Amyloid and tau signatures of brain metabolic decline in preclinical Alzheimer's disease.
ABSTRACT: We aimed to determine the amyloid (A?) and tau biomarker levels associated with imminent Alzheimer's disease (AD) - related metabolic decline in cognitively normal individuals.A threshold analysis was performed in 120 cognitively normal elderly individuals by modelling 2-year declines in brain glucose metabolism measured with [18F]fluorodeoxyglucose ([18F]FDG) as a function of [18F]florbetapir A? positron emission tomography (PET) and cerebrospinal fluid phosphorylated tau biomarker thresholds. Additionally, using a novel voxel-wise analytical framework, we determined the sample sizes needed to test an estimated 25% drugeffect with 80% of power on changes in FDG uptake over 2 years at every brain voxel.The combination of [18F]florbetapir standardized uptake value ratios and phosphorylated-tau levels more than one standard deviation higher than their respective thresholds for biomarker abnormality was the best predictor of metabolic decline in individuals with preclinical AD. We also found that a clinical trial using these thresholds would require as few as 100 individuals to test a 25% drug effect on AD-related metabolic decline over 2 years.These results highlight the new concept that combined A? and tau thresholds can predict imminent neurodegeneration as an alternative framework with a high statistical power for testing the effect of disease-modifying therapies on [18F]FDG uptake decline over a typical 2-year clinical trial period in individuals with preclinical AD.
Project description:This study was designed to test the interaction between amyloid-? and tau proteins as a determinant of metabolic decline in preclinical Alzheimer's disease (AD). We assessed 120 cognitively normal individuals with [18F]florbetapir positron emission tomography (PET) and cerebrospinal fluid (CSF) measurements at baseline, as well as [18F]fluorodeoxyglucose ([18F]FDG) PET at baseline and at 24 months. A voxel-based interaction model was built to test the associations between continuous measurements of CSF biomarkers, [18F]florbetapir and [18F]FDG standardized uptake value ratios (SUVR). We found that the synergistic interaction between [18F]florbetapir SUVR and CSF phosphorylated tau (p-tau) measurements, rather than the sum of their independent effects, was associated with a 24-month metabolic decline in basal and mesial temporal, orbitofrontal, and anterior and posterior cingulate cortices (P<0.001). In contrast, interactions using CSF amyloid-?1-42 and total tau biomarkers did not associate with metabolic decline over a time frame of 24 months. The interaction found in this study further support the framework that amyloid-? and hyperphosphorylated tau aggregates synergistically interact to cause downstream AD neurodegeneration. In fact, the regions displaying the metabolic decline reported here were confined to brain networks affected early by amyloid-? plaques and neurofibrillary tangles. Preventive clinical trials may benefit from using a combination of amyloid-? PET and p-tau biomarkers to enrich study populations of cognitively normal subjects with a high probability of disease progression in studies, using [18F]FDG as a biomarker of efficacy.
Project description:PURPOSE:The PET tracer [18F]florbetapir is a specific fibrillar amyloid-beta (A?) biomarker. During the late scan phase (> 40 min), it provides pathological information about A? status. Early scan phase (0-10 min) can provide FDG-'like' information. The current investigation tested the feasibility of using florbetapir as a dual-phase biomarker in behavioural variant frontotemporal dementia (bvFTD). METHODS:Eight bvFTD patients underwent [18F]florbetapir and FDG-PET scans. Additionally, ten healthy controls and ten AD patients underwent florbetapir-PET only. PET data were acquired dynamically for 60-min post-injection. The bvFTD PET data were used to define an optimal time window, representing blood flow-related pseudo-metabolism ('pseudo-FDG'), of florbetapir data that maximally correlated with the corresponding real FDG SUVR (40-60 min) in a composite neocortical FTD region. RESULTS:A 2 to 5-min time window post-injection of the florbetapir-PET data provided the largest correlation (Pearson's r?=?0.79, p?=?0.02) to the FDG data. The pseudo-FDG images demonstrated strong internal consistency with actual FDG data and were also visually consistent with the bvFTD patients' hypometabolic profiles. The ability to identify bvFTD from blind visual rating of pseudo-FDG images was consistent with previous reports using FDG data (sensitivity?=?75%, specificity?=?85%). CONCLUSIONS:This investigation demonstrates that early phase florbetapir uptake shows a reduction of frontal lobe perfusion in bvFTD, similar to metabolic findings with FDG. Thus, dynamic florbetapir scans can serve as a dual-phase biomarker in dementia patients to distinguish FTD from AD and cognitively normal elderly, removing the need for a separate FDG-PET scan in challenging dementia cases.
