Please wait a minute...
 Home  About the Journal Editorial Board Aims & Scope Peer Review Policy Subscription Contact us
 
Early Edition  //  Current Issue  //  Open Special Issues  //  Archives  //  Most Read  //  Most Downloaded  //  Most Cited
Aging and Disease    2014, Vol. 5 Issue (4) : 218-225     DOI: 10.14336/AD.2014.0500218
|
Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases
David Blokh1, Ilia Stambler2, *
1C.D. Technologies Ltd., Israel
2Department of Science, Technology and Society, Bar Ilan University, Ramat Gan, Israel
Download: PDF(0 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The heterogeneity of parameters is a ubiquitous biological phenomenon, with critical implications for biological systems functioning in normal and diseased states. We developed a method to estimate the level of objects set heterogeneity with reference to particular parameters and applied it to type II diabetes and heart disease, as examples of age-related systemic dysfunctions. The Friedman test was used to establish the existence of heterogeneity. The Newman-Keuls multiple comparison method was used to determine clusters. The normalized Shannon entropy was used to provide the quantitative evaluation of heterogeneity. There was obtained an estimate for the heterogeneity of the diagnostic parameters in healthy subjects, as well as in heart disease and type II diabetes patients, which was strongly related to their age. With aging, as with the diseases, the level of heterogeneity (entropy) was reduced, indicating a formal analogy between these phenomena. The similarity of the patterns in aging and disease suggested a kind of “early aging” of the diseased subjects, or alternatively a “disease-like” aging process, with reference to these particular parameters. The proposed method and its validation on the chronic age-related disease samples may support a way toward a formal mathematical relation between aging and chronic diseases and a formal definition of aging and disease, as determined by particular heterogeneity (entropy) changes.

Keywords parameter heterogeneity      Friedman test      Newman-Keuls method      normalized Shannon entropy      diabetes      heart disease      age related disease      aging      system complexity     
Corresponding Authors: Ilia Stambler   
Issue Date: 10 July 2014
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
David Blokh
Ilia Stambler
Cite this article:   
David Blokh,Ilia Stambler. Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases[J]. Aging and Disease, 2014, 5(4): 218-225.
URL:  
http://www.aginganddisease.org/EN/10.14336/AD.2014.0500218     OR     http://www.aginganddisease.org/EN/Y2014/V5/I4/218
[1] Dussoix P, Vaxillaire M, Iynedjian PB, Tiercy JM, Ruiz J, Spinas GA(1997). Diagnostic heterogeneity of diabetes in lean young adults. Classification based on immunological and genetic parameters. Diabetes, 46:622-631
[2] Radtke MA, Midthjell K, Nilsen TI, Grill V(2009). Heterogeneity of patients with latent autoimmune diabetes in adults: Linkage to autoimmunity is apparent only in those with perceived need for insulin treatment. Diabetes Care, 32:245-250
[3] Bhopal R, Unwin N, White M, Yallop J, Walker L, Alberti KG(1999). Heterogeneity of coronary heart disease risk factors in Indian, Pakistani, Bangladeshi, and European origin populations: cross sectional study. Brit Med J, 319:215-220
[4] Mestroni L, Rocco C, Gregori D, Sinagra G, Di Lenarda A, Miocic S(1999). Familial dilated cardiomyopathy: evidence for genetic and phenotypic heterogeneity. J Am Coll Cardiol, 34:181-190
[5] Lohr JG, Stojanov P, Carter SL, Cruz-Gordillo P, Lawrence MS, Auclair D(2014). Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell, 25:91-101
[6] Marjanovic ND, Weinberg RA, Chaffer CL(2013). Cell plasticity and heterogeneity in cancer. Clin Chem, 59:168-179
[7] Glantz SA Primer of Biostatistics4th editionNew YorkMcGraw-Hill1994
[8] Wu SS, Wang W, Annis DH(2008). On identification of the number of best treatments using the Newman-Keuls Test. Biom J, 50:861-869
[9] Blokh D(2013). Information-theory analysis of cell characteristics in breast cancer patients. Int J Bioinform Biosci, 3:1-5
[10] Blokh D(2012). Clustering financial time series via information-theory analysis and rank statistics. J Patt Reco Res, 7:106-115
[11] Blokh D(2013). Financial time series analysis based on normalized mutual information functions. Int J Cybern Inform, 2:1-8
[12] Berretta R, Moscato P(2010). Cancer biomarker discovery: The entropic hallmark. PLoS One, 5:e12262
