Dataset Information

Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is a neurological disorder with variability in pathology and clinical progression. AD patients may differ in individual-level benefit from amyloid beta removal therapy.

Methods

Random forest models were applied to the EMERGE trial to create an individual-level treatment response (ITR) score which represents individual-level benefit of high-dose aducanumab relative to the placebo. This ITR score was used to test the existence of heterogeneity in treatment effect (HTE).

Results

We found statistical evidence of HTE in the Clinical Dementia Rating-Sum of Boxes (CDR-SB;P = 0.034). The observed CDR-SB benefit was 0.79 points greater in the group with the top 25% of ITR score compared to the remaining 75% (P = 0.020). Of note, the highest treatment responders had lower hippocampal volume, higher plasma phosphorylated tau 181 and a shorter duration of clinical AD at baseline.

Discussion

This ITR analysis provides a proof of concept for precision medicine in future AD research and drug development.

Highlights

Emerging trials have shown a population-level benefit from amyloid beta (Aβ) removal in slowing cognitive decline in early Alzheimer's disease (AD). This work demonstrates significant heterogeneity of individual-level treatment effect of aducanumab in early AD. The greatest clinical responders to Aβ removal therapy have a pattern of more severe neurodegenerative process.

SUBMITTER: Pang M

PROVIDER: S-EPMC10917030 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease.

Pang Menglan M Gabelle Audrey A Saha-Chaudhuri Paramita P Huijbers Willem W Gafson Arie A Matthews Paul M PM Tian Lu L Rubino Ivana I Hughes Richard R de Moor Carl C Belachew Shibeshih S Shen Changyu C

Alzheimer's & dementia : the journal of the Alzheimer's Association 20231026 2

<h4>Introduction</h4>Alzheimer's disease (AD) is a neurological disorder with variability in pathology and clinical progression. AD patients may differ in individual-level benefit from amyloid beta removal therapy.<h4>Methods</h4>Random forest models were applied to the EMERGE trial to create an individual-level treatment response (ITR) score which represents individual-level benefit of high-dose aducanumab relative to the placebo. This ITR score was used to test the existence of heterogeneity i ...[more]

PMID: 37882364

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Project description:Senile plaques, which are mostly composed of beta-amyloid peptide, are the main signature of Alzheimer's disease (AD). Two main forms of beta-amyloid in humans are 40 and 42-amino acid, long; the latter is considered more relevant to AD etiology. The concentration of soluble beta-amyloid-42 (Aβ42) in cerebrospinal fluid (CSF-Aβ42) and the density of amyloid depositions have a strong negative correlation. However, AD patients have lower CSF-Aβ42 levels compared to individuals with normal cognition (NC), even after accounting for this correlation. The goal of this study was to infer deviations of Aβ42 metabolism parameters that underlie this difference using data from the Alzheimer's Disease Neuroimaging Initiative cohort. Aβ42 is released to the interstitial fluid (ISF) by cells and is removed by several processes. First, growth of insoluble fibrils by aggregation decreases the concentration of soluble beta-amyloid in the ISF. Second, Aβ42 is physically transferred from the brain to the CSF and removed with the CSF flow. Finally, there is an intratissue removal of Aβ42 ending in proteolysis, which can occur either in the ISF or inside the cells after the peptide is endocytosed. Unlike aggregation, which preserves the peptide in the brain, transfer to the CSF and intratissue proteolysis together represent amyloid removal. Using a kinetic model of Aβ42 turnover, we found that compared to NC subjects, AD patients had dramatically increased rates of amyloid removal. A group with late-onset mild cognitive impairment (LMCI) also exhibited a higher rate of amyloid removal; however, this was less pronounced than in the AD group. Estimated parameters in the early-onset MCI group did not differ significantly from those in the NC group. We hypothesize that increased amyloid removal is mediated by Aβ42 cellular uptake; this is because CSF flow is not increased in AD patients, while most proteases are intracellular. Aβ cytotoxicity depends on both the amount of beta-amyloid internalized by cells and its intracellular conversion into toxic products. We speculate that AD and LMCI are associated with increased cellular amyloid uptake, which leads to faster disease progression. The early-onset MCI (EMCI) patients do not differ from the NC participants in terms of cellular amyloid uptake. Therefore, EMCI may be mediated by the increased production of toxic amyloid metabolites.

Project description:BackgroundOne component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction.ResultsUsing a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package ( https://github.com/imbs-hl/MBMDRClassifieR ).ConclusionsThe explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

Dataset Information

Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease.

Introduction

Methods

Results

Discussion

Highlights

Publications

Precision medicine analysis of heterogeneity in individual-level treatment response to amyloid beta removal in early Alzheimer's disease.

Similar Datasets

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