Clinical Lipidomics Reveals High Individuality and Sex Specificity of Circulatory Lipid Signatures: A Prospective Healthy Population Study.

Abstract

Lipid metabolism and circulatory lipid levels are tightly associated with the (cardio)metabolic health. Consequently, mass spectrometry (MS)-based lipidomics has emerged as a powerful phenotyping tool in epidemiological, human population, and in clinical intervention studies. However, ensuring high throughput and reproducible measurement of a wide panel of circulatory lipid species in large-scale studies poses a significant challenge. Here, we applied a recently developed quantitative LC-MS/MS lipidomics approach to a subset of 1086 fasted plasma samples belonging to apparently healthy participants from prospective Lausanne population study. This high-coverage and high-throughput HILIC-based methodology allowed for the robust measurement of 782 circulatory lipid species spanning 22 lipid classes and six orders of magnitude-wide concentration range. This was achieved by combining semi-automated sample preparation using a stable isotope dilution approach and the alternate analysis of NIST plasma reference material, as a quality control. Based on NIST QC analysis, median between-batch reproducibility was 8.5%, over the course of analysis of 13 independent batches comprising 1086 samples collected from 364 individuals at three time points. Importantly, the biological variability, per lipid species, was significantly higher than the batch-to-batch analytical variability. Furthermore, the significantly lower between-subject (compared to within-subject) variability, and unsupervised sample clustering demonstrated the high individuality and sex-specificity of circulatory lipidome. The most prominent sex differences were reported for sphingomyelins and ether-linked phospholipids present in significantly higher concentrations in female plasma. The high individuality and sex specificity of circulatory lipidome constitute important advantages for the application of lipidomics in next-generation metabolic health monitoring.