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Objective: To describe the distribution of lipoprotein (a) [Lp(a)] levels in non-arteriosclerotic cardiovascular disease (ASCVD) population in China and explore its influencing factors. Methods: This study was based on a nested case-control study in the CKB study measured plasma biomarkers. Lp(a) levels was measured using a polyclonal antibody-based turbidimetric assay certified by the reference laboratory and ≥75.0 nmol/L defined as high Lp(a). Multiple logistic regression model was used to examine the factors related to Lp(a) levels. Results: Among the 5 870 non-ASCVD population included in the analysis, Lp(a) levels showed a right-skewed distribution, with a M (Q1, Q3) of 17.5 (8.8, 43.5) nmol/L. The multiple logistic regression analysis found that female was associated with high Lp(a) (OR=1.23, 95%CI: 1.05-1.43). The risk of increased Lp(a) levels in subjects with abdominal obesity was significantly reduced (OR=0.68, 95%CI: 0.52-0.89). As TC, LDL-C, apolipoprotein A1(Apo A1), and apolipoprotein B(Apo B) levels increased, the risk of high Lp(a) increased, with OR (95%CI) for each elevated group was 2.40 (1.76-3.24), 2.68 (1.36-4.93), 1.29 (1.03-1.61), and 1.65 (1.27-2.13), respectively. The risk of high Lp(a) was reduced in the HDL-C lowering group with an OR (95%CI) of 0.76 (0.61-0.94). In contrast, an increase in TG levels and the ratio of Apo A1/Apo B(Apo A1/B) was negatively correlated with the risk of high Lp(a), with OR (95%CI) of 0.73 (0.60-0.89) for elevated triglyceride group, and OR (95%CI) of 0.60 (0.50-0.72) for the Apo A1/B ratio increase group (linear trend test P≤0.001 except for Apo A1). However, no correlation was found between Lp(a) levels and lifestyle factors such as diet, smoking, and physical activity. Conclusions: Lp(a) levels were associated with sex and abdominal obesity, but less with lifestyle behaviors.

Original publication




Journal article


Zhonghua Liu Xing Bing Xue Za Zhi

Publication Date





779 - 786


Humans, Lipoprotein(a), China, Case-Control Studies, Female, Cardiovascular Diseases, Male, Risk Factors, Logistic Models, Biomarkers, Middle Aged