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Does white blood cell count predict diabetes incidence in the general Chinese population over time?

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Abstract

Type 2 diabetes is a major global health concern. Recent evidence suggests that inflammation may play a role in the development of this disease. Therefore, immune system markers could serve as prognostic biomarkers for diabetes. The aim of the study was to examine whether white blood cell (WBC) count could predict diabetes incidence in the general Chinese population during a 15-year follow-up. Data were collected in 1992 and again in 2007 from 687 individuals. Questionnaire, physical examination, and laboratory tests were performed using a standardized protocol. To assess the effects of baseline WBC count on the onset of diabetes, Cox’s proportional hazards regression models were used to estimate hazard ratios, and the area under the receiver-operating curve assessed the discriminatory power of anthropometric measures for diabetes. Seventy-four individuals were diagnosed with diabetes during the 15-year follow-up period (incidence: 10.8 %). Time of onset was 11.2 ± 3.8 years. Increased WBC count increased diabetes risk during the follow-up after adjusting for other potential risk factors (P = 0.041). The areas under the receiver-operating curves for WBC count did not significantly predict incident diabetes better than traditional risk factors such as body mass index (BMI) in the general population cohort both at 7–8-year (area under curve (AUC) = 0.06, 95 % CI −0.162–0.282, P = 0.597) and 15-year follow-up (AUC = 0.1, 95 % CI 0.006–0.205, P = 0.065). An increasing WBC count increases the risk of type 2 diabetes incidence. Yet, it was an inappropriate predictor of diabetes in a middle-aged Chinese population compared to traditional risk factors such as BMI.

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References

  1. Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol. 2011;11:98–107.

    Article  CAS  PubMed  Google Scholar 

  2. Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444:860–7.

    Article  CAS  PubMed  Google Scholar 

  3. Donath MY, Ehses JA, Maedler K, et al. Mechanisms of beta-cell death in type 2 diabetes. Diabetes. 2005;54(Suppl. 2):S108–3.

    Article  CAS  PubMed  Google Scholar 

  4. Nakanishi N, Yoshida H, Matsuo Y, et al. White blood-cell count and the risk of impaired fasting glucose or Type II diabetes in middle-aged Japanese men. Diabetologia. 2002;45:42–8.

    Article  CAS  PubMed  Google Scholar 

  5. Vozarova B, Weyer C, Lindsay RS, et al. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes. 2002;51:455–61.

    Article  CAS  PubMed  Google Scholar 

  6. Duncan BB, Schmidt MI, Pankow JS, Atherosclerosis Risk in Communities Study, et al. Low-grade systemic inflammation and the development of type 2 diabetes: the Atherosclerosis Risk in Communities Study. Diabetes. 2003;52:1799–805.

    Article  CAS  PubMed  Google Scholar 

  7. Chao C, Song Y, Cook N, et al. The lack of utility of circulating biomarkers of inflammation and endothelial dysfunction for type 2 diabetes risk prediction among postmenopausal women: the Women’s Health Initiative Observational Study. Arch Intern Med. 2010;170:1557–65.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gkrania-Klotsas E, Ye Z, Cooper AJ, et al. Differential white blood cell count and type 2 diabetes: systematic review and metaanalysis of cross-sectional and prospective studies. PLoS One. 2010;5:e13405.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Du X, Zhu B, Hu G, et al. U-shape association between white blood cell count and the risk of diabetes in young Chinese adults. Diabet Med. 2009;26:955–60.

    Article  CAS  PubMed  Google Scholar 

  10. McNeely MJ, Boyko EJ, Leonetti DL, et al. Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. Diabetes Care. 2003;26:758–63.

    Article  CAS  PubMed  Google Scholar 

  11. The World Health Organization. MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration. J Clin Epidemiol. 1988;41:105–14.

    Article  Google Scholar 

  12. Liu J, Hong Y, D’Agostino Sr RB, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA. 2004;291:2591–9.

    Article  CAS  PubMed  Google Scholar 

  13. Ren J, Grundy SM, Liu J, et al. Long-term coronary heart disease risk associated with very-low-density lipoprotein cholesterol in Chinese: the results of a 15-year Chinese Multi-Provincial Cohort Study (CMCS). Atherosclerosis. 2010;211:27–332.

    Article  Google Scholar 

  14. Liu K, He S, Hong B, et al. Over time, do anthropometric measures still predict diabetes incidence in Chinese Han nationality population from Chengdu community? Int J Endocrinol. 2013;2013:239376.

