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Central European Journal of Medicine

, Volume 4, Issue 1, pp 37–48 | Cite as

Determination and modelling of clinical laboratory data of healthy individuals and patients with end-stage renal failure

  • Agelos Papaioannou
  • George Karamanis
  • Ioannis Rigas
  • Thomas Spanos
  • Zoe Roupa
Research Article
  • 43 Downloads

Abstract

The analyses of 18 biochemical parameters (alanine aminotransferase, albumin, aspartate aminotransferase, calcium, cholesterol, chloride, creatinine, iron, glucose, γ- glutamyl transferase, alkaline phosphatase, phosphorus, potassium, sodium, total protein, triglycerides, uric acid, and urea nitrogen) were performed for 166 healthy individuals and 108 patients with end-stage renal failure (ESRF). The application of cluster analysis proved that there were points of similarity among all 18 biochemical parameters that formed major groups; these groups corresponded to the authors’ assumption of the existence of several overall patterns of biochemical parameters that may be termed “enzyme-specific”; “general health indicator”; “major component excretion”; “blood-specific indicator”; and “protein-specific”. These patterns also appear in the subsets of males and females that were obtained by separation of the general dataset. In addition, the performance of factor analysis similarly proved the validity of this assumption. This projection and modelling method indicated the existence of seven latent factors, which explained 70.05% of the total variance in the system for healthy individuals and more than 72% of the total variance in the system for patients with ESRF. All these results support the probability that a general health indicator could be constructed by taking into account the existing classification groups in the list of biochemical parameters.

Keywords

Healthy individuals End-stage renal failure patients Biochemical parameters Cluster analysis Factor analysis 

