Differences in Metabolic Profiles of Individuals with Heart Failure Using High-Resolution GC/Q-TOF

Significance of the Topic

The metabolic underpinnings of heart failure remain incompletely understood despite its high prevalence and mortality. Detailed profiling of small molecules in blood plasma can reveal disease pathways, support biomarker discovery, and guide the development of targeted therapies for both reduced and preserved ejection fraction heart failure.

Objectives and Study Overview

This study aimed to establish an untargeted metabolomics workflow using high-resolution gas chromatography coupled to quadrupole time-of-flight mass spectrometry (GC/Q-TOF) to compare the plasma metabolic profiles of subjects with heart failure (both HFrEF and HFpEF) against healthy controls. By integrating multiple spectral libraries and advanced software tools, the researchers sought to maximize compound identification, evaluate statistical differences, and explore potential therapeutic insights.

Methodology and Instrumentation

• Sample Groups: Ten plasma samples each from HFrEF patients, HFpEF patients, and healthy controls.
• Extraction and Derivatization: Protein precipitation with acetonitrile:isopropanol:water (3:3:2), drying of 450 µL extract, O-methoximation, followed by trimethylsilylation with MSTFA + 1% TMCS.
• Data Acquisition: Agilent 7250 GC/Q-TOF system with retention-time locking to d27 myristic acid. Electron ionization (EI) at 70 eV and low-energy modes (15, 12, 10 eV), and positive chemical ionization (CI) at 60 eV.
• Chromatography: Agilent 8890 GC, DB-5ms Ultra Inert column (30 m × 0.25 mm, 0.25 µm) with DuraGuard. Oven program: 50 °C for 0.5 min; 10 °C/min to 325 °C, 10 min hold.
• Data Processing: SureMass deconvolution; MassHunter Unknowns Analysis 11.1; Mass Profiler Professional 15.1 for statistics; Molecular Structure Correlator 8.2 for unknown elucidation.
• Spectral Libraries: Agilent accurate mass metabolomics PCDL (>900 curated spectra), unit-mass Fiehn library, NIST23, and a custom MassBank.us PCDL (>9,000 spectra) managed via Agilent ChemVista.

Instrumentation

• Agilent 7250 GC/Q-TOF accurate-mass spectrometer
• Agilent 8890 gas chromatograph
• DB-5ms Ultra Inert column with DuraGuard
• Split/splitless inlet with Ultra Inert liner
• Helium carrier gas at 1 mL/min constant flow

Main Results and Discussion

• Compound Identification: Over 100 library hits per sample after blank subtraction; combining libraries yielded unique contributions from NIST and the accurate mass PCDL.
• Statistical Separation: PCA demonstrated clear clustering of heart failure versus healthy groups.
• Differential Metabolites: Healthy controls showed higher levels of proteinogenic amino acids; heart failure subjects exhibited increased organic acids, sterols, and nitrogen-containing compounds, including medication-related xenobiotics.
• Xenobiotic Findings: Detection of tafamidis in a cardiomyopathy patient and a chlorophenoxy herbicide in another sample.
• Unknown Elucidation: Low-energy EI and CI experiments pinpointed the molecular ion of an unknown feature, and MS/MS analysis with Molecular Structure Correlator suggested a TMS-derivatized pyrimidine triol.

Benefits and Practical Applications of the Method

• Enhanced Coverage: Integration of accurate-mass and unit-mass libraries reduces false positives and maximizes metabolite annotation.
• High Confidence: ExactMass filtering and retention index matching strengthen identification reliability.
• Versatile Workflow: Applicable to a wide range of clinical and biological studies beyond heart failure.
• Unknown Discovery: Advanced deconvolution and structure-elucidation tools uncover novel or unexpected compounds.

Future Trends and Applications

• Expansion to Other Pathologies: Adaptation of the workflow to additional diseases and intervention studies.
• Integration of Emerging Libraries: Inclusion of new spectral databases and crowd-sourced repositories for deeper coverage.
• Machine Learning Integration: Automated annotation and pathway mapping through AI-driven software.
• High-Throughput Screening: Scalable sample preparation and rapid data analysis for large cohort studies.

Conclusion

The described high-resolution GC/Q-TOF workflow, enhanced by multiple spectral libraries and robust software tools, enables comprehensive metabolic profiling of heart failure patients. The approach offers clear statistical separation, confident compound identification—including drug metabolites—and efficient elucidation of unknown features. This platform stands to advance our understanding of disease mechanisms and accelerate the discovery of therapeutic targets.

References

1. Funk M. Epidemiology of heart failure. Crit Care Nurs Clin North Am. 1993;5(4):569–573.
2. Borlaug BA, Paulus WJ. Heart failure with preserved ejection fraction: Pathophysiology, diagnosis, and treatment. Eur Heart J. 2011;32(6):670–679.
3. Beale DJ, Pinu FR, Kouremenos KA, et al. Review of GC-MS approaches to metabolomics-based research. Metabolomics. 2018;14(11):152.
4. Grapp M, Maurer HH, Desel H. Systematic forensic toxicological analysis by GC-MS. Drug Test Anal. 2016;8(8):816–825.
5. Schauer N, Steinhauser D, Strelkov S, et al. GC–MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 2005;579(6):1332–1337.
6. Stein S. Mass spectral reference libraries: An ever-expanding resource for chemical identification. Anal Chem. 2012;84(17):7274–7282.
7. Wohlgemuth G, Mehta SS, Mejia RF, et al. SPLASH, a hashed identifier for mass spectra. Nat Biotechnol. 2016;34:1099–1101.
8. Valdiviez L, Wang S, Sandhu W, et al. Comprehensive accurate mass metabolomics library and its evaluation in targeted and nontargeted data analysis workflows. Agilent Technologies Application Note 5994-5832EN. 2023.
9. Fiehn O. Metabolomics by gas chromatography-mass spectrometry: The combination of targeted and untargeted profiling. Curr Protoc Mol Biol. 2016;114:30.4.1–30.4.32.
10. Agilent GC/Q-TOF PCDL User Guide. Agilent Technologies. 2023.
11. Agilent ChemVista Library Manager Technical Overview. Agilent Technologies. 2023.