Search

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker. Fil: Preische, Oliver. German Center For Neurodegenerative Disease; Alemania. Eberhard Karls Universität Tübingen.; Alemania Fil: Schultz, Stephanie A.. Washington University in St. Louis; Estados Unidos Fil: Apel, Anja. German Center For Neurodegenerative Disease; Alemania. Eberhard Karls Universität Tübingen.; Alemania Fil: Kuhle, Jens. University of Basel; Suiza Fil: Kaeser, Stephan A.. German Center For Neurodegenerative Disease; Alemania. Eberhard Karls Universität Tübingen.; Alemania Fil: Barro, Christian. University of Basel; Suiza Fil: Gräber, Susanne. German Center For Neurodegenerative Disease; Alemania Fil: Kuder Buletta, Elke. German Center For Neurodegenerative Disease; Alemania Fil: LaFougere, Christian. German Center For Neurodegenerative Disease; Alemania Fil: Laske, Christoph. German Center For Neurodegenerative Disease; Alemania. Eberhard Karls Universität Tübingen.; Alemania Fil: Vöglein, Jonathan. German Center for Neurodegenerative Diseases; Alemania. Ludwig Maximilians Universitat; Alemania Fil: Levin, Johannes. German Center for Neurodegenerative Diseases; Alemania. Ludwig Maximilians Universitat; Alemania Fil: Masters, Colin. University of Melbourne; Australia Fil: Martins, Ralph. Edith Cowan University; Australia. Macquarie University; Australia Fil: Schofield, Peter. Neuroscience Research Australia; Australia. University of New South Wales; Australia Fil: Rossor, Martin N.. University College London; Estados Unidos Fil: Graff Radford, Neill. Mayo Clinic Jacksonville. Department of Neurology; Estados Unidos Fil: Salloway, Stephen. Brown University; Estados Unidos Fil: Ghetti, Bernardino. Indiana University. School of Medicine; Estados Unidos Fil: Ringman, John M.. Indiana University. School of Medicine; Estados Unidos Fil: Noble, James M.. Columbia University; Estados Unidos Fil: Chhatwal, Jasmeer. Harvard Medical School; Estados Unidos Fil: Goate, Alison. Icahn School of Medicine at Mount Sinai. Department of Neuroscience; Estados Unidos Fil: Benzinger, Tammie L. S.. Washington University in St. Louis; Estados Unidos Fil: Morris, John. Washington University in St. Louis; Estados Unidos Fil: Bateman, Randall J.. Washington University in St. Louis; Estados Unidos Fil: Wang, Guoqiao. Washington University in St. Louis; Estados Unidos Fil: Fagan, Anne M.. Washington University in St. Louis; Estados Unidos Fil: McDade, Eric M.. Washington University in St. Louis; Estados Unidos Fil: Gordon, Brian. Washington University in St. Louis; Estados Unidos Fil: Jucker, Mathias. University of Tübingen. Hertie Institute for Clinical Brain Research. Department of Cellular Neurology and Department of Psychiatry and Psychotherapy; Alemania. German Center For Neurodegenerative Disease; Alemania Fil: Allegri, Ricardo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease., Grey matter covariance networks are extracted from structural MRI. In the DIAN autosomal dominant Alzheimer’s disease family cohort, we demonstrate for the first time the trajectory of network properties in Alzheimer’s disease. These network properties related to other eurodegeneration markers and can form a non-invasive tool for studying Alzheimer’s disease progression., Graphical Abstract Graphical Abstract

Parietal cortex RNA-sequencing (RNA-seq) data were generated from individuals with and without Alzheimer disease (AD; ncontrol = 13; nAD = 83) from the Knight Alzheimer Disease Research Center (Knight ADRC). Using this and an independent (Mount Sinai Brain Bank (MSBB)) AD RNA-seq dataset, cortical circular RNA (circRNA) expression was quantified in the context of AD. Significant associations were identified between circRNA expression and AD diagnosis, clinical dementia severity and neuropathological severity. It was demonstrated that most circRNA-AD associations are independent of changes in cognate linear messenger RNA expression or estimated brain cell-type proportions. Evidence was provided for circRNA expression changes occurring early in presymptomatic AD and in autosomal dominant AD. It was also observed that AD-associated circRNAs co-expressed with known AD genes. Finally, potential microRNA-binding sites were identified in AD-associated circRNAs for miRNAs predicted to target AD genes. Together, these results highlight the importance of analyzing non-linear RNAs and support future studies exploring the potential roles of circRNAs in AD pathogenesis.