A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer's disease neuropathological hallmarks (that is, amyloid-β plaques and tau ...neurofibrillary tangles) can preserve their cognition over time or are destined to decline. In this large multicenter amyloid and tau positron emission tomography (PET) study (n = 1,325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid PET-positive (A
) and tau PET-positive (T
) in the medial temporal lobe (A
T
) and/or in the temporal neocortex (A
T
) and compared them with A
T
and A
T
groups. Cox proportional-hazards models showed a substantially increased risk for progression to mild cognitive impairment in the A
T
(hazard ratio (HR) = 19.2, 95% confidence interval (CI) = 10.9-33.7), A
T
(HR = 14.6, 95% CI = 8.1-26.4) and A
T
(HR = 2.4, 95% CI = 1.4-4.3) groups versus the A
T
(reference) group. Both A
T
(HR = 6.0, 95% CI = 3.4-10.6) and A
T
(HR = 7.9, 95% CI = 4.7-13.5) groups also showed faster clinical progression to mild cognitive impairment than the A
T
group. Linear mixed-effect models indicated that the A
T
(β = -0.056 ± 0.005, T = -11.55, P < 0.001), A
T
(β = -0.024 ± 0.005, T = -4.72, P < 0.001) and A
T
(β = -0.008 ± 0.002, T = -3.46, P < 0.001) groups showed significantly faster longitudinal global cognitive decline compared to the A
T
(reference) group (all P < 0.001). Both A
T
(P < 0.001) and A
T
(P = 0.002) groups also progressed faster than the A
T
group. In summary, evidence of advanced Alzheimer's disease pathological changes provided by a combination of abnormal amyloid and tau PET examinations is strongly associated with short-term (that is, 3-5 years) cognitive decline in cognitively unimpaired individuals and is therefore of high clinical relevance.
Summary Background As preclinical Alzheimer's disease becomes a target for therapeutic intervention, the overlap between imaging abnormalities associated with typical ageing and those associated with ...Alzheimer's disease needs to be recognised. We aimed to characterise how typical ageing and preclinical Alzheimer's disease overlap in terms of β-amyloidosis and neurodegeneration. Methods We measured age-specific frequencies of amyloidosis and neurodegeneration in individuals with normal cognitive function aged 50–89 years. Potential participants were randomly selected from the Olmsted County (MN, USA) population-based study of cognitive ageing and invited to participate in cognitive and imaging assessments. To be eligible for inclusion, individuals must have been judged clinically to have no cognitive impairment and have undergone amyloid PET,18 F-fluorodeoxyglucose (18 F-FDG) PET, and MRI. Imaging results were obtained from March 28, 2006, to Dec 3, 2013. Amyloid status (positive A+ or negative A– ) was determined by amyloid PET with11 C Pittsburgh compound B. Neurodegeneration status (positive N+ or negative N– ) was determined by an Alzheimer's disease signature18 F-FDG PET or hippocampal volume on MRI. We determined age-specific frequencies of the four groups (amyloid negative and neurodegeneration negative A– N– , amyloid positive and neurodegeneration negative A+ N– , amyloid negative and neurodegeneration positive A– N+ , or amyloid positive and neurodegeneration positive A+ N+ ) cross-sectionally using multinomial regression models. We also investigated associations of group frequencies with APOE ɛ4 status (assessed with DNA extracted from blood) and sex by including these covariates in the multinomial models. Findings The study population consisted of 985 eligible participants. The population frequency of A– N– was 100% (n=985) at age 50 years and fell to 17% (95% CI 11–24) by age 89 years. The frequency of A+ N– increased to 28% (24–32) at age 74 years, then decreased to 17% (11–25) by age 89 years. The frequency of A– N+ increased from age 60 years, reaching 24% (16–34) by age 89 years. The frequency of A+ N+ increased from age 65 years, reaching 42% (31–52) by age 89 years. The results from our multinomial models suggest that A+ N– and A+ N+ were more frequent in APOE ɛ4 carriers than in non-carriers and that A+ N+ was more, and A+ N– less frequent in men than in women. Interpretation Accumulation of amyloid and neurodegeneration are nearly inevitable by old age, but many people are able to maintain normal cognitive function despite these imaging abnormalities. Changes in the frequency of amyloidosis and neurodegeneration with age, which seem to be modified by APOE ɛ4 and sex, suggest that pathophysiological sequences might differ between individuals. Funding US National Institute on Aging and Alexander Family Professorship of Alzheimer's Disease Research.
