Skip to main content

Advertisement

SpringerLink
Log in

Prediction of Type-2 Diabetes Based on Several Element Levels in Blood and Chemometrics

  • Published:
Biological Trace Element Research Aims and scope Submit manuscript

Abstract

The present study was designed to evaluate the levels of eight elements including lithium, zinc, chromium, copper, iron, manganese, nickel and vanadium in whole blood of type-2 diabetes patients, to compare them with age-matched healthy controls and to investigate the feasibility of combining them with an ensemble model for diagnosing purpose. A dataset involving 158 samples, among which 105 were taken from healthy adults and the remaining 53 from patients with type-2 diabetes, was collected. All samples were split into the training set and the test set with the equal size. Based on a simple variable selection, two elements, i.e., chromium and iron, are also picked out as the most important elements. Three kinds of algorithms, i.e., fisher linear discriminate analysis (FLDA), support vector machine (SVM) and decision tree (DT), were used for constructing member models. The best ensemble classifiers constructed on the training set were validated on the independent test set, and the prediction results were compared with those from clinical diagnostics on the same subjects. The results reveal that almost all ensemble classifiers exhibit similar performance, implying that these elements coupled with an appropriate ensemble classifier can serve as a valuable tool of diagnosing diabetes type-2.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Quinn L (2001) Glucose monitoring in the acutely ill patients with type 2 DM. Crit Care Nurs 44:88–98

    Google Scholar 

  2. Naqshbandi M, Harris SB, Esler JG, Antwi-Nsiah F (2008) Global complication rates of type 2 diabetes in. Res Clin Pract 82:1–17

    Article  Google Scholar 

  3. Ward NI, Pim B (1984) Trace element concentrations in blood plasma from diabetic patients and normal individuals. Biol Trace Element Res 6:469–487

    Article  CAS  Google Scholar 

  4. Houstis N, Rosen ED, Lander ES (2006) Reactive oxygen species have a causal role in multiple forms of insulin resistance. Nature 440:944–948

    Article  PubMed  CAS  Google Scholar 

  5. Roberts CK, Sindhu KK (2009) Oxidative stress and metabolic syndrome. Life Sci 84:705–712

    Article  PubMed  CAS  Google Scholar 

  6. Flores CR, Puga MP, Wrobel K, Garay Sevilla ME, Wrobel K (2011) Trace elements status in diabetes mellitus type 2: possible role of the interaction between molybdenum and copper in the progress of typical complications. Diabetes Res Clin Pract 91:333–341

    Article  PubMed  CAS  Google Scholar 

  7. Douglas MT (2003) The importance of trace element speciation in biomedical science. Anal Bioanal Chem 375:1062–1066

    Google Scholar 

  8. Zhai HL, Chen XG, Hu ZD (2003) Study on the relationship between intake of trace elements and breast cancer mortality with chemometric methods. Comput Biol Chem 27:581–586

    Article  PubMed  CAS  Google Scholar 

  9. Bianchi F, Maffini M, Mangia A, Marengo E, Mucchino C (2007) Experimental design optimization for the ICP-AES determination of Li, Na, K, Al, Fe, Mn and Zn in human serum. J Pharm Biomed Anal 43:659–665

    Article  PubMed  CAS  Google Scholar 

  10. Taylor A (1996) Detection and monitoring of disorders of essential trace elements. Ann Clin Biochem 33:486–510

    PubMed  CAS  Google Scholar 

  11. Pasha Q, Malik SA, Iqbal J, Shaheen N, Shah MH (2008) Comparative evaluation of trace metal distribution and correlation in human malignant and benign breast tissues. Biol Trace Element Res 125:30–40

    Article  CAS  Google Scholar 

  12. Patriarca M, Menditto A, Felice GD, Petrucci F, Caroli S, Merli M, Valente C (1998) Recent developments in trace element analysis in the prevention, diagnosis, and treatment of diseases. Microchem J 59:194–202

    Article  CAS  Google Scholar 

  13. Frisk P, Darnerud P, Ola FG, Blomberg J, Ilbäck NG (2007) Sequential trace element changes in serum and blood during a common viral infection in mice. J Trace Elem Med Biol 21:29–36

    Article  PubMed  CAS  Google Scholar 

  14. Celik HA, Aydin HH, Ozsaran A, Kilincsoy N, Batur Y, Ersoz B (2002) Trace elements analysis of ascitic fluid in benign and malignant diseases. J Clin Biochem 35:477–481

    Article  Google Scholar 

  15. Zhang ZY, Zhou HL, Liu SD, Harrington P (2006) Application of Takagi–Sugeno fuzzy systems to classification of cancer patients based on elemental contents in serum samples. Chemom Intell Lab Syst 82:294–299

    Article  CAS  Google Scholar 

  16. Tan C, Chen H, Xia CY (2009) Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm. J Pharm Biomed 49:746–752

    Article  CAS  Google Scholar 

  17. Tan C, Chen H, Xia CY (2009) The prediction of cardiovascular disease based on trace element contents in hair and a classifier of boosting decision stumps. Biol Trace Elem Res 129:9–19

    Article  PubMed  CAS  Google Scholar 

  18. Ren YL, Zhang ZY, Ren YQ, Li W, Wang MC, Xu G (1997) Diagnosis of lung cancer based on metal contents in serum and hair using multivariate statistical methods. Talanta 44:1823–1831

    Article  PubMed  CAS  Google Scholar 

  19. Kazi TG, Afridi HI, Kazi N, Jamali MK, Arain MB, Jalbani N (2008) Copper, chromium, manganese, iron, nickel, and zinc levels in biological samples of diabetes mellitus patients. Biol Trace Elem Res 4:1–18

