Understanding the Underpinnings of the Frontotemporal Lobar Degeneration: Evidence for Benign and Malignant Forms

Background Frontotemporal lobar degeneration (FTLD) encompasses different clinical subtypes but with overlapping features. Establishing whether FTLD represents a continuum or recognizes distinct subgroups may be crucial for diagnostic purposes and therapeutic approaches. Objective To investigate whether cognitive profiles in a large sample of FTLD patients reflect qualitatively distinct subtypes, variants along a single continuum of severity, or severity differences within subtypes. Method Latent class analysis (LCA), exploratory factor analysis (FA), and mixture factor analysis (MFA) modeling were applied to a wide neuropsychological assessment data. LCA corresponds to qualitatively distinct subtypes, FA corresponds to quantitatively severity differences, and MFA allows for both subtypes and severity differences within subtypes. Results The authors consecutively enrolled 314 FTLD patients. A comparison of the different models shows that MFA models provided a superior fit to the data than any of the LCA or exploratory FA models. The “best” MFA model was defined by two continuous variables evaluating the disease severity within two groups of patients (“good” and “bad” performers). These two populations have been called “benign” and “malignant” FTLD. Conclusions FTLD recognizes distinct subgroups beyond the disease severity, namely a benign form and a more malignant form. This observation needs to be taken into account in future clinical trials and for therapeutic approaches. Frontotemporal lobar degeneration (FTLD) encompasses different clinical subtypes but with overlapping features. Establishing whether FTLD represents a continuum or recognizes distinct subgroups may be crucial for diagnostic purposes and therapeutic approaches. To investigate whether cognitive profiles in a large sample of FTLD patients reflect qualitatively distinct subtypes, variants along a single continuum of severity, or severity differences within subtypes. Latent class analysis (LCA), exploratory factor analysis (FA), and mixture factor analysis (MFA) modeling were applied to a wide neuropsychological assessment data. LCA corresponds to qualitatively distinct subtypes, FA corresponds to quantitatively severity differences, and MFA allows for both subtypes and severity differences within subtypes. The authors consecutively enrolled 314 FTLD patients. A comparison of the different models shows that MFA models provided a superior fit to the data than any of the LCA or exploratory FA models. The “best” MFA model was defined by two continuous variables evaluating the disease severity within two groups of patients (“good” and “bad” performers). These two populations have been called “benign” and “malignant” FTLD. FTLD recognizes distinct subgroups beyond the disease severity, namely a benign form and a more malignant form. This observation needs to be taken into account in future clinical trials and for therapeutic approaches.