Abstract Introduction Our goal was to develop cut points for amyloid positron emission tomography (PET), tau PET, flouro-deoxyglucose (FDG) PET, and MRI cortical thickness. Methods We examined five ...methods for determining cut points. Results The reliable worsening method produced a cut point only for amyloid PET. The specificity, sensitivity, and accuracy of cognitively impaired versus young clinically normal (CN) methods labeled the most people abnormal and all gave similar cut points for tau PET, FDG PET, and cortical thickness. Cut points defined using the accuracy of cognitively impaired versus age-matched CN method labeled fewer people abnormal. Discussion In the future, we will use a single cut point for amyloid PET (standardized uptake value ratio, 1.42; centiloid, 19) based on the reliable worsening cut point method. We will base lenient cut points for tau PET, FDG PET, and cortical thickness on the accuracy of cognitively impaired versus young CN method and base conservative cut points on the accuracy of cognitively impaired versus age-matched CN method.
Summary In 2010, we put forward a hypothetical model of the major biomarkers of Alzheimer's disease (AD). The model was received with interest because we described the temporal evolution of AD ...biomarkers in relation to each other and to the onset and progression of clinical symptoms. Since then, evidence has accumulated that supports the major assumptions of this model. Evidence has also appeared that challenges some of our assumptions, which has allowed us to modify our original model. Refinements to our model include indexing of individuals by time rather than clinical symptom severity; incorporation of interindividual variability in cognitive impairment associated with progression of AD pathophysiology; modifications of the specific temporal ordering of some biomarkers; and recognition that the two major proteinopathies underlying AD biomarker changes, amyloid β (Aβ) and tau, might be initiated independently in sporadic AD, in which we hypothesise that an incident Aβ pathophysiology can accelerate antecedent limbic and brainstem tauopathy.
Summary Background A new classification for biomarkers in Alzheimer's disease and cognitive ageing research is based on grouping the markers into three categories: amyloid deposition (A), tauopathy ...(T), and neurodegeneration or neuronal injury (N). Dichotomising these biomarkers as normal or abnormal results in eight possible profiles. We determined the clinical characteristics and prevalence of each ATN profile in cognitively unimpaired individuals aged 50 years and older. Methods All participants were in the Mayo Clinic Study of Aging, a population-based study that uses a medical records linkage system to enumerate all individuals aged 50–89 years in Olmsted County, MN, USA. Potential participants are randomly selected, stratified by age and sex, and invited to participate in cognitive assessments; individuals without medical contraindications are invited to participate in brain imaging studies. Participants who were judged clinically as having no cognitive impairment and underwent multimodality imaging between Oct 11, 2006, and Oct 5, 2016, were included in the current study. Participants were classified as having normal (A−) or abnormal (A+) amyloid using amyloid PET, normal (T−) or abnormal (T+) tau using tau PET, and normal (N−) or abnormal (N+) neurodegeneration or neuronal injury using cortical thickness assessed by MRI. We used the cutoff points of standard uptake value ratio (SUVR) 1·42 (centiloid 19) for amyloid PET, 1·23 SUVR for tau PET, and 2·67 mm for MRI cortical thickness. Age-specific