Differential Analysis of Aging by Sex Using Correlation Analysis of Primary Metabolites

Significance of the Topic

Age and sex influence primary metabolite profiles, contributing to disease susceptibility and functional decline in the elderly. Analytical characterization of these changes supports early detection of metabolic imbalances and informs targeted interventions.

Objectives and Study Overview

This study aimed to quantify serum primary metabolites in healthy individuals aged 70–85 and to elucidate sex-specific and age-dependent variations. Serum from 10 donors (5 men, 5 women) was analyzed by GC-MS and multivariate correlation to reveal characteristic metabolic patterns.

Methodology

Serum samples underwent derivatization and preprocessing according to a standardized metabolomics protocol. GC-MS analysis was performed in multiple reaction monitoring mode targeting 604 metabolites over a 37-minute runtime. Data were processed and visualized using a Multi-omics Analysis Package, enabling pathway mapping, principal component analysis, and correlation statistics.

Instrumentation Used

• GC-MS system: GCMS-TQ™8040 NX equipped for MRM acquisition
• Smart Metabolites Database™ Ver.2 for compound identification and quantitation
• Multi-omics Analysis Package software for data visualization and statistical analysis

Main Results and Discussion

Among 604 targets, 243 compounds were consistently detected and mapped onto metabolic pathways. Chronological analysis revealed divergent glutamate trajectories: levels rose with age in women but declined in men. Correlation mapping identified creatinine as highly associated with glutamate changes (R=0.93), glutamine moderate (R=0.46), and spermine minimal (R=0.10). These findings highlight sex-specific metabolic responses during aging.

Benefits and Practical Applications

This workflow delivers a robust platform for comprehensive primary metabolite profiling, facilitating biomarker discovery and monitoring of sex- and age-related metabolic shifts. It can be applied in clinical research, quality control laboratories, and nutritional or pharmacological studies addressing elderly health.

Future Trends and Applications

• Integration with multi-omics (proteomics, lipidomics) for holistic aging studies
• Expansion to larger, diverse cohorts to validate biomarkers
• Coupling with real-time data analytics and machine learning for predictive modeling
• Application in personalized nutrition, precision medicine, and drug development targeting age-related disorders

Conclusion

The combination of GC-MS profiling with advanced pathway visualization identified distinct sex- and age-dependent alterations in primary metabolism among elderly subjects. This approach enhances our understanding of metabolic health in aging and supports targeted strategies for disease prevention and management.

References

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