Examining differences across cognitive trajectory profiles in late middle‐aged adults: Results from the Wisconsin Registry for Alzheimer's Prevention. (20th December 2022)

• Record Type: Journal Article
• Authors: Du, Lianlian; Hermann, Bruce P; Jonaitis, Erin M.; Burghy, Cory; Cody, Karly Alex; Betthauser, Tobey J; Larget, Bret; Chappell, Richard J.; Janelidze, Shorena; Hansson, Oskar; Johnson, Sterling C.; Koscik, Rebecca Langhough
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 7
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.069423
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Cognitive decline in Alzheimer's disease (AD) and other dementias may accelerate in preclinical phases as brain pathology increases. Here we: 1) developed cognitive trajectory profiles using longitudinally‐based random slope and change point (CP) parameter estimates from a cognitive composite; and 2) examined how AD‐related biomarkers varied across these cognitive profiles. Method: WRAP participants with > = 3 Preclinical Alzheimer's Cognitive Composite (PACC3) scores, dementia‐free at baseline, were included (n = 1068). Amyloid measures from positron emission tomography (PET) [C‐11]Pittsburgh Compound B (PiB; n = 361) scans included Global PiB DVR and proportion PiB+ (Global PiB DVR>1.16). Tau measures included PET MK‐6240 regional entorhinal cortex and hippocampal SUVR (n = 321). Neurodegeneration measures included MRI hippocampal volume and global brain atrophy (GBA; n = 581). Plasma measures included pTau217 (n = 166). Posterior median estimate person‐level CPs, slopes pre‐ and post‐CP, and intercepts at CP for PACC3 were extracted from Bayesian random CP mixed models (BRCPMM; age = time scale) and used to characterize cognitive trajectory profiles (K‐means clustering). We compared demographic, last visit cognitive statuses (cognitively unimpaired‐stable (CU‐S), CU‐declining (CU‐D), and MCI/Dementia), amyloid, tau, neurodegenerations and plasma measures across the cognitive trajectory profiles using analysis of variance, chi‐square and Fisher's exact tests. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Result: Mean(sd) last cognitive assessment age was 66.6(6.6) years; PiB, MK, MRI scans and plasma occurred within mean(sd) 1.1(2.7), 1.6(1.4), .6(3) and .09(.7) years of cognitive assessment. Cluster analysis identified 3 groups of performance patterns representing highest to lowest risk of cognitive decline (high: n = 77(7.2%); intermediate: n = 446(41.8%); and low: n = 545(51.0%); Figure 1 & 2). The high risk group was older, had more females, APOE e4 carriers, and MCI/Dementia at last visit (Table 1). The high risk group also had worse PiB‐amyloid, MK‐tau, pTau217, and MRI‐neurogeneration levels than the lower risk groups (Table2; Figure 3). Conclusion: In this initially non‐demented sample, differences between cognitive clusters across multiple biomarkers and AD risk factors indicate that within‐person PACC3 performance patterns are sensitive to preclinical change. Identifying cognitive trajectory profiles may provide an opportunity for early intervention for high‐risk subjects at the right time for treatment and/or enrollment in a clinical trial.