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Objective <p>Lecanemab effectively reduces brain amyloid-β (Aβ) burden in early Alzheimer’s disease (AD). However, Aβ deposition is spatially heterogeneous.The aim of this study is to test whether baseline β-amyloid (Aβ) deposition phenotype influences lecanemab-related Aβ clearance on Aβ-PET.</p> Methods <p>An Aβ Subtype and Stage Inference (SuStaIn) model was trained to identify Aβ progression phenotypes using cross-sectional Aβ-PET SUVRs from seven brain regions in 303 individuals (249 Aβ+/tau + PET-confirmed Alzheimer’s disease and 54 Aβ−/tau− controls). This trained model was then applied to 39 lecanemab-treated patients with paired baseline and 6‑month Aβ-PET. Global and regional Aβ burden was quantified in Centiloids (CL) and SUVR, respectively. Subtype differences in ΔCL and ΔSUVR were tested. By defining baseline topology and clearance topology using posterior-to-anterior dominance indices (Topo_occ−fron = SUVR of occipital lobe – SUVR of frontal lobe), topology–clearance coupling was assessed using correlation and regression analysis. Additionally, spatial topological correspondence was quantified by modeling ROI-level clearance as a function of ROI-level baseline SUVR within a mixed-effects model with a subject-level random intercept.</p> Results <p>SuStaIn identified two dominant phenotypes consistent with an occipital-onset pattern (subtype 1) and a precuneus–frontal-onset pattern (subtype 2). In the lecanemab cohort, CL decreased by a mean ΔCL of − 27.77 ± 24.51. ΔCL did not differ between subtype 1 (<i>n</i> = 16) and subtype 2 (<i>n</i> = 17), whereas baseline CL strongly predicted ΔCL (<i>r</i> = -0.68, <i>P</i> &lt; 0.001). ROI-wise ΔSUVR differences were not significant after FDR correction, while the ΔSUVR was significantly correlated with baseline SUVR (<i>r</i> = -0.64, <i>P</i> &lt; 0.0001). In subtype-assigned patients (<i>n</i> = 33), a posterior-dominant baseline topology predicted greater posterior clearance. The changed Topo_occ−fron is correlated with baseline values (<i>r</i> = 0.514, <i>P</i> &lt; 0.01) and remained significant after adjusting for baseline CL (β=−0.273, <i>P</i> &lt; 0.01). ROI-level clearance magnitude increased with baseline regional SUVR (mixed-effects β = 0.264, <i>p</i> &lt; 0.001), quantifying high spatial correspondence between deposition and clearance.</p> Conclusion <p>While baseline Aβ phenotype did not influence the magnitude of early global amyloid removal, it predicted the spatial pattern of regional clearance. Quantitative topological correspondence between baseline deposition and early clearance provides in vivo evidence of target engagement and supports topology-informed PET monitoring beyond global CL.</p>

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Baseline amyloid deposition phenotypes inform the spatial distribution of early amyloid clearance with lecanemab in Alzheimer’s disease

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Xiaofeng Dou,
  • Daoyan Hu,
  • Jing Wang,
  • Yan Zhong,
  • Congcong Yu,
  • Hongwei Zhan,
  • Yanxing Chen,
  • Rui Zhou,
  • Xiaohui Zhang,
  • Baorong Zhang,
  • Jiong Zhou,
  • Mei Tian,
  • Hong Zhang,
  • Yaping Yan,
  • Chentao Jin

摘要

Objective

Lecanemab effectively reduces brain amyloid-β (Aβ) burden in early Alzheimer’s disease (AD). However, Aβ deposition is spatially heterogeneous.The aim of this study is to test whether baseline β-amyloid (Aβ) deposition phenotype influences lecanemab-related Aβ clearance on Aβ-PET.

Methods

An Aβ Subtype and Stage Inference (SuStaIn) model was trained to identify Aβ progression phenotypes using cross-sectional Aβ-PET SUVRs from seven brain regions in 303 individuals (249 Aβ+/tau + PET-confirmed Alzheimer’s disease and 54 Aβ−/tau− controls). This trained model was then applied to 39 lecanemab-treated patients with paired baseline and 6‑month Aβ-PET. Global and regional Aβ burden was quantified in Centiloids (CL) and SUVR, respectively. Subtype differences in ΔCL and ΔSUVR were tested. By defining baseline topology and clearance topology using posterior-to-anterior dominance indices (Topo_occ−fron = SUVR of occipital lobe – SUVR of frontal lobe), topology–clearance coupling was assessed using correlation and regression analysis. Additionally, spatial topological correspondence was quantified by modeling ROI-level clearance as a function of ROI-level baseline SUVR within a mixed-effects model with a subject-level random intercept.

Results

SuStaIn identified two dominant phenotypes consistent with an occipital-onset pattern (subtype 1) and a precuneus–frontal-onset pattern (subtype 2). In the lecanemab cohort, CL decreased by a mean ΔCL of − 27.77 ± 24.51. ΔCL did not differ between subtype 1 (n = 16) and subtype 2 (n = 17), whereas baseline CL strongly predicted ΔCL (r = -0.68, P < 0.001). ROI-wise ΔSUVR differences were not significant after FDR correction, while the ΔSUVR was significantly correlated with baseline SUVR (r = -0.64, P < 0.0001). In subtype-assigned patients (n = 33), a posterior-dominant baseline topology predicted greater posterior clearance. The changed Topo_occ−fron is correlated with baseline values (r = 0.514, P < 0.01) and remained significant after adjusting for baseline CL (β=−0.273, P < 0.01). ROI-level clearance magnitude increased with baseline regional SUVR (mixed-effects β = 0.264, p < 0.001), quantifying high spatial correspondence between deposition and clearance.

Conclusion

While baseline Aβ phenotype did not influence the magnitude of early global amyloid removal, it predicted the spatial pattern of regional clearance. Quantitative topological correspondence between baseline deposition and early clearance provides in vivo evidence of target engagement and supports topology-informed PET monitoring beyond global CL.