Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, ...variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain's modular organization and assign each region to a "meta-modular" group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer's dementia and 56 cognitively normal elderly subjects matched 1:2 on age, gender, and education. We found that differences in connectivity we and others have previously observed in Alzheimer's disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer's dementia.
We aimed to (1) assess and compare baseline plasma and CSF neurofilament light (NfL) for cross-sectional and longitudinal associations with neuroimaging or cognition and (2) determine whether change ...in plasma NfL corresponded with change in these outcomes.
Seventy-nine participants without dementia, median age 76 years, had plasma and CSF NfL, neuropsychological testing, and neuroimaging (MRI, amyloid PET, FDG-PET) at the same study visit, and a repeat visit (15 or 30 months later) with both plasma NfL and neuroimaging. Plasma NfL was measured on the Simoa-HD1 Platform and CSF NfL with an in-house ELISA. Linear mixed effects models were used to examine the associations between baseline plasma or CSF NfL and cognitive and neuroimaging outcomes adjusting for age, sex, and education. The relationship between change in plasma NfL and change in the outcomes was assessed using linear regression.
There were no cross-sectional associations between CSF or plasma NfL and any neuroimaging or cognitive measure. Longitudinally, higher baseline plasma NfL was associated with worsening in all neuroimaging measures, except amyloid PET, and global cognition. Higher baseline CSF NfL was associated with worsening in cortical thickness and diffusion MRI. The beta estimates for CSF NfL were similar to those for plasma NfL. Change in plasma NfL was associated with change in global cognition, attention, and amyloid PET.
Elevated baseline plasma NfL is a prognostic marker of cognitive decline and neuroimaging measures of neurodegeneration, and has similar effect sizes to baseline CSF NfL. Change in plasma NfL also tracked with short-term cognitive change.
While amyloid and neurodegeneration are viewed together as Alzheimer disease pathophysiology (ADP), the factors that influence amyloid and AD-pattern neurodegeneration may be considerably different. ...Protection from these ADP factors may be important for aging without significant ADP.
To identify the combined and independent protective factors for amyloid and AD-pattern neurodegeneration in a population-based sample and to test the hypothesis that "exceptional agers" with advanced ages do not have significant ADP because they have protective factors for amyloid and neurodegeneration.
This cohort study conducted a prospective analysis of 942 elderly individuals (70-≥90 years) with magnetic resonance imaging and Pittsburgh compound B-positron emission tomography scans enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive aging in Olmsted County, Minnesota. We operationalized "exceptional aging" without ADP by considering individuals 85 years or older to be without significant evidence of ADP.
We evaluated predictors including demographics, APOE, intellectual enrichment, midlife risk factors (physical inactivity, obesity, smoking, diabetes, hypertension, and dyslipidemia), and the total number of late-life cardiac and metabolic conditions. We used multivariate linear regression models to identify the combined and independent protective factors for amyloid and AD-pattern neurodegeneration. Using a subsample of the cohort 85 years of age or older, we computed Cohen d-based effect size estimations to compare the quantitative strength of each predictor variable in their contribution with exceptional aging without ADP.
The study participants included 423 (45%) women and the average age of participants was 79.7 (5.9) years. Apart from demographics and the APOE genotype, only midlife dyslipidemia was associated with amyloid deposition. Obesity, smoking, diabetes, hypertension, and cardiac and metabolic conditions, but not intellectual enrichment, were associated with greater AD-pattern neurodegeneration. In the 85 years or older cohort, the Cohen d results showed small to moderate effects (effect sizes > 0.2) of several variables except job score and midlife hypertension in predicting exceptional aging without ADP.
The protective factors that influence amyloid and AD-pattern neurodegeneration are different. "Exceptional aging" without ADP may be possible with a greater number of protective factors across the lifespan but warrants further investigation.
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 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.
To increase the opportunity to delay or prevent mild cognitive impairment (MCI) or Alzheimer disease (AD) dementia, markers of early detection are essential. Olfactory impairment may be an important ...clinical marker and predictor of these conditions and may help identify persons at increased risk.
To examine associations of impaired olfaction with incident MCI subtypes and progression from MCI subtypes to AD dementia.
Participants enrolled in the population-based, prospective Mayo Clinic Study of Aging between 2004 and 2010 were clinically evaluated at baseline and every 15 months through 2014. Participants (N = 1630) were classified as having normal cognition, MCI (amnestic MCI aMCI and nonamnestic MCI naMCI), and dementia. We administered the Brief Smell Identification Test (B-SIT) to assess olfactory function.
Mild cognitive impairment, AD dementia, and longitudinal change in cognitive performance measures.