Project description:BACKGROUND:Alzheimer's disease (AD)-related tauopathy can be measured with CSF phosphorylated tau (pTau) and tau PET. We aim to investigate the associations between these measurements and their relative ability to predict subsequent disease progression. METHODS:In 219 cognitively unimpaired and 122 impaired Alzheimer's Disease Neuroimaging Initiative participants with concurrent amyloid-? (A?) PET (18F-florbetapir or 18F-florbetaben), 18F-flortaucipir (FTP) PET, CSF measurements, structural MRI, and cognition, we examined inter-relationships between these biomarkers and their predictions of subsequent FTP and cognition changes. RESULTS:The use of a CSF pTau/A?40 ratio eliminated positive associations we observed between CSF pTau alone and CSF A?42 in the normal A? range likely reflecting individual differences in CSF production rather than pathology. Use of the CSF pTau/A?40 ratio also increased expected associations with A? PET, FTP PET, hippocampal volume, and cognitive decline compared to pTau alone. In A?+ individuals, abnormal CSF pTau/A?40 only individuals (26.7%) were 4 times more prevalent (p?<? 0.001) than abnormal FTP only individuals (6.8%). Furthermore, among individuals on the AD pathway, CSF pTau/A?40 mediates the association between A? PET and FTP PET accumulation, but FTP PET is more closely linked to subsequent cognitive decline than CSF pTau/A?40. CONCLUSIONS:Together, these findings suggest that CSF pTau/A?40 may be a superior measure of tauopathy compared to CSF pTau alone, and CSF pTau/A?40 enables detection of tau accumulation at an earlier stage than FTP among A?+ individuals.
Project description:IMPORTANCE:Age-associated changes in brain imaging and fluid biomarkers are characterized and compared in presenilin 1 (PSEN1)E280A mutation carriers and noncarriers from the world's largest known autosomal dominant Alzheimer disease (AD) kindred. OBJECTIVE:To characterize and compare age-associated changes in brain imaging and fluid biomarkers in PSEN1 E280A mutation carriers and noncarriers. DESIGN, SETTING, AND PARTICIPANTS:Cross-sectional measures of 18F-florbetapir positron emission tomography, 18F-fludeoxyglucose positron emission tomography, structural magnetic resonance imaging, cerebrospinal fluid (CSF), and plasma biomarkers of AD were assessed from 54 PSEN1 E280A kindred members (age range, 20-59 years). MAIN OUTCOMES AND MEASURES:We used brain mapping algorithms to compare regional cerebral metabolic rates for glucose and gray matter volumes in cognitively unimpaired mutation carriers and noncarriers. We used regression analyses to characterize associations between age and the mean cortical to pontine 18F-florbetapir standard uptake value ratios, precuneus cerebral metabolic rates for glucose, hippocampal gray matter volume, CSF A?1-42, total tau and phosphorylated tau181, and plasma A? measurements. Age at onset of progressive biomarker changes that distinguish carriers from noncarriers was estimated using best-fitting regression models. RESULTS:Compared with noncarriers, cognitively unimpaired mutation carriers had significantly lower precuneus cerebral metabolic rates for glucose, smaller hippocampal volume, lower CSF A?1-42, higher CSF total tau and phosphorylated tau181, and higher plasma A?1-42 measurements. Sequential changes in biomarkers were seen at age 20 years (95% CI, 14-24 years) for CSF A?1-42, age 16 years (95% CI, 11-24 years) for the mean cortical 18F-florbetapir standard uptake value ratio, age 15 years (95% CI, 10-24 years) for precuneus cerebral metabolic rate for glucose, age 15 years (95% CI, 7-20 years) for CSF total tau, age 13 years (95% CI, 8-19 years) for phosphorylated tau181, and age 6 years (95% CI, 1-10 years) for hippocampal volume, with cognitive decline up to 6 years before the kindred's estimated median age of 44 years (95% CI, 43-45 years) at mild cognitive impairment diagnosis. No age-associated findings were seen in plasma A?1-42 or A?1-40. CONCLUSIONS AND RELEVANCE:This cross-sectional study provides additional information about the course of different AD biomarkers in the preclinical and clinical stages of autosomal dominant AD.