and sex-specific prevalences of the eight groups were determined using multinomial models combining data from 435 individuals with amyloid PET, tau PET, and MRI assessments, and 1113 individuals who underwent amyloid PET and MRI, but not tau PET imaging. Findings The numbers of participants in each profile group were 165 A−T−N−, 35 A−T+N−, 63 A−T−N+, 19 A−T+N+, 44 A+T−N−, 25 A+T+N−, 35 A+T−N+, and 49 A+T+N+. Age differed by ATN group (p<0·0001), ranging from a median 58 years (IQR 55–64) in A–T–N– and 57 years (54–64) in A–T+N– to a median 80 years (75–84) in A+T–N+ and 79 years (73–87) in A+T+N+. The number of APOE ε4 carriers differed by ATN group (p=0·04), with carriers roughly twice as frequent in each A+ group versus the corresponding A– group. White matter hyperintensity volume (p<0·0001) and cognitive performance (p<0·0001) also differed by ATN group. Tau PET and neurodegeneration biomarkers were discordant in most individuals who would be categorised as stage 2 or 3 preclinical Alzheimer's disease (A+T+N−, A+T−N+, and A+T+N+; 86% at age 65 years and 51% at age 80 years) or with suspected non-Alzheimer's pathophysiology (A−T+N−, A−T−N+, and A−T+N+; 92% at age 65 years and 78% at age 80 years). From age 50 years, A−T−N− prevalence declined and A+T+N+ and A−T+N+ prevalence increased. In both men and women, A−T−N− was the most prevalent until age late 70s. After about age 80 years, A+T+N+ was most prevalent. By age 85 years, more than 90% of men and women had one or more biomarker abnormalities. Interpretation Biomarkers of fibrillar tau deposition can be included with those of β-amyloidosis and neurodegeneration or neuronal injury to more fully characterise the heterogeneous pathological profiles in the population. Both amyloid- dependent and amyloid-independent pathological profiles can be identified in the cognitively unimpaired population. The prevalence of each ATN group changed substantially with age, with progression towards more biomarker abnormalities among individuals who remained cognitively unimpaired. Funding National Institute on Aging (part of the US National Institutes of Health), the Alexander Family Professorship of Alzheimer's Disease Research, the Mayo Clinic, and the GHR Foundation.
Objective:
A workgroup commissioned by the Alzheimer's Association (AA) and the National Institute on Aging (NIA) recently published research criteria for preclinical Alzheimer disease (AD). We ...performed a preliminary assessment of these guidelines.
Methods:
We employed Pittsburgh compound B positron emission tomography (PET) imaging as our biomarker of cerebral amyloidosis, and 18fluorodeoxyglucose PET imaging and hippocampal volume as biomarkers of neurodegeneration. A group of 42 clinically diagnosed AD subjects was used to create imaging biomarker cutpoints. A group of 450 cognitively normal (CN) subjects from a population‐based sample was used to develop cognitive cutpoints and to assess population frequencies of the different preclinical AD stages using different cutpoint criteria.
Results:
The new criteria subdivide the preclinical phase of AD into stages 1 to 3. To classify our CN subjects, 2 additional categories were needed. Stage 0 denotes subjects with normal AD biomarkers and no evidence of subtle cognitive impairment. Suspected non‐AD pathophysiology (SNAP) denotes subjects with normal amyloid PET imaging, but abnormal neurodegeneration biomarker studies. At fixed cutpoints corresponding to 90% sensitivity for diagnosing AD and the 10th percentile of CN cognitive scores, 43% of our sample was classified as stage 0, 16% stage 1, 12 % stage 2, 3% stage 3, and 23% SNAP.
