Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods

Bocancea, Diana I.; Loenhoud, Anita C. van; Groot, Colin; Barkhof, Frederik; Flier, Wiesje M. van der; Ossenkoppele, Rik

doi:10.1212/wnl.0000000000012499

Cited by 61 publications

(42 citation statements)

References 62 publications

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“…In the literature, the observed heterogeneity in preclinical AD is frequently attributed to cognitive resilience (11). Additional factors that have been proposed as possible drivers of resilience, include educational attainment (12,13), cortical thickness (12), personality (14), cardiovascular health (15), and synaptic function (16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Wisch

Butt

Gordon

et al. 2022

Preprint

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Background: Heterogeneity in progression to AD poses challenges for both clinical prognosis and clinical trial implementation. In the absence of a well-defined understanding of future disease trajectory, participants may receive unnecessary treatment or true effects of pharmacological intervention may be obscured. We identified early differences in preclinical Alzheimer Disease (AD) biomarkers, assessed patterns for developing preclinical AD across the Amyloid-Tau-(Neurodegeneration) (AT(N)) framework, and considered potential sources of difference by analyzing the CSF proteome.Methods: 108 participants enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center (ADRC) who completed four or more lumbar punctures and were cognitively normal at baseline were included. Cerebrospinal fluid (CSF) measures of Aβ42, pTau181, and Neurofilament Light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including positron emission tomography (PET) amyloid and tau and structural magnetic resonance imaging (MRI) were repeatedly obtained when available. This allowed for staging individuals according to the AT(N) framework.Results and Conclusion: Growth mixture modeling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and CSF Aβ42. Two groups (AD Biomarker Positive and AD Biomarker Intermediate) had distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were successfully re-classified using only proteomic profiles (AUCAD Biomarker Positive vs AD Biomarker Negatives = 0.970, AUCAD Biomarker Positive vs. Intermediate AD Biomarkers = 0.750, AUCIntermediate AD Biomarkers vs. AD Biomarker Negative = 0.698). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified individuals who became AD Biomarker Positive before age 50. A second group, AD Biomarker Intermediate, developed elevated CSF ptau181 in their mid-60’s before becoming amyloid positive in their mid-70’s. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing vs. other agents) in clinical trials. CSF proteome analysis highlighted additional potential opportunities for non-AT(N) focused therapies, including blood brain barrier-, liver-, and neuroinflammatory-related targets.

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Section: Introductionmentioning

confidence: 99%

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Wisch

Butt

Gordon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Additional factors that have been proposed as possible drivers of resilience, include educational attainment (12,13), cortical thickness (12), personality (14), cardiovascular health (15), and synaptic function (16)(17)(18). A substantial proportion of the resilience literature relies on residual methods which allow researchers to de ne resilience as the source of unexplained variance, rather than identifying clinically meaningful underlying sources (11).…”

Section: Introductionmentioning

confidence: 99%

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Wisch

Butt

Gordon

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Heterogeneity in progression to AD poses challenges for both clinical prognosis and clinical trial implementation. In the absence of a well-defined understanding of future disease trajectory, participants may receive unnecessary treatment or true effects of pharmacological intervention may be obscured. We identified early differences in preclinical Alzheimer Disease (AD) biomarkers, assessed patterns for developing preclinical AD across the Amyloid-Tau-(Neurodegeneration) (AT(N)) framework, and considered potential sources of difference by analyzing the CSF proteome.Methods: 108 participants enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center (ADRC) who completed four or more lumbar punctures and were cognitively normal at baseline were included. Cerebrospinal fluid (CSF) measures of Ab42, pTau181, and Neurofilament Light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including positron emission tomography (PET) amyloid and tau and structural magnetic resonance imaging (MRI) were repeatedly obtained when available. This allowed for staging individuals according to the AT(N) framework.Results: Growth mixture modeling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and CSF Ab42. Two groups (AD Biomarker Positive and AD Biomarker Intermediate) had distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were successfully re-classified using only proteomic profiles (AUCAD Biomarker Positive vs AD Biomarker Negatives = 0.970, AUCAD Biomarker Positive vs. Intermediate AD Biomarkers = 0.750, AUCIntermediate AD Biomarkers vs. AD Biomarker Negative = 0.698). Conclusions: We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified individuals who became AD Biomarker Positive before age 50. A second group, AD Biomarker Intermediate, developed elevated CSF ptau181 in their mid-60’s before becoming amyloid positive in their mid-70’s. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing vs. other agents) in clinical trials. CSF proteome analysis highlighted additional potential opportunities for non-AT(N) focused therapies, including blood brain barrier-, liver-, and neuroinflammatory-related targets.

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“…One popular approach to quantify CR is to measure the residual variance between predicted cognitive performance based on individual's level of brain status and neuropathology and the actual individual's performance (Reed et al, 2010). This residual-based measures offer a more precise measurement of CR (Bocancea et al, 2021). High-reserve individuals exhibit higher actual measured cognitive performance than that predicted.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Cognitive Reserve Quantification

Lee

Liu

Habeck

et al. 2022

Preprint

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Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the structural MRI data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (initially aged 20-80) from RANN, 620 healthy participants (age 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. Cognitive reserve was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (using RANN) to the target domain (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the chosen T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.

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Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods

Cited by 61 publications

References 62 publications

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Proteomic Clusters Underlie Heterogeneity in Preclinical AD Progression

Transfer Learning for Cognitive Reserve Quantification

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