Affiliations: [a]
Department of Neuroscience, University of Padua, Padua, Italy
| [b]
Padova Neuroscience Center (PNC), University of Padua, Padua, Italy
| [c]
IRCCS San Camillo Hospital, Venice, Italy
| [d]
Department of Developmental and Social Psychology, University of Padua, Padua, Italy
| [e]
Department of Philosophy, Sociology, Pedagogy and Applied Psychology, University of Padua, Padua, Italy
| [f]
Human Inspired Technology Research Centre, University of Padua, Padua, Italy
Correspondence:
[*]
Correspondence to: Andrea Zangrossi, Padova Neuroscience Center (PNC), University of Padua, Via Giuseppe Orus 2, 35131 Padua, Italy. Tel.: +39 049 8212623; E-mail: andrea.zangrossi@unipd.it.
Abstract: Background:Alzheimer’s disease (AD) patients show heterogeneous cognitive profiles which suggest the existence of cognitive subgroups. A deeper comprehension of this heterogeneity could contribute to move toward a precision medicine perspective. Objective:In this study, we aimed 1) to investigate AD cognitive heterogeneity as a product of the combination of within- (factors) and between-patients (sub-phenotypes) components, and 2) to promote its assessment in clinical practice by defining a small set of critical tests for this purpose. Methods:We performed factor mixture analysis (FMA) on neurocognitive assessment results of N = 230 patients with a clinical diagnosis of AD. This technique allowed to investigate the structure of cognitive heterogeneity in this sample and to characterize the core features of cognitive sub-phenotypes. Subsequently, we performed a tests selection based on logistic regression to highlight the best tests to detect AD patients in our sample. Finally, the accuracy of the same tests in the discrimination of sub-phenotypes was evaluated. Results:FMA revealed a structure characterized by five latent factors and four groups, which were identifiable by means of a few cognitive tests and were mainly characterized by memory deficits with visuospatial difficulties (“Visuospatial AD”), typical AD cognitive pattern (“Typical AD”), less impaired memory (“Mild AD”), and language/praxis deficits with relatively spared memory (“Nonamnestic AD”). Conclusion:The structure of cognitive heterogeneity in our sample of AD patients, as studied by FMA, could be summarized by four sub-phenotypes with distinct cognitive characteristics easily identifiable in clinical practice. Clinical implications under the precision medicine framework are discussed.
Keywords: Alzheimer’s disease, factor mixture analysis, heterogeneity, precision medicine
