Central insights gained from the first cycle of AG041851 were that the combination of elevated amyloidosis and neurodegeneration greatly increased risk of clinical progression among both clinical normal and mildly impaired persons; and, a novel finding was the definition of an abnormal biomarker category characterized by positive neurodegeneration/neuronal injury biomarkers in the absence of amyloid. We labeled this category suspected non-Alzheimers pathophysiology (SNAP) on the assumption that it represented common non-AD pathologies - e.g., cerebrovascular disease, Lewy body disease, etc. Two modern diagnostic classification systems exist for Alzheimers disease (AD); the National Institute on Aging-Alzheimers Association (NIA-AA) and the International Working Group (IWG). SNAP is not addressed by either of these criteria, yet roughly of elderly clinically normal (CN) and mild cognitive impairment (MCI) persons fall into this category. In addition, neither the NIA-AA nor the IWG criteria include tau PET for classification. We recently led a large international group of senior investigators in proposing a new, descriptive classification scheme for AD biomarkers (Appendix). The seven most widely regarded AD biomarkers are used to create a 3-class biomarker classification scheme called the ATN system. In the ATN system, amyloid biomarkers are amyloid PET and CSF A?42 (denoted by A); tau biomarkers are tau PET and CSF p tau (denoted by T); and, neurodegeneration/neuronal injury biomarkers are FDG PET, anatomic MRI, and CSF t tau (denoted by N). Individuals are classified as positive or negative in each of the three categories leading to eight possible biomarker states (e.g., A-T-N-, A+T+N-, etc.). Neither the NIA-AA nor the IWG criteria categorize individuals in this way and neither addresses the implications of the eight different ATN biomarker permutations.
The aims of this renewal grant will focus on understanding the implications of categorizing individuals into these eight ATN classes. We will use amyloid PET to define A, tau PET to define T, and MRI cortical thickness to define N. Given the current emphasis on individuals who are clinically asymptomatic or have very early signs of cognitive impairment, we will concentrate on individuals who are CN and MCI at baseline.
Our aims are:
Aim 1 : To create fully imaged population-based cohorts of CN and MCI individuals aged 30?90 with baseline amyloid PET, tau PET, and MRI studies who will be followed clinically with visits every 15 months.
Aim 2 : To determine how clinical and demographic characteristics (e.g., age, sex, APOE, indicators of cerebrovascular disease, and baseline cognitive performance) vary across the eight ATN biomarker states.
Aim 3 : To estimate the age and sex specific prevalence rates of the eight ATN biomarker states.
Aim 4 : To determine the associations between the eight ATN biomarker states and cognitive or clinical outcomes and whether covariates (e.g., age, sex, APOE, and cerebrovascular disease) modify rates of cognitive decline.
In this renewal application for AG041851, we proposed a new way of classifying individuals based on biomarkers used in Alzheimer's research. With this system, labeled ATN, Individuals are classified as positive or negative in each of three biomarker categories; amyloidosis (A), tau (T), neurodegeneration/neuronal injury (N), leading to eight possible biomarker states (e.g., A-T-N-, A+T+N-, etc.). We will use amyloid PET to define A, tau PET to define T, and MRI cortical thickness to define N in participants in the Mayo Clinic Study of Aging who are clinically normal or mildly impaired. Our aims focus on understanding the clinical and biological implications of categorizing individuals into these eight ATN states.
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|Kantarci, Kejal; Murray, Melissa E; Schwarz, Christopher G et al. (2017) White-matter integrity on DTI and the pathologic staging of Alzheimer's disease. Neurobiol Aging 56:172-179|
|Jones, David T; Graff-Radford, Jonathan; Lowe, Val J et al. (2017) Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum. Cortex 97:143-159|
|Vemuri, Prashanthi; Lesnick, Timothy G; Przybelski, Scott A et al. (2017) Age, vascular health, and Alzheimer disease biomarkers in an elderly sample. Ann Neurol 82:706-718|
|Vemuri, Prashanthi; Lowe, Val J; Knopman, David S et al. (2017) Tau-PET uptake: Regional variation in average SUVR and impact of amyloid deposition. Alzheimers Dement (Amst) 6:21-30|
|Sarro, Lidia; Tosakulwong, Nirubol; Schwarz, Christopher G et al. (2017) An investigation of cerebrovascular lesions in dementia with Lewy bodies compared to Alzheimer's disease. Alzheimers Dement 13:257-266|
|Staubo, Sara C; Aakre, Jeremiah A; Vemuri, Prashanthi et al. (2017) Mediterranean diet, micronutrients and macronutrients, and MRI measures of cortical thickness. Alzheimers Dement 13:168-177|
|Pink, Anna; Przybelski, Scott A; Krell-Roesch, Janina et al. (2017) Cortical Thickness and Anxiety Symptoms Among Cognitively Normal Elderly Persons: The Mayo Clinic Study of Aging. J Neuropsychiatry Clin Neurosci 29:60-66|
|Josephs, Keith A; Dickson, Dennis W; Tosakulwong, Nirubol et al. (2017) Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer's disease: a longitudinal retrospective study. Lancet Neurol 16:917-924|
|Pink, Anna; Przybelski, Scott A; Krell-Roesch, Janina et al. (2017) Cortical Thickness and Depressive Symptoms in Cognitively Normal Individuals: The Mayo Clinic Study of Aging. J Alzheimers Dis 58:1273-1281|
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