{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T17:17:10Z","timestamp":1789060630785,"version":"build-2803163510"},"reference-count":86,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,12,3]],"date-time":"2023-12-03T00:00:00Z","timestamp":1701561600000},"content-version":"vor","delay-in-days":336,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["NIH R01 AG021155"],"award-info":[{"award-number":["NIH R01 AG021155"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01 AG027161"],"award-info":[{"award-number":["R01 AG027161"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["AARF-19-614533"],"award-info":[{"award-number":["AARF-19-614533"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cognitive decline in Alzheimer\u2019s disease and other dementias typically begins long before clinical impairment. Identifying people experiencing subclinical decline may facilitate earlier intervention. This study developed cognitive trajectory clusters using longitudinally based random slope and change point parameter estimates from a Preclinical Alzheimer\u2019s disease Cognitive Composite and examined how baseline and most recently available clinical\/health-related characteristics, cognitive statuses and biomarkers for Alzheimer\u2019s disease and vascular disease varied across these cognitive clusters. Data were drawn from the Wisconsin Registry for Alzheimer\u2019s Prevention, a longitudinal cohort study of adults from late midlife, enriched for a parental history of Alzheimer\u2019s disease and without dementia at baseline. Participants who were cognitively unimpaired at the baseline visit with \u22653 cognitive visits were included in trajectory modelling (n = 1068). The following biomarker data were available for subsets: positron emission tomography amyloid (amyloid: n = 367; [11C]Pittsburgh compound B (PiB): global PiB distribution volume ratio); positron emission tomography tau (tau: n = 321; [18F]MK-6240: primary regions of interest meta-temporal composite); MRI neurodegeneration (neurodegeneration: n = 581; hippocampal volume and global brain atrophy); T2 fluid-attenuated inversion recovery MRI white matter ischaemic lesion volumes (vascular: white matter hyperintensities; n = 419); and plasma pTau217 (n = 165). Posterior median estimate person-level change points, slopes\u2019 pre- and post-change point and estimated outcome (intercepts) at change point for cognitive composite were extracted from Bayesian Bent-Line Regression modelling and used to characterize cognitive trajectory groups (K-means clustering). A common method was used to identify amyloid\/tau\/neurodegeneration\/vascular biomarker thresholds. We compared demographics, last visit cognitive status, health-related factors and amyloid\/tau\/neurodegeneration\/vascular biomarkers across the cognitive groups using ANOVA, Kruskal\u2013Wallis, \u03c72, and Fisher\u2019s exact tests. Mean (standard deviation) baseline and last cognitive assessment ages were 58.4 (6.4) and 66.6 (6.6) years, respectively. Cluster analysis identified three cognitive trajectory groups representing steep, n = 77 (7.2%); intermediate, n = 446 (41.8%); and minimal, n = 545 (51.0%) cognitive decline. The steep decline group was older, had more females, APOE e4 carriers and mild cognitive impairment\/dementia at last visit; it also showed worse self-reported general health-related and vascular risk factors and higher amyloid, tau, neurodegeneration and white matter hyperintensity positive proportions at last visit. Subtle cognitive decline was consistently evident in the steep decline group and was associated with generally worse health. In addition, cognitive trajectory groups differed on aetiology-informative biomarkers and risk factors, suggesting an intimate link between preclinical cognitive patterns and amyloid\/tau\/neurodegeneration\/vascular biomarker differences in late middle-aged adults. The result explains some of the heterogeneity in cognitive performance within cognitively unimpaired late middle-aged adults.<\/jats:p>","DOI":"10.1093\/braincomms\/fcad333","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T22:39:35Z","timestamp":1701383975000},"source":"Crossref","is-referenced-by-count":13,"title":["Harnessing cognitive trajectory clusterings to examine subclinical decline risk factors"],"prefix":"10.1093","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1662-0883","authenticated-orcid":false,"given":"Lianlian","family":"Du","sequence":"first","affiliation":[{"name":"Wisconsin Alzheimer\u2019s Institute, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53792 , USA"},{"name":"Wisconsin Alzheimer\u2019s Disease Research Center , University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53792 , USA"},{"name":"Department of Medicine, University of 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