Lipid Makeover: How Aging Transforms Adipose and Liver Tissues

Importance of the Topic

Aging reshapes lipid composition in key metabolic organs, undermining energy balance and increasing risk for cardiovascular and metabolic diseases. Understanding age-related lipid remodeling in white and brown adipose tissues and liver is essential for identifying biomarkers of metabolic inflexibility and developing targeted interventions.

Objectives and Overview of the Study

This study applied untargeted lipidomics to characterize how aging alters lipid profiles in three metabolically active tissues of C57BL/6J mice. Young, mature, and old cohorts (n=8 per group, 72 total samples) were compared to map tissue-specific lipid changes and uncover trends associated with chronological aging.

Methodology and Instrumentation

Sample Preparation:

• Tissue samples (10–15 mg) of liver, white adipose tissue, and brown adipose tissue were homogenized with methanol containing an internal standard mix.
• Lipids were extracted via a methanol/MTBE/water protocol, phase separated, and the organic layer dried under nitrogen.
• Extracts were reconstituted in methanol/chloroform before analysis.

Chromatography and Mass Spectrometry:

• Reversed-phase separation on a ZORBAX Eclipse Plus C18 column using a 16-minute gradient.
• Detection on an Agilent 1290 Infinity III Bio LC coupled to a Revident LC/Q-TOF system operated in both positive and negative ion modes (m/z 1700 tuning).
• Iterative MS/MS acquisitions on pooled QC samples for structural confirmation and wide dynamic range profiling.

Data Processing:

• Normalization by internal standard and tissue weight.
• Feature extraction and lipid annotation in MS-DIAL and Agilent Lipid Annotator/MassHunter Explorer workflows.
• Visualization and statistical analysis in lipidR with principal component analysis, volcano plots, and chain-length trend assessments.

Main Results and Discussion

PCA demonstrated clear separation by tissue type, confirming unique lipidomes for liver, WAT, and BAT. Aging led to progressive alterations in lipid abundance: mature vs young groups showed moderate changes, while old vs young comparisons revealed hundreds of differentially abundant species in liver (351), BAT (266), and WAT (256). Key observations included:

• Enrichment of long-chain and very-long-chain triacylglycerols and ether-linked TGs across all tissues with age.
• BAT displayed a shift from shorter to longer lipid chains around a total chain length of 46 carbons in older mice.
• WAT showed stronger positive fold changes for very-long-chain lipids in old vs young comparisons.
• Liver samples from aged mice exhibited increased lipids between 44 and 66 total carbons, indicating altered hepatic lipid metabolism.

These shifts suggest systemic loss of lipid homeostasis, mitochondrial dysfunction, and altered membrane composition in aging tissues.

Benefits and Practical Applications of the Method

The high-resolution Revident LC/Q-TOF platform enables comprehensive detection of low-abundance lipids and broad dynamic range analysis. Untargeted lipidomics provides a powerful screening tool for aging biomarkers, drug efficacy studies, and nutritional interventions. The approach can be integrated into preclinical research pipelines to assess metabolic health and guide therapeutic development.

Future Trends and Possible Applications

Advancements in high-throughput lipidomics and machine learning will further refine lipidome-based aging signatures. Integrating multi-omics data with single-cell and spatial lipidomics will reveal tissue microenvironment remodeling. Translational studies in human cohorts and application to age-related disease models will accelerate biomarker discovery and personalized interventions.

Conclusion

This work highlights how untargeted lipid profiling across adipose and liver tissues uncovers age-dependent accumulation of long-chain and ether lipids. The Revident LC/Q-TOF platform proved effective for deep lipidome coverage, revealing systemic lipid dysregulation in aging. These findings lay the groundwork for future aging biomarker development and targeted metabolic therapies.

Reference

1. Huynh et al. A Comprehensive, Curated, High-Throughput Method for the Detailed Analysis of the Plasma Lipidome. Agilent Application Note 5994-3447EN, 2021.
2. Tsugawa et al. A Lipidome Atlas in MS-DIAL 4. Nature Biotechnology, 2020.
3. Mohamed et al. lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets. Journal of Proteome Research, 2020.