统计学方法与机器学习方法比较，以检测对升高的低密度脂蛋白胆固醇血清水平影响最大的血液学指标。
Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol.
机构信息
International 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.
Medicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran; Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran; Medicinal, Chemistry Department, School of Pharmacy, Mashhad University Medical Sciences, Mashhad, Iran.
出版信息
Chem Phys Lipids. 2024 Nov;265:105446. doi: 10.1016/j.chemphyslip.2024.105446. Epub 2024 Oct 5.
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.
简介
低密度脂蛋白胆固醇（LDL-C）水平升高是心血管疾病（CVD）发展的一个重要危险因素。此外，研究表明全血细胞计数（CBC）指数与血脂异常之间存在关联。我们旨在探讨 CBC 参数与血清 LDL 水平之间的关系。
方法
在一项涉及 9704 名 35-65 岁参与者的前瞻性研究中，进行了综合筛查以估计 LDL-C 水平和 CBC 指标。使用各种分析方法，包括 Logistic 回归（LR）、决策树（DT）、随机森林（RF）、神经网络（NN）和支持向量机（SVM）方法，研究了这些生物标志物与高 LDL-C（LDL-C≥130mg/dL（3.25mmol/L））之间的关系。
结果
本研究发现，年龄、血红蛋白（HGB）、红细胞压积（HCT）、血小板计数（PLT）、淋巴细胞（LYM）、血小板-淋巴细胞比值（PLR）、血小板-高密度脂蛋白比值（PHR）、HGB-淋巴细胞比值（HLR）、红细胞计数（RBC）、中性粒细胞-高密度脂蛋白比值（NHR）和血小板-红细胞比值（PRR）在两组之间均有统计学意义（p<0.05）。另一个重要发现是，红细胞分布宽度（RDW）是女性 LDL 水平升高的一个显著预测指标。此外，在男性中，RDW-血小板比值（RPR）和 PHR 是评估 LDL 升高的最重要指标。
结论
该研究发现，性别使女性 LDL-C 几率增加 52.9%，而年龄和 HCT 分别使几率增加 4.1%和 5.5%。RPR 和 PHR 是两种性别的最有影响的变量。在男性中，升高的 RPR 和 PHR 与 LDL 水平升高呈负相关，RDW 水平是女性的一个统计学显著因素。此外，RDW 是女性 HDL-C 水平升高的一个显著因素。研究表明，女性的 LDL-C 水平较高（女性为 16%，男性为 14%），在年龄、HGB、HCT、PLT、RLR、PHR、RBC、LYM、NHR、RPR 和关键因素（如 RDW 和 SII）等变量方面存在显著差异。
Zotero 插件