Health
CSF protein detects Alzheimer's disease mutation carriers decades before symptoms appear
Long before carriers of autosomal dominant AD mutations notice the first signs of memory loss, changes in their brains are underway at the protein level. In a study published September 26 in the journal Cell, scientists led by Carlos Curuchaga of Washington University in St. Louis further support this concept.
- CSF proteomics PEGs 137 proteins that diverge in ADAD mutation carriers along the AD continuum.
- Before the classic Alzheimer's disease biomarkers were modified, 12 were modified.
- Together, GFAP, NPTX2, PEA15, SMOC1, SMOC2, and TNFRSF1B predicted mutational status better than classical AD biomarkers.
Researchers measured more than 6,000 proteins in the cerebrospinal fluid of Dominantly Inherited Alzheimer's Disease Network (DIAN) participants and identified 137 that are precursors to the pathophysiology of Alzheimer's disease. Twelve of these changed before the traditional AD biomarkers Aβ or p-tau. Together, six of them were able to more accurately predict a person's mutation carrier status. Of note, all 137 were also significantly biased in the CSF of sporadic Alzheimer's disease patients, suggesting that this finding applies across the most definitive family forms.
Alzforum featured this study earlier this year when it was published in a medRxiv preprint (News for February 2024). Since then, researchers have reanalyzed the proteomic treasure trove using new statistical methods and reproduced them using other proteomic techniques. Read on for some highlights from our latest findings.
Co-lead authors Yuanyuan Shen and Jigyasha Timsina used the Somascan proteomics platform to measure 6,163 proteins in the CSF of 286 mutation carriers and 184 non-carriers. The researchers used estimated years of onset (EYO) as a timescale and placed each participant along a pseudo-time trajectory of disease progression. They divided the sample into discovery and replication cohorts and ultimately identified 137 proteins that differentiate carriers from non-carriers at some point along their trajectory. In the LOAD experiment, concentrations of each of 137 were increased or decreased in the CSF of 848 patients with biomarker-confirmed sporadic AD compared to 915 healthy controls.
trifecta of trouble. As mutation carriers approach EYO, three stages of CSF proteomic changes unfold, each characterized by distinct biological pathways. [Courtesy of Shen at al., Cell, 2024.]
This change suggests the sequential involvement of multiple biological pathways. This unfolds in three stages as participants approach the EYO. The early stages are characterized by altered stress responses, glutamate metabolism, and mitochondrial damage in neurons. Metaphase characterized by apoptotic proteins. Then, there is the late pre-symptomatic stage, when microglia and cell communication proteins begin to malfunction.
p-tau, total tau, and Aβ42 changed 17, 12, and 11 years before EYO, respectively, while 12 other proteins changed even earlier. These included the extracellular matrix protein SMOC1 at age 31, followed by DNAJB9 at age 26, and SMOC2 at age 20 before EYO.
Could any of these proteins predict the status of mutation carriers? Using a machine learning approach and dividing the cohort into training and test samples, the scientists found that the six individuals who played the role Identified the executives. They are GFAP, NPTX2, PEA15, SMOC1, SMOC2, and TNFRSF1B. With 91% accuracy, this group had stronger predictive power than traditional AD markers, but none reached 80%.
The researchers tested this predictive panel using other proteomic methods. Using O-link, an antibody-based quantification technique, we were able to measure all but PEA15. The remaining five proteins predicted mutation carrier status with 83.7% accuracy and distinguished between presymptomatic and symptomatic carriers with 87.9% accuracy. Three of the proteins (SMOC1, GFAP, and NPTX2) are included in Alamar's CNS Nulisa platform. This is a new technology that uses nucleic acid tags to amplify antibody signals and detect very small amounts of protein (August 2024 Conference News). Even using just these three proteins, Nulisa's measurements revealed mutation carriers and their clinical status with 78.1 percent and 87.6 percent accuracy, respectively. —Jessica Shugart
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