ActiveBrain & Nervous SystemNIHR-supported projectPsychology & Behaviour

Remote Blood Biomarker Sampling for Detection of Early Alzheimer’s Disease and Neurodegeneration in Older Adults Living in the Community (REBEL),

In plain English

AI plain-English summary

A single finger-prick blood sample, collected at home and posted to a lab, could flag whether an older adult is developing early Alzheimer’s disease. This matters because current dementia diagnosis often happens late, after significant brain damage has already occurred. Many people in the early stages never see a specialist, and the only reliable tests—PET scans or lumbar punctures—are expensive, invasive, and unavailable outside hospitals. The REBEL study asks whether a simple blood test, combined with a short computerised thinking-and-memory assessment done at home, can reliably predict who is at risk years before symptoms become obvious. If the approach works, it could transform dementia detection from a hospital-based, late-stage event into a routine, community-based screening—like checking cholesterol or blood pressure. GPs could identify high-risk patients earlier, refer them for confirmatory tests, and open the door to treatments that work best when started early. The infrastructure for postal blood sampling already exists for other conditions; this research would test whether it can be extended to neurodegeneration at scale. The study itself is a large-scale validation, not a fundamental science project—its success depends on whether the biomarkers and cognitive tests hold up in a real-world, diverse older population.

View original technical description
This project will deliver a large-scale blood biomarker study to determine the predictive power of remotely collected blood biomarkers in combination with computerised neuropsychology for detection of dementia risk and early AD

Researchers

Anne Corbett (Principal Investigator)

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Original classification

Theme 3 Evaluation of new technologies and technology-supported intervention trials

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.