Skip to main content

Clinical risk model assessment for cardiovascular autonomic dysfunction in the general Chinese population

Journal of Endocrinological Investigation volume 38, pages 615–622 (2015)Cite this article

Abstract

Background

The aim of this study was to evaluate the prevalence of cardiovascular autonomic (CA) dysfunction in the general Chinese population (instead of focusing on only patients with diabetes) and to develop a clinical risk model for the disease.

Methods and materials

We evaluated CA dysfunction prevalence in a dataset based on a population sample consisting of 2,092 individuals. Clinical risk models were derived from exploratory sets using multiple logistic regression analysis. The performance of the clinical risk models was tested in the validation sets.

Results

CA dysfunction prevalence was 18.50 % in the general Chinese population, while the prevalence was 24.14 % in individuals aged ≥60 years. Its prevalence was 31.17, 24.69, and 21.26 % in patients with diabetes, and hypertensive, and metabolic syndrome populations, respectively. Finally, we developed clinical risk models involving seven risk factors. The mean area under the receiver-operating curve was 0.758 (95 % CI 0.724–0.793) for these models. The mean sensitivity and specificity of the clinical risk models was 75.0 and 66.2 %, respectively.

Conclusion

CA dysfunction prevalence was high in the general Chinese population, and its prevalence was more frequent in individuals with diabetes, and hypertensive, and metabolic syndrome. Clinical risk models with a high value for predicting CA dysfunction were developed. CA dysfunction has become a major public health problem in China that requires strategies aimed at the prevention and treatment of the disease.

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

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1

Abbreviations

AUC:

Area under the receiver-operating curve

BP:

Blood pressure

BMI:

Body mass index

BSA:

Body surface area

CA:

Cardiovascular autonomic

Ccr:

Creatinine clearance rate

CI:

Confidence intervals

Cr:

Creatinine

DM:

Diabetes

FPG:

Fasting plasma glucose

HbAlc:

Glycosylated hemoglobin

HDL:

High-density lipoprotein cholesterol

HF:

High frequency

HL:

Hosmer–Lemeshow

HOMA-IR:

Homeostasis model assessment insulin resistance estimate

HRV:

Heart rate variability

IDF:

International Diabetes Federation

LDL:

Low-density lipoprotein cholesterol

LF:

Low frequency

LR:

Logistic linear regression

MetS:

Metabolic syndrome

OGTT:

Oral glucose tolerance test

OR:

Odds ratios

PBG:

Postprandial blood glucose

PH:

Hypertension

RACE:

Rapid autonomic cardiovascular evaluation

TC:

Serum total cholesterol

TG:

Triglyceride

WC:

Waist circumference

UA:

Uric acid

References

  1. Spallone V, Ziegler D, Freeman R, Bernardi L, Frontoni S, Pop-Busui R, Stevens M, Kempler P, Hilsted J, Tesfaye S, Low P, Valensi P (2011) Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management. Diabetes Metab Res Rev 27:639–653

    Article  PubMed  Google Scholar 

  2. Garruti G, Giampetruzzi F, Vita MG, Pellegrini F, Lagioia P, Stefanelli G, Bellomo-Damato A, Giorgino F (2012) Links between metabolic syndrome and cardiovascular autonomic dysfunction. Exp Diabetes Res 2012:615835

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Hazari MA, Khan RT, Reddy BR, Hassan MA (2012) Cardiovascular autonomic dysfunction in type 2 diabetes mellitus and essential hypertension in a South Indian population. Neurosciences (Riyadh) 17:173–175

    Google Scholar 

  4. Iodice V, Low DA, Vichayanrat E, Mathias CJ (2011) Cardiovascular autonomic dysfunction in MSA and Parkinson’s disease: similarities and differences. J Neurol Sci 310:133–138

    Article  PubMed  Google Scholar 

  5. Ziegler D, Zentai C, Perz S, Rathmann W, Haastert B, Meisinger C, Lowel H (2006) Selective contribution of diabetes and other cardiovascular risk factors to cardiac autonomic dysfunction in the general population. Exp Clin Endocrinol Diabetes 114:153–159

    Article  CAS  PubMed  Google Scholar 

  6. Min KB, Min JY, Paek D, Cho SI, Son M (2008) Is 5-minute heart rate variability a useful measure for monitoring the autonomic nervous system of workers? Int Heart J 49:175–181

    Article  PubMed  Google Scholar 

  7. McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS, Evidence-Based Medicine Working Group (2000) Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. JAMA 284:79–84

    Article  CAS  PubMed  Google Scholar 

  8. Chen ZY, Liu JH, Liang K, Liang WX, Ma SH, Zeng GJ, Xiao SY, He JG (2012) The diagnostic value of a multivariate logistic regression analysis model with transvaginal power Doppler ultrasonography for the prediction of ectopic pregnancy. J Int Med Res 40:184–193

    Article  PubMed  Google Scholar 

  9. Tuerlinckx D, Bodart E, Jamart J, Glupczynski Y (2009) Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis: a European study. Pediatr Infect Dis J 28:394–397

