Genetic Study Links Specific Blood Lipids to Pre-eclampsia and Eclampsia Risk

Genetically predicted levels of a specific blood fat, triacylglycerol (50:1), are associated with increased risk of pre-eclampsia, eclampsia, and hypertension, according to a new study published in Artery Research. The work, led by Jia Zhu, Jin Tang, Shuping Liu, Xue Xiang, and Youqiong Xiang, combined Mendelian randomization analysis with network toxicology and molecular simulations to build a mechanistic hypothesis about how individual plasma lipid species might contribute to these dangerous pregnancy complications. The authors stress that their findings are genetically supported associations and computational hypotheses, not experimentally confirmed biology.

Pre-eclampsia and eclampsia are among the most serious complications of pregnancy. Pre-eclampsia is characterized by new-onset hypertension and proteinuria during mid-to-late pregnancy, and its progression to eclampsia can involve seizures and coma, threatening both mother and fetus. Globally, these conditions account for approximately 2 to 8 percent of all pregnancies and are even more common in developing countries, where they rank among the leading direct causes of maternal mortality. Despite established clinical monitoring and management protocols, the complex underlying biology and the lack of effective early predictive markers mean many patients still miss out on timely treatment, the authors note in their introduction.

Previous research has implicated lipid metabolism disorders in the onset and progression of pre-eclampsia, with epidemiological studies linking pregnancy lipid changes to hypertensive disorders and basic research suggesting lipid metabolites can influence endothelial function, oxidative stress, and inflammation. However, most of that evidence comes from observational studies, which cannot establish causality. Mendelian randomization addresses this by using genetic variants as instrumental variables, exploiting the random allocation of alleles at conception to reduce confounding and reverse causation. The technique infers whether an exposure, here a blood lipid, has a causal effect on an outcome, here disease risk, based on genetic proxies.

For the lipid exposure data, the team used summary statistics from a lipidomics genome-wide association study of the GeneRISK cohort, comprising 7,174 Finnish individuals and covering 179 plasma lipid molecules across 13 functional categories, including sterols, sphingolipids, glycerophospholipids, and glycerolipids. Fasting plasma samples were analyzed by high-throughput shotgun lipidomics, and genotyping was performed with the HumanCoreExome array. Disease outcomes came from the FinnGen Consortium R12 dataset, covering pre-eclampsia, eclampsia, and hypertension, with all study participants of European ancestry. The authors describe the lipid dataset as the largest genetic study of the plasma lipidome to date.

The analysis used inverse-variance weighting as the primary method, with weighted median, weighted mode, simple mode, and MR-Egger regression as complementary checks. Instrumental variables were selected at a significance threshold of P < 5 × 10⁻⁵, clumped with a linkage disequilibrium threshold of r² < 0.001, and required to have F-statistics above 10 to exclude weak instruments. In total, 4,506 SNPs met the screening criteria, with 3,214 SNPs used for the pre-eclampsia analysis, 3,224 for eclampsia, and 3,224 for hypertension.

The results revealed significant associations between 16 plasma lipid species and pre-eclampsia, and between 8 lipid species and eclampsia, with 14 lipids significantly associated with hypertension. Triacylglycerol (50:1) showed the strongest single association with eclampsia, with an odds ratio of 3.579 (95% CI 1.785–7.176), and was also positively associated with pre-eclampsia (OR = 1.145, 95% CI 1.055–1.242) and hypertension. Other risk lipids for pre-eclampsia included phosphatidylethanolamine (O-16:1_18:2), phosphatidylcholine (18:0_18:2), sphingomyelin (d34:1), triacylglycerol (54:3), and diacylglycerol (18:1_18:1), while several phosphatidylcholine species and sterol esters showed negative associations. Sensitivity analyses found no significant heterogeneity or horizontal pleiotropy for the pre-eclampsia and eclampsia results, although some heterogeneity appeared in the hypertension analysis, and triacylglycerol (54:3) showed horizontal pleiotropy in the hypertension results, a caveat the authors flag explicitly.

