Skip to main content

Blood transcriptome profiling as potential biomarkers of suboptimal health status: potential utility of novel biomarkers for predictive, preventive, and personalized medicine strategy


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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6



ATP-binding cassette


Area under the curve


Bone morphogenetic proteins


Biological process


Cellular component


Complementary DNA


Confidence interval


Cardiovascular disease


Differentially expressed genes


Extracellular signal-regulated kinase-1


False discovery rate


Gene Ontology


Interquartile ranges


Kyoto Encyclopedia of Genes and Genomes


Molecular function


Nonalcoholic fatty liver disease


Protein–protein interaction


Preventive, predictive, and personalized medicine


RNA sequencing


Receiver operating characteristic


Standard deviation


Suboptimal health status


Suboptimal health status questionnaire-25


Search tool for the retrieval of interacting genes


Type 2 diabetes mellitus


  1. 1.

    Golubnitschaja O, Watson ID, Topic E, Sandberg S, Ferrari M, Costigliola V. Position paper of the EPMA and EFLM: a global vision of the consolidated promotion of an integrative medical approach to advance health care. EPMA J. 2013;4(1):12.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Golubnitschaja O, Baban B, Boniolo G, Wang W, Bubnov R, Kapalla M, et al. Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J. 2016;7:23.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Golubnitschaja O, Costigliola V, Epma. General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine. EPMA J. 2012;3(1):14.

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Adua E, Roberts P, Wang W. Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population. EPMA J. 2017;8(4):345–55.

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Ge S, Xu X, Zhang J, Hou H, Wang H, Liu D, et al. Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study. EPMA J. 2019;10(1):1–8.

    Google Scholar 

  6. 6.

    Yan Y, Dong J, Liu Y, Yang X, Li M, Shia G, et al. Association of suboptimal health status and cardiovascular risk factors in urban Chinese workers. J Urban Health. 2012;89(2):329–38.

    PubMed  Google Scholar 

  7. 7.

    Wang Y, Liu X, Qiu J, Wang H, Liu D, Zhao Z, et al. Association between Ideal Cardiovascular Health Metrics and Suboptimal Health Status in Chinese Population. Sci Rep. 2017;7(1):14975.

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Yan Y, Liu Y, Li M, Hu P, Guo A, Yang X, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19(6):333–41.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kupaev V, Borisov O, Marutina E, Yan YX, Wang W. Integration of suboptimal health status and endothelial dysfunction as a new aspect for risk evaluation of cardiovascular disease. EPMA J. 2016;7(1):19.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Anto EO, Roberts P, Coall D, Turpin CA, Adua E, Wang Y, et al. Integration of suboptimal health status evaluation as a criterion for prediction of preeclampsia is strongly recommended for healthcare management in pregnancy: a prospective cohort study in a Ghanaian population. EPMA J. 2019;10(3):211–26.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wang Y, Ge S, Yan Y, Wang A, Zhao Z, Yu X, et al. China suboptimal health cohort study: rationale, design and baseline characteristics. J Transl Med. 2016;14(1):291.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Gerner C, Costigliola V, Golubnitschaja O. Multiomic patterns in body fluids: technological challenge with a great potential to implement the advanced paradigm of 3P medicine. Mass Spectrom Rev. 2020;39(5–6):442–51.

    CAS  PubMed  Google Scholar 

  14. 14.

    Yan Y, Wu LJ, Xiao H, Wang S, Dong J, Wang W. Latent class analysis to evaluate performance of plasma cortisol, plasma catecholamines, and SHSQ-25 for early recognition of suboptimal health status. EPMA J. 2018;9(3):299–305.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Alzain MA, Asweto CO, Zhang J, Fang H, Zhao Z, Guo X, et al. Telomere length and accelerated biological aging in the China suboptimal health cohort: a case-control study. OMICS. 2017;21(6):333–9.

    CAS  PubMed  Google Scholar 

  16. 16.

    Sun Q, Xu X, Zhang J, Sun M, Tian Q, Li Q, et al. Association of suboptimal health status with intestinal microbiota in Chinese youths. J Cell Mol Med. 2019;24(2):1837–47.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wang H, Tian Q, Zhang J, Liu H, Zhang X, Cao WJ, et al. Population-based case-control study revealed metabolomic biomarkers of suboptimal health status in Chinese population-potential utility for innovative approach by predictive, preventive, and personalized medicine. EPMA J. 2020;11(2):147–60.

  18. 18.

    Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. Transcriptome profiling in human diseases: new advances and perspectives. Int J Mol Sci. 2017;18(8):1652.

    PubMed Central  Google Scholar 

  21. 21.

    Yan Y, Dong J, Liu Y, Zhang J, Song M, He Y, et al. Association of suboptimal health status with psychosocial stress, plasma cortisol and mRNA expression of glucocorticoid receptor alpha/beta in lymphocyte. Stress. 2015;18(1):29–34.

