Dataset Information

Harmonization and standardization of biofluid-based biomarker measurements for AT(N) classification in Alzheimer's disease.

ABSTRACT: Fluid biomarkers are currently measured in cerebrospinal fluid and blood for Alzheimer's disease diagnosis and are promising targets for drug development and for patients' follow-up in clinical trials. These biomarkers have been grouped in an unbiased research framework, the amyloid (Aβ), tau, and neurodegeneration (AT[N]) biomarker system to aid patients' early diagnosis and stratification. Metrological approaches relying on mass spectrometry have been used for the development of reference materials and reference measurement procedures. Despite their excellent performances as clinical tools, fluid biomarkers often present an important between-laboratory variation. Standardization efforts were carried out on the biomarkers currently included in the AT(N) classification system, involving the collaboration of national metrology institutes, clinicians, researchers, and in vitro diagnostic providers. This article provides an overview of current activities towards standardization. These reference methods and reference materials may be used for recalibration of immunoassays and the establishment of standardized cutoff values allowing a better stratification of Alzheimer's disease patients.

Highlights

The AT(N) biomarker system allows stratifying AD patients on the basis of biomarker profiles.Fluid biomarker measurements often present an important between-laboratory variation preventing the establishment of standardized cutoff values.Overview on the standardization initiatives involving the fluid biomarkers currently included in the AT(N) framework.

SUBMITTER: Giangrande C

PROVIDER: S-EPMC10432775 | biostudies-literature | 2023 Jul-Sep

REPOSITORIES: biostudies-literature

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Harmonization and standardization of biofluid-based biomarker measurements for AT(N) classification in Alzheimer's disease.

Giangrande Chiara C Delatour Vincent V Andreasson Ulf U Blennow Kaj K Gobom Johan J Zetterberg Henrik H

Alzheimer's & dementia (Amsterdam, Netherlands) 20230701 3

Fluid biomarkers are currently measured in cerebrospinal fluid and blood for Alzheimer's disease diagnosis and are promising targets for drug development and for patients' follow-up in clinical trials. These biomarkers have been grouped in an unbiased research framework, the amyloid (Aβ), tau, and neurodegeneration (AT[N]) biomarker system to aid patients' early diagnosis and stratification. Metrological approaches relying on mass spectrometry have been used for the development of reference mate ...[more]

PMID: 37600860

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Project description:Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with the following diagnoses: cognitive normal (CN; n=137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found that classifications using embedded MRI features generally outperformed (p<0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p=0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: =0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.

Dataset Information

Harmonization and standardization of biofluid-based biomarker measurements for AT(N) classification in Alzheimer's disease.

Highlights

Publications

Harmonization and standardization of biofluid-based biomarker measurements for AT(N) classification in Alzheimer's disease.

Similar Datasets

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