One striking finding was the divergent behavior of a single lipid across diseases. Phosphatidylethanolamine (O-16:1_18:2) was positively associated with pre-eclampsia risk (OR = 1.091) but negatively associated with eclampsia risk (OR = 0.418), a pattern the authors say has rarely been systematically discussed and may reflect differential regulatory mechanisms across these pregnancy complications. Triacylglycerol (50:1), by contrast, was consistently associated with increased risk across all three outcomes, which the authors argue strengthens its causal status through genetic instruments and highlights the value of lipid-species-level resolution, since lipids within the same broad class can exert distinct or even opposite biological effects depending on their fatty acyl chain composition and saturation.

To move from association to mechanism, the team turned to network toxicology. They retrieved 100 predicted toxicity-related targets of triacylglycerol (50:1) from the SwissTargetPrediction database, along with 2,068 targets for pre-eclampsia and 2,102 for eclampsia from GeneCards, and identified 51 intersecting genes. Protein–protein interaction network analysis highlighted five hub targets: prostaglandin-endoperoxide synthase 2 (PTGS2), peroxisome proliferator activated receptor gamma (PPARG), peroxisome proliferator activated receptor alpha (PPARA), the Jun proto-oncogene (JUN), and nuclear receptor subfamily 3 group C member 1 (NR3C1). Gene Ontology and KEGG enrichment analyses suggested that triacylglycerol (50:1) may contribute to pre-eclampsia and eclampsia through the PPAR signaling pathway, alongside ovarian steroidogenesis, neuroactive ligand–receptor interaction, efferocytosis, and steroid hormone biosynthesis.

Molecular docking then tested whether the selected triacylglycerol (50:1) structure could physically engage PPARA and PPARG. The predicted binding free energies were −6.3 and −7.3 kcal·mol⁻¹ respectively, both below the −5.0 kcal·mol⁻¹ threshold the authors used to indicate favorable binding. The ligand formed six hydrogen bonds with residues THR-279, ALA-333, and TYR-334 of PPARA and six hydrophobic contacts with VAL-324, TYR-334, HIS-440, and VAL-444, while with PPARG it established two hydrogen bonds with SER-289 and LEU-453 plus seven hydrophobic interactions. Molecular dynamics simulations run for 100 nanoseconds with GROMACS showed root mean square deviations within 1.0 nm, stable radii of gyration between 1.9 and 2.0 nm, and persistent hydrogen bonding, with the ligand occupying the same binding pocket throughout the simulation under the specified computational conditions.

The authors are careful about what these simulations do and do not show. They state that the in silico findings generate a testable mechanistic hypothesis but do not independently establish receptor activation or functional regulation, and that the lipidomic annotation triacylglycerol (50:1) may represent multiple molecular and positional isomers, whereas the structural analyses used only one explicitly selected structure. They also acknowledge the lack of experimental validation, the possibility that dataset heterogeneity limits generalizability across populations, and the fact that the analyses, based on GWAS summary statistics and computational modeling, cannot yet be translated into clinical screening or treatment guidance. Future work, they write, should validate these lipid markers in longitudinal maternal plasma samples, assess assay reproducibility, and determine whether they improve prediction beyond existing clinical and biochemical risk models. For now, the study offers a genetically supported map of which individual blood lipids track with pregnancy hypertensive disease risk, and a concrete molecular hypothesis, centered on the PPAR pathway, that laboratory experiments can now put to the test.

Subject of Research: Genetic and computational analysis of plasma lipid species associated with pre-eclampsia and eclampsia risk

Article Title: Deciphering the Role of Plasma Lipids in Pre-eclampsia and Eclampsia: Insights from Mendelian Randomization, Network Toxicology, and Molecular Dynamics Simulations

Article References: Zhu, J., Tang, J., Liu, S., Xiang, X., & Xiang, Y. (2026). Deciphering the Role of Plasma Lipids in Pre-eclampsia and Eclampsia: Insights from Mendelian Randomization, Network Toxicology, and Molecular Dynamics Simulations. Artery Research, 32(1), Article 38. https://doi.org/10.1007/s44200-026-00125-y

Image Credits: AI Generated

DOI: 10.1007/s44200-026-00125-y

Keywords: pre-eclampsia, eclampsia, hypertension, plasma lipidome, triacylglycerol, Mendelian randomization, network toxicology, molecular docking, molecular dynamics simulation, PPAR signaling pathway, GWAS, pregnancy complications