Yu-Xiang Yan1,2, Li-Juan Wu1,2, Huan-Bo Xiao3, Shuo Wang1, Jing Dong4, Wei Wang1,2,5,6. 1. 1Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China. 2. Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. 3. 3Department of Preventive Medicine, Yanjing Medical College, Capital Medical University, Beijing, China. 4. 4Health Management Center, Xuanwu Hospital, Capital Medical University, Beijing, China. 5. 5School of Public Health, Taishan Medical University, Tai'an, China. 6. 6School of Medical and Health Sciences, Edith Cowan University, Perth, WA 6027 Australia.
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.
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.
Entities:
Keywords:
Catecholamine; Cortisol; Early recognition; Latent class analysis; Prediction; Suboptimal health status
Authors: Matias Brødsgaard Grynderup; Ole Mors; Åse Marie Hansen; Johan Hviid Andersen; Jens Peter Bonde; Anette Kærgaard; Linda Kærlev; Sigurd Mikkelsen; Reiner Rugulies; Jane Frølund Thomsen; Henrik Albert Kolstad Journal: Occup Environ Med Date: 2013-03-08 Impact factor: 4.402