# Exploring the prediction model and core genes for coronary artery disease in non-obese steatotic liver disease patients

## TL;DR

This study explores how non-obese steatotic liver disease is linked to coronary artery disease and identifies genes that could help predict heart disease in these patients.

## Contribution

The study introduces a novel diagnostic model and identifies HNF4A and LTBP4 as key genes associated with both non-obese steatotic liver disease and coronary artery disease.

## Key findings

- Non-obese steatotic liver disease patients have a higher risk of coronary artery disease than obese patients and non-SLD individuals.
- A diagnostic model with an area under the curve of 0.846 was developed for predicting CAD in non-obese SLD patients.
- HNF4A and LTBP4 were identified as core genes with significant diagnostic value for both non-obese SLD and CAD.

## Abstract

Non-obese steatotic liver disease (SLD) refers to a metabolic disorder characterized by ectopic fat deposition in the liver, but without increased subcutaneous adipose tissue and normal body mass index (BMI) in patients. Emerging evidence indicates that non-obese SLD is associated with coronary artery disease (CAD). However, the mechanisms underlying their mutual relationship remain undefined.

We retrospectively analyzed 8,722 subjects and constructed a prediction model for diagnosing CAD in non-obese SLD patients. Then, public datasets from the Gene Expression Omnibus (GEO) were retrieved for further bioinformatics analysis, and machine learning algorithms were used to screen candidate core genes.

Through the analysis of clinical data, we found that the risk of CAD in non-obese SLD patients was significantly higher than that in obese SLD patients and individuals without SLD. We constructed a nomogram for predicting CAD in non-obese SLD patients, and the area under the curve for training and validation sets was 0.846 and 0.732, respectively. We analyzed the non-obese SLD dataset (GSE89632) and CAD dataset (GSE113079) and overlapped the differentially expressed genes (DEGs) in these two datasets. We found that there were 28 overlapping upregulated DEGs and 66 overlapping downregulated DEGs. The protein–protein interaction network generated a 94-edge network, and the top 40 hub genes were selected using the maximal clique centrality algorithm. The candidate core genes, including HNF4A and LTBP4, were screened based on machine learning algorithms. The receiver operating characteristic results showed that these two genes have considerable diagnostic value for non-obese SLD and CAD.

We found a close correlation between non-obese SLD and CAD. Our study developed a novel diagnostic model to predict CAD in non-obese SLD patients with promising predictive performance. In addition, through comprehensive bioinformatics analysis and machine learning algorithms, two key core genes, HNF4A and LTBP4, were identified to be associated with both non-obese SLD and CAD.