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Association between serum hypertriglyceridemia and hematological indices: data mining approaches.

Somayeh Ghiasi Hafezi1,2, Amin Mansoori3, Alireza Kooshki4

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High triglyceride levels are linked to specific hematological factors like Red cell distribution width/Lymphocyte and Platelets/high-density lipoprotein ratios. Machine learning models accurately predict triglyceride levels, offering new insights into their associations.

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Area of Science:

  • Hematology
  • Biochemistry
  • Data Science

Background:

  • High triglyceride (TG) levels are closely linked with various hematological factors.
  • Serum TG concentration is a critical component of lipid profiles, impacting systemic diseases.
  • Understanding the interplay between TG and hematology is essential for disease management.

Purpose of the Study:

  • To investigate the association between serum triglyceride concentrations and hematological factors.
  • To identify key hematological predictors of high TG levels.
  • To explore the utility of machine learning in analyzing these relationships.

Main Methods:

  • Utilized data from 9704 participants (ages 35-65) from the MASHAD cohort (2007-2020).
  • Employed machine learning algorithms: logistic regression, decision tree, and random forest.
  • Analyzed associations between normal and high TG levels with various hematological parameters.

Main Results:

  • Identified significant predictors of TG levels, including Red cell distribution width/Lymphocyte (RLR), Red cell distribution width/Platelets (RPR), and Platelets/high-density lipoprotein (PHR).
  • Found age to be statistically associated with TG levels in women, potentially due to menopausal changes.
  • The Random Forest (RF) model demonstrated high accuracy in predicting TG levels for both genders.

Conclusions:

  • The study successfully modeled associations between serum TG and key hematological factors (RLR, RPR, PHR).
  • Results highlight potential interactions between TG and other hematological parameters.
  • Further research is warranted to elucidate the mechanisms and pathophysiology underlying these findings.