Dataset Information

Diagnosing Alzheimer's disease: are we any nearer to useful biomarker-based, non-invasive tests?

ABSTRACT:

Background

Alzheimer's disease (AD) accounts for 60-80% of cases of dementia and causes significant morbidity in patients and carers, and expense for health and social services. There is a need for a validated, non-invasive and cheap test to diagnose early AD, as diagnosis may enable prompt treatment and service planning.

Aim

To identify emerging biomarker-based tests for the early diagnosis of AD which could be available for use in primary or generalist care in the near future.

Design

Horizon scanning review.

Methods

We searched online sources to identify emerging non-invasive, biomarker-based tests. Tests were included if they used blood, saliva or urine; and there was evidence of use in trials in patients with AD. For tests licensed for use in clinical or research settings we requested information from the developer on the intended place of use and plans for availability in Europe.

Results

We identified 6 biomarker-based tests of which 5 are available for research or clinical use. The closest to market were AclarusDX™ (ExonHit Therapeutics) a gene signature test, and INNO-BIA plasma A? forms assay (Innogenetics N.V.) which may be CE marked for clinical use in 2015. We found no evidence of clinical utility or cost.

Conclusion

Although biomarker-based tests are nearing clinical availability and may have a future role to help target AD-specific treatment and guide prognosis, they are not yet ready for trials of clinical utility in primary care.

SUBMITTER: Fletcher LC

PROVIDER: S-EPMC3677379 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Diagnosing Alzheimer's disease: are we any nearer to useful biomarker-based, non-invasive tests?

Fletcher Lydia C B LC Burke Katie E KE Caine Paul L PL Rinne Natasha L NL Braniff Charlotte A CA Davis Hannah R HR Miles Kathryn A KA Packer Claire C

GMS health technology assessment 20130411

<h4>Background</h4>Alzheimer's disease (AD) accounts for 60-80% of cases of dementia and causes significant morbidity in patients and carers, and expense for health and social services. There is a need for a validated, non-invasive and cheap test to diagnose early AD, as diagnosis may enable prompt treatment and service planning.<h4>Aim</h4>To identify emerging biomarker-based tests for the early diagnosis of AD which could be available for use in primary or generalist care in the near future.<h ...[more]

PMID: 23755087

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Project description:Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood plasma, has been demonstrated as a promising approach to the diagnosis and prevention of AD. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in the whole blood transcriptome. Hence, we hypothesized that the establishment of a diagnostic model based on the metabolic signatures of blood is a workable strategy. To that end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, network analysis, etc., were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via the Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD groups, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation, fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression compared with S1 and the non-AD group. These findings indicated that S2 probably has a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 (95%CI: 0.70, 0.77) in the training dataset, 0.71 (95%CI: 0.65, 0.77) in the testing dataset, and an AUC of 0.99 (95%CI: 0.96, 1.00) in one external validation dataset. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using the blood transcriptome and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD.

Dataset Information

Diagnosing Alzheimer's disease: are we any nearer to useful biomarker-based, non-invasive tests?

Background

Aim

Design

Methods

Results

Conclusion

Publications

Diagnosing Alzheimer's disease: are we any nearer to useful biomarker-based, non-invasive tests?

Similar Datasets

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