Interpretation:
This cross‐sectional evaluation of the NIA‐AA criteria for preclinical AD indicates that the 1–3 staging criteria coupled with stage 0 and SNAP categories classify 97% of CN subjects from a population‐based sample, leaving only 3% unclassified. Future longitudinal validation of the criteria will be important ANN NEUROL 2012;
Summary Background In a 2014 cross-sectional analysis, we showed that amyloid and neurodegeneration biomarker states in participants with no clinical impairment varied greatly with age, suggesting ...dynamic within-person processes. In this longitudinal study, we aimed to estimate rates of transition from a less to a more abnormal biomarker state by age in individuals without dementia, as well as to assess rates of transition to dementia from an abnormal state. Methods Participants from the Mayo Clinic Study of Aging (Olmsted County, MN, USA) without dementia at baseline were included in this study, a subset of whom agreed to multimodality imaging. Amyloid PET (with11 C-Pittsburgh compound B) was used to classify individuals as amyloid positive (A+ ) or negative (A− ).18 F-fluorodeoxyglucose (18 F-FDG)-PET and MRI were used to classify individuals as neurodegeneration positive (N+ ) or negative (N− ). We used all observations, including those from participants who did not have imaging results, to construct a multistate Markov model to estimate four different age-specific biomarker state transition rates: A− N− to A+ N− ; A− N− to A− N+ (suspected non-Alzheimer's pathology); A+ N− to A+ N+ ; and A− N+ to A+ N+ . We also estimated two age-specific rates to dementia: A+ N+ to dementia and A− N+ to dementia. Using these state-to-state transition rates, we estimated biomarker state frequencies by age. Findings At baseline (between Nov 29, 2004, to March 7, 2015), 4049 participants did not have dementia (3512 87% were clinically normal and 537 13% had mild cognitive impairment). 1541 individuals underwent imaging between March 28, 2006, to April 30, 2015. Transition rates were low at age 50 years and, with one exception, exponentially increased with age. At age 85 years compared with age 65 years, the rate was nearly 11-times higher (17·2 vs 1·6 per 100 person-years) for the transition from A− N− to A− N+ , three-times higher (20·8 vs 6·1) for A+ N− to A+ N+ , and five-times higher (13·2 vs 2·6) for A− N+ to A+ N+ . The rate of transition was also increased at age 85 years compared with age 65 years for A+ N+ to dementia (7·0 vs 0·8) and for A− N+ to dementia (1·7 vs 0·6). The one exception to an exponential increase with age was the transition rate from A− N− to A+ N− , which increased from 4·0 transitions per 100 person-years at age 65 years to 6·9 transitions per 100 person-years at age 75 and then plateaued beyond that age. Estimated biomarker frequencies by age from the multistate model were similar to cross-sectional biomarker frequencies. Interpretation Our transition rates suggest that brain ageing is a nearly inevitable acceleration toward worse biomarker and clinical states. The one exception is the transition to amyloidosis without neurodegeneration, which is most dynamic from age 60 years to 70 years and then plateaus beyond that age. We found that simple transition rates can explain complex, highly interdependent biomarker state frequencies in our population. Funding National Institute on Aging, Alexander Family Professorship of Alzheimer's Disease Research, the GHR Foundation.
Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of ...tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer's clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer's disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.
