Multiplex Cerebrospinal Fluid Proteomics Identifies Biomarkers for Diagnosis and Prediction of Alzheimer's Disease

Medical Laboratory Science Neurology Medical Imaging Life Sciences Medicine Cell Biology
Alzheimer's Disease Cerebrospinal Fluid Proteomics Biomarkers Diagnosis Prediction

Cerebrospinal Fluid Proteomics Study for Diagnosis and Prediction of Alzheimer’s Disease

Background and Research Objectives

Alzheimer’s disease (AD) is a neurodegenerative disease that leads to memory loss and cognitive decline, and currently, there is no effective cure globally. Traditionally, the pathological features of AD include β-amyloid (Aβ) plaques and tau protein neurofibrillary tangles. However, these features reflect only part of the complex pathological process of AD and fail to fully reveal its biological basis. In recent years, disease-modifying treatments targeting Aβ and tau have shown poor results, further highlighting the importance of expanding AD biomarker research. To accurately diagnose and prognose AD, especially in the early stages of the disease, it is crucial to identify more high-performance biomarkers.

Paper Source and Authors

This paper was published in the journal “Nature Human Behaviour,” titled “Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer’s disease.” The research team comes from the Department of Neurology at Huashan Hospital, the Frontier Science Center for Brain Research at Fudan University, among other institutions. The study utilized data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and conducted a large-scale cerebrospinal fluid (CSF) proteomics analysis to discover potential biomarkers for AD.

Research Process

The study included the following main steps:

  1. Study Subjects and Data Collection: A selection of 707 participants from the ADNI database, including cognitively normal (CN), mild cognitive impairment (MCI), and AD dementia patients. Each participant underwent detailed CSF proteomics analysis, along with testing for core AD biomarkers (Aβ42 and phosphorylated tau).

  2. Differential Expression Analysis: Identification of 262 differentially expressed proteins between biologically defined AD (A+T+) and CN (A−T−), and 50 differentially expressed proteins between clinically diagnosed AD dementia and normal cognition.

  3. Protein Importance Ranking and Diagnostic Accuracy Evaluation: Using the Light Gradient Boosting Machine (LGBM) classifier to select the most distinguishing proteins and evaluate their diagnostic accuracy both individually and in combination.

  4. Independent External Cohort Validation: Validation of the selected proteins’ performance in the Parkinson’s Progression Markers Initiative (PPMI) cohort to further determine their clinical applicability.

  5. Association Analysis and Pathway Enrichment: Evaluating the association of selected proteins with core AD biomarkers, cognitive decline, and protein changes at different AD stages, to explore their dynamic changes in the disease process.

Major Research Results

Identification of Differentially Expressed Proteins

Among the 707 participants, the research team identified 262 differentially expressed proteins between biologically defined AD and CN and 50 differentially expressed proteins between clinically diagnosed AD dementia and normal cognition. The larger number of differentially expressed proteins in the former suggests that biological diagnosis can more comprehensively reflect AD’s pathological characteristics.

Protein Importance Ranking and Diagnostic Accuracy

Using the LGBM classifier, the research team ultimately selected the top four proteins distinguishing AD from CN (YWHAG, SMOC1, PIGR, and TMOD2) and the top five proteins distinguishing AD dementia from normal cognition (ACHE, YWHAG, PCSK1, MMP10, and IRF1). These proteins demonstrated excellent performance in distinguishing biologically defined AD and CN (AUC=0.987) and clinically diagnosed AD dementia and normal cognition (AUC=0.975).

Validation of Proteins in Independent External Cohorts

In the PPMI cohort, the research team validated the diagnostic capability of these proteins. YWHAG, SMOC1, and TMOD2 performed excellently in distinguishing early stages of AD (e.g., preclinical AD) from normal cognition, with AUC values of 0.934, 0.997, and 0.974, respectively, further proving their potential for early diagnosis.

Dynamic Changes and Association Analysis of Proteins

The study found that YWHAG and SMOC1 are upregulated in the preclinical stage of AD and persist throughout the disease progression. This finding suggests that these proteins can be used not only for early diagnosis but also help monitor disease progression. Additionally, these proteins are closely associated with core AD biomarkers (e.g., Aβ42, p-tau181, and t-tau), brain metabolism decline, hippocampal atrophy, and cognitive decline, further proving their significance in the pathological process of AD.

Research Conclusions and Significance

This study identified YWHAG, SMOC1, TMOD2, and PIGR as key biomarkers for AD diagnosis and prediction through high-throughput CSF proteomics analysis, and constructed highly accurate diagnostic models with combinations of four and five proteins. The validation results in independent external cohorts and neuropathological verification further support the clinical application potential of these proteins. The research also revealed dynamic changes of these proteins at different AD stages, indicating their importance in early diagnosis and disease progression monitoring.

These findings provide new biomarker tools for AD diagnosis and prediction, revealing the multifactorial pathological characteristics of AD, and providing new perspectives for future clinical trials and therapeutic strategies. By further developing clinically applicable detection tools, these protein biomarkers are expected to play a crucial role in early diagnosis and personalized treatment of AD.

Research Highlights

  1. High-throughput Proteomics Analysis: The study covered 6,361 CSF proteins, offering a wide candidate pool of biomarkers.
  2. High-accuracy Diagnostic Models: The constructed models with combinations of four and five proteins performed excellently in diagnosing AD and predicting disease progression.
  3. Independent Validation: Results in the PPMI cohort further support the clinical applicability of these proteins.
  4. Dynamic Change Analysis: The study revealed dynamic changes of key proteins at different AD stages, underscoring their importance in early diagnosis and progression monitoring.

These results provide crucial scientific evidence for early diagnosis, prognosis evaluation, and treatment strategies of AD, laying a solid foundation for future clinical applications.