Research Background and Objectives

With over 50 million people worldwide affected by cognitive impairment disorders, this number is expected to double by 2050. Alzheimer’s disease (AD) is the most common form of dementia, characterized by extracellular amyloid-beta (Aβ) plaques and intracellular tau protein aggregates in the brain. Over the past two decades, the AD field has focused on using biomarkers to support diagnosis and prognosis rather than relying solely on clinical symptoms. This study aims to create a robust biological model for staging AD using cerebrospinal fluid (CSF) biomarkers.

Research Source

This study was completed by multiple scholars, including Gemma Salvadó and others, from various research institutions across multiple countries and regions, such as Lund University in Sweden, the Washington University School of Medicine, and the Amsterdam VU University Medical Center in the Netherlands. The research findings were published in the May 2024 issue of the journal Nature Aging.

Research Design and Methods

The study included 426 participants from the Biofinder-2 project and 222 participants from the Knight Alzheimer’s Disease Research Center. Researchers used the subtype and stage inference (SUSTAIN) algorithm to sort data from multiple CSF biomarkers to construct a staging model. The biomarkers used included: Aβ42/40, p-tau (such as pt217/t217, pt205/t205), and other tau proteins (such as mtbr-tau243 and non-phosphorylated mid-region tau). Through this model analysis, researchers were able to align biomarker changes in the AD progression process with characteristic pathological features and predict the risk of clinical decline.

Main Research Results

The SUSTAIN algorithm identified a single biomarker sequence and revealed five CSF biomarkers, namely Aβ42/40, pt217/t217, pt205/t205, mtbr-tau243, and non-phosphorylated mid-region tau, which constitute a reliable staging model. These CSF stages (0-5) were correlated with other AD-related biomarker abnormalities such as Aβ-PET and Tau-PET and consistent with longitudinal biomarker changes during AD progression. Higher baseline CSF stages were associated with an increased risk of clinical decline. This clinical study emphasizes the existence of a common molecular pathway underlying AD pathophysiology, suggesting that a single CSF sampling is sufficient to accurately indicate AD pathology and represent the disease progression stage. The proposed staging model has profound implications for enhancing diagnostic and prognostic assessments and clinical trial design.

Research Conclusion and Significance

This CSF-based biomarker model has significant scientific value for staging AD and provides practical value for AD diagnosis and prognosis assessment. The proposed staging model underscores the importance of addressing the problem, the novelty of the research methods or workflows used, and the specificity of the research objectives. Additionally, the model’s application value lies in its potential future use in clinical practice and clinical trials to select optimal participants and as an alternative prognostic measurement tool.

Research Highlights and Other Information

The study’s highlight is the highly accurate and stable performance of the new biological staging model for AD, successfully replicated in independent samples. The study also demonstrated the practical application potential of the new model in predicting pathological changes in AD, cognitive decline risk, and assessing clinical progression. Moreover, the study utilized various advanced algorithms and biomarker analysis methods, leading the entire research field. As for other valuable content, the study also mentioned the relationship between CSF biomarkers and laboratory imaging indicators such as uptake value ratios and cortical thickness.