Skip to main content
SpringerLink
Log in

Negative associations of morning serum cortisol levels with obesity: the Henan rural cohort study

  • Original Article
  • Published:
Journal of Endocrinological Investigation Aims and scope Submit manuscript

Abstract

Aims

To evaluate the associations of morning serum cortisol levels with obesity defined by different indices in Chinese rural populations.

Materials and methods

A cross-sectional study was performed including 6198 participants (2566 males and 3632 females). Serum cortisol was collected in morning and quantified by liquid chromatography‐tandem mass spectrometry. Obesity was defined by body mass index (BMI), body fat percentage (BFP), waist-to-height ratio (WHtR), waist circumference (WC), visceral fat index (VFI) and waist-to-hip ratio (WHR). Both multivariable liner regression, logistic regression and restrictive cubic splines models were used to estimate the gender-specific relationships between cortisol levels and obesity defined by different indices, respectively.

Results

After adjusting for potential confounders, serum cortisol was negatively associated with different obesity measures, except obese females defined by BFP (for instance, overall obesity defined by BMI, Quartile 4 vs. Quartile 1, odds ratio (OR) = 0.25, 95% confidence interval (CI):0.15, 0.41 in males, and OR = 0.58, 95% CI: 0.42,0.80 in females, central obesity defined by WC, OR = 0.52, 95% CI:0.39,0.69 in males and OR = 0.63, 95% CI:0.51,0.77 in females). Similarly, restrictive cubic splines showed the nonlinear relationship between high levels of cortisol and different obesity indices. Furthermore, ROC curve analysis indicated that cortisol could improve the discrimination of model with common biomarkers.

Conclusion

Morning serum cortisol were negatively related to obesity defined by different indices in Chinese rural populations. In addition, cortisol could be as a biomarker for prediction of obesity in males.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. World Health Organization (2018) http://www.who.int/mediacentre/factsheets/fs311/en/. (Accessed 8 Jan 670 2018)

  2. Zhou M, Wang H, Zeng X, Yin P, Zhu J et al (2019) Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 394(10204):1145–1158. https://doi.org/10.1016/S0140-6736(19)30427-1

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hu S, Gao RL, Liu LS et al (2019) The report of China cardiovascular disease in China-2018 profiles. Chinese Circ J 34(03):209–220. https://doi.org/10.3969/j.issn.1000-3614.2019.03.001

    Article  Google Scholar 

  4. Liu X, Wu W, Mao Z, Huo W, Tu R et al (2018) Prevalence and influencing factors of overweight and obesity in a Chinese rural population: the Henan Rural Cohort Study. Sci Rep 8(1):13101. https://doi.org/10.1038/s41598-018-31336-2

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Johansson M, Carreras-Torres R, Scelo G, Purdue MP, Mariosa D et al (2019) The influence of obesity-related factors in the etiology of renal cell carcinoma-A mendelian randomization study. PLoS Med 16(1):e1002724. https://doi.org/10.1371/journal.pmed.1002724

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. China OoTSCo (Jan 22, 2017) Notice of the General Office of the State Council on printing and distributing China’s medium and long­term Plan for prevention and treatment of chronic diseases (2017–2025). http://www.gov.cn/zhengce/content/2017-02/14/content_5167886.htm

  7. Kumari M, Chandola T, Brunner E, Kivimaki M (2010) A nonlinear relationship of generalized and central obesity with diurnal cortisol secretion in the Whitehall II study. J Clin Endocrinol Metab 95(9):4415–4423. https://doi.org/10.1210/jc.2009-2105

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Power C, Li L, Hertzman C (2006) Associations of early growth and adult adiposity with patterns of salivary cortisol in adulthood. J Clin Endocrinol Metab 91(11):4264–4270. https://doi.org/10.1210/jc.2006-0625

    Article  PubMed  CAS  Google Scholar 

  9. Champaneri S, Xu X, Carnethon MR, Bertoni AG, Seeman T et al (2013) Diurnal salivary cortisol is associated with body mass index and waist circumference: the Multiethnic Study of Atherosclerosis. Obesity (Silver Spring) 21(1):E56-63. https://doi.org/10.1002/oby.20047

    Article  CAS  Google Scholar 

  10. Rosmond R, Holm G, Bjorntorp P (2000) Food-induced cortisol secretion in relation to anthropometric, metabolic and haemodynamic variables in men. Int J Obes Relat Metab Disord 24(4):416–422. https://doi.org/10.1038/sj.ijo.0801173

    Article  PubMed  CAS  Google Scholar 

  11. Therrien F, Drapeau V, Lalonde J, Lupien SJ, Beaulieu S et al (2007) Awakening cortisol response in lean, obese, and reduced obese individuals: effect of gender and fat distribution. Obesity (Silver Spring) 15(2):377–385. https://doi.org/10.1038/oby.2007.509

