| Title: |
ABIDEing with automated cerebrospinal fluid assays; update of an MCI to dementia prediction model. |
| Authors: |
van der Veere, Pieter J.1,2,3 (AUTHOR); van Harten, Argonde C.4,5,6 (AUTHOR) a.vanharten@amsterdamumc.nl; van Maurik, Ingrid S.7,8,9 (AUTHOR); Teunissen, Charlotte E.2,4,10 (AUTHOR); Barkhof, Frederik11,12,13 (AUTHOR); Vos, Stephanie J. B.14 (AUTHOR); Frölich, Lutz15 (AUTHOR); Kornhuber, Johannes16 (AUTHOR); Wiltfang, Jens17,18,19 (AUTHOR); Maier, Wolfgang20 (AUTHOR); Peters, O.21,22 (AUTHOR); Rüther, Eckart23 (AUTHOR); Frisoni, Giovanni B.24 (AUTHOR); Spiru, Luiza25,26 (AUTHOR); Freund‐Levi, Yvonne27,28,29 (AUTHOR); Wallin, Åsa K.30 (AUTHOR); Hampel, Harald31 (AUTHOR); Tsolaki, Magda32 (AUTHOR); Kłoszewska, Iwona33 (AUTHOR); Mecocci, Patrizia34,35 (AUTHOR) |
| Source: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association. Dec2025 Supplement, Vol. 21 Issue S7, p1-5. 5p. |
| Abstract: |
Background: Automated cerebrospinal fluid (CSF) biomarker assays have largely replaced manual immunoassays for measuring amyloid pathology. Their relevance is increasing as amyloid‐targeting therapies (ATTs) are becoming available for amyloid‐positive mild cognitively impaired (MCI) individuals. Therefore, we refitted and validated the ABIDE model, predicting progression from MCI to dementia, with CSF measurements from the automated Elecsys platform. Additionally, we evaluated the performance in an amyloid‐positive subpopulation, potentially eligible for ATTs. Method: We combined data from MCI participants of seven single‐centre and multicentre observational cohorts: Amsterdam Dementia Cohort (n =648), Alzheimer's Disease Neuroimaging Initiative (n =544), BioFINDER (n =212), European Medical Information Framework for Alzheimer's Disease (n =809), Lleida (n =88), National Alzheimer's Coordinating Centre (n =63), and Wisconsin Alzheimer's Disease Cohort (n =9). Participants were included with MCI at baseline, a baseline Mini‐Mental State Examination, either a magnetic resonance imaging hippocampal volume or CSF Aβ1‐42 and pTau181 measurements, and at least six months of follow‐up. Elecsys was used in 737 (31%) participants. A Cox model was used to predict time to dementia using the variables in the previous ABIDE model (Maurik et al. 2019). Model discrimination and calibration were evaluated with leave‐one‐cohort‐out cross‐validation. Calibration was assessed in the pooled cohort (PC) and amyloid‐positive (APos) subgroup, stratified by predicted risk: PC/APos1 (P86). Result: Of 2372 MCI participants (Table 1; 70±8yrs, 57%F; 41% amyloid‐positive) with a median follow‐up of 2.1yrs, 997 (42%; 563 [58%] amyloid‐positive) developed dementia (IQR:1.3‐3.2yrs). The refitted coefficients resemble the prior model, except for a larger effect of the Aβ1‐42*pTau interaction (Table 2). Discrimination was similar to the prior ABIDE model, with Harrell's C of 0.70 (95%CI:0.69‐0.71), and calibration was good in the pooled cohort, amyloid‐positive subgroup (Figure 1), and across CSF assays. In the amyloid‐positive subgroup, all four risk groups had a substantial progression risk with a median predicted progression time of 6.3yrs (95%CI:6.1‐6.6) in APos1, 3.7yrs (95%CI:3.5‐4.0) in APos2, 3.0yrs (95%CI:2.8‐3.0) in APos3, and 2.0yrs (95%CI:2.0‐2.1) in APos4. Conclusion: We updated the ABIDE model for predicting MCI to dementia progression with automated CSF measurements. The model was well calibrated in amyloid‐positive patients and may support clinical discussions regarding the initiation of ATTs. [ABSTRACT FROM AUTHOR] |
| Database: |
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