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Somayeh Ghiasi Hafezi1, Bahareh Behkamal2, Mohammad Rashidmayvan3
1International UNESCO center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
Insights
Complete blood count (CBC) parameters like hemoglobin and platelet count are linked to high LDL cholesterol. Red cell distribution width (RDW) is a key predictor in women, while RDW-PLT ratio and PLT-HDL ratio are crucial for men.
Area of Science:
- Cardiovascular Disease Research
- Hematology
- Biomarker Discovery
Background:
- Elevated low-density lipoprotein-cholesterol (LDL-C) is a major risk factor for cardiovascular diseases (CVD).
- Associations between complete blood count (CBC) indices and dyslipidemia have been observed.
- Understanding these links can aid in CVD risk assessment.
Purpose of the Study:
- To investigate the relationship between CBC parameters and serum LDL levels.
- To identify specific CBC biomarkers predictive of high LDL-C.
- To explore sex-specific differences in these associations.
Main Methods:
- Prospective study of 9704 participants (aged 35-65).
- Comprehensive screening of LDL-C levels and CBC indicators.
- Analysis using Logistic Regression, Decision Tree, Random Forest, Neural Network, and Support Vector Machine.
Main Results:
- Age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-HDL ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were significantly associated with LDL levels (p<0.05).
- Red cell distribution width (RDW) significantly predicted higher LDL in women.
- RDW-PLT ratio (RPR) and PHR were most influential for men, while RDW was key for women.
Conclusions:
- Sex influences LDL-C odds, with females showing higher odds (52.9%).
- Age and HCT also increase LDL-C odds (4.1% and 5.5%, respectively).
- RPR and PHR are key predictors for both genders; RDW is significant for women, particularly for high HDL-C levels.
Introduction:
Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL.
Method:
In a prospective study involving 9704 participants aged 35-65 years, comprehensive screening was conducted to estimate LDL-C levels and CBC indicators. The association between these biomarkers and high LDL-C (LDL-C≥130 mg/dL (3.25 mmol/L)) was investigated using various analytical methods, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) methodologies.
Result:
The present study found that age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-High-Density Lipoprotein (HDL) ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were all statistically significant between the two groups (p<0.05). Another important finding was that red cell distribution width (RDW) was a significant predictor for higher LDL levels in women. Furthermore, in men, RDW-PLT ratio (RPR) and PHR were the most important indicators for assessing the elevated LDL levels.
Conclusion:
The study found that sex increases LDL-C odds in females by 52.9 %, while age and HCT increase it by 4.1 % and 5.5 %, respectively. RPR and PHR were the most influential variables for both genders. Elevated RPR and PHR were negatively correlated with increased LDL levels in men, and RDW levels was a statistically significant factor for women. Moreover, RDW was a significant factor in women for high levels of HDL-C. The study revealed that females have higher LDL-C levels (16 % compared to 14 % of males), with significant differences across variables like age, HGB, HCT, PLT, RLR, PHR, RBC, LYM, NHR, RPR, and key factors like RDW and SII.
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