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References

  1. [1]
    Martin M.V., Barroso S., Herraez O., De Sande F., Caravaca F., Cystatin C as a renal function estimator in advanced chronic renal failure stages, Nefrologia, 2006, 26, 433–438PubMedGoogle Scholar
  2. [2]
    White C., Akbari A., Hussain N., Dinh L., Filler G., Lepage N., et al., Estimating Glomerular Filtration Rate in Kidney Transplantation: A Comparison between Serum Creatinine and Cystatin C-Based Methods, J. Am. Soc. Nephro.l, 2005, 16, 3763–3770CrossRefGoogle Scholar
  3. [3]
    Soveri I., Renal Dysfunction and Cardiovascular Disease, Acta Universitatis Upsaliensis, Uppsala, 2006Google Scholar
  4. [4]
    Zappitelli M., Joseph L., Gupta I.R., Bell L., Paradis G., Validation of child serum creatinine-based prediction equations for glomerular filtration rate, Pediatr. Nephrol., 2007, 22, 272–281PubMedCrossRefGoogle Scholar
  5. [5]
    Tabak M.A., Christenson P.C., Fine R.N., Prediction of the Progression of Chronic Renal Failure in Children: Are Current Models Accurate?, Pediatrics, 1986, 6, 1007–1012Google Scholar
  6. [6]
    Walser M., Drew H.H., Guldan J.L., Prediction of glomerular filtration rate from serum creatinine concentration in advanced chronic renal failure, Kidney Int., 1993, 44, 1145–1148PubMedCrossRefGoogle Scholar
  7. [7]
    Kuan Y., Hossain M., Surman J., El Nahas M., Haylor J., GRF prediction using the NDRD and Cockroft and Gault equations in patients with end-stage renal disease. Nephrol. Dial. Transplant., 2005, 20, 2394–2401PubMedCrossRefGoogle Scholar
  8. [8]
    Fontsere N., Bonal J., Navarro M., Riba J., Fraile M., Torres F., Romero R., A comparison of prediction equations for estimating glomerular filtration rate in adult patients with chronic kidney disease stages 4–5, Nephron Clin. Pract., 2006, 104, c160–c168PubMedCrossRefGoogle Scholar
  9. [9]
    Grubb A., Numan U., Bjork J., Lindstrom V., Rippe B., Sterner G., Christensson A., Simple cystatin C-based prediction equation for glomerular filtration rate compared with the modification of diet in renal disease prediction equation for adults and the Schwartz and the Counahan-Barratt prediction equations for children, Clin. Chem., 2005, 51, 1420–1431PubMedCrossRefGoogle Scholar
  10. [10]
    Johnston N., Low-Density Lipoprotein Oxidation and Renal Dysfunction, Acta Universitatis Upsaliensis, Uppsala, 2006Google Scholar
  11. [11]
    Rossing P., Prediction, progression and prevention of diabetic nephropathy. The Minkowski Lecture 2005, Diabetologia, 2006, 49, 11–19PubMedCrossRefGoogle Scholar
  12. [12]
    Zoccali C., Mallamaci F., Tripepi G., Parlongo S., Cutrupi S., Benedetto F.A., et al. Norepinephrine and concentric hypertrophy in patients with end-stage renal disease, Hypertension, 2002, 40, 41–46PubMedCrossRefGoogle Scholar
  13. [13]
    Forestieri P., Formato A., Pilone V., Romano A., Monda A., Rhabdomyolysis after gastrectomy: increase in muscle enzymes does not predict fatel outcome, Obes. Surg., 2008, 18, 349–351CrossRefGoogle Scholar
  14. [14]
    Svensson M., Sundkvist G., Arnqvist H.J., Bjork E., Blohme G., Bolinder J., et. al., Signs of nephropathy may occur early in young adults with diabetes despite modern diabetes management, Diabetes Care, 2003, 26, 2903–2909PubMedCrossRefGoogle Scholar
  15. [15]
    Dyachenko P., Monselise A., Shustak A., Ziv M., Rozenman D., Nail disorders in patients with chronic renal failure and undergoing haemodialysis treatment: a case-control study, J. Eur. Acad. Dermatol. Venereol., 2007, 21, 340–344PubMedCrossRefGoogle Scholar
  16. [16]
    Srnak MJ., Levey AS., Schoolwerth A.C., Coresh J., Culleton B., et. al., Kidney disease as a risk factor for development of cardiovascular disease, Circulation, 2003, 108, 2154–2169CrossRefGoogle Scholar
  17. [17]
    Malyszko J., Malyszko JS., Pawlak K., Mysliwiec M., Hepcidin, iron status, and renal function in chronic renal failure, kidney transplantation, and hemodialysis, Am. J. Hematol., 2006, 81, 832–837PubMedCrossRefGoogle Scholar
  18. [18]
    Iseki K., Uehara H., Nishime K., Tokuyama K., Yoshihara K., et. al.. Impact of the initial levels of laboratory variables on survival in chronic dialysis patients. Am. J. Kidney Dis., 1996, 4, 541–548CrossRefGoogle Scholar
  19. [19]
    Geddes C.C., Van Dijk C.W., McArthur S., Metcalfe W., Jager K., et. al., The ERA-EDTA cohortcomparison of methods to predict survival on renal replacement therapy, Nephrol. Dial. Transplant., 2006, 21, 945–956PubMedCrossRefGoogle Scholar
  20. [20]
    Akgul A., Bilgic A., Ibis A., Ozdemir FN., Arat Z., Haberal M., Is uric acid a predictive factor for Graft dysfunction in renal transplant recipients?, Transplantation Proceedings, 2007, 39, 1023–1026PubMedCrossRefGoogle Scholar
