Differences in lipid profile results of high-triglyceride serum samples detected by four different analytical systems
10.3760/cma.j.cn114452-20250228-00117
- VernacularTitle:不同分析系统检测高甘油三酯血清血脂结果差异分析
- Author:
Ruohong CHEN
1
;
Jingyao CAI
1
;
Xing LYU
1
;
Xin LIU
1
;
Shiqi HE
1
;
Min HU
1
;
Sisheng YI
1
Author Information
1. 中南大学湘雅二医院检验医学科，长沙410011
- Publication Type:Journal Article
- Keywords:
Hypertriglyceridemia;
Cholesterol, LDL;
Cholesterol, HDL;
Cholesterol, non-HDL;
Lipid management
- From:
Chinese Journal of Laboratory Medicine
2025;48(7):869-878
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To compare the differences among four routine lipid testing systems in detecting high triglyceride (TG) serum samples and evaluate the accuracy and consistency of the four homogeneous low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) reagents using vertical auto profile (VAP) as the reference method.Methods:A retrospective study was conducted on 249 serum samples with elevated TG levels collected from the Department of Laboratory Medicine at the Second Xiangya Hospital of Central South University between January and October 2024. TG, total cholesterol (TC), LDL-C, and HDL-C were measured using four homogeneous detection systems: Beckman Coulter (USA), Wako Pure Chemical Industries (Japan), Mindray (China), and Roche Diagnostics (Germany). VAP was used to analyze lipoprotein subfractions, including very-low-density lipoprotein cholesterol (VLDL-C), intermediate-density lipoprotein cholesterol (IDL-C), LDL-C, lipoprotein(a) cholesterol [Lp(a)-C], and HDL-C. The mean coefficient of variation ( CV) across the four systems was calculated for each parameter. Pearson correlation and ordinal logistic regression (OLR) were used to assess correlations between the four HDL-C/LDL-C systems and VAP. Bland-Altman plots were generated to evaluate biases, and deviations were calculated. For parameters with significant deviations, multivariate linear regression and standardized coefficients were used to analyze correlations between biases and lipoprotein subfractions. Based on the Chinese Guidelines for Lipid Management (2023), LDL-C and non-HDL-C treatment goals were categorized into five risk levels (ultra-high, high, moderate, high-risk, and low-risk). VAP results defined LDL-C/non-HDL-C intervals, and the four systems′ concordance in risk classification was evaluated. Samples were grouped into A, B, C, D ( n=63, 62, 62, 62) by TG concentration, and ANOVA, chi-square, and Fisher exact tests assessed intergroup differences. Results:The mean CVs across systems for TG, TC, LDL-C, HDL-C, and non-HDL-C were 2.98%, 1.76%, 18.10%, 5.60%, 2.58%, respectively. Pearson correlations between LDL-C results (Beckman, Wako, Mindray, Roche) and VAP were 0.889, 0.854, 0.899, and 0.973; mean relative deviations were 54.8%, 41.0%, 49.3%, and 3.6%; classification accuracies were 6.0% (15/249), 21.3% (53/249), 9.2% (23/249), and 76.7% (191/249). HDL-C deviations were 18.7%, 15.1%, 11.1%, and 8.7%, with correlations ( r) of 0.883, 0.911, 0.959, and 0.950 (all P<0.001). LDL-C means showed no intergroup differences (A-D), but CV increased with TG levels ( P<0.001). HDL-C means and CVs showed no significant intergroup differences. Beckman, Wako, and Mindray LDL-C results exhibited significant positive biases correlated with TG and VLDL-C (multivariate regression; P<0.05); VLDL-C had the strongest influence (standardized coefficients: 0.820, 0.394, 0.813; P<0.001). Non-HDL-C classifications matched VAP in 92.4% (Beckman), 85.9% (Wako), 94.0% (Mindray), and 93.2% (Roche), with no intergroup differences. Conclusion:For high-TG sera, Beckman, Wako, and Mindray LDL-C exhibited significant positive biases correlated with TG and VLDL-C, while Roche LDL-C showed minimal deviation. TG, TC, HDL-C, and non-HDL-C results showed minimal variation across the four systems. All systems demonstrated comparable accuracy for non-HDL-C compared to VAP. The non-HDL-C measured by the four detection systems demonstrates high accuracy and consistency in atherosclerotic cardiovascular disease risk stratification and lipid-lowering goal assessment, and it is unaffected by TG levels.