    CAS  PubMed  Google Scholar 

  22. 22.

    Sweetman E, Ryan M, Edgar C, MacKay A, Vallings R, Tate W. Changes in the transcriptome of circulating immune cells of a New Zealand cohort with myalgic encephalomyelitis/chronic fatigue syndrome. Int J Immunopathol Pharmacol. 2019;33:2058738418820402.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Tomas-Roig J, Havemann-Reinecke U. Gene expression signature in brain regions exposed to long-term psychosocial stress following acute challenge with cannabinoid drugs. Psychoneuroendocrinology. 2019;102:1–8.

    CAS  PubMed  Google Scholar 

  24. 24.

    Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    PubMed  Google Scholar 

  25. 25.

    Soldatos CR, Dikeos DG, Paparrigopoulos TJ. Athens Insomnia Scale: validation of an instrument based on ICD-10 criteria. J Psychosom Res. 2000;48(6):555–60.

    CAS  PubMed  Google Scholar 

  26. 26.

    Shear MK, Vander Bilt J, Rucci P, Endicott J, Lydiard B, Otto MW, et al. Reliability and validity of a structured interview guide for the Hamilton Anxiety Rating Scale (SIGH-A). Depress Anxiety. 2001;13(4):166–78.

    CAS  PubMed  Google Scholar 

  27. 27.

    Bagby RM, Ryder AG, Schuller DR, Marshall MB. The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry. 2004;161(12):2163–77.

    PubMed  Google Scholar 

  28. 28.

    Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30.

    CAS  Google Scholar 

  29. 29.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 2003;31(1):258–61.

    Google Scholar 

  32. 32.

    Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    R Foundation for Statistical Computing. R: a language and environment for statistical computing. 2017. Accessed 22 Feb 2021.

  34. 34.

    Golubnitschaja O, Costigliola V. Common origin but individual outcomes: time for new guidelines in personalized healthcare. Per Med. 2010;7(5):561–8.

    PubMed  Google Scholar 

  35. 35.

    Van Schaftingen E. A protein from rat liver confers to glucokinase the property of being antagonistically regulated by fructose 6-phosphate and fructose 1-phosphate. Eur J Biochem. 1989;179(1):179–84.

    PubMed  Google Scholar 

  36. 36.

    Agius L. Glucokinase and molecular aspects of liver glycogen metabolism. Biochem J. 2008;414(1):1–18.

    CAS  PubMed  Google Scholar 

  37. 37.

    Haeusler RA, Camastra S, Astiarraga B, Nannipieri M, Anselmino M, Ferrannini E. Decreased expression of hepatic glucokinase in type 2 diabetes. Mol Metab. 2015;4(3):222–6.

    CAS  PubMed  Google Scholar 

  38. 38.

    Peter A, Stefan N, Cegan A, Walenta M, Wagner S, Konigsrainer A, et al. Hepatic glucokinase expression is associated with lipogenesis and fatty liver in humans. J Clin Endocrinol Metab. 2011;96(7):E1126–30.

    PubMed  Google Scholar 

  39. 39.

    Orho-Melander M, Melander O, Guiducci C, Perez-Martinez P, Corella D, Roos C, et al. Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes. 2008;57(11):3112–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Diabetes Genetics Initiative of Broad Institute of H, Mit LU, Novartis Institutes of BioMedical R, Saxena R, Voight BF, Lyssenko V, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(5829):1331–6.

    Google Scholar 

  41. 41.

    Zain SM, Mohamed Z, Mohamed R. Common variant in the glucokinase regulatory gene rs780094 and risk of nonalcoholic fatty liver disease: a meta-analysis. J Gastroenterol Hepatol. 2015;30(1):21–7.

    CAS  PubMed  Google Scholar 

  42. 42.

    Kaushik A, Kaushik M. Recent updates on glucokinase activators and glucokinase regulatory protein disrupters for the treatment of type 2 diabetes mellitus. Curr Diabetes Rev. 2019;15(3):205–12.

    CAS  PubMed  Google Scholar 

  43. 43.

    Fishman GI, Eddy RL, Shows TB, Rosenthal L, Leinwand LA. The human connexin gene family of gap junction proteins: distinct chromosomal locations but similar structures. Genomics. 1991;10(1):250–6.

    CAS  PubMed  Google Scholar 

  44. 44.

    Beyer EC, Paul DL, Goodenough DA. Connexin family of gap junction proteins. J Membr Biol. 1990;116(3):187–94.

    CAS  PubMed  Google Scholar 

  45. 45.

