Investigating the relationship between blood factors and HDL-C levels in the bloodstream using machine learning
Somayeh Ghiasi Hafezi1,2, Marzieh Hosseini3, Mahtab Panahi4
1Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
Insights
Hematological inflammation significantly impacts high-density lipoprotein cholesterol (HDL-C) levels. Neutrophil-to-lymphocyte ratio (NHR) and lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR) are key predictors, offering insights into cardiometabolic health.
Area of Science:
- Biochemistry
- Cardiovascular Health
- Data Science
Background:
- Investigates the link between blood lipid components and metabolic disorders.
- Focuses on high-density lipoprotein cholesterol (HDL-C) and its importance for cardiovascular health.
- Explores how various blood factors influence HDL-C levels.
Purpose of the Study:
- To identify gender-specific hematological predictors of HDL-C levels.
- To explore the relationship between blood inflammation and HDL-C homeostasis.
- To provide insights into the pathophysiological role of inflammatory pathways in cardiometabolic disorders.
Main Methods:
- Utilized a dataset of 9704 participants with normal and low HDL-C levels.
- Employed data mining techniques including logistic regression (LR), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), XGBoost (XGB), and neural networks (NN).
- Identified predictive models for HDL-C using DT and confirmed predictor importance with SHAP analysis.
Main Results:
- Identified gender-specific hematological predictors for HDL-C.
- Logistic regression demonstrated the highest performance among the tested models.
- Neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) and lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR) were key predictors for males and females, respectively, alongside LYM, NEUT, and WBC.
Conclusions:
- Blood inflammation plays a role in HDL-C homeostasis.
- A complex interplay exists between inflammation, HDL-C levels, and cardiometabolic health.
- Modulating hematological inflammation may offer a strategy for preventing or treating cardiometabolic disorders.
Introduction:
The study investigates the relationship between blood lipid components and metabolic disorders, specifically high-density lipoprotein cholesterol (HDL-C), which is crucial for cardiovascular health. It uses logistic regression (LR), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), XGBoost (XGB), and neural networks (NN) algorithms to explore how blood factors affect HDL-C levels in the bloodstream.
Method:
The study involved 9704 participants, categorized into normal and low HDL-C levels. Data was analyzed using a data mining approach such as LR, DT, RF, KNN, XGB, and NN to predict HDL-C measurement. Additionally, DT was used to identify the predictive model for HDL-C measurement.
Result:
This study identified gender-specific hematological predictors of HDL-C levels using multiple ML models. Logistic regression exhibited the highest performance. NHR and LHR were the most influential predictors in males and females, respectively, with SHAP analysis confirming their critical roles alongside LYM, NEUT, and WBC in HDL-C classification.
Discussion:
The results show that blood inflammation plays a role in HDL-C homeostasis. The mechanisms of these relationships are not fully understood, but a complex interplay between inflammation and HDL-C levels as well as cardiometabolic health is evident. These findings support the pathophysiological role of inflammatory pathways in cardiometabolic disorders and provide insights into how modulation of hematological inflammation may contribute to disease prevention or treatment.
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