Alzheimer’s disease (AD) is an international health concern that has a devastating effect on patients and families. While several genetic risk factors for AD have been identified much of the genetic variance in AD remains unexplained. There are limited published assessments of the familiality of Alzheimer’s disease. Here we present the largest genealogy-based analysis of AD to date.
We assessed the familiality of AD in The Utah Population Database (UPDB), a population-based resource linking electronic health data repositories for the state with the computerized genealogy of the Utah settlers and their descendants. We searched UPDB for significant familial clustering of AD to evaluate the genetic contribution to disease. We compared the Genealogical Index of Familiality (GIF) between AD individuals and randomly selected controls and estimated the Relative Risk (RR) for a range of family relationships. Finally, we identified pedigrees with a significant excess of AD deaths.
The GIF analysis showed that pairs of individuals dying from AD were significantly more related than expected. This excess of relatedness was observed for both close and distant relationships. RRs for death from AD among relatives of individuals dying from AD were significantly increased for both close and more distant relatives. Multiple pedigrees had a significant excess of AD deaths.
These data strongly support a genetic contribution to the observed clustering of individuals dying from AD. This report is the first large population-based assessment of the familiality of AD mortality and provides the only reported estimates of relative risk of AD mortality in extended relatives to date. The high-risk pedigrees identified show a true excess of AD mortality (not just multiple cases) and are greater in depth and width than published AD pedigrees. The presence of these high-risk pedigrees strongly supports the possibility of rare predisposition variants not yet identified.
Positron Emission Tomography (PET) of brain amyloid-beta is a technology that is becoming more available, but its clinical utility in medical practice requires careful definition. In order to provide guidance to dementia care practitioners, patients and caregivers, the Alzheimer Association and the Society of Nuclear Medicine and Molecular Imaging convened the Amyloid Imaging Taskforce (AIT). The AIT considered a broad range of specific clinical scenarios in which amyloid PET could potentially be appropriately used. Peer-reviewed, published literature was searched to ascertain available evidence relevant to these scenarios, and the AIT developed a consensus of expert opinion. While empirical evidence of impact on clinical outcomes is not yet available, a set of specific Appropriate Use Criteria (AUC) were agreed upon that define the types of patients and clinical circumstances in which amyloid PET could be used. Both appropriate and inappropriate uses were considered and formulated, and are reported and discussed here. Because both dementia care and amyloid PET technology are in active development, these AUC will require periodic reassessment. Future research directions are also outlined, including diagnostic utility and patient-centered outcomes.
Estimates of incident dementia, and cognitive impairment, not dementia (CIND) (or the related mild cognitive impairment (MCI)) are important for public health and clinical care policy. In this paper, we report US national incidence rates for dementia and CIND.
Participants in the Aging, Demographic and Memory Study (ADAMS) were evaluated for cognitive impairment using a comprehensive in-home assessment. A total of 456 individuals age 72 and older, who were not demented at baseline were followed longitudinally from August 2001 to December 2009. An expert consensus panel assigned a diagnosis of normal cognition, CIND, or dementia and its subtypes. Using a population-weighted sample, we estimated the incidence of dementia, Alzheimer’s disease (AD), vascular dementia (VaD), and CIND by age. We also estimated the incidence of progression from CIND to dementia.
The incidence of dementia was 33.3 (s.e. = 4.2) per 1000 person-years and 22.9 (s.e. =2.9) per 1000 person-years for AD. The incidence of CIND was 60.4 (s.e.= 7.2) cases per 1000 person-years. An estimated 120.3 (s.e.=16.9) individuals per 1000 person-years progressed from CIND to dementia. Over a 5.9 year period, about 3.4 million individuals aged 72 and older in the US developed incident dementia; of which approximately 2.3 million developed AD and about 637,000 developed VaD. Over this same period, almost 4.8 million individuals developed incident CIND.
The incidence of CIND is greater than the incidence of dementia, and those with CIND are at high risk of progressing to dementia, making CIND a potentially valuable target for treatments aimed at slowing cognitive decline.
