Skip to main content

Advertisement

SpringerLink
Log in

Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study

  • Research
  • Published:
EPMA Journal Aims and scope Submit manuscript

Abstract

Background

The prevalence of diabetes, constituted chiefly by type 2 diabetes mellitus (T2DM), is a global public health threat. Suboptimal health status (SHS), a physical state between health and disease, might contribute to the progression or development of T2DM.

Methods

We conducted a prospective cohort study, based on the China Suboptimal Health Cohort Study (COACS), to understand the impact of SHS on the progress of T2DM. We examined associations between SHS and T2DM outcomes using multivariable logistic regression models and constructed predictive models for T2DM onset based on SHS.

Results

A total of 61 participants developed T2DM after an average of 3.1 years of follow-up. Participants with higher SHS scores had more T2DM outcomes (p = 0.036). Moreover, compared with the lowest quartile of SHS scores, participants with fourth, third, and second quartile SHS scores were found to be associated with a 1.7-fold, 1.6-fold, and 1.5-fold risk of developing T2DM, respectively. The predictive model constructed with SHS had higher discriminatory power (AUC = 0.848) than the model without SHS (AUC = 0.795).

Conclusions

The present study suggests that a higher SHS score is associated with a higher incidence of T2DM. SHS is a new independent risk factor for T2DM and has the capability to act as a predictive tool for T2DM onset. The evaluation of SHS combined with the analysis of modifiable risk factors for SHS allows the risk stratification of T2DM, which may consequently contribute to the prevention of T2DM development. These findings might require further validation in a longer-term follow-up study.

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

Abbreviations

SHS :

suboptimal health status

SHSQ-25:

Suboptimal Health Status Questionnaire-25

T2DM :

type 2 diabetes mellitus

COACS :

China Suboptimal Health Cohort Study

NCD :

noncommunicable chronic diseases

BMI :

body mass index

SBP :

systolic blood pressure

DBP :

diastolic blood pressure

FPG :

fasting plasma glucose

TC :

total cholesterol

TG :

triglyceride

LDL-C :

low-density lipoprotein cholesterol

HDL-C :

high-density lipoprotein cholesterol

RR :

relative risk

OR :

odds ratio

CI :

confidence interval

ANOVA :

analysis of variance

ROC :

receiver operating characteristic

AUC :

area under the ROC curve

References

  1. Ramachandran A, Snehalatha C, Shetty AS, Nanditha A. Trends in prevalence of diabetes in Asian countries. World J Diabetes. 2012;3(6):110–7.

    Article  Google Scholar 

  2. Yang WY, Lu JM, Weng JP, Jia WP, Ji LN, Xiao JZ, et al. Prevalence of diabetes among men and women in China. New Engl J Med. 2010;362(12):1090–101.

    Article  CAS  Google Scholar 

  3. Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215–22.

    Article  Google Scholar 

  4. 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(6):333–41.

    Article  Google Scholar 

  5. Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28.

    Article  Google Scholar 

  6. Adua E, Roberts P, Wang W. Incorporation of suboptimal health status as a potential risk assessment for type II diabetes mellitus: a case-control study in a Ghanaian population. EPMA J. 2017;8(4):345–55.

    Article  Google Scholar 

  7. 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(2):329–38.

    Article  Google Scholar 

  8. Ma C, Xu W, Zhou L, Ma S, Wang Y. Association between lifestyle factors and suboptimal health status among Chinese college freshmen: a cross-sectional study. BMC Public Health. 2018;18(1):105.

    Article  Google Scholar 

  9. Chen J, Cheng J, Liu Y, Tang Y, Sun X, Wang T, et al. Associations between breakfast eating habits and health-promoting lifestyle, suboptimal health status in southern China: a population based, cross sectional study. J Transl Med. 2014;12:348.

    Article  Google Scholar 

  10. Bi J, Huang Y, Xiao Y, Cheng J, Li F, Wang T, et al. Association of lifestyle factors and suboptimal health status: a cross-sectional study of Chinese students. BMJ Open. 2014;4(6):e005156.

    Article  Google Scholar 

  11. Wu S, Xuan Z, Li F, Xiao W, Fu X, Jiang P, et al. Work-recreation balance, health-promoting lifestyles and suboptimal health status in southern China: a cross-sectional study. Int J Environ Res Public Health. 2016;13(3):E339.

    Article  Google Scholar 

  12. 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(1):19.

    Article  Google Scholar 

  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(1):14975.

    Article  Google Scholar 

  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(6):333–9.