Of the 1630 participants who were cognitively normal at the time of the smell test, 33 died before follow-up and 167 were lost to follow-up. Among the 1430 cognitively normal participants included, the mean (SD) age was 79.5 (5.3) years, 49.4% were men, the mean duration of education was 14.3 years, and 25.4% were APOE ε4 carriers. Over a mean 3.5 years of follow-up, there were 250 incident cases of MCI among 1430 cognitively normal participants. We observed an association between decreasing olfactory identification, as measured by a decrease in the number of correct responses in B-SIT score, and an increased risk of aMCI. Compared with the upper B-SIT quartile (quartile Q 4, best scores), hazard ratios (HRs) (95% CI) were 1.12 (0.65-1.92) for Q3 (P = .68); 1.95 (1.25-3.03) for Q2 (P = .003); and 2.18 (1.36-3.51) for Q1 (P = .001) (worst scores; P for trend <.001) after adjustment for sex and education, with age as the time scale. There was no association with naMCI. There were 64 incident dementia cases among 221 prevalent MCI cases. The B-SIT score also predicted progression from aMCI to AD dementia, with a significant dose-response with worsening B-SIT quartiles. Compared with Q4, HR (95% CI) estimates were 3.02 (1.06-8.57) for Q3 (P = .04); 3.63 (1.19-11.10) for Q2 (P = .02); and 5.20 (1.90-14.20) for Q1 (P = .001). After adjusting for key predictors of MCI risk, B-SIT (as a continuous measure) remained a significant predictor of MCI (HR, 1.10 95% CI, 1.04-1.16; P < .001) and improved the model concordance.
Olfactory impairment is associated with incident aMCI and progression from aMCI to AD dementia. These findings are consistent with previous studies that have reported associations of olfactory impairment with cognitive impairment in late life and suggest that olfactory tests have potential utility for screening for MCI and MCI that is likely to progress.
The association between gait speed and cognition has been reported; however, there is limited knowledge about the temporal associations between gait slowing and cognitive decline among cognitively ...normal individuals.
The Mayo Clinic Study of Aging is a population-based study of Olmsted County, Minnesota, United States, residents aged 70-89 years. This analysis included 1,478 cognitively normal participants who were evaluated every 15 months with a nurse visit, neurologic evaluation, and neuropsychological testing. The neuropsychological battery used nine tests to compute domain-specific (memory, language, executive function, and visuospatial skills) and global cognitive z-scores. Timed gait speed (m/s) was assessed over 25 feet (7.6 meters) at a usual pace. Using mixed models, we examined baseline gait speed (continuous and in quartiles) as a predictor of cognitive decline and baseline cognition as a predictor of gait speed changes controlling for demographics and medical conditions.
Cross-sectionally, faster gait speed was associated with better performance in memory, executive function, and global cognition. Both cognitive scores and gait speed declined over time. A faster gait speed at baseline was associated with less cognitive decline across all domain-specific and global scores. These results were slightly attenuated after excluding persons with incident mild cognitive impairment or dementia. By contrast, baseline cognition was not associated with changes in gait speed.
Our study suggests that slow gait precedes cognitive decline. Gait speed may be useful as a reliable, easily attainable, and noninvasive risk factor for cognitive decline.
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.
Objective
To investigate the associations between age, vascular health, and Alzheimer disease (AD) imaging biomarkers in an elderly sample.
Methods
We identified 430 individuals along the cognitive ...continuum aged >60 years with amyloid positron emission tomography (PET), tau PET, and magnetic resonance imaging (MRI) scans from the population‐based Mayo Clinic Study of Aging. A subset of 329 individuals had fluorodeoxyglucose (FDG) PET. We ascertained presently existing cardiovascular and metabolic conditions (CMC) from health care records and used the summation of presence/absence of hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke as a surrogate for vascular health. We used global amyloid from Pittsburgh compound B PET, entorhinal cortex tau uptake (ERC‐tau) from tau‐PET, and neurodegeneration in AD signature regions from MRI and FDG‐PET as surrogates for AD pathophysiology. We dichotomized participants into CMC = 0 (CMC−) versus CMC > 0 (CMC+) and tested for age‐adjusted group differences in AD biomarkers. Using structural equation models (SEMs), we assessed the impact of vascular health on AD biomarker cascade (amyloid leads to tau leads to neurodegeneration) after considering the direct and indirect age, sex, and apolipoprotein E effects.
Results
CMC+ participants had significantly greater neurodegeneration than CMC− participants but did not differ by amyloid or ERC‐tau. The SEMs showed that (1) vascular health had a significant direct and indirect impact on neurodegeneration but not on amyloid; and (2) vascular health, specifically the presence of hyperlipidemia, had a significant direct impact on ERC‐tau.
Interpretation
Vascular health had quantifiably greater impact on neurodegeneration in AD regions than on amyloid deposition. Longitudinal studies are warranted to clarify the relationship between tau deposition and vascular health. Ann Neurol 2017;82:706–718
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