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Type 1 diabetes mellitus is a chronic metabolic disorder characterized by an absolute deficiency of insulin resulting from the autoimmune destruction of pancreatic β-cells. Although it can occur at any age, it is most commonly diagnosed in childhood, adolescence, or early adulthood. The loss of insulin production impairs cellular glucose uptake, resulting in persistent hyperglycemia and necessitating lifelong insulin therapy.Autoimmune Destruction of β-CellsThe hallmark of type 1...
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Type 1 diabetes mellitus arises from an immune-mediated destruction of pancreatic β-cells, resulting in an absolute deficiency of insulin. This process develops in genetically susceptible individuals when autoimmunity, environmental exposures, and immunologic dysregulation converge to trigger a targeted attack on the insulin-producing cells of the pancreas. The β-cells are located within the islets of Langerhans and are essential for regulating blood glucose by facilitating cellular...
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A closed-loop microneedle-integrated physiological model for predictive glycemic management.

Marco Fratus1, Muhammad A Alam1

  • 1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906.

Proceedings of the National Academy of Sciences of the United States of America
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

A new physics-based framework predicts how microneedle (MN) designs impact diabetes management systems. This approach optimizes continuous glucose monitoring (CGM) and therapy delivery, reducing trial-and-error for better glycemic control.

Keywords:
closed-loop systemdiabetesmicroneedlesphysics-based modelingwearable devices

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Area of Science:

  • Biomedical Engineering
  • Physiological Modeling
  • Wearable Technology

Background:

  • Continuous glucose monitoring (CGM) and on-demand therapy are crucial for managing chronic diseases like diabetes.
  • Current methods for optimizing device design in closed-loop diabetes systems are inefficient and empirical.
  • Predicting device design's impact on system performance, especially for microneedle (MN) integration, remains a challenge.

Purpose of the Study:

  • To introduce a physics-based framework integrating microneedle (MN) designs for sensing and therapy with a physiological model of glycemic control.
  • To establish compact relationships between material, chemical, and geometrical MN features and key performance metrics.
  • To create a predictive model for glycemic regulation by linking device design to system behavior.

Main Methods:

  • Developed a physics-based framework incorporating microneedle (MN) designs for sensing and therapy.
  • Formulated theories for three sensing MN types (hollow, porous/swellable, nanostructured) and one therapeutic patch.
  • Integrated MN theories with a physiological model to predict glycemic regulation.

Main Results:

  • The framework establishes relationships between MN design features and performance metrics like response time and insulin delivery rate.
  • Simulations examined the impact of disease progression, MN design, and MN sensitivity on glucose levels and time-in-range.
  • Demonstrated the framework's ability to predict plasma glucose levels and time-in-range metrics.

Conclusions:

  • The physics-based framework provides a foundation for a digital twin in diabetes management.
  • This approach streamlines microneedle (MN) patch design for next-generation CGM technology.
  • Minimizes glycemic events and reduces trial-and-error in developing diabetes management devices.