[13] Greenier JL, Van Rompay KK, Montefiori D, Earl P, Moss B, Marthas ML(2005). Simian immunodeficiency virus (SIV) envelope quasispecies transmission and evolution in infant rhesus macaques after oral challenge with uncloned SIVmac251: increased diversity is associated with neutralizing antibodies and improved survival in previously immunized animals. Virol J, 2:e11
[14] Zvarova J, Studeny M(1997). Information theoretical approach to constitution and reduction of medical data. Int J Med Inform, 45:65-74
[15] Blokh D, Stambler I, Afrimzon E, Shafran Y, Korech E, Sandbank J(2007). The information-theory analysis of Michaelis–Menten constants for detection of breast cancer. Cancer Detect Prev, 31:489-498
[16] Blokh D, Zurgil N, Stambler I, Afrimzon E, Shafran Y, Korech E, Sandbank J, Deutsch M(2008). An information-theoretical model for breast cancer detection. Methods Inf Med, 47:322-327
[17] Blokh D, Stambler I, Afrimzon E, Platkov M, Shafran Y, Korech E(2009). Comparative analysis of cell parameter groups for breast cancer detection. Comput Methods Programs Biomed, 94:239-249
[18] Gutierrez Diez PJ, Russo IH, Russo J The Evolution of the Use of Mathematics in Cancer ResearchNew YorkSpringer2012
[19] Lawson AB(1994). Using spatial Gaussian priors to model heterogeneity in environmental epidemiology. Statistician, 43:69-76
[20] Foulley JL, Quaas RL(1995). Heterogeneous variances in Gaussian linear mixed models. Genet Sel Evol, 27:211-228
[21] Böhning D, Malzahn U, Dietz E, Schlattmann P, Viwatwongkasem C, Biggeri A(2002). Some general points in estimating heterogeneity variance with the DerSimonian–Laird estimator. Biostatistics, 3:445-457
[22] Kanematsu N, Komori M, Yonai S, Ishizaki A(2009). Dynamic splitting of Gaussian pencil beams in heterogeneity-correction algorithms for radiotherapy with heavy charged particles. Phys Med Biol, 54:2015-2027
[23] UCI Machine Learning RepositoryPima Indians Diabetes Data Set Original owner: US National Institute of Diabetes and Digestive and Kidney DiseasesDonor of Database: The Johns Hopkins University, Accessed January 2014, Retrieved from: http://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
[24] Baier LJ, Hanson RL(2004). Genetic studies of the etiology of type 2 diabetes in Pima Indians. Hunting for pieces to a complicated puzzle Diabetes, 53:1181-1186
[25] UCI Machine Learning RepositoryHeart Disease Data Set. Creator: V.A. Medical Center, Long Beach and Cleveland Clinic FoundationAccessed January 2014, Retrieved from: http://archive.ics.uci.edu/ml/datasets/Heart+Disease
[26] Conover WJ Practical Nonparametric StatisticsNew YorkWiley-Interscience1999
[27] Alter O, Brown PO, Botstein D(2000). Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA, 97:10101-10106
[28] Rosso OA, Masoller C(2009). Detecting and quantifying temporal correlations in stochastic resonance via information theory measures. Eur Phys J B, 69:37-43
[29] Elsasser WM(1984). Outline of a theory of cellular heterogeneity. Proc Natl Acad Sci USA, 81:5126-5129
[30] Lapidot MB(1987). Does the brain age uniformly? Evidence from effects of smooth pursuit eye movements on verbal and visual tasks. J Gerontol, 42:329-331
[31] Chen D, Bruno J, Easlon E, Lin SJ, Cheng HL, Alt FW, Guarente L(2008). Tissue-specific regulation of SIRT1 by calorie restriction. Genes Dev, 22:1753-1757
[32] Hayflick L(2007). Entropy explains aging, genetic determinism explains longevity, and undefined terminology explains misunderstanding both. PLoS Genet, 3:2351-2354
[33] Bruce EN, Bruce MC, Vennelaganti S(2009). Sample entropy tracks changes in EEG power spectrum with sleep state and aging. J Clin Neurophysiol, 26:257-266
[34] Borg FG, Laxåback G(2010). Entropy of balance – some recent results. J Neuroeng Rehabil, 7:e38
[35] Lipsitz LA, Goldberger AL(1992). Loss of ’complexity’ and aging: potential applications of fractals and chaos theory to senescence. JAMA, 267:1806-1809
[36] Goldberger AL(1996). Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet, 347:1312-1314
[37] Goldberger AL, Peng CK, Lipsitz LA(2002). What is physiologic complexity and how does it change with aging and disease?. Neurobiol Aging, 23:23-26
[38] Vyazovskiy LV, Harris KD Sleep and the single neuron: the role of global slow oscillations in individual cell rest. Nat Rev Neurosci, 14:443-451
[1] Feng Tang,Meng-Hao Pan,Yujie Lu,Xiang Wan,Yu Zhang,Shao-Chen Sun. Involvement of Kif4a in Spindle Formation and Chromosome Segregation in Mouse Oocytes[J]. A&D, 2018, 9(4): 623-633.
[2] J. Thomas Mock,Sherilynn G Knight,Philip H Vann,Jessica M Wong,Delaney L Davis,Michael J Forster,Nathalie Sumien. Gait Analyses in Mice: Effects of Age and Glutathione Deficiency[J]. A&D, 2018, 9(4): 634-646.
