Potential for Predicting Leqembi Treatment Response Using Blood Biomarkers Suggested

Korea University Guro Hospital research team conducts long-term follow-up on 153 Leqembi-treated patients

Link between changes in p-tau217 and cognitive outcomes identified

Reduction in p-tau217 observed from three months after treatment initiation

Korea University Guro Hospital announced on August 20 that the research team led by Professor Sung-Hoon Kang from the Department of Neurology has presented the possibility of monitoring the therapeutic response to Leqembi, an Alzheimer's disease medication, by observing changes in the blood biomarker p-tau217, as well as the potential to predict future cognitive outcomes.

The research team, including Professor Kang and researcher Yujeong Park, utilized K-LEARN, a real-world practice-based prospective research cohort, to follow long-term changes in blood biomarkers in 153 patients with early Alzheimer's disease who received Leqembi treatment. The study's findings were published in the international academic journal "Alzheimer's & Dementia."

The team repeatedly performed blood tests before and after Leqembi treatment, analyzing long-term changes in p-tau217, a biomarker highly sensitive to brain amyloid pathology. The results showed that p-tau217 decreased significantly from three months after the initiation of treatment. In particular, the most pronounced decline was observed between three and six months of treatment, after which the biomarker levels stabilized.

(From left) Professor Seonghoon Kang, Department of Neurology, Guro Hospital, Korea University, and Researcher Yujeong Park, Department of Neurology, Guro Hospital, Korea University. Korea University Guro Hospital

Subsequently, by analyzing the changes in p-tau217 during the initial six months of treatment, the research team identified two groups of patients who displayed different response patterns.

Group A showed a significant decrease in p-tau217 during the first six months after treatment, along with a relatively slower rate of cognitive decline up to 12 months. By contrast, Group B exhibited a smaller reduction in p-tau217 during the initial six months, and their cognitive function deteriorated more rapidly than Group A over time.

Additionally, patients with hypertension tended to show a relatively smaller decline in p-tau217. The research team suggested that vascular risk factors may be associated with the biological response to anti-amyloid therapy.

Leqembi is a disease-modifying therapy (DMT) that slows the progression of Alzheimer's disease by removing amyloid beta accumulated in the brain. Although it was introduced in Korea in December 2024, its high treatment cost and variability in patient response have highlighted the need for biomarkers that can objectively assess therapeutic efficacy in clinical practice.

This study is meaningful in demonstrating that early changes in p-tau217 following the initiation of Leqembi in real-world clinical settings may be associated with subsequent cognitive outcomes. Notably, rather than relying on a single biomarker level at a certain time point, the study suggests it is possible to evaluate individual patient responses by tracking the change patterns during the course of treatment.

Professor Kang said, "These results show that early changes in p-tau217 have the potential to be used for assessing clinical response to Leqembi in the future," adding, "It is particularly significant that we confirmed the association between biomarker changes in the first six months of treatment and subsequent cognitive outcomes in real clinical settings."

He continued, "We plan to conduct further studies so that blood biomarkers can be used to more objectively monitor individual patient responses and to help develop therapeutic strategies going forward."

K-LEARN is a research cohort that tracks the clinical course of Leqembi-treated patients, as well as blood biomarkers, MRI, amyloid PET, and changes in cognitive function over long periods using standardized methods. Professor Kang's team is building a repository of real-world clinical data on anti-amyloid therapy based on K-LEARN. The team plans to pursue further research evaluating individual patient responses by integrating blood biomarkers with imaging and clinical information.

This content was produced with the assistance of AI translation services.