EPMA Journal

, Volume 9, Issue 3, pp 299–305 | Cite as

Latent class analysis to evaluate performance of plasma cortisol, plasma catecholamines, and SHSQ-25 for early recognition of suboptimal health status

  • Yu-Xiang Yan
  • Li-Juan Wu
  • Huan-Bo Xiao
  • Shuo Wang
  • Jing Dong
  • Wei WangEmail author



Chronic stress is associated with suboptimal health status (SHS) which is a new public health challenge in China and worldwide. Plasma stress hormones may act as potential objective biomarkers for SHS measure. This study was aimed to evaluate the diagnostic performance of plasma cortisol, catecholamine adrenaline/noradrenaline, and SHS questionnaires (SHSQ) for SHS using latent class analysis (LCA) in the absence of a gold standard.


A cross-sectional study was conducted among 868 employees in Beijing. The SHS questionnaires-25 (SHSQ-25) was distributed, and plasma cortisol, adrenaline, and noradrenaline were measured in the survey. LCA was used to assess the performance of both subjective and objective measures for SHS recognition.


Akaike information criterion (AIC) and consistent AIC (CAIC) was 14.11 and 54.48 respectively, indicating that the model was well fitted. The sensitivity and specificity of plasma cortisol were 0.836 (95% CI 0.811–0.861) and 0.840 (95% CI 0.816–0.864), respectively. The area under curve (AUC) of receiver operating characteristic (ROC) of SHSQ-25 was 0.743 (95% CI 0.709–777), while the AUC of plasma adrenaline was 0.688 (95% CI 0.651–0.725). The prevalence of SHS in the investigated population was 34.78%.


Plasma cortisol is a valuable biomarker for SHS detection, whereas SHSQ-25 is more suitable for SHS screening in the population-based health survey. The accuracy and applicability of plasma adrenaline are inferior to cortisol and SHSQ-25, respectively. LCA has merit to evaluate performance of plasma cortisol, catecholamines, and SHSQ-25 for recognition of SHS in the absence of a gold standard test.


Suboptimal health status Cortisol Catecholamine Latent class analysis Early recognition Prediction 



adrenocorticotropic hormone


Akaike information criterion


consistent Akaike information criterion


corticotropin-releasing factor






latent class analysis


predictive, preventive and personalized medicine


receiver operating characteristic


suboptimal health status


suboptimal health status questionnaire-25


sympathetic nervous system



This study was supported by the National Natural Science Foundation (81102208, 81573214), the Beijing Municipal Natural Science Foundation (7162020), and the Scientific Research Project of Beijing Municipal Educational Committee (KM201510025006).

Author contributions

Yu-Xiang Yan and Wei Wang designed the study; Jing Dong and Shuo Wang collected the data; Yu-Xiang Yan and Li-Juan Wu conducted the experiments’ statistical analyses. Yu-Xiang Yan and Huan-Bo Xiao conducted the experiments. All authors interpreted the data, and all authors contributed to writing. All authors have approved the final manuscript.

Compliance with ethical standards

The study was approved by the Ethical Committee of Capital Medical University and was conducted in accordance with Good Clinical Practice within the tenets of the Declaration of Helsinki. Each participant was required to sign an informed consent form before being enrolled in the study and prior to any measurements being taken.

