Transcriptomic Characteristics of Suboptimal Health Status: Potential Utility of Novel Biomarkers for Predictive, Preventive, and Personalized Medicine Strategy

Abstract Background The early diagnosis of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising “omics” technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive diagnosis and targeted prevention of SHS. Methods To explore the potential diagnostic biomarkers, biological functions, and signaling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Results Transcriptome analysis identified a total of 46 differentially expressed genes, in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts was selected as candidate diagnostic biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub gene related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM and TMEM98. A transcriptome diagnosis model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% Confidence Interval (CI): 73.9%-92.7%), a specificity of 90.0% (95% CI: 82.4%-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882–0.994). Conclusion Blood transcripts are potentially objective biomarkers for the SHS diagnosis. These findings determine the potential utility of SHS-related transcriptomic biomarkers for PPPM of chronic diseases.