Monitoring T cells could prevent type 1 diabetes: Study

Scripps scientists have discovered that analyzing a certain type of immune cell in the blood can help identify people at risk of developing type 1 diabetes, a fatal autoimmune disease. If the new approach is validated in future research, it could be used to select people with potential for a treatment that inhibits the autoimmune process, making type 1 diabetes a preventable condition.

In the study, which appeared in Science Translational Medicine on July 5, 2023, researchers isolated T cells (a type of immune cell) from mouse and human blood samples. By analyzing the T cells that cause type 1 diabetes, they were able to separate at-risk patients from those who had active autoimmunity and those who did not have any significant autoimmunity – with 100% accuracy in a small sample.

The study’s senior author, Luke Teitan, MD, PhD, professor in the Department of Immunology and Microbiology, says, “These findings represent a major step forward because they offer the possibility of capturing this autoimmune process, while preventing or delaying diabetes.” There’s still time to do it.” in Scripps Research.

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The study’s first authors were graduate student Siddharth Sharma and research assistants Josh Boyer and Zhuqian Tan, all of the Teyton lab at the time of the study. Type 1 diabetes occurs when the immune system destroys the insulin-producing “islet cells” of the pancreas.

The autoimmune process that underlies type 1 diabetes can start and stop over many years. Exactly how the process begins is not well understood, although it is known to involve genetic factors and may be triggered by routine viral infections. When it occurs, it usually occurs in childhood or early adulthood, and requires lifelong insulin replacement. Researchers estimate that about two million people in the US alone have type 1 diabetes.

In 2022, the US Food and Drug Administration approved an immune-suppressing therapy that may protect islet cells and at least delay the onset of diabetes by months to years if given in the early stages of autoimmunity. However, doctors don’t have a good way of identifying people who might benefit from this type of treatment. They have traditionally checked for anti-islet antibody levels in patient blood samples, but this antibody response is not a very accurate measure of autoimmune progression.

“Anti-islet antibody levels are difficult to predict on an individual level, and type 1 diabetes is fundamentally a T cell-driven disease,” Teyton says.

In the study, Teyton and his team engineered protein complexes to mimic a mix of immune proteins and insulin fragments that specialized T cells called CD4 T cells normally recognize to initiate an autoimmune response. They used these structures as bait to capture anti-insulin CD4 T cells in blood samples. They then analyzed gene activity within the captured T cells and protein expression on the cells to assess their activation status.

In this way, they were able to develop a classification algorithm that correctly identified which at-risk patients in a set of nine had ongoing anti-islet autoimmunity. Teyton now hopes to validate the CD4 T cell-based approach with a long-term study in a larger cohort of participants, comparing this approach to the traditional approach of quantifying anti-islet antibodies.

Teyton and his colleagues are also working to make the process of isolating and analyzing anti-islet T cells in blood samples more affordable and convenient, so that it can be more easily used in clinical settings.

“If we can develop this into a useful method for identifying at-risk patients and tracking their autoimmune status, we’ll not only have a way to get the right people into treatment, but we’ll also be able to monitor their disease progression and evaluate potential new preventive therapies,” Teyton says.