Project description:It may be possible to classify patients with Aβ positive (+) mild cognitive impairment (MCI) into fast and slow decliners according to their biomarker status. In this study, we aimed to develop a risk prediction model to predict fast decline in the Aβ+ MCI population using multimodal biomarkers. We included 186 Aβ+ MCI patients who underwent florbetapir PET, brain MRI, cerebrospinal fluid (CSF) analyses, and FDG PET at baseline. We defined conversion to dementia within 3 years (= fast decline) as the outcome. The associations of potential covariates (MCI stage, APOE4 genotype, corrected hippocampal volume (HV), FDG PET SUVR, AV45 PET SUVR, CSF Aβ, total tau (t-tau), and phosphorylated tau (p-tau)) with the outcome were tested and nomograms were constructed using logistic regression models in the training dataset (n=124, n of fast decliners=52). The model was internally validated with the testing dataset (n=62, n of fast decliners=22). The multivariable analysis (including CSF t-tau) showed that MCI stage (late MCI vs. early MCI; OR 15.88, 95% CI 4.59, 54.88), APOE4 (OR 5.65, 95% CI 1.52, 20.98), corrected HV*1000 (OR 0.22, 95% CI 0.09, 0.57), FDG SUVR*10 (OR 0.43, 95% CI 0.27, 0.71), and loge CSF t-tau (OR 6.20, 95% CI 1.48, 25.96) were associated with being fast decliners. In the second model including CSF p-tau instead of t-tau, the above associations remained the same, with a significant association between loge CSF p-tau (OR 4.53, 95% CI 1.26, 16.31) and fast decline. The constructed nomograms showed excellent predictive performance (90%) on validation with the testing dataset. Among Aβ+ MCI patients, our findings suggested that multimodal AD biomarkers are significantly associated with being classified as fast decliners. A nomogram incorporating these biomarkers might be useful in early treatment decisions or stratified enrollment of this population into clinical trials.
Project description:Background:Fluorodeoxyglucose f18 positron emission tomography (18F-FDG PET) is regarded as the only functional neuroimaging biomarker for degeneration which can be used to increase the certainty of Alzheimer's disease (AD) pathophysiological process in research settings or as an optional clinical tool where available. Although a decline in FDG metabolism was confirmed in some regions known to be associated with AD, there was little known about the genetic association of FDG metabolism in AD cohorts. In this study, we present the first genome-wide association study (GWAS) analysis of brain FDG metabolism. Methods:A total of 222 individuals were included from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) cohort. All subjects were restricted to non-Hispanic Caucasians and met all quality control (QC) criteria. Associations of 18F-FDG with the genetic variants were assessed using PLINK 1.07 under the additive genetic model. Genome-wide associations were visualized using a software program R 3.2.3. Results:One significant SNP rs12444565 in RNA-binding Fox1 (RBFOX1) was found to have a strong association with 18F-FDG (P=6.06×10-8). Rs235141, rs79037, rs12526331 and rs12529764 were identified as four suggestive loci associated with 18F-FDG. Conclusions:Our study results suggest that a genome-wide significant SNP (rs12444565) in the RBFOX1, and four suggestive loci (rs235141, rs79037, rs12526331 and rs12529764) are associated with 18F-FDG.
