Simultaneously evaluating the effect of baseline levels and longitudinal changes in disease biomarkers on cognition in dominantly inherited Alzheimer's disease

Abstract

IntroductionAs the role of biomarkers is increasing in Alzheimer's disease (AD) clinical trials, it is critical to use a comprehensive temporal biomarker profile that reflects both baseline and longitudinal assessments to establish a more precise association between the change in biomarkers and change in cognition. Because age of onset of dementia symptoms is highly predictable, and there are relatively few age-related comorbidities, the Dominantly Inherited Alzheimer Network autosomal dominant AD population affords a unique opportunity to investigate these relationships in a well-characterized population.MethodsA novel joint statistical model was used to simultaneously evaluate how a comprehensive AD biomarker profile predicts change in cognition using amyloid positron emission tomography (PET), CSF Aβ42, CSF total tau and Ptau181, cortical metabolism using [F-18] fluorodeoxyglucose–PET, and hippocampal volume from participants enrolled in the Dominantly Inherited Alzheimer Network (n = 262) with mean (SD) duration of follow-up of 2.7 (1.2) years.ResultsBaseline amyloid PET levels and CSF biomarkers were associated with change in cognition in contrast to the rate of change of brain metabolism and hippocampal volume, which predicted change in cognition.ConclusionsThis study suggests that the baseline value of amyloid PET and CSF Aβ42 measures may be useful for screening participants for AD trials; however, brain hippocampus atrophy and hypometabolism are only useful as repeated longitudinal assessments for tracking cognition and disease progression. This suggests that measures of amyloid plaques predict future cognitive decline, but only longitudinal measures of neurodegeneration correlate with cognitive decline. The novel statistical model used in this study can be easily applied to any pair of outcomes and has potential to be widely used by the AD research community.

Site-Specific Cerebrospinal Fluid Tau Hyperphosphorylation in Response to Alzheimer’s Disease Brain Pathology: Not All Tau Phospho-Sites are Hyperphosphorylated

“…Tau PET uptake typically correlates with clinical severity and poorer cognitive performance [8][9][10][11][12][13]. Tau PET measures suggest pathological tau begins to aggregate around the time of symptom onset [14][15][16]. CSF total tau (t-tau) and phosphorylated tau (p-tau) levels, which reflect the circulating pool of soluble tau released by central nervous system cells [17], may enable earlier assessment of tau abnormalities prior to the deposition of NFTs in brain parenchyma [18,19].…”

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

“…Despite extensive literature on the associations between biomarkers and cognition, only few clinical trials and observational studies performed a complete A/T/N characterization of their participants included, using different biomarker modalities within the different categories (Dumurgier et al, 2017; Salloway et al, 2018; Wang et al, 2018). Therefore, in the present study, we aimed to investigate which A/T/N biomarkers (except for tau-PET) at baseline were associated with short-term cognitive decline in a population comprising the whole spectrum of AD, including cognitively healthy controls (HC), MCI, and AD dementia patients.…”

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

“…Various disease progression and sub-type approaches have been proposed and developed. These include survival and multi-state models for investigating transitions between disease states ( Hubbard and Zhou, 2011 ; Vos et al, 2013 ; van den Hout, 2016 ; Wei and Kryscio, 2016 ; Robitaille et al, 2018 ; Zhang et al, 2019 ); mixed effects models (linear, generalized, non-linear) that incorporate subject-specific random effects and can be extended to handle latent time shifts, random change points, latent factors, processes and classes, hidden states, and multiple outcomes ( Hall et al, 2000 ; Jedynak et al, 2012 ; Liu et al, 2013 ; Proust-Lima et al, 2013 ; Donohue et al, 2014 ; Samtani et al, 2014 ; Lai et al, 2016 ; Zhang et al, 2016 ; Geifman et al, 2018 ; Li et al, 2018 ; Wang et al, 2018 ; Lorenzi et al, 2019 ; Proust-Lima et al, 2019 ; Villeneuve et al, 2019 ; Younes et al, 2019 ; Bachman et al, 2020 ; Kulason et al, 2020 ; Raket, 2020 ; Segalas et al, 2020 ; Williams et al, 2020 ) and can be combined with models for event-history data ( Marioni et al, 2014 ; Blanche et al, 2015 ; Proust-Lima et al, 2016 ; Rouanet et al, 2016 ; Li et al, 2017 ; Iddi et al, 2019 ; Li and Luo, 2019 ; Wu et al, 2020 ); event-based models which attempt to model the pathological cascade of events occurring as the disease develops and progresses through disease stages ( Fonteijn et al, 2012 ; Young et al, 2014 ; Chen et al, 2016 ; Goyal et al, 2018 ; Oxtoby et al, 2018 ); and various clustering approaches for discovering risk stratification/disease progression groups and endotypes. For example, those based on hierarchical, partitioning and model-based clustering algorithms/methods ( Dong et al, 2016 ; Racine et al, 2016 ; Dong et al, 2017 ; ten Kate et al, 2018 ; Young et al, 2018 ).…”