    Article  PubMed  Google Scholar 

  10. Vinik AI, Ziegler D (2007) Diabetic cardiovascular autonomic neuropathy. Circulation 115:387–397

    Article  PubMed  Google Scholar 

  11. Vujosevic S, Zamaklar M, Belada N, Stamatovic S (2012) Mortality after acute myocardial infarction: significance of cardiovascular diabetic autonomic neuropathy (CDAN). Med Arh 66:296–299

    Article  Google Scholar 

  12. Ding W, Zhou L, Bao Y, Yang Y, Lu B, Wu X, Hu R (2011) Autonomic nervous function and baroreflex sensitivity in hypertensive diabetic patients. Acta Cardiol 66:465–470

    PubMed  Google Scholar 

  13. Laitinen T, Lindstrom J, Eriksson J, Ilanne-Parikka P, Aunola S, Keinanen-Kiukaanniemi S, Tuomilehto J, Uusitupa M (2011) Cardiovascular autonomic dysfunction is associated with central obesity in persons with impaired glucose tolerance. Diabet Med 28:699–704

    Article  CAS  PubMed  Google Scholar 

  14. Chanudet X, Bonnevie L, Bauduceau B (2007) Coronary heart disease and cardiovascular autonomic neuropathy in the elderly diabetic. Diabetes Metab 33(Suppl 1):S19–31

    Article  PubMed  Google Scholar 

  15. Chang YW, Lin JD, Chen WL, Yen CF, Loh CH, Fang WH, Wu LW (2012) Metabolic syndrome and short-term heart rate variability in adults with intellectual disabilities. Res Dev Disabil 33:1701–1707

    Article  PubMed  Google Scholar 

  16. Li Z, Tang ZH, Zeng F, Zhou L (2013) Associations between the severity of metabolic syndrome and cardiovascular autonomic function in a Chinese population. J Endocrinol Invest 36:993–999

    CAS  PubMed  Google Scholar 

  17. Grundy SM, Hansen B, Smith SC Jr, Cleeman JI, Kahn RA (2004) Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association conference on scientific issues related to management. Circulation 109:551–556

    Article  PubMed  Google Scholar 

  18. Swartz MD, Yu RK, Shete S (2008) Finding factors influencing risk: comparing Bayesian stochastic search and standard variable selection methods applied to logistic regression models of cases and controls. Stat Med 27:6158–6174

    Article  PubMed Central  PubMed  Google Scholar 

  19. Levy PS, Stolte K (2000) Statistical methods in public health and epidemiology: a look at the recent past and projections for the next decade. Stat Methods Med Res 9:41–55

    Article  CAS  PubMed  Google Scholar 

  20. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J, Shan Z, Liu J, Tian H, Ji Q, Zhu D, Ge J, Lin L, Chen L, Guo X, Zhao Z, Li Q, Zhou Z, Shan G, He J (2010) Prevalence of diabetes among men and women in China. N Engl J Med 362:1090–1101

    Article  CAS  PubMed  Google Scholar 

  21. Wang JG, Li Y (2012) Characteristics of hypertension in the Chinese population. Curr Hypertens Rep 14:410–415

    Article  PubMed  Google Scholar 

  22. Deepa M, Farooq S, Datta M, Deepa R, Mohan V (2007) Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: the Chennai urban rural epidemiology study (CURES-34). Diabetes Metab Res Rev 23:127–134

    Article  CAS  PubMed  Google Scholar 

  23. Stein PK, Barzilay JI, Domitrovich PP, Chaves PM, Gottdiener JS, Heckbert SR, Kronmal RA (2007) The relationship of heart rate and heart rate variability to non-diabetic fasting glucose levels and the metabolic syndrome: the cardiovascular health study. Diabet Med 24:855–863

    Article  CAS  PubMed  Google Scholar 

  24. Vassallo M, Allen SC (1997) A study of autonomic cardiovascular reflexes in elderly patients with pneumonia. Int J Clin Pract 51:438–441

    CAS  PubMed  Google Scholar 

  25. Clark CV, Mapstone R (1986) Age-adjusted normal tolerance limits for cardiovascular autonomic function assessment in the elderly. Age Ageing 15:221–229

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This study was supported by the grant from China National Grant on Science and Technology (Grant Number: 30570740). We thank the grant from the China National Grant on Science and Technology to support the study.

Conflict of interest

All authors have no conflicts of interest.

Author information

Affiliations

  1. Department of Endocrinology and Metabolism, Huashan Hospital of Fudan University, Building 0#, NO. 12 Wulumuqi Mid Road, Shanghai, 200040, China

    L. Zhang, Z.-H. Tang, F. Zeng, Z. Li, L. Zhou & Y. Li

Authors
  1. L. Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Z.-H. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Z. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Y. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Li.

Additional information

L. Zhang and Z.-H. Tang contributed equally to this work.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Tang, ZH., Zeng, F. et al. Clinical risk model assessment for cardiovascular autonomic dysfunction in the general Chinese population. J Endocrinol Invest 38, 615–622 (2015). https://doi.org/10.1007/s40618-014-0229-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40618-014-0229-8

Keywords

  • Cardiovascular autonomic dysfunction
  • Prevalence
  • General Chinese population
  • Clinical risk model