Biomarkers for the Diagnosis of Alzheimer’s Disease in Clinical Practice: The Role of CSF Biomarkers during the Evolution of Diagnostic Criteria

Dulewicz, Maciej; Kulczyńska-Przybik, Agnieszka; Mroczko, Piotr; Kornhuber, Johannes; Lewczuk, Piotr; Mroczko, Barbara

doi:10.3390/ijms23158598

Cited by 12 publications

(10 citation statements)

References 84 publications

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“…To develop screening tests with greater specificity, further research is needed to identify additional biomarkers beyond the main CSF biomarkers currently in use for AD (Aβ42, t-tau, and p-tau), such as Aβ42/Aβ40 ratio and α-synuclein. 82,83 Blood-based biomarkers hold immense promise for evaluating AD prognosis due to their high accessibility and low cost. However, standardized assays and procedures need to be developed to facilitate the comparison of biomarker measurements across different batches and laboratories.…”

Section: Remaining Challengesmentioning

confidence: 99%

“…Early diagnosis of AD is important for prevention and planning therapeutic strategies. To develop screening tests with greater specificity, further research is needed to identify additional biomarkers beyond the main CSF biomarkers currently in use for AD (Aβ42, t‐tau, and p‐tau), such as Aβ42/Aβ40 ratio and α‐synuclein 82,83 …”

Section: Prognostic Biomarkersmentioning

confidence: 99%

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Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Winchester,

Harshfield,

Shi

et al. 2023

Alzheimer's & Dementia

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With the increase in large multimodal cohorts and high‐throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice.Highlights Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real‐world validation, and interdisciplinary collaboration are required.

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Section: Remaining Challengesmentioning

confidence: 99%

Section: Prognostic Biomarkersmentioning

confidence: 99%

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Winchester,

Harshfield,

Shi

et al. 2023

Alzheimer's & Dementia

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“…For the calculation of the limit of blank (LoB) and limit of detection (LoD), 24 BC samples were analyzed, and parameters were calculated according to Armbruster et al [24] using Equations ( 1) and (2). Afterward, values were translated into particle concentrations using the calculated calibration curve.…”

Section: Analytical Validation: Detection and Quantification Limitsmentioning

confidence: 99%

“…Alzheimer's disease (AD) is a progressive brain disease that causes increasing deterioration of mental abilities. AD is mainly characterized by misfolding and aggregation of amyloid-β (Aβ) peptides and Tau proteins into amyloid plaques and neurofibrillary tangles [1,2]. For decades, these deposits were considered the primary cause of disease onset and progression.…”

Section: Introductionmentioning

confidence: 99%

Development and Implementation of an Internal Quality Control Sample to Standardize Oligomer-Based Diagnostics of Alzheimer’s Disease

et al. 2023

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Protein misfolding and aggregation are pathological hallmarks of various neurodegenerative diseases. In Alzheimer’s disease (AD), soluble and toxic amyloid-β (Aβ) oligomers are biomarker candidates for diagnostics and drug development. However, accurate quantification of Aβ oligomers in bodily fluids is challenging because extreme sensitivity and specificity are required. We previously introduced surface-based fluorescence intensity distribution analysis (sFIDA) with single-particle sensitivity. In this report, a preparation protocol for a synthetic Aβ oligomer sample was developed. This sample was used for internal quality control (IQC) to improve standardization, quality assurance, and routine application of oligomer-based diagnostic methods. We established an aggregation protocol for Aβ1–42, characterized the oligomers by atomic force microscopy (AFM), and assessed their application in sFIDA. Globular-shaped oligomers with a median size of 2.67 nm were detected by AFM, and sFIDA analysis of the Aβ1–42 oligomers yielded a femtomolar detection limit with high assay selectivity and dilution linearity over 5 log units. Lastly, we implemented a Shewhart chart for monitoring IQC performance over time, which is another important step toward quality assurance of oligomer-based diagnostic methods.

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“…Various biomarkers [ 48 ] provide valuable information on diagnosing and understanding the mechanisms underlying dementia [ 49 , 50 ]. Some of these are neurofilaments [ 51 ], Aβ42 [ 52 , 53 ], tau [ 54 , 55 ], GAP-43 [ 56 ], neurogranin [ 57 ], trem2 [ 58 ], neuron-specific enolase [ 59 ], YKL-40 [ 60 ], and neuroregulin [ 61 ] among others.…”

Section: Specific Challengesmentioning

confidence: 99%

A Perspective: Challenges in Dementia Research

Stecker

2022

Medicina

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Although dementia is a common and devastating disease that has been studied intensely for more than 100 years, no effective disease modifying treatment has been found. At this impasse, new approaches are important. The purpose of this paper is to provide, in the context of current research, one clinician’s perspective regarding important challenges in the field in the form of specific challenges. These challenges not only illustrate the scope of the problems inherent in finding treatments for dementia, but can also be specific targets to foster discussion, criticism and new research. One common theme is the need to transform research activities from small projects in individual laboratories/clinics to larger multinational projects, in which each clinician and researcher works as an integral part. This transformation will require collaboration between researchers, large corporations, regulatory/governmental authorities and the general population, as well as significant financial investments. However, the costs of transforming the approach are small in comparison with the cost of dementia.

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Biomarkers for the Diagnosis of Alzheimer’s Disease in Clinical Practice: The Role of CSF Biomarkers during the Evolution of Diagnostic Criteria

Cited by 12 publications

References 84 publications

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Development and Implementation of an Internal Quality Control Sample to Standardize Oligomer-Based Diagnostics of Alzheimer’s Disease

A Perspective: Challenges in Dementia Research

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