Predicting MCI outcome with clinically available MRI and CSF biomarkers
TL;DR: In this paper, the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI).
Abstract: Objective: To determine the ability of clinically available volumetric MRI (vMRI) and CSF biomarkers, alone or in combination with a quantitative learning measure, to predict conversion to Alzheimer disease (AD) in patients with mild cognitive impairment (MCI). Methods: We stratified 192 MCI participants into positive and negative risk groups on the basis of 1) degree of learning impairment on the Rey Auditory Verbal Learning Test; 2) medial temporal atrophy, quantified from Food and Drug Administration–approved software for automated vMRI analysis; and 3) CSF biomarker levels. We also stratified participants based on combinations of risk factors. We computed Cox proportional hazards models, controlling for age, to assess 3-year risk of converting to AD as a function of risk group and used Kaplan-Meier analyses to determine median survival times. Results: When risk factors were examined separately, individuals testing positive showed significantly higher risk of converting to AD than individuals testing negative (hazard ratios [HR] 1.8– 4.1). The joint presence of any 2 risk factors substantially increased risk, with the combination of greater learning impairment and increased atrophy associated with highest risk (HR 29.0): 85% of patients with both risk factors converted to AD within 3 years, vs 5% of those with neither. The presence of medial temporal atrophy was associated with shortest median dementia-free survival (15 months). Conclusions: Incorporating quantitative assessment of learning ability along with vMRI or CSF biomarkers in the clinical workup of MCI can provide critical information on risk of imminent conversion to AD. Neurology ® 2011;77:1619–1628
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University of California, Irvine1, University of California, Los Angeles2, Indiana University3, University of Kansas4, Alzheimer's Association5, University of Pittsburgh6, University of California, San Francisco7, University of Michigan8, Veterans Health Administration9, University of Pennsylvania10
TL;DR: There is a need to devote considerable time and attention to patient education and shared decision-making in patients with MCI, and amyloid imaging may be a tool to aid clinicians.
Abstract: Mild cognitive impairment (MCI) has an uncertain etiology and prognosis and may be challenging for clinicians to discuss with patients and families. Amyloid imaging may aid specialists in determining MCI etiology and prognosis, but creates novel challenges related to disease labeling. We convened a workgroup to formulate recommendations for clinicians providing care to MCI patients. Clinicians should use the MCI diagnosis to validate patient and family concerns and educate them that the patient’s cognitive impairment is not normal for his or her age and education level. The MCI diagnosis should not be used to avoid delivering a diagnosis of dementia. For patients who meet Appropriate Use Criteria after standard-of-care clinical workup, amyloid imaging may position specialists to offer more information about etiology and prognosis. Clinicians must set appropriate expectations, including ensuring that patients and families understand the limitations of amyloid imaging. Communication of negative results should include that patients remain at elevated risk for dementia and that negative scans do not indicate a specific diagnosis or signify brain health. Positive amyloid imaging results should elicit further monitoring and conversations about appropriate advance planning. Clinicians should offer written summaries, including referral to appropriate social services. In patients with MCI, there is a need to devote considerable time and attention to patient education and shared decision-making. Amyloid imaging may be a tool to aid clinicians. Careful management of patient expectations and communication of scan results will be critical to the appropriate use of amyloid imaging information.
51 citations
TL;DR: This paper reviews UNHCR’s efforts to improve the quality of its programmes in the following technical sectors: data and information management, shelter; education; public health; reproductive health and HIV; nutrition and food security; water, sanitation and hygiene; information management; livelihoods; and the environment.
Abstract: This paper reviews UNHCR’s efforts to improve the quality of its programmes in the following technical sectors: data and information management; shelter; education; public health; reproductive health and HIV; nutrition and food security; water, sanitation and hygiene (WASH); information management; livelihoods; and the environment. It also updates on cooperation with development actors in enabling durable solutions for refugees and other persons of concern.
50 citations
TL;DR: It might be suggested that anatomical, cognitive, and neurophysiological markers may be considered as "first order" predictors of progression to AD, while APOE or cognitive reserve proxies might play a more secondary role.
Abstract: Recent proposals of diagnostic criteria within the healthy aging-Alzheimer's disease (AD) continuum stressed the role of biomarker information. More importantly, such information might be critical to predict those mild cognitive impairment (MCI) patients at a higher risk of conversion to AD. Usually, follow-up studies utilize a reduced number of potential markers although the conversion phenomenon may be deemed as multifactorial in essence. In addition, not only biological but also cognitive markers may play an important role. Considering this background, we investigated the role of cognitive reserve, cognitive performance in neuropsychological testing, hippocampal volumes, APOE genotype, and magnetoencephalography power sources to predict the conversion to AD in a sample of 33 MCI patients. MCIs were followed up during a 2-year period and divided into two subgroups according to their outcome: The "stable" MCI group (sMCI, 21 subjects) and the "progressive" MCI group (pMCI, 12 subjects). Baseline multifactorial information was submitted to a hierarchical logistic regression analysis to build a predictive model of conversion to AD. Results indicated that the combination of left hippocampal volume, occipital cortex theta power, and clock drawing copy subtest scores predicted conversion to AD with a 100% of sensitivity and 94.7% of specificity. According to these results it might be suggested that anatomical, cognitive, and neurophysiological markers may be considered as "first order" predictors of progression to AD, while APOE or cognitive reserve proxies might play a more secondary role.
50 citations
Cites background or result from "Predicting MCI outcome with clinica..."
...4% of sensibility and specificity, respectively, values that may be considered within the top rank reported in the literature (see [67])....
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...Second, our findings confirmed that screening tests such as the CDT, probably less influenced by educational factors as compared with the MMSE, are also reliable severity measures [67, 70, 73]....
