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Maternal circulating lipid metabolic heterogeneity in preeclampsia: machine learning identifies clinically distinct
Jianfeng Gan1, Li Cao1, Jiejie Shao1
1Obstetrics and Gynecology Hospital of Fudan University, No. 419, Road Fangxie, Shanghai, 200011, China.
Background:
Preeclampsia is associated with dyslipidemia. Maternal circulating lipid profiles may reflect underlying placental-metabolic interactions. We aimed to identify distinct maternal circulating lipid metabolic subtypes and assess their association with clinical outcomes among preeclamptic women.
Methods:
A retrospective cohort study of 1,124 women with preeclampsia (2018-2022) was conducted. Third-trimester serum lipids (total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)) were analyzed. Unsupervised k-means clustering (Euclidean distance, k = 4 optimized by consensus culmulative distribution function) was used to identify subtypes. Clinical characteristics and pregnancy outcomes were compared across clusters.
Results:
We identified four lipid metabolic subtypes of preeclampsia with distinct clinical profiles. Four subtypes were identified: Cluster 1 (low metabolic subtype, LMS, 33.5%), Cluster 2 (high HDL subype, HHS, 35.0%), Cluster 3 (high triglyceride subtype, HTS, 11.7%), and Cluster 4 (high metabolic subtype, HMS, 19.8%). The HTS subtype with elevated triglycerides and highest pre-pregnancy body mass index (median 23.92 kg/m2), had the lowest rates of preterm birth (3.1%, p = 0.016) and low birth weight (3.8%, p < 0.001). Conversely, the HMS subtype exhibited the highest blood pressure and proteinuria levels.
Conclusions:
This study establishes lipid-driven subtypes of preeclampsia with distinct clinical trajectories, suggesting placental lipid handling may be a key modulator in disorder heterogeneity. This heterogeneity underscores the importance of lipid-based stratification for understanding placental dysfunction in preeclampsia.
