Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder Using Multivariate Analysis - Besga, Ariadna; Gonzalez, Itxaso; Echeburua, Enrique; Savio, Alexandre; Ayerdi, Borja; Chyzhyk, Darya; Madrigal, Jose L. M.; Leza, Juan C.; Graña, Manuel; Gonzalez-Pinto, Ana Maria (Frontiers in Aging Neuroscience, 2015)
textbfBackground 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 features related to neuroinflammation. Improved means to differentiate between LOBD and AD in elder subjects will help to select the best personalized treatment. Objective The aim of this study \textcolor{red}{was} to assess the relative significance of clinical observations, neuropsychological tests, and \textcolor{red}{specific} \textcolor{red}{blood plasma }biomarkers (inflammatory and neurotrophic), separately and combined, in the \textcolor{red}{differential diagnosis} of LOBD versus AD. The \textcolor{red}{significance} assessment \textcolor{red}{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) \textcolor{red}{was} recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers \textcolor{red}{were} obtained at recruitment time. Methods We appl\textcolor{red}{ied} multivariate machine learning classification methods to discriminate subjects from HC, AD and LOBD populations in the study. We analyze\textcolor{red}{d} of feature sets \textcolor{red}{combining} clinical observations, neuropshycological measures, and biological markers, including inflammation biomarkers. \textcolor{red}{A feature set containing variables showing significative differences for each classification contrast was tested also.} Furthermore, a battery of classifier approaches \textcolor{red}{were} applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance \textcolor{red}{was} evaluated in a leave-one-out \textcolor{red}{(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%, AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26%, AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76%, AUC 88.46%.} Conclusions It is feasible to build CAD systems for discrimination among LOBD and AD \textcolor{red}{on the basis of a reduced set of clinical variables} to assist the clinician in this difficult differential