Homogeneous clusters of Alzheimer's disease patient population

被引:10
作者
Gamberger, Dragan [1 ]
Zenko, Bernard [2 ]
Mitelpunkt, Alexis [3 ]
Lavrac, Nada [2 ,4 ]
机构
[1] Rudjer Boskovic Inst, Bijenicka 54, Zagreb 10000, Croatia
[2] Jozef Stefan Inst, Ljubljana, Slovenia
[3] Tel Aviv Univ, Tel Aviv, Israel
[4] Univ Nova Gorica, Nova Gorica, Slovenia
基金
欧盟地平线“2020”; 美国国家卫生研究院;
关键词
Alzheimer's disease; Clustering; Biomarker identification; Brain injuries; HEAD-INJURY;
D O I
10.1186/s12938-016-0183-0
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Background: Identification of biomarkers for the Alzheimer's disease (AD) is a challenge and a very difficult task both for medical research and data analysis. Methods: We applied a novel clustering tool with the goal to identify subpopulations of the AD patients that are homogeneous in respect of available clinical as well as in respect of biological descriptors. Results: The main result is identification of three clusters of patients with significant problems with dementia. The evaluation of properties of these clusters demonstrates that brain atrophy is the main driving force of dementia. The unexpected result is that the largest subpopulation that has very significant problems with dementia has besides mild signs of brain atrophy also large ventricular, intracerebral and whole brain volumes. Due to the fact that ventricular enlargement may be a consequence of brain injuries and that a large majority of patients in this subpopulation are males, a potential hypothesis is that such medical status is a consequence of a combination of previous traumatic events and degenerative processes. Conclusions: The results may have substantial consequences for medical research and clinical trial design. The clustering methodology used in this study may be interesting also for other medical and biological domains.
引用
收藏
页数:14
相关论文
共 21 条
[1]  
Agrawal R., 1998, SIGMOD Record, V27, P94, DOI 10.1145/276305.276314
[2]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[3]   MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM [J].
DEMPSTER, AP ;
LAIRD, NM ;
RUBIN, DB .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01) :1-38
[4]   Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study [J].
Doraiswamy, P. M. ;
Sperling, R. A. ;
Johnson, K. ;
Reiman, E. M. ;
Wong, T. Z. ;
Sabbagh, M. N. ;
Sadowsky, C. H. ;
Fleisher, A. S. ;
Carpenter, A. ;
Joshi, A. D. ;
Lu, M. ;
Grundman, M. ;
Mintun, M. A. ;
Skovronsky, D. M. ;
Pontecorvo, M. J. .
MOLECULAR PSYCHIATRY, 2014, 19 (09) :1044-1051
[5]   Neuropsychology of sports-related head injury: Dementia Pugilistica to Post Concussion Syndrome [J].
Erlanger, DM ;
Kutner, KC ;
Barth, JT ;
Barnes, R .
CLINICAL NEUROPSYCHOLOGIST, 1999, 13 (02) :193-209
[6]  
Ester M, 1996, P 2 INT C KNOWLEDGE, DOI DOI 10.5555/3001460.3001507
[7]  
Galili T, 2014, LECT NOTES ARTIF INT, V8777, P73, DOI 10.1007/978-3-319-11812-3_7
[8]   Identification of Gender Specific Biomarkers for Alzheimer's Disease [J].
Gamberger, Dragan ;
Zenko, Bernard ;
Mitelpunkt, Alexis ;
Lavrac, Nada .
BRAIN INFORMATICS AND HEALTH (BIH 2015), 2015, 9250 :57-66
[9]  
Gamberger D, 2015, LECT N BIOINFORMAT, V9043, P134, DOI 10.1007/978-3-319-16483-0_13
[10]  
Gamberger D, 2014, LECT NOTES ARTIF INT, V8777, P87, DOI 10.1007/978-3-319-11812-3_8