The early identification of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) in 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 identification and targeted prevention of SHS. To explore the potential biomarkers, biological functions, and signalling 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. Transcriptome analysis identified a total of 46 differentially expressed genes (DEGs), in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts were selected as candidate predictive 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 genes related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM, and TMEM98. A transcriptome predictive 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). In the present study, we demonstrated that blood (buffy coat) samples appear to be a very promising and easily accessible biological material for the transcriptomic analyses focused on the objective identification of SHS by using our transcriptome predictive model. The pattern of particularly determined DEGs can be used as predictive transcriptomic biomarkers for the identification of SHS in an individual who may, subjectively, feel healthy, but at the level of subcellular mechanisms, the changes can provide early information about potential health problems in this person. Our findings also indicate the potential therapeutic targets in dealing with chronic diseases related to SHS, such as T2DM and CVD, and an early onset of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases, as well as the findings suggest the targets for personalized interventions as promoted in PPPM.
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Area under the curve
Bone morphogenetic proteins
Differentially expressed genes
Extracellular signal-regulated kinase-1
False discovery rate
Kyoto Encyclopedia of Genes and Genomes
Nonalcoholic fatty liver disease
Preventive, predictive, and personalized medicine
Receiver operating characteristic
Suboptimal health status
Suboptimal health status questionnaire-25
Search tool for the retrieval of interacting genes
Type 2 diabetes mellitus
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The authors acknowledge the participants and their families who donated their time and effort in helping to make this study possible.
This work was partially supported by the “Beijing Talents Project (2020A17)” and the National Natural Science Foundation of China (Grant Numbers: 81872682 & 81773527). HW was supported by the China Scholarship Council (CSC 201708110200).
Ethical approval and consent to participate
This study was approved by the Ethics Committee of the Weifang University, Weifang, China. Written informed consent was obtained from each participant at the beginning of the study. The ethics approval was given in compliance with the Declaration of Helsinki.
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Abbreviations for all particular genes can be found at the following link: www.ncbi.nlm.nih.gov/gene/
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Wang, H., Tian, Q., Zhang, J. et al. Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy. EPMA Journal 12, 103–115 (2021). https://doi.org/10.1007/s13167-021-00238-1
- Predictive preventive personalized medicine
- Transcriptome profiling
- Suboptimal health status
- Predictive biomarkers
- Novel transcriptomic biomarkers
- Transcriptome predictive model
- ATP-binding cassette transporter
- Glucokinase regulator