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Yan YX, Wang W. Advances in research of suboptimal health status. Chin J Public Health (in Chin). 2008;24:1037–8.Google Scholar
  2. 2.
    Chen SW. Job stress models, depressive disorders and work performance of engineers in microelectronics industry. Int Arch Occup Environ Health. 2011;84:91–103.CrossRefPubMedGoogle Scholar
  3. 3.
    Grynderup MB, Mors O, Hansen ÅM, Andersen JH, Bonde JP, Kærgaard A, et al. Work-unit measures of organisational justice and risk of depression: a 2-year cohort study. Occup Environ Med. 2013;70:380–5.CrossRefPubMedGoogle Scholar
  4. 4.
    Pranita A, Balsubramaniyan B, Phadke AV, Tambe DB, Apte GM, Kharche JS, et al. Association of occupational & prediabetes statuses with obesity in middle aged women. J Clin Diagn Res. 2013;7:1311–3.PubMedPubMedCentralGoogle Scholar
  5. 5.
    Liang YZ, Dong J, Zhang J, Wang S, He Y, Yan YX. Identification of neuroendocrine stress response-related circulating microRNAs as biomarkers for type 2 diabetes mellitus and insulin resistance. Front Endocrinol. 2018;9:132.CrossRefGoogle Scholar
  6. 6.
    Du CL. Workplace justice and psychosocial work hazards in association with return to work in male workers with coronary heart diseases: a prospective study. Int J Cardiol. 2013;166:745–7.CrossRefPubMedGoogle Scholar
  7. 7.
    Park J, Kim Y, Cheng Y, Horie S. A comparison of the recognition of overwork-related cardiovascular disease in Japan, Korea, and Taiwan. Ind Health. 2012;50:17–23.CrossRefPubMedGoogle Scholar
  8. 8.
    Yan YX, Liu YQ, Li M, Hu PF, Guo AM, Yang XH, et al. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19:333–41.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Yan YX, Dong J, Li M, Yang SS, Wang W. Establish the cut off point for suboptimal health status using SHSQ-25. Chin Health Stat (in Chin). 2011;28:256–8.Google Scholar
  10. 10.
    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;14:291.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    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;7:19.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yan YX, Dong J, Liu YQ, Zhang J, Song MS, 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;18:29–34.CrossRefPubMedGoogle Scholar
  13. 13.
    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;7:14975.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    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;21:333–9.CrossRefPubMedGoogle Scholar
  15. 15.
    McEwen BS. Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators. Eur J Pharmacol. 2008;583:174–85.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Nonogaki K, Iguchi A. Stress, acute hyperglycemia, and hyperlipidemia role of the autonomic nervous system and cytokines. Trends Endocrinol Metab. 1997;8:192–7.CrossRefPubMedGoogle Scholar
  17. 17.
    Juster RP, McEwen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev. 2010;35:2–16.CrossRefPubMedGoogle Scholar
  18. 18.
    Hellhammer DH, Wüst S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology. 2009;34:163–71.CrossRefPubMedGoogle Scholar
  19. 19.
    Inder WJ, Dimeski G, Russell A. Measurement of salivary cortisol in 2012 - laboratory techniques and clinical indications. Clin Endocrinol. 2012;77:645–51.CrossRefGoogle Scholar
  20. 20.
    van Smeden M, Naaktgeboren CA, Reitsma JB, Moons KG, de Groot JA. Latent class models in diagnostic studies when there is no reference standard - a systematic review. Am J Epidemiol. 2013;179:423–31.CrossRefPubMedGoogle Scholar
  21. 21.
    Dong J, Liang YZ, Zhang J, Wu LJ, Wang S, Hua Q, et al. Potential role of lipometabolism-related microRNAs in peripheral blood mononuclear cells as biomarkers for coronary artery disease. J Atheroscler Thromb. 2017;24:430–41.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Liang YZ, Chu X, Meng SJ, Zhang J, Wu LJ, Yan YX. Relationship between stress-related psychosocial work factors and suboptimal health among Chinese medical staff: a cross-sectional study. BMJ Open. 2018;8:e018485.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Becker AJ, Uckert S, Stief CG, Truss MC, Machtens S, Scheller F, et al. Plasma levels of cavernous and systemic norepinephrine and epinephrine in men during different phases of penile erection. J Urol. 2000;164:573–7.CrossRefPubMedGoogle Scholar
  24. 24.
    Drew L, Lewis JB. poLCA: an R package for polytomous variable latent class analysis. J Stat Softw. 2011;42:1–29.Google Scholar
  25. 25.
    Collins J, Huynh M. Estimation of diagnostic test accuracy without full verification: a review of latent class methods. Stat Med. 2014;33:4141–69.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Lanza ST, Collins LM, Lemmon DR, Schafer JL. PROC LCA: a SAS procedure for latent class analysis. Struct Equ Model. 2007;14:671–94.CrossRefGoogle Scholar
  27. 27.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Ulrich-Lai YM, Herman JP. Neural regulation of endocrine and autonomic stress responses. Nat Rev Neurosci. 2009;10:397–409.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    McEwen BS. Allostasis and allostatic load: implications for neuropsychopharmacology. Neuropsychopharmacology. 2000;22:108–24.CrossRefPubMedGoogle Scholar
  30. 30.
    Yan YX, Dong J, Liu YQ, Yang XH, Li M, Shia G, et al. Association of suboptimal health status and cardiovascular risk factors in urban Chinese workers. J Urban Health. 2012;89:329–38.CrossRefPubMedGoogle Scholar
  31. 31.
    Gholami N, Hosseini Sabzvari B, Razzaghi A, Salah S. Effect of stress, anxiety and depression on unstimulated salivary flow rate and xerostomia. J Dent Res Dent Clin Dent Prospects. 2017;11:247–52.PubMedPubMedCentralGoogle Scholar

Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2018

Authors and Affiliations

  • Yu-Xiang Yan
    • 1
    • 2
  • Li-Juan Wu
    • 1
    • 2
  • Huan-Bo Xiao
    • 3
  • Shuo Wang
    • 1
  • Jing Dong
    • 4
  • Wei Wang
    • 1
    • 2
    • 5
    • 6
    Email author
  1. 1.Department of Epidemiology and Biostatistics, School of Public HealthCapital Medical UniversityBeijingChina
  2. 2.Municipal Key Laboratory of Clinical EpidemiologyBeijingChina
  3. 3.Department of Preventive Medicine, Yanjing Medical CollegeCapital Medical UniversityBeijingChina
  4. 4.Health Management Center, Xuanwu HospitalCapital Medical UniversityBeijingChina
  5. 5.School of Public HealthTaishan Medical UniversityTai’anChina
  6. 6.School of Medical and Health SciencesEdith Cowan UniversityPerthAustralia

Personalised recommendations