Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics

Abstract

Background: Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics.

Methods: In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia.

Results: In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis".

Conclusion: The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.

Keywords: Chinese; LC-MS; risk; sarcopenia; untargeted metabonomics.

Figure 1

OPLS-DA score plot comparing the new-onset sarcopenia group with the matched control group.

Figure 2

Statistical validation of the OPLS-DA model by permutation testing with 200 iterations.

Figure 3

Volcano plot of the differential metabolites filtered by the univariate analysis between the new-onset sarcopenia and matched control groups.

Figure 4

The pie chart illustrates the classification and quantity of significantly disturbed metabolites.

Figure 5

Hierarchical clustering revealed the profiles of important differential metabolites in samples from the new-onset sarcopenia and matched control groups. Blue indicates decreased levels, while red indicates increased levels.

Figure 6

(A) Pathway analysis indicates that sphingolipid metabolism is the most statistically enriched pathway. (B) Metabolic pathway analysis based on plasma metabolites.