Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline

Abstract

Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration will not directly reflect the disease state, but rather the effect of the cognitive decline on the patient's predisease cognitive capability. Patients with high predisease cognitive capabilities tend to score better on cognitive tests relative to patients with low predisease cognitive capabilities at the same disease stage. Thus, a single assessment with a cognitive test is not adequate for determining the stage of an AD patient. Methods and Findings: I developed a joint statistical model that explicitly modeled disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. The developed model takes the form of a nonlinear mixed-effects model. Maximum-likelihood estimation in this model induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline in AD that spans approximately 15 years from the earliest subjective cognitive deficits to severe AD dementia. It was demonstrated how direct modeling of latent factors that modify the observed data patterns provide a scaffold for understanding disease progression, biomarkers and treatment effects along the continuous time progression of disease. Conclusions: The suggested framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.