A Centiloid cut‐off to help predict true amyloid accumulation
Ariane Bollack, Lyduine E. Collij, David Vállez García, Mahnaz Shekari, Daniele Altomare, Pierre Payoux, Bruno Dubois, Oriol Grau‐Rivera, Mercè Boada Rovira, Agneta K Nordberg, Zuzana Walker, Philip Scheltens, Michael Schöll, Robin Wolz, Mark E Schmidt, Rossella Gismondi, Andrew W. Stephens, Christopher Buckley, Giovanni B Frisoni, Bernard J Hanseeuw, Pieter Jelle Visser, Rik Vandenberghe, Alexander Drzezga, Maqsood Yaqub, Ronald Boellaard, Pawel J Markiewicz, David M Cash, Gill Farrar, Juan Domingo Gispert, Frederik Barkhof- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
Longitudinal amyloid‐PET‐based imaging endpoints are often included in clinical trials, where they provide critical evidence of treatment efficacy. We assessed the longitudinal variability of the Centiloid (CL) scale and its ability to predict early amyloid accumulation.
Method
Longitudinal amyloid‐PET with 18F‐flutemetamol and 18F‐florbetaben (N = 711 at follow‐up 1, time interval: 3.0±1.3years; N = 98 at follow‐up 2, 5.1±0.7years) was conducted in predominantly cognitively unimpaired participants of the AMYPAD Prognostic and Natural History Study (PNHS). Quantification of scans was performed using the MR‐based CL pipeline and visually read (VR) by local certified assessors. Longitudinal change in CL was modelled using linear mixed effects models corrected for age, sex, and education. Subjects were classified based on VR status over time (i.e., Stable VR‐, VR‐Converters or Stable VR+) and based on amyloid accumulation over time (i.e., Accumulator or Non‐accumulator; Accumulator if annualised rate of change [ARC]>95th percentile of a subset of 110 subjects for whom no amyloid accumulation was expected, selection criteria in Table‐1). Finally, a baseline CL cut‐off to identify future Accumulators was derived from a receiver operating characteristic (ROC) curve analysis.
Result
Participants had a median age of 65 years, 57% were females, 41% were APOE‐e4 carriers, and 18% had baseline VR+ scan (Table‐1). The 95th percentile of ARC in the stable 110 subjects was 2.7 CL/year. Baseline CL was higher in Accumulators compared to Non‐accumulators (24.8 versus 8.7 CL, p<.001). Based on the ROC analysis, the optimal baseline CL threshold to predict Accumulators was 12.1 CL (sensitivity = 67% and specificity = 74%, Figure B‐C‐D), in line with the baseline CL threshold that best predicts VR conversion (11.6 CL). Baseline CL values and ARC were also higher in Stable VR+ and VR‐Converters compared to Stable VR‐ (p<.001), but no significant difference in ARC was found between VR‐Converters and Stable VR+ (Table‐1, Figure A). Results were robust across tracers.
Conclusion
Increases over 2.7 CL/year can be indicative of reliable amyloid accumulation. A CL cut‐off of ∼12CL can help identify subjects more likely to accumulate amyloid in the short future. Annualised rates of change in amyloid deposition where highly comparable across tracers.