    Article  CAS  Google Scholar 

  15. Hou H, Feng X, Li Y, Meng Z, Guo D, Wang F, et al. Suboptimal health status and psychological symptoms among Chinese college students: a perspective of predictive, preventive and personalised health. EPMA J. 2018;9(4):367–77.

    Article  Google Scholar 

  16. 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(1):291.

    Article  Google Scholar 

  17. DeSalvo KB. Public health 3.0: applying the 2015-2020 dietary guidelines for Americans. Public Health Rep. 2016;131(4):518–21.

    Article  Google Scholar 

  18. U.S. Department of Health and Human Services and U.S. Department of Agriculture. 2015–2020 Dietary Guidelines for Americans. 8th Edn. 2015. Available at https://health.gov/dietaryguidelines/2015/guidelines/.

  19. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    Article  Google Scholar 

  20. Wang W, Russell A, Yan Y. Global Health Epidemiology Reference G. Traditional Chinese medicine and new concepts of predictive, preventive and personalized medicine in diagnosis and treatment of suboptimal health. EPMA J. 2014;5(1):4.

    Article  Google Scholar 

  21. Collins GS, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103.

    Article  Google Scholar 

  22. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33:46–62.

    Article  Google Scholar 

  23. Joseph JJ, Echouffo-Tcheugui JB, Talegawkar SA, Effoe VS, Okhomina V, Carnethon MR, et al. Modifiable lifestyle risk factors and incident diabetes in African Americans. Am J Prev Med. 2017;53(5):e165–e74.

    Article  Google Scholar 

  24. Bancks MP, Kershaw K, Carson AP, Gordon-Larsen P, Schreiner PJ, Carnethon MR. Association of modifiable risk factors in young adulthood with racial disparity in incident type 2 diabetes during middle adulthood. JAMA. 2017;318(24):2457–65.

    Article  Google Scholar 

  25. Yaffe K. Modifiable risk factors and prevention of dementia: what is the latest evidence? JAMA Intern Med. 2018;178(2):281–2.

    Article  Google Scholar 

  26. Chan JC, Zhang Y, Ning G. Diabetes in China: a societal solution for a personal challenge. Lancet Diabetes Endocrinol. 2014;2(12):969–79.

  27. Turi KN, Buchner DM, Grigsby-Toussaint DS. Predicting risk of type 2 diabetes by using data on easy-to-measure risk factors. Prev Chronic Dis. 2017;14:E23.

    Article  Google Scholar 

Download references

Funding

This work was supported by grants from the National Natural Science Foundation of China (NSFC) (81673247, 81872682, and 81773527), the Joint Project of the Australian National Health & Medical Research Council (NHMRC), and the NSFC (NHMRC APP1112767, NSFC 81561128020), Beijing Nova Program (Z141107001814058), and China Scholarship Council (CSC-2017).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, 10 Youanmen Xitoutiao, Beijing, 100069, China

    Siqi Ge, Jie Zhang, Hao Wang, Di Liu, Xiaoyu Zhang, Manshu Song, Youxin Wang & Wei Wang

  2. Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

    Siqi Ge

  3. School of Public Health, Taishan Medical University, Taian, 271000, China

    Xizhu Xu, Haifeng Hou, Dong Li & Wei Wang

  4. School of Medical and Health Sciences, Edith Cowan University, Perth, WA6027, Australia

    Hao Wang, Manshu Song, Youxin Wang & Wei Wang

  5. Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, 100093, China

    Yong Zhou

Authors
  1. Siqi Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xizhu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haifeng Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Di Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Manshu Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Youxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YW, YZ, and WW conceived the study. SG, XX, JZ, and MS performed the investigation and collected the data. SG, HW, DL, and XZ performed the statistical analysis. SG, XX, HH, and YZ wrote the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yong Zhou or Youxin Wang.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethical approval and consent to participate

The study was conducted according to the guidelines of Helsinki Declaration. Approvals have been obtained from Ethical Committees of the Staff Hospital of Jidong Oil-field of Chinese National Petroleum, and Capital Medical University. Written informed consent has also been obtained from each of the participants.

Additional information

Publisher’s Note

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

Electronic supplementary material

ESM 1

(DOCX 215 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ge, S., Xu, X., Zhang, J. et al. Suboptimal health status as an independent risk factor for type 2 diabetes mellitus in a community-based cohort: the China suboptimal health cohort study. EPMA Journal 10, 65–72 (2019). https://doi.org/10.1007/s13167-019-0159-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13167-019-0159-9

Keywords

Search

Navigation