    Article  Google Scholar 

  20. Beckett GJ, Arthur JR (2005) Selenium and endocrine systems. J Endocrinol 184:455–65

    Article  PubMed  CAS  Google Scholar 

  21. Zheng Y, Li XK, Wang Y, Cai L (2008) The role of zinc, copper and iron in the pathogenesis of diabetes and diabetic complications: therapeutic effects by chelators. Hemoglobin 32:135–45

    Article  PubMed  CAS  Google Scholar 

  22. Wrobel K, Garay-Sevilla ME, Malacara JM, Fajardo ME, Wrobel K (1999) Effect of chromium on glucose tolerance, serum cholesterol and triglyceride levels in occupational exposure to trivalent speciers in type 2 diabetic patients and in control subjects. Trace Elem Electrolytes 16:199–205

    CAS  Google Scholar 

  23. Meyer JA, Spence DM (2009) A perspective on the role of metals in diabetes: past findings and possible future directions.1:32-49.

  24. Thompson KH, Orvig C (2006) Vanadium in diabetes: 100 years from phase 0 to phase I. J Inorg Biochem 100:1925–1935

    Article  PubMed  CAS  Google Scholar 

  25. Sakurai H, Adachi Y (2005) The pharmacology of the insulinomimetic effect of zinc complexes. 18:319-323.

  26. Senofonte O, Violante N, Caroli S (2000) Assessment of reference values for elements in human hair of urban school boys. J Trace Elem Med Biol 14:6–13

    Article  PubMed  CAS  Google Scholar 

  27. Afridi HI, Kazi TG, Kazi N, Baig JA, Jamali MK, Arain MB (2009) Status of essential trace metals in biological samples of diabetic mother and their neonates. Arch Gynecol Obstet 280:415–423

    Article  PubMed  CAS  Google Scholar 

  28. Afridi HI, Kazi TG, Kazi N, Jamali MK, Arain MB, Jalbani N (2008) Evaluation of status of toxic metals in biological samples of diabetes mellitus patients. Diabetes Res Clin Pract 80:280–288

    Article  PubMed  CAS  Google Scholar 

  29. Edwards JR, Prozialeck WC (2009) Cadmium, diabetes and chronic kidney disease. Toxicol Appl Pharmacol 238:289–299

    Article  PubMed  CAS  Google Scholar 

  30. Hasan NA (2009) Effects of trace elements on albumin and lipoprotein glycation in diabetic retinopathy. Saudi Med J 30:1263–1271

    PubMed  Google Scholar 

  31. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Computation 4:1–58

    Article  Google Scholar 

  32. Krogh A, Sollich P (1997) Statistical mechanics of ensemble learning. Phys Rev E 55:811–825

    Article  Google Scholar 

  33. Breiman L (1996) Bagging predictors. Mach Learning 24:123–140

    Google Scholar 

  34. Vapnik VN (1995) The nature of statistical learning theory. Springer, NewYork

    Google Scholar 

  35. Amendolia SR, Ganadu ML, Golosio B, Masala GL, Mura GM (2003) A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening. Chemom Intell Lab Syst 69:13–27

    Article  CAS  Google Scholar 

  36. Li D (2009) Support vector machine and trace element method for pattern recognition of type-2 diabetes. Dissertation, Shenyang Pharmaceutical University, Shenyang

    Google Scholar 

  37. Li D, Li YC, Wang LL, Qu LM, Zhang YQ, Zhao CJ (2009) Simultaneous determination of eleven trace elements in human blood by inductively coupled plasma optical emission spectrometry. Journal of Shenyang Pharmaceutical University 26(7):539–542

    CAS  Google Scholar 

  38. Chen DD, Li D, Liu J, Zhao CJ (2007) Determination of trace elements in urine of the normal persons in Shenyang City. Guangdong Trace Elem Sci 14:14–17

    Google Scholar 

  39. Stanimirova I, Walczak B, Massart DL, Simeonov V (2004) A comparison between two robust PCA algorithms. Chemom Intell Lab Syst 71:83–95

    Article  CAS  Google Scholar 

  40. Tan C, Li ML, Qin X (2008) Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples. Anal Sci 24:647–653

    Article  PubMed  CAS  Google Scholar 

  41. Hernández-Caraballo EA, Rivas F, Pérez AG, Marcó-Parra LM (2005) Evaluation of chemometric techniques and artificial neural networks for cancer screening using Cu, Fe, Se and Zn concentrations in blood serum. Anal Chim Acta 533:161–168

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Sichuan Youth Science and Technology Foundation (09ZQ026-066), Youth Foundation of Yibin University (2010Q11), Key Research Foundation of Yibin University (2011Z22), and Innovative Research and Teaching Team Program of Yibin University (Cx201104).

Author information

Authors and Affiliations

  1. Hospital, Yibin University, Yibin, 644007, People’s Republic of China

    Hui Chen

  2. Department of Chemistry and Chemical Engineering, Yibin University, Yibin, 644007, People’s Republic of China

    Chao Tan

  3. Computational Physics Key Laboratory of Sichuan Province, Yibin University, Yibin, 644007, People’s Republic of China

    Chao Tan

Authors
  1. Hui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Tan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, H., Tan, C. Prediction of Type-2 Diabetes Based on Several Element Levels in Blood and Chemometrics. Biol Trace Elem Res 147, 67–74 (2012). https://doi.org/10.1007/s12011-011-9306-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12011-011-9306-4

Keywords

Search

Navigation