Abstract
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. 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. 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. 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. 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%. 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.
Highlights
Bipolar disorder (BD) is a chronic mood disorder associated with cognitive, affective, and functional impairment, often appearing at youth, or even earlier, whose age of onset may be determined by environmental conditions (Bauer et al, 2014b, 2015a,b; Martinez-Cengotitabengoa et al, 2014)
Though Alzheimer’s disease (AD) and Late onset bipolar disorder (LOBD) are considered distinct and unrelated clinical entities, there is a trend in recent years to question whether there is a link between both disorders based on the overlapping symptoms and the increased successful use of well-established BD treatments, i.e., Lithium, to treat dementia (Takeshi et al, 2006)
Neuropsychological variables are statistically significant in the comparisons with controls, but in the comparison LOBD vs. AD, only the memory tests have significant differences
Summary
Bipolar disorder (BD) is a chronic mood disorder associated with cognitive, affective, and functional impairment, often appearing at youth (around age 20 years), or even earlier, whose age of onset may be determined by environmental conditions (Bauer et al, 2014b, 2015a,b; Martinez-Cengotitabengoa et al, 2014). Inflammation and oxidative stress have been found as common pathophysiological processes underlying AD (Akiyama et al, 2000; Kamer et al, 2008; Sardi et al, 2011) and LOBD (Goldstein et al, 2009; Konradi et al, 2012; Leboyer et al, 2012; Lee et al, 2013; Bauer et al, 2014a; Hope et al, 2015), as well as many other neuropsychological illness, such as depression and mania (Brydon et al, 2009; Dickerson et al, 2013; Castanon et al, 2014; Singhal et al, 2014) These disorders seem to be epigenetically linked to decrease transcriptional activity. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment
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