Project description:Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, we examined (1) cross-sectional relationships between amyloid deposition, hypometabolism, and cognition, and (2) associations between amyloid and hypometabolism measurements and longitudinal cognitive measurements.We examined associations between mean cortical florbetapir uptake, mean (18) F-fluorodeoxyglucose-positron emission tomography (FDG-PET) within a set of predefined regions, and Alzhiemer's Disease Assessment Scale (ADAS-cog) performance in 426 ADNI participants (126 normal, 162 early mild cognitive impairment [EMCI], 85 late MCI [LMCI], 53 Alzheimer disease [AD] patients). For a subset of these (76 normal, 81 LMCI) we determined whether florbetapir and FDG-PET were associated with retrospective decline in longitudinal ADAS-cog measurements.Twenty-nine percent of normal subjects, 43% of EMCI patients, 62% of LMCI patients, and 77% of AD patients were categorized as florbetapir positive. Florbetapir was negatively associated with concurrent FDG and ADAS-cog in both MCI groups. In longitudinal analyses, florbetapir-positive subjects in both normal and LMCI groups had greater ongoing ADAS-cog decline than those who were florbetapir negative. However, in normal subjects, florbetapir positivity was associated with greater ADAS-cog decline than FDG, whereas in LMCI, FDG positivity was associated with greater decline than florbetapir.Although both hypometabolism and ?-amyloid (A?) deposition are detectable in normal subjects and all diagnostic groups, A? showed greater associations with cognitive decline in normal participants. In view of the minimal cognitive deterioration overall in this group, this suggests that amyloid deposition has an early and subclinical impact on cognition that precedes metabolic changes. At moderate and later stages of disease (LMCI/AD), hypometabolism becomes more pronounced and more closely linked to ongoing cognitive decline.
Project description:OBJECTIVES:Many predictive or influencing factors have emerged in investigations of the cognitive reserve model of patients with Alzheimer's disease (AD). For example, neuronal injury, which correlates with cognitive decline in AD, can be assessed by [18F]-fluorodeoxyglucose positron-emission-tomography (FDG-PET), structural magnetic resonance imaging (MRI) and total tau in cerebrospinal fluid (CSFt-tau), all according to the A/T/N-classification. The aim of this study was to calculate residual cognitive performance based on neuronal injury biomarkers as a surrogate of cognitive reserve, and to test the predictive value of this index for the individual clinical course. METHODS:110 initially mild cognitive impaired and demented subjects (age 71 ± 8 years) with a final diagnosis of AD dementia were assessed at baseline by clinical mini-mental-state-examination (MMSE), FDG-PET, MRI and CSFt-tau. All neuronal injury markers were tested for an association with clinical MMSE and the resulting residuals were correlated with years of education. We used multiple regression analysis to calculate the expected MMSE score based on neuronal injury biomarkers and covariates. The residuals of the partial correlation for each biomarker and the predicted residualized memory function were correlated with individual cognitive changes measured during clinical follow-up (27 ± 13 months). RESULTS:FDG-PET correlated highly with clinical MMSE (R = -0.49, p < .01), whereas hippocampal atrophy to MRI (R = -0.15, p = .14) and CSFt-tau (R = -0.12, p = .22) showed only weak correlations. Residuals of all neuronal injury biomarker regressions correlated significantly with education level, indicating them to be surrogates of cognitive reserve. A positive residual was associated with faster cognitive deterioration at follow-up for the residuals of stand-alone FDG-PET (R = -0.36, p = .01) and the combined residualized memory function model (R = -0.35, p = .02). CONCLUSIONS:These findings suggest that subjects with higher cognitive reserve had accumulated more pathology, which subsequently caused a faster cognitive decline over time. Together with previous findings suggesting that higher reserve is associated with slower cognitive decline, we propose a biphasic reserve effect, with an initially protective phase followed by more rapid decompensation once the protection is overwhelmed.
Project description:To elucidate the relationship between cerebrospinal fluid (CSF) total-tau (T-tau) and phosphorylated tau (P-tau) with the tau PET ligand 18F-AV-1451 in Alzheimer's disease (AD), we examined 30 cognitively healthy elderly (15 with preclinical AD), 14 prodromal AD, and 39 AD dementia patients. CSF T-tau and P-tau were highly correlated (R = 0.92, P < 0.001), but they were only moderately associated with retention of 18F-AV-1451, and mainly in demented AD patients. 18F-AV-1451, but not CSF T-tau or P-tau, was strongly associated with atrophy and cognitive impairment. CSF tau was increased in preclinical AD, despite normal 18F-AV-1451 retention. However, not all dementia AD patients exhibited increased CSF tau, even though 18F-AV-1451 retention was always increased at this disease stage. We conclude that CSF T-tau and P-tau mainly behave as biomarkers of "disease state", since they appear to be increased in many cases of AD at all disease stages, already before the emergence of tau aggregates. In contrast, 18F-AV-1451 is a biomarker of "disease stage", since it is increased in clinical stages of the disease, and is associated with brain atrophy and cognitive decline.
Project description:The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-? AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion: 18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.