    Article  CAS  Google Scholar 

  12. Steptoe A, Kunz-Ebrecht SR, Brydon L, Wardle J (2004) Central adiposity and cortisol responses to waking in middle-aged men and women. Int J Obes Relat Metab Disord 28(9):1168–1173. https://doi.org/10.1038/sj.ijo.0802715

    Article  PubMed  CAS  Google Scholar 

  13. Incollingo Rodriguez AC, Epel ES, White ML, Standen EC, Seckl JR et al (2015) Hypothalamic-pituitary-adrenal axis dysregulation and cortisol activity in obesity: a systematic review. Psychoneuroendocrinology 62:301–318. https://doi.org/10.1016/j.psyneuen.2015.08.014

    Article  PubMed  CAS  Google Scholar 

  14. Karastergiou K, Smith SR, Greenberg AS, Fried SK (2012) Sex differences in human adipose tissues—the biology of pear shape. Biol Sex Differ 3(1):13. https://doi.org/10.1186/2042-6410-3-13

    Article  PubMed  PubMed Central  Google Scholar 

  15. Aleksandrova K, Drogan D, Boeing H, Jenab M, Bas Bueno-de-Mesquita H et al (2014) Adiposity, mediating biomarkers and risk of colon cancer in the European prospective investigation into cancer and nutrition study. Int J Cancer 134(3):612–621. https://doi.org/10.1002/ijc.28368

    Article  PubMed  CAS  Google Scholar 

  16. Kalupahana NS, Moustaid-Moussa N, Claycombe KJ (2012) Immunity as a link between obesity and insulin resistance. Mol Aspects Med 33(1):26–34. https://doi.org/10.1016/j.mam.2011.10.011

    Article  PubMed  CAS  Google Scholar 

  17. Liu X, Mao Z, Li Y, Wu W, Zhang X et al (2019) Cohort Profile: The Henan Rural Cohort: a prospective study of chronic non-communicable diseases. Int J Epidemiol 48(6):1756–1756j. https://doi.org/10.1093/ije/dyz039

    Article  PubMed  Google Scholar 

  18. Kosicka K, Siemiatkowska A, Krzyscin M, Breborowicz GH, Resztak M et al (2015) Glucocorticoid metabolism in hypertensive disorders of pregnancy: analysis of plasma and urinary cortisol and cortisone. PLoS ONE 10(12):e0144343. https://doi.org/10.1371/journal.pone.0144343

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Wei D, Liu X, Jiang J, Tu R, Qiao D et al (2020) Mineralocorticoids, glucose homeostasis and type 2 diabetes mellitus: The Henan Rural Cohort study. J Diabetes Complications 34(5):107558. https://doi.org/10.1016/j.jdiacomp.2020.107558

    Article  PubMed  Google Scholar 

  20. China WGoOi (2004) Guidelines on prevention and Control of overweight and obesity in Chinese adults (excerpts). J Nutr 01:1–4

    Google Scholar 

  21. International Diabetes Federation (2006) The IDF consensus worldwide definition of the metabolic syndrome. https://www.idf.org/our-activities/advocacy-awareness/resources-and-tools/60:idfconsensus-worldwide-definitionof-the-metabolic-syndrome.html. (Accessed 1 Dec 2017)

  22. Minematsu K, Takamura N, Goto K, Honda S, Aoyagi K et al (2011) A proposed method for the evaluation of body fat in Japanese adults that predicts obesity. Nutr Res 31(2):113–121. https://doi.org/10.1016/j.nutres.2011.01.008

    Article  PubMed  CAS  Google Scholar 

  23. Oh SK, Cho AR, Kwon YJ, Lee HS, Lee JW (2018) Derivation and validation of a new visceral adiposity index for predicting visceral obesity and cardiometabolic risk in a Korean population. PLoS ONE 13(9):e0203787. https://doi.org/10.1371/journal.pone.0203787

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. World Health Organization (2008) Waist circumference and waist-hip ratio report of a WHO expert consultation. http://apps.who.int/iris/bitstream/10665/44583/1/9789241501491_eng.pdf. (Accessed 1 Oct 2020)

  25. Srinivasan SR, Wang R, Chen W, Wei CY, Xu J et al (2009) Utility of waist-to-height ratio in detecting central obesity and related adverse cardiovascular risk profile among normal weight younger adults (from the Bogalusa Heart Study). Am J Cardiol 104(5):721–724. https://doi.org/10.1016/j.amjcard.2009.04.037

    Article  PubMed  Google Scholar 

  26. Fan M, Lyu J, He P (2014) Chinese guidelines for data processing and analysis concerning the International Physical Activity Questionnaire. Zhonghua Liu Xing Bing Xue Za Zhi 35(8):961–964

    PubMed  Google Scholar 

  27. Kroenke K, Spitzer RL, Williams JB (2003) The Patient health questionnaire-2: validity of a two-item depression screener. Med Care 41(11):1284–1292. https://doi.org/10.1097/01.MLR.0000093487.78664.3C

