Defining lipedema's molecular hallmarks by multi-omics approach for disease prediction in women

Leon G. Straub, Jan Bernd Funcke, Nolwenn Joffin, Chanmin Joung, Sara Al-Ghadban, Shangang Zhao, Qingzhang Zhu, Ilja L. Kruglikov, Yi Zhu, Paul R. Langlais, Ruth Gordillo, Karen L. Herbst, Philipp E. Scherer

Research output: Contribution to journalArticlepeer-review

Abstract

Lipedema is a chronic disease in females characterized by pathologic subcutaneous adipose tissue expansion and hitherto remains without druggable targets. In this observational study, we investigated the molecular hallmarks of lipedema using an unbiased multi-omics approach. We found adipokine dysregulation in lipedema patients participating in a cross-sectional clinical study (ClinicalTrial.gov, NCT02838277), pointing towards the adipocyte as a key player. Analyses of newly generated transcriptomic (SRA, PRJNA940039) and proteomic (ProteomeXchange, PXD058489) datasets of early- and late-stage lipedema samples revealed a local downregulation of factors involved in inflammation. Concomitantly, factors involved in cellular respiration, oxidative phosphorylation, as well as in mitochondrial organization were upregulated. Measuring a cytokine and chemokine panel in the serum of non-menopausal women, we observed little systemic changes in inflammatory markers, but a trend towards increased VEGF. Metabolomic and lipidomic analyses highlighted altered circulating glutamic acid, glutathione, and sphingolipid levels, suggesting a broader dysregulation of metabolic and inflammatory processes. We subsequently benchmarked a set of models to accurately predict lipedema using serum factor measurements (sLPM). Our study of the molecular signature of lipedema thus provides not only potential targets for therapeutic intervention, but also candidate markers of disease development and progression.

Original languageEnglish (US)
Article number156191
JournalMetabolism: Clinical and Experimental
Volume168
DOIs
StatePublished - Jul 2025

Keywords

  • Adipose tissue
  • Complement
  • Disease prediction
  • ElasticNet
  • Inflammation
  • Lipedema
  • Machine learning
  • Multi-omics
  • Random Forest classifier
  • Support vector machine

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

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