In Brain : a journal of neurology
Heterogeneity in progression to Alzheimer Disease poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical Alzheimer Disease biomarkers, assessed patterns for developing preclinical Alzheimer Disease across the Amyloid-Tau-(Neurodegeneration) framework, and considered potential sources of difference by analyzing the cerebrospinal fluid proteome. 108 participants enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of Aβ42, pTau181, and Neurofilament Light chain as well as proteomics values were evaluated. Imaging biomarkers, including positron emission tomography amyloid and tau and structural magnetic resonance imaging were repeatedly obtained when available. Individuals were staged according to the Amyloid-Tau-(Neurodegeneration) framework. Growth mixture modeling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by cerebrospinal fluid pTau181 and Aβ42. Two groups (Alzheimer Disease Biomarker Positive and Intermediate Alzheimer Disease Biomarker) had distinct progression from normal biomarker status to having biomarkers consistent with preclinical Alzheimer Disease. A third group (Alzheimer Disease Biomarker Negative) did not develop abnormal Alzheimer Disease biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive vs AD Biomarker Negatives = 0.857, AUCAD Biomarker Positive vs. Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers vs. AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before age 50. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing vs. other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.
Wisch Julie K, Butt Omar H, Gordon Brian A, Schindler Suzanne E, Fagan Anne M, Henson Rachel L, Yang Chengran, Boerwinkle Anna H, Benzinger Tammie L S, Holtzman David M, Morris John C, Cruchaga Carlos, Ances Beau M
2022-Dec-21
Alzheimer disease, biomarkers, heterogeneity, machine learning, proteome
