RNA-Sequencing Analysis Identification of Potential Biomarkers for Diagnosis of Sarcopenia

Abstract

Sarcopenia is geriatric disease associated with increased mortality and disability. Early diagnosis and intervention are required to prevent it. This study investigated biomarkers for sarcopenia by using combination of comprehensive clinical data and messenger RNA sequencing (RNA-seq) analysis obtained from peripheral blood mononuclear cells. We enrolled total 114 older adults aged 66 – 94 years (52 sarcopenia diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus and 62 normal older people). We used clinical data which were not included diagnosis criteria of sarcopenia, and stride length showed significance by logistic regression analysis (Bonferroni corrected p = .012, odds ratio = 0.06, 95% confidence interval [CI]: 0.05 to 0.40). RNA-seq analysis detected six differential expressed genes (FAR1, GNL2, HERC5, MRPL47, NUBP2, and S100A11). We also performed gene set enrichment analysis and detected two functional modules (i.e., hub genes, MYH9 and FLNA). By using any combination of the nine candidates and basic information (age and sex), risk-prediction models were constructed. The best model by using a combination of stride length, HERC5, S100A11, and FLNA, achieved a high area under the curve (AUC) of 0.91 in a validation cohort (95% CI: 0.78 to 0.95). The quantitative PCR results of the three genes were consistent with the trend observed in the RNA-seq results. When BMI was added, the model achieved a high AUC of 0.95 (95% CI: 0.84 to 0.99). We have discovered potential biomarkers for the diagnosis of sarcopenia. Further refinement may lead to their future practical use in clinical use.

Multidisciplinary approach to sarcopenia: a narrative review

“…Future research should focus on discovering and validating novel biomarkers, such as blood-based markers, genetic factors, or imaging-based parameters, that can help predict the risk, progression, and response to treatment in sarcopenia. In their study, Furutani et al [ 90 ] combined messenger RNA analysis from mononuclear cells in serum with clinical information to create a model with high diagnostic sensitivity. There have also been studies that have attempted to use circulating microRNAs (miRNAs) as biomarkers.…”