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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 Wang
Research

Abstract

Background

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.

Methods

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.

Results

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%.

Conclusion

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.

Keywords

Suboptimal health status Cortisol Catecholamine Latent class analysis Early recognition Prediction 

Abbreviations

ACTH

adrenocorticotropic hormone

AIC

Akaike information criterion

CAIC

consistent Akaike information criterion

CRF

corticotropin-releasing factor

GC

glucocorticoid

HPA

hypothalamus-pituitary-adrenal

LCA

latent class analysis

PPPM

predictive, preventive and personalized medicine

ROC

receiver operating characteristic

SHS

suboptimal health status

SHSQ-25

suboptimal health status questionnaire-25

SNS

sympathetic nervous system

Notes

Acknowledgements

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.

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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
  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

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