  21. [21]
    Musso C., Liakopoulos V., De Miguel R., Imperiali N., Algranati L., Transtubular potassium concentration gradient: comparison between healthy old people and chronic renal failure patients, Int. Urol. Nephrol., 2006, 38, 387–390Google Scholar
  22. [22]
    Gilbertson D.T., Liu J., Xue J.L., Louis T.A., Solid C.A., Ebben J.P., et al., Projecting the number of patients with end-stage renal disease in the United States to the year 2015, J. Am. Soc. Nephrol., 2005, 16, 3736–3741PubMedCrossRefGoogle Scholar
  23. [23]
    Perazella M.A., Khan S., Increased mortality in chronic kidney disease: a call to action, Am. J. Med. Sci., 2006, 331, 150–153PubMedCrossRefGoogle Scholar
  24. [24]
    Sarnak M.J., Levey A.S., Schoolwerth A.C., Coresh J., Culleton B., et. al., Kidney disease as a risk factor for development of cardiovascular disease, Circulation, 2003, 108, 2154–69PubMedCrossRefGoogle Scholar
  25. [25]
    Tonelli M., Wiebe N., Culleton B., House A., Rabbat C., Fok M., et al., Chronic kidney disease and mortality risk: a systematic review, J, Am. Soc. Nephrol., 2006, 17, 2034–47Google Scholar
  26. [26]
    Papaioannou A., Simeonov V., Plageras P., Dovriki E., Spanos T., Multivariate statistical interpretation of laboratory clinical data, Central European J. Med., 2007, 3, 319–334CrossRefGoogle Scholar
  27. [27]
    Slockbower JM., Blumenfeld TA. (ed), Collection and handling of Laboratory Speciments, Philadelphic: Lippincott Co, 201, 1983Google Scholar
  28. [28]
    NCCLS: “Procedures for the Collection of Diagnostic Blood Specimens by Venipuncture”, NCCLS, Document H3-A3 Wayne, PA: NCCLS, 1991Google Scholar
  29. [29]
    NCCLS: “Procedures for the Collection of Diagnostic Blood Specimens by Skin Puncture”, NCCLS Document H4-A3 Wayne, PA: NCCLS, 1991Google Scholar
  30. [30]
    NCCLS: “Internal Quality Control Testing: Principles and Definitions”, NCCLS, Document C24-A Wayne, PA: NCCLS, 1991Google Scholar
  31. [31]
    Kafka M.T., Internal quality control, proficiency testing and the clinical relevance of laboratory testing, Arch. Pathol. Lab. Med., 1988, 112, 449–53PubMedGoogle Scholar
  32. [32]
    Deming S., Chemometrics: an Overview, Clin. Chem., 1986, 32, 1702–1706PubMedGoogle Scholar
  33. [33]
    Vogt W., Nagel D., Cluster Analysis in Diagnosis, Clin. Chem., 1992, 38, 182–198Google Scholar
  34. [34]
    Vanderginste B., Massart DL., Buydens L., De Jong S., Lewi P., Smeyers-Verbeke J., Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1998Google Scholar
  35. [35]
    Massart D.L,. Kaufman L., The Interpretation of analytical chemical data by the Use of Cluster Analysis, J. Wiley, New York, 1983Google Scholar
  36. [36]
    Winkel P., Patterns and Clusters-Multivariate Approach for Interpreting Clinical Chemistry Results, Clin. Chem., 1973, 19, 1329–1338PubMedGoogle Scholar
  37. [37]
    Grams R., Lezotte D., and Gudat J., Establishing a Multivariate Clinical Laboratory Data Base, J. Med. Sys.. 1978, 2, 355–362CrossRefGoogle Scholar
  38. [38]
    Nguyen H., Altinger J., Carrieri-Kohlman V., Gormley J., Paul S., Stulbarg M., Factor Analysis of Laboratory and Clinical Measurements of Dyspnea in Patients with Chronic Obstructive Pulmonary Disease, Journal of Pain and Symptom Management, 2003, 25, 118–127PubMedCrossRefGoogle Scholar
  39. [39]
    Plomteux G., Multivariate Analysis of an Enzymic Profile for the Differential Diagnosis of Viral Hepatitis, Clin. Chem., 1980, 26, 1897–1899PubMedGoogle Scholar
  40. [40]
    Poupard J., Gagnon B., Stanhope M., Stewart C., Methods for Data Mining from Large multinational Surveillance Studies, Antimicrobial Agents and Chemotherapy, 2002, 46, 2409–2419PubMedCrossRefGoogle Scholar
  41. [41]
    Chang C.C., Cheng C.S., A structural design of clinical decision support system for chronic diseases risk management, Central European J. Med., 2007, 2, 129–139CrossRefGoogle Scholar

Copyright information

© © Versita Warsaw and Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Agelos Papaioannou
    • 1
  • George Karamanis
    • 2
  • Ioannis Rigas
    • 3
  • Thomas Spanos
    • 4
  • Zoe Roupa
    • 5
  1. 1.Section of Clinical Chemistry and Biochemistry, Department of Medical Laboratories, Faculty of Health and CareTechnological and Education Institute of LarissaLarissaGreece
  2. 2.Biochemical LaboratoryGeneral Hospital of KavalaKavalaGreece
  3. 3.Department of Animal ProductionTechnological and Education Institute of LarissaLarissaGreece
  4. 4.Education and Technological Institute of KavalaKavalaGreece
  5. 5.Nursing DepartmentTechnological and Education Institute of LarissaLarissaGreece

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