LipiDecipher: A Structure-Oriented Analytical Framework for Interpretable Clinical Lipidomics

Clinical lipidomics can capture disease-associated molecular alterations at high resolution, yet translating complex lipid species data into interpretable biological insight remains challenging. Existing workflows often emphasize statistical discrimination while underutilizing the structural information embedded in lipid species. To address this gap, we developed LipiDecipher, a structure-oriented analytical framework designed to summarize lipidomic alterations into interpretable structural patterns and to provide database-supported biological contextualization.

LipiDecipher integrates differential lipid analysis, structure-resolved summarization, multivariate discrimination, and knowledge-based lipid-to-protein/pathway contextualization. We applied this framework to a retrospective serum lipidomics dataset comprising healthy controls and patients with acute myocardial infarction or post-PCI recurrent myocardial infarction. To improve transparency and robustness, the revised analysis includes sex-disaggregated reporting, covariate-adjusted sensitivity analyses for sex and age, and internal separation stability assessment of category-specific LDA projections through resampling-based feature stability analysis, repeated cross-validation, and permutation testing.

The framework identified distinct lipid alterations across study groups, including changes in phosphatidylinositols, ceramides, and triglyceride remodeling patterns. These alterations became more interpretable when summarized at the structural level, including lipid class composition, acyl-chain length, and degree of unsaturation. Internal discrimination analyses suggested separability between groups, while repeated resampling highlighted a subset of recurrently selected lipid features. Knowledge-based mapping prioritized lipid-associated biological contexts related to glycerophospholipid metabolism, sphingolipid metabolism, membrane remodeling, inflammatory signaling, and energy-related processes. Importantly, these protein- and pathway-level outputs are presented as database-supported hypotheses rather than direct evidence of target engagement or pathway activation in the studied cohort.

LipiDecipher provides a structure-oriented and interpretation-focused framework for clinical lipidomics. In a retrospective acute myocardial infarction cohort, it enabled the prioritization of candidate lipid signatures and biologically plausible hypotheses from complex lipidomic data. These findings support its use as a hypothesis-generating analytical tool, while external validation and experimental follow-up remain necessary before mechanistic or clinical claims can be established.