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

  • Siqi Ge
  • Xizhu Xu
  • Jie Zhang
  • Haifeng Hou
  • Hao Wang
  • Di Liu
  • Xiaoyu Zhang
  • Manshu Song
  • Dong Li
  • Yong ZhouEmail author
  • Youxin WangEmail author
  • Wei Wang
Research
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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.

Keywords

Suboptimal health status Type 2 diabetes mellitus Risk factor Predictive preventive personalized medicine 

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

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Notes

Authors’ 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.

Funding information

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).

Compliance with ethical standards

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.

Supplementary material

13167_2019_159_MOESM1_ESM.docx (216 kb)
ESM 1 (DOCX 215 kb)

References

  1. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  2. 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.PubMedCrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Wang W, Yan Y. Suboptimal health: a new health dimension for translational medicine. Clin Transl Med. 2012;1(1):28.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 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.PubMedCrossRefGoogle Scholar
  8. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  9. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 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.PubMedCrossRefGoogle Scholar
  12. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 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.PubMedCrossRefGoogle Scholar
  15. 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.PubMedCrossRefGoogle Scholar
  16. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    DeSalvo KB. Public health 3.0: applying the 2015-2020 dietary guidelines for Americans. Public Health Rep. 2016;131(4):518–21.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 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. 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.PubMedCrossRefGoogle Scholar
  20. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  21. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Yaffe K. Modifiable risk factors and prevention of dementia: what is the latest evidence? JAMA Intern Med. 2018;178(2):281–2.PubMedCrossRefGoogle Scholar
  26. 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.Google Scholar
  27. 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.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Clinical Epidemiology, School of Public HealthCapital Medical UniversityBeijingChina
  2. 2.Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
  3. 3.School of Public HealthTaishan Medical UniversityTaianChina
  4. 4.School of Medical and Health SciencesEdith Cowan UniversityPerthAustralia
  5. 5.Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina

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