Homogeneous clusters of Alzheimer’s disease patient population

BackgroundIdentification of biomarkers for the Alzheimer’s disease (AD) is a challenge and a very difficult task both for medical research and data analysis.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.

[1]  Vikas Singh,et al.  Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population , 2011, NeuroImage.

[2]  Isabelle Guyon,et al.  Clustering: Science or Art? , 2009, ICML Unsupervised and Transfer Learning.

[3]  Hans Förstl,et al.  Mild cognitive impairment and dementia: the importance of modifiable risk factors. , 2011, Deutsches Arzteblatt international.

[4]  C. Jack,et al.  Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer’s disease in veterans, using the Alzheimer’s Disease Neuroimaging Initiative , 2014, Alzheimer's & Dementia.

[5]  M. Poca,et al.  Ventricular enlargement after moderate or severe head injury: a frequent and neglected problem. , 2005, Journal of neurotrauma.

[6]  J. Barth,et al.  Neuropsychology of sports-related head injury: Dementia Pugilistica to Post Concussion Syndrome. , 1999, The Clinical neuropsychologist.

[7]  Mark E. Schmidt,et al.  The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception , 2012, Alzheimer's & Dementia.

[8]  Cheng Wang,et al.  Millions of random rules , 2004 .

[9]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10]  S. Horvath,et al.  Unsupervised Learning With Random Forest Predictors , 2006 .

[11]  et al.,et al.  Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) , 2009, NeuroImage.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  George Jewell,et al.  Florbetapir F 18 amyloid PET and 36-month cognitive decline:a prospective multicenter study , 2014, Molecular Psychiatry.

[14]  Naren Ramakrishnan,et al.  Redescription Mining: Structure Theory and Algorithms , 2005, AAAI.

[15]  Valerie Carr,et al.  High-resolution imaging of medial temporal lobe subfield structure and function in mild cognitive impairment , 2013, Alzheimer's & Dementia.

[16]  Alexis Mitelpunkt,et al.  Erratum: Categorize, Cluster, and Classify: A 3-C Strategy for Scientific Discovery in the Medical Informatics Platform of the Human Brain Project , 2014, Discovery Science.

[17]  Nada Lavrac,et al.  Multilayer Clustering: A Discovery Experiment on Country Level Trading Data , 2014, Discovery Science.

[18]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[19]  D. Knopman,et al.  Mild cognitive impairment and on to dementia , 2010, Neurology.

[20]  Bernard Zenko,et al.  Identification of Gender Specific Biomarkers for Alzheimer's Disease , 2015, BIH.

[21]  Bernard Zenko,et al.  Multilayer Clustering: Biomarker Driven Segmentation of Alzheimer's Disease Patient Population , 2015, IWBBIO.

[22]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[23]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.