The Functional Activities Questionnaire (FAQ) and Alzheimer’s Disease Assessment Scale – cognitive subscale (ADAS-cog) are frequently-used indices of cognitive decline in Alzheimer’s disease (AD). The goal of this study was to compare FDG-PET and clinical measurements in a large sample of elderly subjects with memory disturbance. We examined relationships between glucose metabolism in FDG-PET regions of interest (FDG-ROIs), and ADAS-cog and FAQ scores in AD and mild cognitive impairment (MCI) patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Low glucose metabolism at baseline predicted subsequent ADAS-cog and FAQ decline. In addition, longitudinal glucose metabolism decline was associated with concurrent ADAS-cog and FAQ decline. Additionally, a power analysis revealed that FDG-ROI values have greater statistical power than ADAS-cog to detect attenuation of cognitive decline in AD and MCI patients. Glucose metabolism is a sensitive measure of change in cognition and functional ability in AD and MCI, and has value in predicting future cognitive decline.
FDG-PET; Alzheimer’s disease; Mild Cognitive Impairment
The clinical diagnosis of dementing diseases largely depends upon the subjective interpretation of patient symptoms. Consensus panels are frequently used in research to determine diagnoses when definitive pathological findings are unavailable. Nevertheless, research on group decision-making indicates many factors can adversely influence panel performance.
To determine conditions that improve consensus panel diagnosis.
Comparison of neuropathological diagnoses with individual and consensus panel diagnoses based on clinical summaries, FDG-PET scans, and summaries with scans.
Expert and trainee individual and consensus panel deliberations using a modified Delphi method in a pilot research study of the diagnostic utility of FDG-PET imaging.
Patients and Methods
Forty-five patients with pathologically confirmed Alzheimer’s disease or frontotemporal dementia. Statistical measures of diagnostic accuracy, agreement, and confidence for individual raters and panelists before and after consensus deliberations.
The consensus protocol using trainees and experts surpassed the accuracy of individual expert diagnoses when clinical information elicited diverse judgments. In these situations, consensus was 3.5 times more likely to produce positive rather than negative changes in the accuracy and diagnostic certainty of individual panelists. A rule that forced group consensus was at least as accurate as majority and unanimity rules.
Using a modified Delphi protocol to arrive at a consensus diagnosis is a reasonable substitute for pathologic information. This protocol improves diagnostic accuracy and certainty when panelist judgments differ and is easily adapted to other research and clinical settings while avoiding potential pitfalls of group decision-making.
Fluorodeoxyglucose positron emission tomography (FDG-PET) studies report characteristic patterns of cerebral hypometabolism in probable Alzheimer's disease (pAD) and amnestic mild cognitive impairment (aMCI). This study aims to characterize the consistency of regional hypometabolism in pAD and aMCI patients enrolled in the AD Neuroimaging Initiative (ADNI) using statistical parametric mapping (SPM) and bootstrap resampling, and to compare bootstrap based reliability index to the commonly used type-I error approach with or without correction for multiple comparisons. Batched SPM5 was run for each of 1,000 bootstrap iterations to compare FDG-PET images from 74 pAD and 142 aMCI patients, respectively, to 82 normal controls. Maps of the hypometabolic voxels detected for at least a specific percentage of times over the 1000 runs were examined and compared to an overlap of the hypometabolic maps obtained from 3 randomly partitioned independent sub-datasets. The results from the bootstrap derived reliability of regional hypometabolism in the overall data set were similar to that observed in each of the three non-overlapping sub-sets using family-wise error. Strong but non-linear association was found between the bootstrap based reliability index and the type-I error. For threshold p=0.0005, pAD was associated with extensive hypometabolic voxels in the posterior cingulate/precuneus and parietotemporal regions with reliability between 90% and 100%. Bootstrap analysis provides an alternative to the parametric family-wise error approach used to examine consistency of hypometabolic brain voxels in pAD and aMCI patients. These results provide a foundation for the use of bootstrap analysis characterize statistical ROIs or search regions in both cross-sectional and longitudinal FDG PET studies. This approach offers promise in the early detection and tracking of AD, the evaluation of AD-modifying treatments, and other biologically or clinical important measurements using brain images and voxel-based data analysis techniques.
Alzheimer's Disease; MCI; FDG PET; Reproducibility of Results; Reliability; Bootstrap Resampling; Familywise Error; SPM
This is a progress report of the Alzheimer's Disease Neuroimaging Initiative (ADNI) PET Core.
