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

Dipeptidyl Peptidase 4 Inhibitors01:23

Dipeptidyl Peptidase 4 Inhibitors

165
Dipeptidyl peptidase 4 (DPP-4) is a serine protease widely distributed in the body. It's involved in the inactivation of GLP-1 and GIP hormones, which are crucial for insulin regulation. DPP-4 inhibitors, such as sitagliptin (Januvia), saxagliptin (Onglyza), linagliptin (Tradjenta), alogliptin (Nesina), and vildagliptin (Galvus), help increase the proportion of active GLP-1, enhancing insulin secretion. These inhibitors work by competitively binding to DPP-4. This binding causes a...
165
Oral Hypoglycemic Agents: Biguanides and Glitazones01:26

Oral Hypoglycemic Agents: Biguanides and Glitazones

165
Biguanides, particularly metformin (Glucophage), are insulin sensitizers that enhance glucose uptake, thereby reducing insulin resistance. Unlike sulfonylureas, metformin doesn't prompt insulin secretion, which helps to curb hypoglycemia risk. Metformin is beneficial in treating conditions like polycystic ovary syndrome due to its insulin-resistance reduction capability. The drug's primary action involves curtailing hepatic gluconeogenesis, a significant contributor to high blood...
165

You might also read

Related Articles

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

Sort by
Same author

Prenatal exposure to mixed toxic metals and childhood blood pressure: the mediating role of amino acid and carnitine metabolism.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Pilot-scale biomass pyrolysis dual fluidized bed with in-situ biochar recovery for high-quality bio-oil and negative carbon emissions.

Bioresource technology·2026
Same author

Therapeutic effects and mechanisms of Artemisia species on metabolic diseases: A systematic review.

Medicine·2026
Same author

The fate and bioaccessibility of arsenic in an abandoned smelter site: Distribution, mobility and potential risks.

Journal of hazardous materials·2026
Same author

Pan-immune inflammation value and neutrophil-to-albumin ratio predict hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke: a dual-center cohort study.

Frontiers in nutrition·2026
Same author

Assessment of the mechanical performance of a novel CT-linac treatment couch.

Frontiers in oncology·2026

Related Experiment Video

Updated: Jun 1, 2025

An In Ovo Model for Testing Insulin-mimetic Compounds
06:09

An In Ovo Model for Testing Insulin-mimetic Compounds

Published on: April 23, 2018

10.3K

A data-driven machine learning algorithm to predict the effectiveness of inulin intervention against type II

Shuheng Yang1, Ralf Weiskirchen2, Wenjing Zheng1

  • 1School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, China.

Frontiers in Nutrition
|January 22, 2025
PubMed
Summary

A machine learning model effectively predicts which type 2 diabetes mellitus (T2DM) patients benefit from inulin nutritional therapy. Key factors like HbA1c and glucose levels help personalize treatment for better outcomes.

Keywords:
XGBoostinulinmachine-learning algorithmtreatment decisiontype 2 diabetes

More Related Videos

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
07:15

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

9.5K
Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test OGTT and Insulin Tolerance Test ITT
08:13

Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test OGTT and Insulin Tolerance Test ITT

Published on: January 7, 2018

67.9K

Related Experiment Videos

Last Updated: Jun 1, 2025

An In Ovo Model for Testing Insulin-mimetic Compounds
06:09

An In Ovo Model for Testing Insulin-mimetic Compounds

Published on: April 23, 2018

10.3K
An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
07:15

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

Published on: July 31, 2019

9.5K
Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test OGTT and Insulin Tolerance Test ITT
08:13

Study of In Vivo Glucose Metabolism in High-fat Diet-fed Mice Using Oral Glucose Tolerance Test OGTT and Insulin Tolerance Test ITT

Published on: January 7, 2018

67.9K

Area of Science:

  • Endocrinology and Metabolism
  • Nutritional Science
  • Computational Biology

Background:

  • Rising incidence of type 2 diabetes mellitus (T2DM) necessitates advanced management strategies.
  • Nutritional therapy, particularly inulin supplementation, is a key component in T2DM care.
  • Identifying suitable T2DM patients for inulin intervention requires predictive tools.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting inulin treatment effectiveness in T2DM patients.
  • To identify key patient characteristics influencing the response to inulin therapy.

Main Methods:

  • Utilized data from a previous study on T2DM patients undergoing inulin intervention.
  • Employed LASSO regression for feature selection and XGBoost for predictive model development.
  • Evaluated model performance using accuracy, specificity, positive/negative predictive values, ROC, calibration, and decision curves.

Main Results:

  • Inulin intervention successfully reduced glycated hemoglobin (HbA1c) in 62.93% of 758 T2DM patients.
  • Key predictors identified by LASSO regression included HbA1c, glucose variability, fasting glucose, HDL, age, and BMI.
  • The XGBoost model achieved high performance metrics, with training set accuracy of 0.819 and testing set accuracy of 0.709.

Conclusions:

  • The XGBoost-SHAP framework effectively predicts inulin intervention outcomes in T2DM.
  • This approach enables personalized treatment by assessing individual patient features and prediction abilities.
  • Establishes a valuable link between machine learning and nutritional therapy for T2DM management.