EPMA Journal

, Volume 10, Issue 1, pp 65–72 | Cite as

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



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.


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.


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).


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.


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



suboptimal health status


Suboptimal Health Status Questionnaire-25


type 2 diabetes mellitus


China Suboptimal Health Cohort Study


noncommunicable chronic diseases


body mass index


systolic blood pressure


diastolic blood pressure


fasting plasma glucose


total cholesterol




low-density lipoprotein cholesterol


high-density lipoprotein cholesterol


relative risk


odds ratio


confidence interval


analysis of variance


receiver operating characteristic


area under the ROC curve


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)


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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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