Alzheimer’s Disease Biomarkers and Future Decline in Cognitive Normal Older Adults

Dumurgier, Julien; Hanseeuw, Bernard; Hatling, Frances B.; Judge, Kelly; Schultz, Aaron P.; Chhatwal, Jasmeer P.; Blacker, Deborah; Sperling, Reisa A.; Johnson, Keith A.; Hyman, Bradley T.; Gómez-Isla, Teresa

doi:10.3233/jad-170511

Cited by 85 publications

(51 citation statements)

References 45 publications

(42 reference statements)

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“…In the last few years, the relationships between biomarkers and cognition have been intensively studied [25][26][27][28][29]48] and some studies have suggested a stronger association between cognition and tau than between cognition and A␤ [11,13,49]. According to the literature, A␤ accumulates first in the neocortex and then in subcortical areas [50].…”

Section: Discussionmentioning

confidence: 99%

“…Since there is recent literature regarding the relationships between cognitive function and AD CSF biomarkers in cognitively normal subjects [25][26][27][28][29], the present research pushed further by focusing on a specific sample of cognitively healthy subjects with normal AD CSF biomarker levels. The aim of the present study was to examine and follow up this well-characterized sample in order to detect demographical, structural and biological variables related to the earliest cognitive changes in aging.…”

Section: Introductionmentioning

confidence: 99%

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Tau Protein is Associated with Longitudinal Memory Decline in Cognitively Healthy Subjects with Normal Alzheimer’s Disease Cerebrospinal Fluid Biomarker Levels

Tort‐Merino

Olives

León

et al. 2019

JAD

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Background: We investigated a sample of cognitively healthy subjects with normal Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarker levels to identify the earliest variables related to longitudinal memory changes. Objective: Employing a new highly demanding learning and memory test (the Ancient Farming Equipment Test; AFE-T), we aimed to investigate whether a biomarker related to neurodegeneration (i.e., CSF tau) was associated with longitudinal memory decline.Methods: Thirty-two cognitively and biologically normal (CBN) subjects underwent MRI, neuropsychological assessment, and the AFE-T at baseline and 18 months later. To explore the relationship between cognitive performance and relevant factors, a linear model was set up. For a secondary analysis that further explore the effect of tau, the subjects were divided into CBN-Tau ↓ (tau < 228.64 pg/ml; n = 16) and CBN-Tau ↑ (tau > 228.64 pg/ml; n = 16). We also performed voxel-based morphometry (VBM) to identify regions of grey matter volume that would predict both baseline and longitudinal cognitive performance. Results: Our main finding was an association between CSF tau and longitudinal memory decline measured with AFE-T (B = -0.17, p < 0.05; r = -0.414; p < 0.01), and further analyses showed different evolvement between subgroups, with an accelerated decline in individuals with higher tau (F(1,31) = 8.37; p < 0.01). VBM results suggested that AFE-T performance is related to grey matter volume in a medial temporal, middle frontal, and posterior cerebellar network at baseline, and that there are strategic brain areas driving the longitudinal cognitive changes. Conclusions: The present findings provide evidence for structural and biological markers linked to cognitive aging by highlighting the role of tau, a marker of neurodegeneration, which can be related with the earliest memory changes in healthy subjects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tau Protein is Associated with Longitudinal Memory Decline in Cognitively Healthy Subjects with Normal Alzheimer’s Disease Cerebrospinal Fluid Biomarker Levels

Tort‐Merino

Olives

León

et al. 2019

JAD

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“…In addition, however, using the multimodal prognostic index vs. Ab status alone to stratify individuals reduces the sample size required to observe a clinically meaningful change in the stereotypical pattern of pathological tau accumulation by 46%. The benefit of combining multimodal data for stratification in early AD has been previously reported in the context of future changes in cognition [25][26][27][28][29][30] . These previous studies have shown that grey matter atrophy and cortical Ab burden relate to separable patterns of future cognitive decline 25,26,29,30 .…”

Section: Figure 7 Discussionmentioning

confidence: 89%

Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

Giorgio¹,

Baker²,

Landau³

et al. 2020

Preprint

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The earliest stages of Alzheimer's disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to β-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD.

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“…In addition, however, using the multimodal prognostic index vs. A status alone to stratify individuals reduces the sample size required to observe a clinically meaningful change in the stereotypical pattern of pathological tau accumulation by 46%. The benefit of combining multimodal data for stratification in early AD has been previously reported in the context of future changes in cognition [25][26][27][28][29][30] . These previous studies have shown that grey matter atrophy and cortical A burden relate to separable patterns of future cognitive decline 25,26,29,30 .…”

Section: Figure 7 Discussionmentioning

confidence: 89%

“…The benefit of combining multimodal data for stratification in early AD has been previously reported in the context of future changes in cognition [25][26][27][28][29][30] . These previous studies have shown that grey matter atrophy and cortical A burden relate to separable patterns of future cognitive decline 25,26,29,30 . Given the known association between longitudinal changes in tau and cognitive decline in preclinical AD 6 , our results further support the benefit of combining continuous values of A and medial temporal grey matter density for prognostication in early AD.…”

Section: Figure 7 Discussionmentioning

confidence: 89%

Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

Giorgio

Baker

Landau

et al. 2020

Preprint

View full text Add to dashboard Cite

The earliest stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to β-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD.

show abstract

Alzheimer’s Disease Biomarkers and Future Decline in Cognitive Normal Older Adults

Cited by 85 publications

References 45 publications

Tau Protein is Associated with Longitudinal Memory Decline in Cognitively Healthy Subjects with Normal Alzheimer’s Disease Cerebrospinal Fluid Biomarker Levels

Tau Protein is Associated with Longitudinal Memory Decline in Cognitively Healthy Subjects with Normal Alzheimer’s Disease Cerebrospinal Fluid Biomarker Levels

Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

Predicting future regional tau accumulation in asymptomatic and early Alzheimer’s disease

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