Unraveling the protein-metabolite network of sarcopenia in plasma: A large-scale Mendelian randomization study

Some plasma molecules may have an effect on sarcopenia, but it is not fully understood. We aimed to comprehensively explore the causal effects of plasma proteins and metabolites on sarcopenia traits, and to unravel their network.

A two-sample Mendelian randomization design was adopted. The levels of 4,907 plasma proteins from 35,559 Icelanders, and 1,400 plasma metabolites from 8,299 Europeans, were set as exposures. Low handgrip strength, appendicular lean mass, and usual walking pace from Europeans were set as outcomes. Inverse-variance weighted (IVW) and four other methods, along with sensitivity analyzes, were performed to estimate the causal effects. Enrichment and pathway analyzes were conducted to present their characteristics. IVW was used to estimate the bidirectional relationships between sarcopenia-related proteins and metabolites, and to visualize them within a network.

We identified 76 relationships between proteins and sarcopenia traits. The absolute values of causal effects (β_IVW) ranging from 0.01 to 0.35. IL2, AIF1, GDNF, CXCL13, LRRTM3, and SLPI were the top six proteins ranked by causal effects. Additionally, 22 relationships between metabolites and sarcopenia traits were identified, with absolute values of β_IVW ranging from 0.02 to 0.22. Sulfate and serine/pyruvate ratio had the highest values. The network diagram showed some key nodes, such as ISOC1, GSTA1, tryptophan and 5α-androstan-3α,17β-diol monosulfate.

This work unraveled a molecular network of sarcopenia in plasma for the first time and identified some key proteins and metabolites. It may help to understand the mechanisms of sarcopenia, providing new insights for predicting, diagnosing and treating sarcopenia.