” Lu said.

Lu created the classification model behind how the app works. Ava Smith, a graduate student in the Graduate Biomedical Sciences (GBS) program under the mentorship of professor Matthew Might, Ph.D., built the user interface and supporting functionality to turn it into a web app.

Future investigations

Researchers said further studies are required to determine the causes of the increase in severe diabetes subtypes after the pandemic, but potential explanations may include direct biological effects from SARS-CoV-2 infection and indirect effects from the pandemic lockdowns and the resulting disruption of diabetes services.

“While further studies are required to examine the potential causes of this phenomenon, diagnosing these diabetes subtypes and identifying trends early will support better tailoring of diabetes management strategies and should improve diabetes control and outcomes,” Lu said.

Other collaborators on this study include Precision Diabetes Workgroup members from the UCDC, PMI, and School of Nursing: Li Peng, Ph.D., Andrew B. Crouse, Ph.D., Tiffany Grimes, R.N, Ava N. Smith, MBA, Matthew Might, Ph.D., and Fernando Ovalle, M.D., and Anath Shalev, M.D.

Researchers who are interested in studying precision diabetes at UAB, can apply for the UCDC Seed Funding Program for Precision Diabetes through June 19, 2026.