    Article  PubMed  Google Scholar 

  28. Whooley MA, Avins AL, Miranda J, Browner WS (1997) Case-finding instruments for depression. Two questions are as good as many. J Gen Intern Med 12(7):439–445. https://doi.org/10.1046/j.1525-1497.1997.00076.x

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Kroenke K, Spitzer RL, Williams JB, Monahan PO, Lowe B (2007) Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med 146(5):317–325. https://doi.org/10.7326/0003-4819-146-5-200703060-00004

    Article  PubMed  Google Scholar 

  30. J Committee for Developing Chinese guidelines on P, Treatment of Dyslipidemia in A (2007) Chinese guidelines on prevention and treatment of dyslipidemia in adults. Zhonghua Xin Xue Guan Bing Za Zhi 35(5):390–419

    Google Scholar 

  31. Mattsson C, Reynolds RM, Simonyte K, Olsson T, Walker BR (2009) Combined receptor antagonist stimulation of the hypothalamic-pituitary-adrenal axis test identifies impaired negative feedback sensitivity to cortisol in obese men. J Clin Endocrinol Metab 94(4):1347–1352. https://doi.org/10.1210/jc.2008-2054

    Article  PubMed  CAS  Google Scholar 

  32. Roelfsema F, Pereira AM, Veldhuis JD (2014) Impact of adiposity and fat distribution on the dynamics of adrenocorticotropin and cortisol rhythms. Curr Obes Rep 3(4):387–395. https://doi.org/10.1007/s13679-014-0118-7

    Article  PubMed  Google Scholar 

  33. Livingstone DE, Jones GC, Smith K, Jamieson PM, Andrew R et al (2000) Understanding the role of glucocorticoids in obesity: tissue-specific alterations of corticosterone metabolism in obese Zucker rats. Endocrinology 141(2):560–563. https://doi.org/10.1210/endo.141.2.7297

    Article  PubMed  CAS  Google Scholar 

  34. Masuzaki H, Paterson J, Shinyama H, Morton NM, Mullins JJ et al (2001) A transgenic model of visceral obesity and the metabolic syndrome. Science 294(5549):2166–2170. https://doi.org/10.1126/science.1066285

    Article  PubMed  CAS  Google Scholar 

  35. Rask E, Olsson T, Soderberg S, Andrew R, Livingstone DE et al (2001) Tissue-specific dysregulation of cortisol metabolism in human obesity. J Clin Endocrinol Metab 86(3):1418–1421. https://doi.org/10.1210/jcem.86.3.7453

    Article  PubMed  CAS  Google Scholar 

  36. Luo Z, Li Y, Hou Y, Liu X, Jiang J et al (2019) Gender-specific prevalence and associated factors of major depressive disorder and generalized anxiety disorder in a Chinese rural population: the Henan rural cohort study. BMC Public Health 19(1):1744. https://doi.org/10.1186/s12889-019-8086-1

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

The authors thank the participants, coordinators, and administrators for their supports, and laboratory for the facility support at the school of Public Health Zhengzhou University during the study. In addition, the authors heartfelt thanks for the anonymous reviewers for their helpful suggestions on the quality improvement of our present paper.

Funding

This research was supported by the National Key Research and Development Program of China (Grant No: 2019YFC1710002, 2016YFC0900803), the National Natural Science Foundation of China (Grant No: 21806146, 21607136), the Postdoctoral Science Foundation of China (Grant No: 2016M602264, 2020T130604), the Excellent Youth Development Foundation of Zhengzhou University (Grant No: 2018ZDGGJS052), and the Science and Technique Foundation of Henan Province (Grant No: 202102310046).

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People’s Republic of China

    K. Fan, D. Wei, X. Liu, Y. He, H. Tian, R. Tu, P. Liu, L. Zhang, D. Qiao, X. Liu, J. Hou, L. Li, C. Wang, G. Zhang & Z. Mao

  2. Department of Occupational and Environmental Health Sciences, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, 450001, Henan, People’s Republic of China

    L. Nie & W. Huo

Authors
  1. K. Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Y. He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. H. Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R. Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. L. Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. L. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. D. Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. X. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J. Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. L. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. C. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. W. Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. G. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Z. Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to G. Zhang or Z. Mao.

Ethics declarations

Conflict of interest

The author(s) declare that they have no conflict of interest.

Ethical approval

The protocol of this study was conducted according to the “Helsinki” declaration and approved by the Life Science Ethics Committee of Zhengzhou University (Code: [2015] MEC (S128)).

Informed consent

Written informed consent were obtained from all participants included in the study.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 37 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, K., Wei, D., Liu, X. et al. Negative associations of morning serum cortisol levels with obesity: the Henan rural cohort study. J Endocrinol Invest 44, 2581–2592 (2021). https://doi.org/10.1007/s40618-021-01558-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40618-021-01558-9

Keywords

Search

Navigation