PROPOSE: This study aims to explore the use of the Centiloid (CL) method in amyloid-β PET quantification to evaluate distinct cognitive aging stages, investigating subjects' mismatch classification using different cut-points for amyloid-β positivity. PROCEDURES: The CL equation was applied in four groups of individuals: SuperAgers (SA), healthy age-matched controls (AC), healthy middle-aged controls (MC), and Alzheimer's disease (AD). The amyloid-β burden was calculated and compared between groups and quantitative variables. Three different cut-points (Jack CR, Wiste HJ, Weigand SD, et al., Alzheimer's Dement 13:205-216, 2017; Salvadó G, Molinuevo JL, Brugulat-Serrat A, et al., Alzheimer's Res Ther 11:27, 2019; and Amadoru S, Doré V, McLean CA, et al., Alzheimer's Res Ther 12:22, 2020) were applied in CL values to differentiate the earliest abnormal pathophysiological accumulation of Aβ and the established Aβ pathology. RESULTS: The AD group exhibited a significantly increased Aβ burden compared to the MC, but not AC groups. Both healthy control (MC and AC) groups were not significantly different. Visually, the SA group showed a diverse distribution of CL values compared with MC; however, the difference was not significant. The CL values have a moderate and significant relationship between Aβ visual read, RAVLT DR and MMSE. Depending on the cut-point used, 10 CL, 19 CL, or 30 CL, 7.5% of our individuals had a different classification in the Aβ positivity. For the AC group, we obtained about 40 to 60% of the individuals classified as positive. CONCLUSION: SuperAgers exhibited a similar Aβ load to AC and MC, differing in cognitive performance. Independently of cut-point used (10 CL, 19 CL, or 30 CL), three SA individuals were classified as Aβ positive, showing the duality between the individual's clinics and the biological definition of Alzheimer's. Different cut-points lead to Aβ positivity classification mismatch in individuals, and an extra care is needed for individuals who have a CL value between 10 and 30 CL.
PROPOSE: This study aims to explore the use of the Centiloid (CL) method in amyloid-β PET quantification to evaluate distinct cognitive aging stages, investigating subjects' mismatch classification using different cut-points for amyloid-β positivity. PROCEDURES: The CL equation was applied in four groups of individuals: SuperAgers (SA), healthy age-matched controls (AC), healthy middle-aged controls (MC), and Alzheimer's disease (AD). The amyloid-β burden was calculated and compared between groups and quantitative variables. Three different cut-points (Jack CR, Wiste HJ, Weigand SD, et al., Alzheimer's Dement 13:205-216, 2017; Salvadó G, Molinuevo JL, Brugulat-Serrat A, et al., Alzheimer's Res Ther 11:27, 2019; and Amadoru S, Doré V, McLean CA, et al., Alzheimer's Res Ther 12:22, 2020) were applied in CL values to differentiate the earliest abnormal pathophysiological accumulation of Aβ and the established Aβ pathology. RESULTS: The AD group exhibited a significantly increased Aβ burden compared to the MC, but not AC groups. Both healthy control (MC and AC) groups were not significantly different. Visually, the SA group showed a diverse distribution of CL values compared with MC; however, the difference was not significant. The CL values have a moderate and significant relationship between Aβ visual read, RAVLT DR and MMSE. Depending on the cut-point used, 10 CL, 19 CL, or 30 CL, 7.5% of our individuals had a different classification in the Aβ positivity. For the AC group, we obtained about 40 to 60% of the individuals classified as positive. CONCLUSION: SuperAgers exhibited a similar Aβ load to AC and MC, differing in cognitive performance. Independently of cut-point used (10 CL, 19 CL, or 30 CL), three SA individuals were classified as Aβ positive, showing the duality between the individual's clinics and the biological definition of Alzheimer's. Different cut-points lead to Aβ positivity classification mismatch in individuals, and an extra care is needed for individuals who have a CL value between 10 and 30 CL.
Authors: Christopher C Rowe; Svetlana Pejoska; Rachel S Mulligan; Gareth Jones; J Gordon Chan; Samuel Svensson; Zsolt Cselényi; Colin L Masters; Victor L Villemagne Journal: J Nucl Med Date: 2013-04-10 Impact factor: 10.057
Authors: William E Klunk; Henry Engler; Agneta Nordberg; Yanming Wang; Gunnar Blomqvist; Daniel P Holt; Mats Bergström; Irina Savitcheva; Guo-feng Huang; Sergio Estrada; Birgitta Ausén; Manik L Debnath; Julien Barletta; Julie C Price; Johan Sandell; Brian J Lopresti; Anders Wall; Pernilla Koivisto; Gunnar Antoni; Chester A Mathis; Bengt Långström Journal: Ann Neurol Date: 2004-03 Impact factor: 10.422
Authors: Christopher C Rowe; Uwe Ackerman; William Browne; Rachel Mulligan; Kerryn L Pike; Graeme O'Keefe; Henry Tochon-Danguy; Gordon Chan; Salvatore U Berlangieri; Gareth Jones; Kerryn L Dickinson-Rowe; Hank P Kung; Wei Zhang; Mei Ping Kung; Daniel Skovronsky; Thomas Dyrks; Gerhard Holl; Sabine Krause; Matthias Friebe; Lutz Lehman; Stefanie Lindemann; Ludger M Dinkelborg; Colin L Masters; Victor L Villemagne Journal: Lancet Neurol Date: 2008-01-10 Impact factor: 44.182
Authors: Frank Jessen; Rebecca E Amariglio; Rachel F Buckley; Wiesje M van der Flier; Ying Han; José Luis Molinuevo; Laura Rabin; Dorene M Rentz; Octavio Rodriguez-Gomez; Andrew J Saykin; Sietske A M Sikkes; Colette M Smart; Steffen Wolfsgruber; Michael Wagner Journal: Lancet Neurol Date: 2020-01-17 Impact factor: 44.182