Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


Preferences help
enabled [disable] Abstract
Number of results

Journal

2009 | 4 | 4 | 433-443

Article title

Chemometrics as an option to assess clinical data from diabetes mellitus type 2 patients

Content

Title variants

Languages of publication

EN

Abstracts

EN
The present study deals with the application of two major multivariate statistical approaches - Cluster Analysis (CA) and Principal Components Analysis (PCA) as an option for assessment of clinical data from diabetes mellitus type 2 patients. One hundred clinical cases of patients are considered as object of the statistical classification and modeling, each one of them characterized by 34 various clinical parameters. The goal of the study was to find patterns of similarity, both between the patients and the clinical tests. Each group of similarity is interpreted revealing at least five clusters of correlated parameters or five latent factors, which determine the data structure. Relevant explanation of the clustering is found based on the pattern of similarity like glucose level, anthropometric data, enzyme level, liver function, kidney function etc. It is assumed that this classification could be of help in optimizing the performance of clinical test for this type of patients and for designing a pattern for the role of the different groups of test in determining the metabolic syndrome of the patients.

Publisher

Journal

Year

Volume

4

Issue

4

Pages

433-443

Physical description

Dates

published
1 - 12 - 2009
online
3 - 10 - 2009

Contributors

  • Department of Chemistry and Biochemistry, Faculty of Medicine, Medical University of Sofia, Zdrave Str. 2, 1431, Sofia, Bulgaria
  • Laboratory of Environmental Physics, Georgi Nadjakov Institute of Solid State Physics, Bulgarian Academy of Sciences, Tzarigradsko Chaussee 72, 1784, Sofia, Bulgaria
  • Chair of Analytical Chemistry, Faculty of Chemistry, University of Sofia “St. Kl. Okhridski”, J. Bourchier Blvd. 1, 1164, Sofia, Bulgaria

References

  • [1] S. Wild, G. Roglic, A. Green, R. Sucree and H. King: ldGlobal prevalence of diabetes: estimates for the year 2000 and projections for 2030rd, Diabetes Care, Vol. 27 (2004), pp 1047–1053 http://dx.doi.org/10.2337/diacare.27.5.1047[Crossref]
  • [2] American Diabetes Association: ldEconomic costs of diabetes in the US in 2002rd, Diabetes Care, Vol. 26, (2003), pp. 917–932 http://dx.doi.org/10.2337/diacare.26.7.2194
  • [3] P. Zimmet, K.G.M.M. Alberti and J. Shaw: ldGlobal and societal implication of the diabetes epidemicrd, Nature, Vol. 414 (2001), pp. 782–787 http://dx.doi.org/10.1038/414782a[Crossref]
  • [4] A. Sekigawa, H. Eguchi, M. Tominagawa, K. Igarashi, T. Abe, H. Manaka, H. Sasaki, H. Fukuyama, T. Kato, Y. Kiohora and M. Fujishima: ldPrevalence of type 2 diabetes mellitus and impaired glucose tolerance in a rural area of Japan: The Funagata Diabetes Studyrd, Journal of Diabetes Complications, Vol. 14, (2000), pp. 78–83 http://dx.doi.org/10.1016/S1056-8727(00)00074-X[Crossref]
  • [5] A.G. Schranz: ldAbnormal glucose tolerance in the Maltese: a population-based longitudinal study of the natural history of NIDDM and IGT in Maltard, Diabetes Research and Clinical Practice, Vol. 7,(1989), pp. 7–16 http://dx.doi.org/10.1016/0168-8227(89)90038-7[Crossref]
  • [6] G.K. Dowse, H. Gareebo, P.Z. Zimmet, K.G. Alberti, J. Tuomilehto, D. Fateed, L.G. Brisonette and C.F. Finch: ldHigh prevalence of NIDDM and impaired glucose tolerance in Indian, Creole, and Chinese Mauritius Noncommunicable Disease Study Grouprd, Diabetes, Vol. 39, (1990), pp. 390–396 http://dx.doi.org/10.2337/diabetes.39.3.390[Crossref]
  • [7] N.S. Levitt, J.M. Katzenellenbogen, D. Bradshaw, M.N. Hoffman and F. Bounici: ldThe prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africard, Diabetes Care, Vol. 16, (1993), pp. 601–607 http://dx.doi.org/10.2337/diacare.16.4.601[Crossref]
  • [8] J.C. Mbanya, J. Ngogang, J.N. Salah, E. Minkoulou and B. Balkau: ldPrevalence of NIDDM and impaired glucose tolerance in a rural and urban population in Cameroonrd, Diabetologia, Vol. 40, (1997), pp. 824–829 http://dx.doi.org/10.1007/s001250050755[Crossref]
  • [9] V.R. Collins, G.K. Dowse, P.M. Toelupe, T.T. Imo, F.L. Aloaina, R.A. Spark and P.Z. Zimmet: ldIncreasing prevalence of NIDDM in the Pacific island population of Western Samoa over 13-year periodrd, Diabetes Care, Vol. 17, (1994), pp. 288–296 http://dx.doi.org/10.2337/diacare.17.4.288[Crossref]
  • [10] D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte and L. Kaufman: Chemometrics: a textbook, Elsevier, Amsterdam, 1988
  • [11] A. Papaioannou, V. Simeonov, P. Plageras, E. Dovriki and T. Spanos: ldMultivariate statistical interpretation of laboratory clinical datard, Central European Journal of Medicine, Vol. 2(3), (2007), pp. 319–334 http://dx.doi.org/10.2478/s11536-007-0035-1[WoS][Crossref]
  • [12] J. Poupard, B. Gagnon, M. Stanhope and C. Stewart: ldMethods for data mining from large multinational surveillance studiesrd, Antimicrobial Agents Chemistry, Vol. 46, (2002), pp. 2409–2419 http://dx.doi.org/10.1128/AAC.46.8.2409-2419.2002[Crossref]
  • [13] H. Kraemer, J. Measelle, M. Essex, T. Boyce and D. Kupfer: ldA new approach to integrating data from multiple informants in psychiatric assessment and research: mixing and matching contexts and perspectivesrd, American Journal of Psychiatry, Vol. 160, (2003), pp. 371–381 http://dx.doi.org/10.1176/appi.ajp.160.9.1566[Crossref]
  • [14] D. L. Massart and L. Kaufman: The interpretation of analytical chemical data by the use of cluster analysis, J. Wiley & Sons, New York, 1983
  • [15] B. Vandeginste, D. L. Massart, L. Buydens, S. De Long, P. Lewi and J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Elsevier, Amsterdam, 1998
  • [16] J. W. Einax, K. H. Zwanziger and S. Geiss: Chemometrics in Environmental Analysis, VCH Weinheim, Germany, 1998

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.-psjd-doi-10_2478_s11536-009-0059-9
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.