The Core has supervised the acquisition, quality control, and analysis of longitudinal [18F]fluorodeoxyglucose PET (FDG-PET) data in approximately half of the ADNI cohort. In an “add on” study, approximately 100 subjects also underwent scanning with [11C]PIB-PET for amyloid imaging. The Core developed quality control procedures and standardized image acquisition by developing an imaging protocol that has been widely adopted in academic and pharmaceutical industry studies. Data processing provides users with scans that have identical orientation and resolution characteristics despite acquisition on multiple scanner models. The Core labs have used a number of different approaches to characterize differences between subject groups (AD, MCI, controls), to examine longitudinal change over time in glucose metabolism and amyloid deposition, and to assess the use of FDG-PET as a potential outcome measure in clinical trials.
ADNI data indicate that FDG-PET increases statistical power over traditional cognitive measures, might aid subject selection, and could substantially reduce the sample size in a clinical trial. PIB-PET data showed expected group differences, and identified subjects with significant annual increases in amyloid load across the subject groups. The next activities of the PET core in ADNI will entail developing standardized protocols for amyloid imaging using the [18F]-labeled amyloid imaging agent AV45, which can be delivered to virtually all ADNI sites.
ADNI has demonstrated the feasibility and utility of multicenter PET studies and is helping to clarify the role of biomarkers in the study of aging and dementia.
PET; fluorodeoxyglucose; amyloid imaging; biomarkers
In mostly small single-center studies, Alzheimer’s disease (AD) is associated with characteristic and progressive reductions in fluorodeoxyglucose positron emission tomography (PET) measurements of the regional cerebral metabolic rate for glucose (CMRgl). The AD Neuroimaging Initiative (ADNI) is acquiring FDG PET, volumetric magnetic resonance imaging, and other biomarker measurements in a large longitudinal multi-center study of initially mildly affected probable AD (pAD) patients, amnestic mild cognitive impairment (aMCI) patients, who are at increased AD risk, and cognitively normal controls (NC), and we are responsible for analyzing the PET images using statistical parametric mapping (SPM). Here we compare baseline CMRgl measurements from 74 pAD patients and 142 aMCI patients to those from 82 NC, we correlate CMRgl with categorical and continuous measures of clinical disease severity, and we compare apolipoprotein E (APOE) ε4 carriers to non-carriers in each of these subject groups. In comparison with NC, the pAD and aMCI groups each had significantly lower CMRgl bilaterally in posterior cingulate, precuneus, parietotemporal and frontal cortex. Similar reductions were observed when categories of disease severity or lower Mini-Mental State Exam (MMSE) scores were correlated with lower CMRgl. However, when analyses were restricted to the pAD patients, lower MMSE scores were significantly correlated with lower left frontal and temporal CMRgl. These findings from a large, multi-site study support previous single-site findings, supports the characteristic pattern of baseline CMRgl reductions in AD and aMCI patients, as well as preferential anterior CMRgl reductions after the onset of AD dementia.
Alzheimer’s disease; MCI; MMSE; Positron Emission Tomography
The neuropsychological test battery from the Uniform Data Set (UDS) of the Alzheimer’s Disease Centers (ADC) program of the National Institute on Aging (NIA) consists of brief measures of attention, processing speed, executive function, episodic memory and language. This paper describes development of the battery and preliminary data from the initial UDS evaluation of 3,268 clinically cognitively normal men and women collected over the first 24 months of utilization. The subjects represent a sample of community-dwelling, individuals who volunteer for studies of cognitive aging. Subjects were considered “clinically cognitively normal” based on clinical assessment, including the Clinical Dementia Rating scale and the Functional Assessment Questionnaire. The results demonstrate performance on tests sensitive to cognitive aging and to the early stages of Alzheimer disease (AD) in a relatively well-educated sample. Regression models investigating the impact of age, education, and gender on test scores indicate that these variables will need to be incorporated in subsequent normative studies. Future plans include: 1) determining the psychometric properties of the battery; 2) establishing normative data, including norms for different ethnic minority groups; and 3) conducting longitudinal studies on cognitively normal subjects, individuals with mild cognitive impairment, and individuals with AD and other forms of dementia.
