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Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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HUPA-UCM diabetes dataset.

J Ignacio Hidalgo1, Jorge Alvarado2, Marta Botella3

  • 1Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.

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|July 1, 2024
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This dataset offers valuable insights into type 1 diabetes management. It includes continuous glucose monitoring, insulin, and lifestyle data to develop predictive models for glucose levels and sleep impacts.

Keywords:
DiabetesGlucose predictionMachine learningT1DM

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Area of Science:

  • Biomedical Informatics
  • Endocrinology
  • Data Science

Background:

  • Type 1 diabetes mellitus (T1DM) management requires continuous monitoring of glucose levels and understanding influencing factors.
  • Integrating physiological and lifestyle data can enhance predictive modeling for T1DM.

Purpose of the Study:

  • To present a comprehensive dataset for T1DM research, encompassing continuous glucose monitoring (CGM), insulin, nutrition, and activity data.
  • To facilitate the development of predictive models for glucose fluctuations, hypoglycemia, and hyperglycemia in individuals with T1DM.
  • To investigate the intricate relationships between sleep patterns and glycemic control in T1DM.

Main Methods:

  • Collected data from 25 individuals with T1DM over at least 14 days.
  • Utilized FreeStyle Libre 2 CGMs for glucose readings and Fitbit Ionic smartwatches for activity and sleep tracking.
  • Recorded insulin doses and meal carbohydrate intake (grams).

Main Results:

  • The dataset includes synchronized CGM, insulin, meal, step, calorie, heart rate, and sleep data.
  • This data enables the exploration of correlations between lifestyle factors, sleep, and glycemic variability.
  • Previous analyses have demonstrated the utility of this dataset for machine learning-based glucose prediction.

Conclusions:

  • This dataset provides a rich resource for advancing T1DM research and personalized management strategies.
  • It supports the development of sophisticated predictive models for glucose control and the identification of key influencing variables.
  • Further research using this dataset can elucidate the impact of sleep on glycemic outcomes in T1DM.