Lipoprotein(a) in pregnancy: A systematic review and meta-analysis

Pregnancy induces significant metabolic changes, including altered lipid profiles, yet the role of lipoprotein(a) [Lp(a)] remains unclear. While Lp(a) levels rise during pregnancy and may be linked to complications like pre-eclampsia and gestational diabetes, existing studies show inconsistent findings. This meta-analysis aims to investigate Lp(a) levels in normal and high-risk pregnancy and its associated outcomes.

This meta-analysis followed PRISMA guidelines, systematically searching PubMed, Scopus, Web of Science, and Embase for studies comparing Lp(a) levels in normal pregnant vs. non-pregnant women, or high-risk vs. normal pregnancies. Data extraction, quality assessment, and statistical analysis were performed independently by two reviewers. Heterogeneity was assessed using I² statistics, with sensitivity analyses and GRADE evaluating evidence certainty.

Forty-one studies were included (31 meta-analyzed), predominantly small cross-sectional studies. Compared with non-pregnant women, Lp(a) levels were modestly higher in healthy pregnancy (MD = 5.02 mg/dL, p = 0.01; very low-certainty evidence - GRADE). In women with pre-eclampsia, Lp(a) levels were significantly elevated compared to normotensive pregnancies (MD = 11.92 mg/dL, 95 % CI: 7.68 to 16.16, p < 0.00001; very low-certainty evidence - GRADE). Subgroup analyses demonstrated that this association was consistent across different assay methods and was also observed in both early-onset (≤34 weeks; MD = 12.91 mg/dL) and late-onset (>34 weeks; MD = 10.60 mg/dL) pre-eclampsia. No significant difference was observed in women with gestational diabetes (MD = -8.85 mg/dL, p = 0.39; very low-certainty- GRADE).

This meta-analysis found higher Lp(a) levels in pregnancy, especially in pre-eclampsia, but not in gestational diabetes. While these findings suggest a possible link between Lp(a) and hypertensive pregnancy complications, further studies are needed to confirm clinical relevance and establish predictive utility.