[3] Jiayu Wu,Weiying Ren,Li Li,Man Luo,Kan Xu,Jiping Shen,Jia Wang,Guilin Chang,Yi Lu,Yiming Qi,Binger Xu,Yuting He,Yu Hu. Effect of Aging and Glucagon-like Peptide 2 on Intestinal Microbiota in SD Rats[J]. A&D, 2018, 9(4): 566-577.
[4] Carmen G Vinagre,Fatima R Freitas,Carlos H de Mesquita,Juliana C Vinagre,Ana Carolina Mariani,Roberto Kalil-Filho,Raul C Maranhão. Removal of Chylomicron Remnants from the Bloodstream is Delayed in Aged Subjects[J]. A&D, 2018, 9(4): 748-754.
[5] Yao-Chih Yang,Cheng-Yen Tsai,Chien-Lin Chen,Chia-Hua Kuo,Chien-Wen Hou,Shi-Yann Cheng,Ritu Aneja,Chih-Yang Huang,Wei-Wen Kuo. Pkcδ Activation is Involved in ROS-Mediated Mitochondrial Dysfunction and Apoptosis in Cardiomyocytes Exposed to Advanced Glycation End Products (Ages)[J]. A&D, 2018, 9(4): 647-663.
[6] Aurore Marie,Johann Meunier,Emilie Brun,Susanna Malmstrom,Veronique Baudoux,Elodie Flaszka,Gaëlle Naert,François Roman,Sylvie Cosnier-Pucheu,Sergio Gonzalez-Gonzalez. N-acetylcysteine Treatment Reduces Age-related Hearing Loss and Memory Impairment in the Senescence-Accelerated Prone 8 (SAMP8) Mouse Model[J]. A&D, 2018, 9(4): 664-673.
[7] Wenjun Li,Gourav Roy Choudhury,Ali Winters,Jude Prah,Wenping Lin,Ran Liu,Shao-Hua Yang. Hyperglycemia Alters Astrocyte Metabolism and Inhibits Astrocyte Proliferation[J]. A&D, 2018, 9(4): 674-684.
[8] Yali Chen,Mengmei Yin,Xuejin Cao,Gang Hu,Ming Xiao. Pro- and Anti-inflammatory Effects of High Cholesterol Diet on Aged Brain[J]. A&D, 2018, 9(3): 374-390.
[9] Wenzhi Sun,Jiewen Tan,Zhuo Li,Shibao Lu,Man Li,Chao Kong,Yong Hai,Chunjin Gao,Xuehua Liu. Evaluation of Hyperbaric Oxygen Treatment in Acute Traumatic Spinal Cord Injury in Rats Using Diffusion Tensor Imaging[J]. A&D, 2018, 9(3): 391-400.
[10] Jie Zhen,Tong Lin,Xiaochen Huang,Huiqiang Zhang,Shengqi Dong,Yifan Wu,Linlin Song,Rong Xiao,Linhong Yuan. Association of ApoE Genetic Polymorphism and Type 2 Diabetes with Cognition in Non-Demented Aging Chinese Adults: A Community Based Cross-Sectional Study[J]. A&D, 2018, 9(3): 346-357.
[11] Tao Yan,Poornima Venkat,Michael Chopp,Alex Zacharek,Peng Yu,Ruizhuo Ning,Xiaoxi Qiao,Mark R. Kelley,Jieli Chen. APX3330 Promotes Neurorestorative Effects after Stroke in Type One Diabetic Rats[J]. A&D, 2018, 9(3): 453-466.
[12] Changjun Yang,Kelly M. DeMars,Eduardo Candelario-Jalil. Age-Dependent Decrease in Adropin is Associated with Reduced Levels of Endothelial Nitric Oxide Synthase and Increased Oxidative Stress in the Rat Brain[J]. A&D, 2018, 9(2): 322-330.
[13] Lin-Yuan Zhang,Pan Lin,Jiaji Pan,Yuanyuan Ma,Zhenyu Wei,Lu Jiang,Liping Wang,Yaying Song,Yongting Wang,Zhijun Zhang,Kunlin Jin,Qian Wang,Guo-Yuan Yang. CLARITY for High-resolution Imaging and Quantification of Vasculature in the Whole Mouse Brain[J]. A&D, 2018, 9(2): 262-272.
[14] Weiming Hu,Junwu Wu,Wenjing Jiang,Jianguo Tang. MicroRNAs and Presbycusis[J]. A&D, 2018, 9(1): 133-142.
[15] Barbara Strasser,Konstantinos Volaklis,Dietmar Fuchs,Martin Burtscher. Role of Dietary Protein and Muscular Fitness on Longevity and Aging[J]. A&D, 2018, 9(1): 119-132.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © 2014 Aging and Disease, All Rights Reserved.
Address: Aging and Disease Editorial Office 3400 Camp Bowie Boulevard Fort Worth, TX76106 USA
Fax: (817) 735-0408 E-mail: editorial@aginganddisease.org
Powered by Beijing Magtech Co. Ltd