We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer’s disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker’s diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.
The promise of Alzheimer’s disease (AD) biomarkers has led to their incorporation in new diagnostic criteria and in therapeutic trials; however, significant barriers exist to widespread use. Chief among these is the lack of internationally accepted standards for quantitative metrics. Hippocampal volumetry is the most widely studied quantitative magnetic resonance imaging (MRI) measure in AD and thus represents the most rational target for an initial effort at standardization.
Methods and Results
The authors of this position paper propose a path toward this goal. The steps include: 1) Establish and empower an oversight board to manage and assess the effort, 2) Adopt the standardized definition of anatomic hippocampal boundaries on MRI arising from the EADC-ADNI hippocampal harmonization effort as a Reference Standard, 3) Establish a scientifically appropriate, publicly available Reference Standard Dataset based on manual delineation of the hippocampus in an appropriate sample of subjects (ADNI), and 4) Define minimum technical and prognostic performance metrics for validation of new measurement techniques using the Reference Standard Dataset as a benchmark.
Although manual delineation of the hippocampus is the best available reference standard, practical application of hippocampal volumetry will require automated methods. Our intent is to establish a mechanism for credentialing automated software applications to achieve internationally recognized accuracy and prognostic performance standards that lead to the systematic evaluation and then widespread acceptance and use of hippocampal volumetry. The standardization and assay validation process outlined for hippocampal volumetry is envisioned as a template that could be applied to other imaging biomarkers.
Alzheimer’s disease; biomarkers; Magnetic resonance imaging; hippocampus; biomarker standards
This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer’s disease (AD), compares it to other biological, cognitive and clinical measures, and demonstrate its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual’s fluorodeoxyglucose positron emission tomography (FDG PET) image corresponds to that in probable AD patients, and is generated using a fully automated voxel-based image analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18 months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p = 9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCI’s or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, have the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.
hypometabolic convergence index; Alzheimer’s disease; FDG; PET; MCI; hippocampal volume
To evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomography scans with 18F-fluorodeoxyglucose (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD).
Twelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere – frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex and posterior cingulate cortex. Results were compared to neuropathological diagnoses.
Academic medical centers
45 patients with pathologically confirmed FTLD (n=14) or AD (n=31)
Raters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27/31, 87%) than in patients with FTLD (7/14, 50%) (p = 0.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD.
Temporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelihood of FTLD, even when temporoparietal hypometabolism is present. Ultimately, the accurate interpretation of FDG-PET scans in patients with dementia cannot rest on the presence or absence of a single region of hypometabolism, but must take into account the relative hypometabolism of all brain regions.
Alzheimer's disease (AD) is characterized by specific and progressive reductions in fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the cerebral metabolic rate for glucose (CMRgl), some of which may precede the onset of symptoms. In this report, we describe twelve-month CMRgl declines in 69 probable AD patients, 154 amnestic mild cognitive impairment (MCI) patients, and 79 cognitively normal controls (NCs) from the AD Neuroimaging Initiative (ADNI) using statistical parametric mapping (SPM). We introduce the use of an empirically predefined statistical region-of-interest (sROI) to characterize CMRgl declines with optimal power and freedom from multiple comparisons, and we estimate the number of patients needed to characterize AD-slowing treatment effects in multi-center randomized clinical trials (RCTs). The AD and MCI groups each had significant twelve-month CMRgl declines bilaterally in posterior cingulate, medial and lateral parietal, medial and lateral temporal, frontal and occipital cortex, which were significantly greater than those in the NC group and correlated with measures of clinical decline. Using sROIs defined based on training sets of baseline and follow-up images to assess CMRgl declines in independent test sets from each patient group, we estimate the need for 66 AD patients or 217 MCI patients per treatment group to detect a 25% AD-slowing treatment effect in a twelve-month, multi-center RCT with 80% power and two-tailed alpha=0.05, roughly one-tenth the number of the patients needed to study MCI patients using clinical endpoints. Our findings support the use of FDG PET, brain-mapping algorithms and empirically pre-defined sROIs in RCTs of AD-slowing treatments.