Holtzman, David M.

AbstractBackgroundRecent advances in the development of novel Alzheimer’s disease (AD) measures of amyloid, tau, and neurodegeneration in cerebrospinal fluid (CSF) and blood have enabled a better understanding of the links between amyloid‐beta (Aβ), tau species and neurodegeneration in the brain, CSF, and blood. The discoveries of novel tau species in CSF and blood, including specific phospho‐tau (p‐tau) and truncated species containing the microtubule binding region of tau (MTBR‐tau) which aggregates in tau tangles, have greatly expanded our understanding of tau biology and tau target development. The longitudinal Aβ, tau, and neurofilament light chain (NfL) changes previously measured in CSF are now being measured accurately in blood, enabling interpretation and understanding of AD progression in much larger and more robust populations.MethodWe analyzed longitudinal CSF and/or blood plasma mass spectrometry measures of Aβ42/Aβ40, multiple p‐tau species (including p‐tau217, 231, 181, 205, etc.), total tau, MTBR‐tau (e.g., MTBR‐tau243, MTBR‐tau299, and MTBR‐tau354), and NfL in sporadic AD and dominantly inherited AD cohorts. Mass spectrometry results were compared to amyloid and tau aggregation measures by Positron Emission Tomography (PET) scans and clinical and cognitive measures in local and international clinical cohorts including ADNI, AIBL, BioFINDER, Mayo Clinic, the Knight ADRC, and the DIAN.ResultThe longitudinal results indicate that a pathophysiological cascade of events begin with altered CSF and blood plasma Aβ42/Aβ40 ratio, followed by increases in amyloid plaques as measured by amyloid PET, associated with increased phosphorylation of specific CSF tau species (e.g., p‐tau217, p‐tau181, etc.), before increases in p‐tau205, NfL, total tau concentrations, hypometabolism, and atrophy. Finally, some species of MTBR‐tau increase before or with tau aggregation by tau PET and onset of clinical symptoms and correlate with longitudinal tau aggregation and clinical progression.ConclusionThese findings indicate that CSF and blood plasma Aβ, p‐tau, MTBR‐tau and NfL measures are highly precise biomarkers of brain amyloidosis, tauopathy, and neurodegeneration and can accurately identify stages of preclinical and clinical AD.

Abstract 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.

AbstractIntroductionFour less well‐studied but promising “emerging” cerebrospinal fluid (CSF) biomarkers are elevated in late‐onset Alzheimer disease (AD): neurogranin, synaptosomal‐associated protein‐25 (SNAP‐25), visinin‐like protein 1 (VILIP‐1), and chitinase‐3‐like protein 1 (YKL‐40).MethodsCSF neurogranin, SNAP‐25, VILIP‐1, and YKL‐40 were measured in families carrying autosomal‐dominant AD mutations.ResultsThe four emerging CSF biomarkers were significantly elevated in the mutation carriers (n = 235) versus noncarriers (n = 145). CSF SNAP‐25, VILIP‐1, and YKL‐40 were altered very early in the AD time course, approximately 15–19 years before estimated symptom onset. All CSF biomarkers predicted important AD‐related outcomes including performance on a cognitive composite, brain amyloid burden as measured by amyloid positron emission tomography, and the estimated years from symptom onset.DiscussionEarly abnormalities in CSF tTau, pTau, SNAP‐25, VILIP‐1, and YKL‐40 suggest that synaptic damage, neuronal injury, and neuroinflammation begin shortly after the commencement of brain amyloid accumulation.

Abstract In 2011, the National Institute on Aging and Alzheimer's Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer's disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer's Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer's Association Research Framework, Alzheimer's disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six‐stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker‐based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker‐based research should not be considered a template for all research into age‐related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β‐amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.