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

• Authors: Agelos Papaioannou , George Karamanis , Ioannis Rigas , Thomas Spanos , Zoe Roupa
• Languages of publication: EN

Abstracts

EN

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

EN

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

• Publisher: De Gruyter Open
• Journal: Open Medicine
• Year: 2009
• Volume: 4
• Issue: 1
• Pages: 37-48

Dates

published

1 - 3 - 2009

online

11 - 2 - 2009

Contributors

author

Agelos Papaioannou

• Section of Clinical Chemistry and Biochemistry, Department of Medical Laboratories, Faculty of Health and Care, Technological and Education Institute of Larissa, 41110, Larissa, Greece

author

George Karamanis

• Biochemical Laboratory, General Hospital of Kavala, 65201, Kavala, Greece

author

Ioannis Rigas

• Department of Animal Production, Technological and Education Institute of Larissa, 41110, Larissa, Greece

author

Thomas Spanos

• Education and Technological Institute of Kavala, 65403, Kavala, Greece

author

Zoe Roupa

• Nursing Department, Technological and Education Institute of Larissa, 41110, Larissa, Greece

References

• [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, 433–438
• [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, 3763–3770 http://dx.doi.org/10.1681/ASN.2005050512[Crossref]
• [3] Soveri I., Renal Dysfunction and Cardiovascular Disease, Acta Universitatis Upsaliensis, Uppsala, 2006
• [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, 272–281 http://dx.doi.org/10.1007/s00467-006-0322-0[WoS][Crossref]
• [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, 1007–1012
• [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, 1145–1148 http://dx.doi.org/10.1038/ki.1993.361[Crossref]
• [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, 2394–2401 http://dx.doi.org/10.1093/ndt/gfi076[Crossref]
• [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–c168 http://dx.doi.org/10.1159/000095476[Crossref]
• [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, 1420–1431 http://dx.doi.org/10.1373/clinchem.2005.051557[Crossref]
• [10] Johnston N., Low-Density Lipoprotein Oxidation and Renal Dysfunction, Acta Universitatis Upsaliensis, Uppsala, 2006
• [11] Rossing P., Prediction, progression and prevention of diabetic nephropathy. The Minkowski Lecture 2005, Diabetologia, 2006, 49, 11–19 http://dx.doi.org/10.1007/s00125-005-0077-3
• [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, 41–46 http://dx.doi.org/10.1161/01.HYP.0000022063.50739.60[Crossref]
• [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, 349–351 http://dx.doi.org/10.1007/s11695-007-9356-z[WoS][Crossref]
• [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, 2903–2909 http://dx.doi.org/10.2337/diacare.26.10.2903[Crossref]
• [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, 340–344 http://dx.doi.org/10.1111/j.1468-3083.2006.01925.x[WoS][Crossref]
• [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, 2154–2169 http://dx.doi.org/10.1161/01.CIR.0000095676.90936.80[Crossref]
• [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, 832–837 http://dx.doi.org/10.1002/ajh.20657[Crossref]
• [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, 541–548 http://dx.doi.org/10.1016/S0272-6386(96)90465-5[Crossref]
• [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, 945–956 http://dx.doi.org/10.1093/ndt/gfi326[Crossref]
• [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, 1023–1026 http://dx.doi.org/10.1016/j.transproceed.2007.03.028[Crossref]
• [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, 387–390 [Crossref]
• [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, 3736–3741 http://dx.doi.org/10.1681/ASN.2005010112
• [23] Perazella M.A., Khan S., Increased mortality in chronic kidney disease: a call to action, Am. J. Med. Sci., 2006, 331, 150–153 http://dx.doi.org/10.1097/00000441-200603000-00007[Crossref]
• [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, 2154–69 http://dx.doi.org/10.1161/01.CIR.0000095676.90936.80[Crossref]
• [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, 2034–47 [Crossref]
• [26] Papaioannou A., Simeonov V., Plageras P., Dovriki E., Spanos T., Multivariate statistical interpretation of laboratory clinical data, Central European J. Med., 2007, 3, 319–334 http://dx.doi.org/10.2478/s11536-007-0035-1[WoS][Crossref]
• [27] Slockbower JM., Blumenfeld TA. (ed), Collection and handling of Laboratory Speciments, Philadelphic: Lippincott Co, 201, 1983
• [28] NCCLS: “Procedures for the Collection of Diagnostic Blood Specimens by Venipuncture”, NCCLS, Document H3-A3 Wayne, PA: NCCLS, 1991
• [29] NCCLS: “Procedures for the Collection of Diagnostic Blood Specimens by Skin Puncture”, NCCLS Document H4-A3 Wayne, PA: NCCLS, 1991
• [30] NCCLS: “Internal Quality Control Testing: Principles and Definitions”, NCCLS, Document C24-A Wayne, PA: NCCLS, 1991
• [31] Kafka M.T., Internal quality control, proficiency testing and the clinical relevance of laboratory testing, Arch. Pathol. Lab. Med., 1988, 112, 449–53
• [32] Deming S., Chemometrics: an Overview, Clin. Chem., 1986, 32, 1702–1706 [PubMed]
• [33] Vogt W., Nagel D., Cluster Analysis in Diagnosis, Clin. Chem., 1992, 38, 182–198
• [34] Vanderginste B., Massart DL., Buydens L., De Jong S., Lewi P., Smeyers-Verbeke J., Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1998
• [35] Massart D.L,. Kaufman L., The Interpretation of analytical chemical data by the Use of Cluster Analysis, J. Wiley, New York, 1983
• [36] Winkel P., Patterns and Clusters-Multivariate Approach for Interpreting Clinical Chemistry Results, Clin. Chem., 1973, 19, 1329–1338
• [37] Grams R., Lezotte D., and Gudat J., Establishing a Multivariate Clinical Laboratory Data Base, J. Med. Sys.. 1978, 2, 355–362 http://dx.doi.org/10.1007/BF02221901[Crossref]
• [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, 118–127 http://dx.doi.org/10.1016/S0885-3924(02)00690-5[Crossref]
• [39] Plomteux G., Multivariate Analysis of an Enzymic Profile for the Differential Diagnosis of Viral Hepatitis, Clin. Chem., 1980, 26, 1897–1899
• [40] Poupard J., Gagnon B., Stanhope M., Stewart C., Methods for Data Mining from Large multinational Surveillance Studies, Antimicrobial Agents and Chemotherapy, 2002, 46, 2409–2419 http://dx.doi.org/10.1128/AAC.46.8.2409-2419.2002[Crossref]
• [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, 129–139 http://dx.doi.org/10.2478/s11536-007-0021-7[Crossref]

Identifiers

DOI

10.2478/s11536-008-0085-z