    Schulz R, Gorge PM, Gorbe A, Ferdinandy P, Lampe PD, Leybaert L. Connexin 43 is an emerging therapeutic target in ischemia/reperfusion injury, cardioprotection and neuroprotection. Pharmacol Ther. 2015;153:90–106.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Squecco R, Sassoli C, Nuti F, Martinesi M, Chellini F, Nosi D, et al. Sphingosine 1-phosphate induces myoblast differentiation through Cx43 protein expression: a role for a gap junction-dependent and -independent function. Mol Biol Cell. 2006;17(11):4896–910.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Urist MR. Bone: formation by autoinduction. Science. 1965;150(3698):893–9.

    CAS  Google Scholar 

  48. 48.

    Dyer LA, Pi X, Patterson C. The role of BMPs in endothelial cell function and dysfunction. Trends Endocrinol Metab. 2014;25(9):472–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Wang RN, Green J, Wang Z, Deng Y, Qiao M, Peabody M, et al. Bone Morphogenetic Protein (BMP) signaling in development and human diseases. Genes Dis. 2014;1(1):87–105.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Roskoski R Jr. ERK1/2 MAP kinases: structure, function, and regulation. Pharmacol Res. 2012;66(2):105–43.

    CAS  PubMed  Google Scholar 

  51. 51.

    Muslin AJ. MAPK signalling in cardiovascular health and disease: molecular mechanisms and therapeutic targets. Clin Sci (Lond). 2008;115(7):203–18.

    CAS  Google Scholar 

  52. 52.

    Tanti JF, Jager J. Cellular mechanisms of insulin resistance: role of stress-regulated serine kinases and insulin receptor substrates (IRS) serine phosphorylation. Curr Opin Pharmacol. 2009;9(6):753–62.

    CAS  PubMed  Google Scholar 

  53. 53.

    Kim EK, Choi EJ. Pathological roles of MAPK signaling pathways in human diseases. Biochim Biophys Acta. 2010;1802(4):396–405.

    CAS  PubMed  Google Scholar 

  54. 54.

    Qian S, Golubnitschaja O, Zhan X. Chronic inflammation: key player and biomarker-set to predict and prevent cancer development and progression based on individualized patient profiles. EPMA J. 2019;10(4):365–81.

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Higgins CF. ABC transporters: from microorganisms to man. Annu Rev Cell Biol. 1992;8:67–113.

    CAS  PubMed  Google Scholar 

  56. 56.

    Sun H, Molday RS, Nathans J. Retinal stimulates ATP hydrolysis by purified and reconstituted ABCR, the photoreceptor-specific ATP-binding cassette transporter responsible for Stargardt disease. J Biol Chem. 1999;274(12):8269–81.

    CAS  PubMed  Google Scholar 

  57. 57.

    Berge KE, Tian H, Graf GA, Yu L, Grishin NV, Schultz J, et al. Accumulation of dietary cholesterol in sitosterolemia caused by mutations in adjacent ABC transporters. Science. 2000;290(5497):1771–5.

    CAS  PubMed  Google Scholar 

  58. 58.

    Chen Y, Weng Z, Liu Q, Shao W, Guo W, Chen C, et al. FMO3 and its metabolite TMAO contribute to the formation of gallstones. Biochim Biophys Acta Mol Basis Dis. 2019;1865(10):2576–85.

    CAS  PubMed  Google Scholar 

  59. 59.

    Zhu Y, Li T, Din AU, Hassan A, Wang Y, Wang G. Beneficial effects of Enterococcus faecalis in hypercholesterolemic mice on cholesterol transportation and gut microbiota. Appl Microbiol Biotechnol. 2019;103(7):3181–91.

    CAS  PubMed  Google Scholar 

  60. 60.

    Yeghiazaryan K, Flammer J, Golubnitschaja O. Predictive molecular profiling in blood of healthy vasospastic individuals: clue to targeted prevention as personalised medicine to effective costs. EPMA J. 2010;1(2):263–72.

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    Ding G, Zhao X, Wang Y, Song D, Chen D, Deng Y, et al. Evaluation of the relationship between cognitive impairment and suboptimal health status in a northern Chinese population: a cross-sectional study. J Glob Health. 2020;10(1):010804.

    PubMed  PubMed Central  Google Scholar 

Download references


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).

Author information




HW and YW participated in the design of the study. HW, QT, JZ, HL, WC, XZ, XL, LW, MS and YK performed participant enrollment and collected the samples. HW, QT and JXZ performed the Transcriptome analysis. HW and QT performed the statistical analysis and drafted the manuscript. YK, WW and YW revised the manuscript.

Corresponding author

Correspondence to Youxin Wang.

Ethics declarations

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.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abbreviations for all particular genes can be found at the following link:

Supplementary Information

Below is the link to the electronic supplementary material.


(PDF 120 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation


  • Predictive preventive personalized medicine
  • Transcriptome profiling
  • Suboptimal health status
  • Predictive biomarkers
  • Novel transcriptomic biomarkers
  • Transcriptome predictive model
  • ATP-binding cassette transporter
  • Neurodegeneration
  • Glucokinase regulator