Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

4.2K
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...
4.2K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

2.0K
For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
2.0K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

458
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
458
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

4.8K
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...
4.8K
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

13.7K
Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
13.7K
Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

766
Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
766

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The best diagnostic approach for classifying ischemic stroke onset time: A systematic review and meta-analysis.

Neuroradiology·2025
Same author

Sparsity regularization enhances gene selection and leukemia subtype classification via logistic regression.

Leukemia research·2025
Same author

Data mining techniques in breast cancer diagnosis at the cellular-molecular level.

Journal of cancer research and clinical oncology·2023
Same journal

Association between osteoporosis and arthritis: Results from the NHANES and Mendelian randomization study.

Metabolism open·2026
Same journal

What's in a name? From PCOS to polyendocrine metabolic ovarian syndrome: A metabolic reframing, promise, controversies, and challenges ahead.

Metabolism open·2026
Same journal

Corrigendum to "Efficacy and safety of semaglutide injection in comparison with reference semaglutide for chronic weight management in Indian adults with obesity: A phase III randomized non-inferiority trial" [Metabol Open 30 (2026) 100460].

Metabolism open·2026
Same journal

Sex differences in the association between bone mineral density and lipid profiles in people with type 2 diabetes in southwest China.

Metabolism open·2026
Same journal

Ensemble learning uncovers novel metabolomic biomarkers for early osteoporosis prediction in Tibetan plateau populations.

Metabolism open·2026
Same journal

Early ICU glycemic trajectory phenotypes predict short- and long-term mortality in critically ill patients: a dual-database cohort study.

Metabolism open·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Machine learning-based binary classification of elevated HbA1c (≥6.5 %) for risk assessment.

Dler Hussein Kadir1,2, Azhin Mohammed Khudhur1, Hewir Abdulqadir Khidir1

  • 1Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq.

Metabolism Open
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a predictive model for glycated hemoglobin (HbA1c) using clinical and demographic factors. Age and metabolic indicators like HDL, VitaminD3, VDL, and GPT are key predictors for long-term glycemic management and diabetes risk assessment.

Keywords:
AUCClinical predictorsDiabetes risk assessmentHbA1cLogistic regressionPredictive modeling

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Related Experiment Videos

Last Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Area of Science:

  • Endocrinology and Metabolism
  • Clinical Diagnostics
  • Biostatistics

Background:

  • Glycated hemoglobin (HbA1c) is a crucial biomarker for assessing long-term glycemic control and diabetes risk.
  • Identifying key clinical and demographic variables can enhance early risk assessment and intervention strategies.
  • Integrating lipid profiles, liver enzymes, and demographic data can improve HbA1c prediction models.

Purpose of the Study:

  • To develop a comprehensive HbA1c prediction model by integrating clinical indicators and demographic characteristics.
  • To identify significant clinical and demographic variables that influence HbA1c levels.
  • To improve early diabetes risk assessment and personalized medical care.

Main Methods:

  • Logistic regression analysis was employed to evaluate 482 cases.
  • A stepwise selection process was utilized to identify the most important predictive factors.
  • The model's predictive ability was assessed using the area under the curve (AUC).

Main Results:

  • The final prediction model demonstrated strong predictive ability with an AUC of 0.797.
  • Significant predictors included age (OR=1.085), glutamate pyruvate transaminase (GPT) (OR=1.011), high-density lipoprotein (HDL) (OR=0.969), VitaminD3 (OR=1.023), and very low-density lipoprotein (VDL) (OR=1.016).
  • Age was the strongest positive predictor, while HDL showed a protective effect.

Conclusions:

  • Age, along with indicators of liver and metabolic function (GPT, HDL, VitaminD3, VDL), significantly impacts HbA1c variability.
  • The integration of common clinical and demographic data into predictive models facilitates early diabetes risk assessment.
  • These findings support individualized medical care and improved patient outcomes in diabetes management.