ArticleGenes2023
An Artificial Intelligence Prediction Model of Insulin Sensitivity, Insulin Resistance, and Diabetes Using Genes Obtained through Differential Expression.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Western Diet Induces Changes in Gene Expression in Multiple Tissues During Early Insulin Resistance and Glucose Intolerance in Male C57BL/6 Mice.Current issues in molecular biology · 2025Article
- Obesity as a Multifactorial Chronic Disease: Molecular Mechanisms, Systemic Impact, and Emerging Digital Interventions.Current issues in molecular biology · 2025Review
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insulin is a powerful pleiotropic hormone that affects processes such as cell growth, energy expenditure, and carbohydrate, lipid, and protein metabolism. The molecular mechanisms by which insulin regulates muscle metabolism and the underlying defects that cause insulin resistance have not been fully elucidated. This study aimed to perform a microarray data analysis to find differentially expressed genes. The analysis has been based on the data of a study deposited in Gene Expression Omnibus (GEO) with the identifier "GSE22309". The selected data contain samples from three types of patients after taking insulin treatment: patients with diabetes (DB), patients with insulin sensitivity (IS), and patients with insulin resistance (IR). Through an analysis of omics data, 20 genes were found to be differentially expressed (DEG) between the three possible comparisons obtained (DB vs. IS, DB vs. IR, and IS vs. IR); these data sets have been used to develop predictive models through machine learning (ML) techniques to classify patients with respect to the three categories mentioned previously. All the ML techniques present an accuracy superior to 80%, reaching almost 90% when unifying IR and DB categories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.