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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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...
Diabetes: Management and Pharmacotherapy01:15

Diabetes: Management and Pharmacotherapy

The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Diabetic Retinopathy01:27

Diabetic Retinopathy

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Hyperglycemia01:29

Hyperglycemia

Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose levels exceed 180 mg/dL two...
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Diabetes Mellitus: Introduction

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Leveraging Machine Learning to Assess Post-COVID-19 Glycemic Control in Diabetic Patients.

Marie Lluberes-Contreras1, Eduardo Figueroa-Santiago2, Hamid-Reza Kohan-Ghadr3

  • 1Bioinformatics and Data Science Laboratory, Department of Computer Science, University of Puerto Rico, San Juan, PR 00925, USA.

International Journal of Environmental Research and Public Health
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

COVID-19 infection did not significantly alter long-term glycemic control (Hemoglobin A1c) for most adults with diabetes. However, factors like body mass index and insulin use influenced post-infection blood sugar changes.

Keywords:
COVID-19HbA1cdiabetesdisease diagnosiselectronic health recordsepidemic predictionmachine learning

Related Experiment Videos

Area of Science:

  • Endocrinology
  • Infectious Diseases
  • Data Science

Background:

  • Hemoglobin A1c (HbA1c) is crucial for monitoring diabetes and predicting complications.
  • The COVID-19 pandemic's long-term effects on diabetes glycemic control are not well understood.
  • SARS-CoV-2 infection may have metabolic consequences.

Purpose of the Study:

  • To assess the impact of COVID-19 infection on long-term glycemic control (HbA1c) in adults with diabetes.
  • To identify factors associated with significant post-infection HbA1c changes using machine learning.

Main Methods:

  • Retrospective analysis of harmonized electronic health records (n=93,320).
  • Paired statistical analysis of HbA1c levels pre- and post-COVID-19 infection.
  • Supervised machine learning classifiers (random forest) evaluated for predictive performance.

Main Results:

  • 71% of patients showed no statistically significant change in average HbA1c post-infection.
  • When changes occurred, decreases in HbA1c were more frequent than increases.
  • Machine learning identified body mass index, insulin use, age, and socioeconomic factors as key predictors of glycemic change.

Conclusions:

  • COVID-19 infection generally does not substantially alter long-term glycemic control in the majority of individuals with diabetes.
  • Individual clinical and socioeconomic factors play a role in post-infection glycemic variability.
  • Personalized monitoring and management strategies may be necessary for vulnerable patient subgroups.