The purpose of this study was to use serial imaging to gain insight into the sequence of pathologic events in Alzheimer's disease, and the clinical features associated with this sequence. We measured ...change in amyloid deposition over time using serial 11C Pittsburgh compound B (PIB) positron emission tomography and progression of neurodegeneration using serial structural magnetic resonance imaging. We studied 21 healthy cognitively normal subjects, 32 with amnestic mild cognitive impairment and 8 with Alzheimer's disease. Subjects were drawn from two sources—ongoing longitudinal registries at Mayo Clinic, and the Alzheimer's disease Neuroimaging Initiative (ADNI). All subjects underwent clinical assessments, MRI and PIB studies at two time points, approximately one year apart. PIB retention was quantified in global cortical to cerebellar ratio units and brain atrophy in units of cm3 by measuring ventricular expansion. The annual change in global PIB retention did not differ by clinical group (P = 0.90), and although small (median 0.042 ratio units/year overall) was greater than zero among all subjects (P < 0.001). Ventricular expansion rates differed by clinical group (P < 0.001) and increased in the following order: cognitively normal (1.3 cm3/year) < amnestic mild cognitive impairment (2.5 cm3/year) < Alzheimer's disease (7.7 cm3/year). Among all subjects there was no correlation between PIB change and concurrent change on CDR-SB (r = −0.01, P = 0.97) but some evidence of a weak correlation with MMSE (r =−0.22, P = 0.09). In contrast, greater rates of ventricular expansion were clearly correlated with worsening concurrent change on CDR-SB (r = 0.42, P < 0.01) and MMSE (r =−0.52, P < 0.01). Our data are consistent with a model of typical late onset Alzheimer's disease that has two main features: (i) dissociation between the rate of amyloid deposition and the rate of neurodegeneration late in life, with amyloid deposition proceeding at a constant slow rate while neurodegeneration accelerates and (ii) clinical symptoms are coupled to neurodegeneration not amyloid deposition. Significant plaque deposition occurs prior to clinical decline. The presence of brain amyloidosis alone is not sufficient to produce cognitive decline, rather, the neurodegenerative component of Alzheimer's disease pathology is the direct substrate of cognitive impairment and the rate of cognitive decline is driven by the rate of neurodegeneration. Neurodegeneration (atrophy on MRI) both precedes and parallels cognitive decline. This model implies a complimentary role for MRI and PIB imaging in Alzheimer's disease, with each reflecting one of the major pathologies, amyloid dysmetabolism and neurodegeneration.
Altered iron metabolism has been hypothesized to be associated with Alzheimer's disease pathology, and prior work has shown associations between iron load and beta amyloid plaques. Quantitative ...susceptibility mapping (QSM) is a recently popularized MR technique to infer local tissue susceptibility secondary to the presence of iron as well as other minerals. Greater QSM values imply greater iron concentration in tissue. QSM has been used to study relationships between cerebral iron load and established markers of Alzheimer's disease, however relationships remain unclear. In this work we study QSM signal characteristics and associations between susceptibility measured on QSM and established clinical and imaging markers of Alzheimer's disease. The study included 421 participants (234 male, median age 70 years, range 34–97 years) from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center; 296 (70%) had a diagnosis of cognitively unimpaired, 69 (16%) mild cognitive impairment, and 56 (13%) amnestic dementia. All participants had multi-echo gradient recalled echo imaging, PiB amyloid PET, and Tauvid tau PET. Variance components analysis showed that variation in cortical susceptibility across participants was low. Linear regression models were fit to assess associations with regional susceptibility. Expected increases in susceptibility were found with older age and cognitive impairment in the deep and inferior gray nuclei (pallidum, putamen, substantia nigra, subthalamic nucleus) (betas: 0.0017 to 0.0053 ppm for a 10 year increase in age, p = 0.03 to <0.001; betas: 0.0021 to 0.0058 ppm for a 5 point decrease in Short Test of Mental Status, p = 0.003 to p<0.001). Effect sizes in cortical regions were smaller, and the age associations were generally negative. Higher susceptibility was significantly associated with higher amyloid PET SUVR in the pallidum and putamen (betas: 0.0029 and 0.0012 ppm for a 20% increase in amyloid PET, p = 0.05 and 0.02, respectively), higher tau PET in the basal ganglia with the largest effect size in the pallidum (0.0082 ppm for a 20% increase in tau PET, p<0.001), and with lower cortical gray matter volume in the medial temporal lobe (0.0006 ppm for a 20% decrease in volume, p = 0.03). Overall, these findings suggest that susceptibility in the deep and inferior gray nuclei, particularly the pallidum and putamen, may be a marker of cognitive decline, amyloid deposition, and off-target binding of the tau ligand. Although iron has been demonstrated in amyloid plaques and in association with neurodegeneration, it is of insufficient quantity to be reliably detected in the cortex using this implementation of QSM.
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