  15. World Health Organization. Multination monitoring of treads and determinants in cardiovascular disease (MONICA project) and manual of operation. Geneva: Cardiovascular Disease Unit; 1993.

    Google Scholar 

  16. Ferrières J, Ruidavets JB. Association between resting heart rate and hypertension treatment in a general population. Am J Hypertens. 1999;12:628–31.

    Article  PubMed  Google Scholar 

  17. He S, Shu Y, He J, et al. The effects of initial and subsequent adiposity status on diabetes mellitus. Int J Cardiol. 2013;168:511–4.

    Article  PubMed  Google Scholar 

  18. Liu K, Wang Y, He J, et al. Is pulse pressure a predictor of diabetes in Chinese Han nationality population? 15-year prospective study in Chengdu community. Int J Cardiol. 2014;176:529–32.

    Article  PubMed  Google Scholar 

  19. He J, He S, Liu K, et al. The TG/HDL-C ratio might be a surrogate for insulin resistance in Chinese Nonobese Women. Int J Endocrinol. 2014;2014:105168.

  20. Twig G, Afek A, Shamiss A, et al. White blood cells count and incidence of type 2 diabetes in young men. Diabetes Care. 2013;36:276–82.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jiang H, Yan WH, Li CJ, et al. Elevated white blood cell count is associated with higher risk of glucose metabolism disorders in middle-aged and elderly Chinese people. Int J Environ Res Public Health. 2014;11:5497–509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Senn JJ, Klover PJ, Nowak IA, et al. Interleukin-6 induces cellular insulin resistance in hepatocytes. Diabetes. 2002;51:3391–9.

    Article  CAS  PubMed  Google Scholar 

  23. Demmer RT, Squillaro A, Papapanou PN, et al. Periodontal infection, systemic inflammation, and insulin resistance: results from the continuous National Health and Nutrition Examination Survey (NHANES) 1999–2004. Diabetes Care. 2012;35:2235–42.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lee CT, Harris SB, Retnakaran R, et al. White blood cell subtypes, insulin resistance and β-cell dysfunction in high-risk individuals—the PROMISE cohort. Clin Endocrinol. 2014;81:536–41.

    Article  CAS  Google Scholar 

  25. Dandona P, Aljada A, Chaudhuri A, et al. Endothelial dysfunction, inflammation and diabetes. Rev Endocr Metab Disord. 2004;5:189–97.

    Article  CAS  PubMed  Google Scholar 

  26. Gustafson B. Adipose tissue, inflammation and atherosclerosis. J AtherosclerThromb. 2010;17:332–41.

    CAS  Google Scholar 

  27. Carvalheira JB, Qiu Y, Chawla A. Blood spotlight on leukocytes and obesity. Blood. 2013;122:3263–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Raynor LA, Pankow JS, Duncan BB, et al. Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Care. 2013;36:70–6.

    Article  CAS  PubMed  Google Scholar 

  29. Sattar N, Wannamethee SG, Forouhi NG. Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008;51:926–40.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This study was supported by a project from the National Eighth Five-Year Research Plan, China (grant no. 85-915-01-02) and by Mega-projects of Science Research for the 11th Five-year Plan, China (grant no. 2006BAI01A01).

Author contributions

Qi Liu and Ying Xu equally contributed to the article.

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Authors and Affiliations

  1. Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, China

    Qi Liu, Ying Xu, Kai Liu, Sen He, Rufeng Shi & Xiaoping Chen

Authors
  1. Qi Liu
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  2. Ying Xu
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  3. Kai Liu
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  4. Sen He
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  5. Rufeng Shi
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  6. Xiaoping Chen
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Correspondence to Xiaoping Chen.

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Conflict of interest

The authors declare that they have no conflict of interests.

Funding

This study was supported by a project from the China’s Eighth National 5-Year Research Plan (grant No. 85-915-01-02) and by Mega-projects of Science Research for China’s 11th 5-year Plan (grant no. 2006BAI01A01).

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Qi Liu and Ying Xu contributed equally to this work.

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Liu, Q., Xu, Y., Liu, K. et al. Does white blood cell count predict diabetes incidence in the general Chinese population over time?. Int J Diabetes Dev Ctries 37, 195–200 (2017). https://doi.org/10.1007/s13410-016-0521-6

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  • DOI: https://doi.org/10.1007/s13410-016-0521-6

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