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

Area of Science:

• Clinical lipidomics
• Systems biology
• Biomedical data science

Background:

• Clinical lipidomics offers high-resolution molecular insights into disease but struggles with data interpretation.
• Current methods often prioritize statistical differences over structural lipid information.
• LipiDecipher addresses this by summarizing lipidomic alterations into interpretable structural patterns.

Purpose of the Study:

• To introduce LipiDecipher, a structure-oriented analytical framework for clinical lipidomics.
• To translate complex lipid species data into understandable biological insights.
• To provide database-supported biological contextualization for lipidomic findings.

Main Methods:

• LipiDecipher integrates differential lipid analysis, structure-resolved summarization, and multivariate discrimination.
• It incorporates knowledge-based lipid-to-protein/pathway contextualization.
• The framework was applied to a retrospective serum lipidomics dataset of acute myocardial infarction (AMI) patients and controls, with sex-disaggregated and covariate-adjusted analyses.

Main Results:

• The framework identified distinct lipid alterations in AMI patients, including changes in phosphatidylinositols, ceramides, and triglyceride remodeling.
• Summarizing alterations by lipid structure (class, acyl-chain length, unsaturation) enhanced interpretability.
• Knowledge-based mapping prioritized biologically relevant contexts such as glycerophospholipid and sphingolipid metabolism, membrane remodeling, and inflammatory signaling.

Conclusions:

• LipiDecipher offers a structure-oriented, interpretation-focused approach for clinical lipidomics.
• It successfully prioritized candidate lipid signatures and generated plausible hypotheses in an AMI cohort.
• The framework serves as a valuable hypothesis-generating tool, requiring external validation for clinical application.