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040			_cH12O
041			_aeng
100			_92441 _aMuñoz Madrigal, José Luis _eInstituto de Investigación i+12
100			_91995 _aLeza, Juan Carlos _eInstituto de Investigación i+12
245	0	0	_aDiscrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. _h[artículo]
260			_bFrontiers in aging neuroscience, _c2015
300			_a7:231.
500			_aFormato Vancouver: Besga A, González I, Echeburua E, Savio A, Ayerdi B, Chyzhyk D et al. Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. Front Aging Neurosci. 2015 Dec 14;7:231.
501			_aPMID: 26696883 PMC4677464
504			_aContiene 72 referencias
520			_aBackground: Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. Objective: The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. Materials: A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. Methods: We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Results: Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. Conclusion: It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.
710			_9625 _aInstituto de Investigación imas12
856			_uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4677464/ _yAcceso libre
942			_2ddc _cART _n0