Synchronized sigmoidal mixed‐effects model for dynamics of cognitive decline relative to onset of Alzheimer’s disease in aging adults in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.

Abstract

BackgroundModeling the dynamics of Alzheimer’s disease (AD) biomarkers over the entire continuum of AD progression is important, yet challenging due to limited resources to collect longitudinal biomarkers from the aging population with fully observed clinical spectrum of AD. This study proposed and applied a synchronized sigmoidal mixed‐effects model to characterize dynamics of longitudinal memory performance using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The model leveraged time to AD onset as the time scale and additionally allowed inclusion of participants without AD onset, drastically expanding future applications.MethodADNI participants with observed mild cognitive impairment (MCI) and/or AD onset (n = 312, mean (SD) baseline age 74.9 (6.44) years) were included (Table 1). A memory composite previously built in ADNI was leveraged for all analyses. A synchronized sigmoidal mixed‐effects model was constructed for dynamics of memory performance with parameters for initial memory level, magnitude of decline, and half‐life of decline. For participants with observed MCI but not AD onset, an additional parameter (t0 ) quantifying the time from MCI onset to AD was incorporated (Figure 1). We considered random effects for all parameters and allowed t0 to vary by age at MCI onset (nonlinearly), sex, apolipoprotein E (APOE)‐ε4 status and their interactions.ResultThe mean initial harmonized memory score is 0.24 (95% CI: 0.17‐0.32). The mean decline in the harmonized memory score is 1.53 (95% CI: 1.43‐1.64). The mean time when the harmonized memory score declined by half is 0.57 years before AD onset (95% CI: 0.32‐0.82). Female is associated with faster progression from MCI onset to AD (p = 0.002). Age at MCI onset is nonlinearly associated with MCI‐to‐AD progression (p < 0.001) and APOE‐ε4 status interacts with age at MCI onset on MCI‐to‐AD progression (p = 0.002) (Figure 2).ConclusionThe proposed synchronized sigmoidal mixed effect model can be used to characterize dynamics of AD biomarkers relative to AD onset using participants with and without AD onset. A model to estimate duration of MCI‐to‐AD progression can be simultaneously included for synchronization purpose, which identified gender, age at MCI onset and APOE‐ε4 status as factors associated with MCI‐to‐AD progression.

Generating the sigmoid form in neuroscience: A threshold-crossing model

“…There is no reason to suppose that the rate of loss in normal controls is now or ever will be the same as in mild cognitive impairment or diagnosed disease. However, this homogeneity assumption is the norm 21,22 . What is known is that the rate of loss cognitive performance is slowest in normal controls, and fastest in those diagnosed 23 .…”

“…Just as an infection-like model would predict. It is common for logistic curves to be fitted to such clinical data 8 (see Fig 1c Kang et al).…”