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Published on: March 21, 2021
Integrating genetics and transcriptome analyses identify potential biomarkers and immune interactions in metabolic
Wei Fu1, Ning Chang2, Han Liang3
1Department of Geriatrics, Xijing Hospital, Air Force Medical University, No. 127, Changle West Road, Xi'an, 710032, Shaanxi, China.
Background:
Increasing evidence has indicated that metabolic syndrome (MetS) exists in a close link with sarcopenia; however, the potential mechanism and biomarkers between them remain uninvestigated. This study leverages integrative genetics and transcriptome to identify potential biomarkers and immune interactions in MetS-related sarcopenia.
Methods:
We used genome-wide association studies summary statistics for linkage disequilibrium score regression and MiXeR analyses to explore shared genetic architecture between MetS and sarcopenia-related traits. Causal associations were assessed via Mendelian randomization (MR), causal analysis using the summary effect, and summary data-based MR. Cross-phenotype association analysis identified pleiotropic variants, while transcriptome-wide association study revealed shared pleiotropic genes. Single-cell RNA sequencing mapped gene distribution across immune cells and intercellular communication. A clinical predictive model and MetS animal model validated the pleiotropic genes.
Results:
LDSC analyses uncovered an aggregate group of 13 pairs demonstrating notable genetic correlations. A significant genetic overlap took place between MetS and sarcopenia-related traits. The causal association of MetS with WP and ALM were found. Furthermore, we determined 79 shared risk novel SNPs and 9 pleiotropic genes. These genes had a strong connection to immune cell infiltration, immune-related markers, and immun-related processes. We demonstrated varying levels of gene expression across different immune cell types using scRNA-seq data. Nine genes were selected for developing a clinical predictive model. For the predictive chart used to predict binary risks, the area under the curve in the training data was 0.75, and in the validation data it was 0.72. Finally, five genes were confirmed in the MetS animal model based on mRNA expression level.
Conclusion:
This research offers compelling proof of a shared genetic architecture and uncovered immune interactions between MetS and sarcopenia. Our unique innovation reveals the comorbidity mechanism of metabolic syndrome and sarcopenia from a genetic perspective. The discovery of pleiotropic genes provided potential targets for intervention in MetS-related sarcopenia.
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