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...According to this, Heister and colleagues [67] claimed that neuropsychological tests might be better conceptualized as severity measures rather than “predictors”....
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...Some authors stated that since AD and MCI diagnosis are based on the severity of cognitive dysfunction, the use of neuropsychological performance as a predictor of conversion is circular [67]....
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...Interestingly, in three of these four studies [67, 69, 70] the combination of mesial temporal atrophy and cognitive performance were best predictors of progression to AD, while CR or APOE4 made no contribution....
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01 Jan 2012
TL;DR: In this paper, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density, and automatic estimations of ICV were evaluated against the manual segmentation.
Abstract: BACKGROUND AND PURPOSE: Brain size is commonly described in relation to ICV, whereby accurate assessment of this quantity is fundamental. Recently, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density. ICV can be measured automatically within minutes from QRAPMASTER outputs and a dedicated software, SyMRI. Automatic estimations of ICV were evaluated against the manual segmentation. MATERIALS AND METHODS: In 19 healthy subjects, manual segmentation of ICV was performed by 2 neuroradiologists (Obs1, Obs2) by using QBrain software and conventional T2-weighted images. The automatic segmentation from the QRAPMASTER output was performed by using SyMRI. Manual corrections of the automatic segmentation were performed (corrected-automatic) by Obs1 and Obs2, who were blinded from each other. Finally, the repeatability of the automatic method was evaluated in 6 additional healthy subjects, each having 6 repeated QRAPMASTER scans. The time required to measure ICV was recorded. RESULTS: No significant difference was found between reference and automatic (and correctedautomatic) ICV (P .25). The mean difference between the reference and automatic measurement was 4.84 19.57 mL (or 0.31 1.35%). Mean differences between the reference and the corrected-automatic measurements were 0.47 17.95 mL (0.01 1.24%) and 1.26 17.68 mL (0.06 1.22%) for Obs1 and Obs2, respectively. The repeatability errors of the automatic and the corrected-automatic method were 1%. The automatic method required 1 minute 11 seconds (SD 12 seconds) of processing. Adding manual corrections required another 1 minute 32 seconds (SD 38 seconds). CONCLUSIONS: Automatic and corrected-automatic quantification of ICV showed good agreement with the reference method. SyMRI software provided a fast and reproducible measure of ICV. ABBREVIATIONS: CoV coefficient of variation; ICV intracranial volume; Obs1 observer 1; Obs 2 observer 2; PD proton density; QRAPMASTER quantification of relaxation times and proton density by multi-echo acquisition of a saturation-recovery using turbo spin-echo readout
49 citations
TL;DR: Together with neuropsychological testing, imaging can improve the prediction of worsening to Alzheimer disease among patients with mild cognitive impairment, and these findings have huge implications for research on therapeutic approaches to Alzheimer Disease.
Abstract: In 2011, a new set of new guidelines for the research diagnosis of three stages of Alzheimer disease was promulgated by the US National Institute of Aging and the Alzheimer Association. For the first time, they include the diagnosis of presymptomatic Alzheimer disease, recognizing that the disease process begins years before cognitive impairment develops. Awareness of this fact has largely been driven by neuroimaging, and particularly by imaging amyloid β (abeta) deposition in the brain, a procedure approved by the US Food and Drug Administration for clinical use in April 2012.
In Alzheimer disease, abeta deposition antecedes, probably by decades, the onset of cognitive impairment. In brain regions with greatest abeta deposition, synaptic dysfunction can be imaged beginning at preclinical stages. In regions that are not identical with the ones with greatest abeta deposition but heavily connected with them, regional atrophy and loss of white-matter anisotropy can be detected later in the course of the disease, near the time when mild cognitive impairment supervenes. Together with neuropsychological testing, imaging can improve the prediction of worsening to Alzheimer disease among patients with mild cognitive impairment.
These findings have huge implications for research on therapeutic approaches to Alzheimer disease. For instance, while so far only patients with the clinical diagnosis have been treated with immunotherapy targeting abeta removal, a consensus is building that to be effective, this therapy should be given in the preclinical stages of the disease, which are assessed most advantageously by means of neuroimaging.
47 citations
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Johns Hopkins University1, Johns Hopkins University School of Medicine2, Mayo Clinic3, McGill University4, Harvard University5, University of California, Irvine6, University of Pittsburgh7, Columbia University Medical Center8, Eli Lilly and Company9, Washington University in St. Louis10, UCL Institute of Neurology11, VU University Medical Center12, Alzheimer's Association13, Northwestern University14, National Institutes of Health15
TL;DR: The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available.
Abstract: The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.
13,710 citations
Brigham and Women's Hospital1, University of California, San Diego2, University of California, Davis3, Rush University Medical Center4, University of Washington5, Washington University in St. Louis6, University of Tokyo7, Mayo Clinic8, Oregon Health & Science University9, University of Texas at Dallas10, University of Melbourne11, Eli Lilly and Company12, Columbia University13, University of California, San Francisco14, Alzheimer's Association15, National Institutes of Health16
TL;DR: A conceptual framework and operational research criteria are proposed, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies and it is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD.
Abstract: The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.
5,671 citations
TL;DR: This work proposes a model that relates disease stage to AD biomarkers in which Abeta biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegnerative biomarker become abnormal later, and correlate with clinical symptom severity.
Abstract: Summary Currently available evidence strongly supports the position that the initiating event in Alzheimer's disease (AD) is related to abnormal processing of β-amyloid (Aβ) peptide, ultimately leading to formation of Aβ plaques in the brain. This process occurs while individuals are still cognitively normal. Biomarkers of brain β-amyloidosis are reductions in CSF Aβ 42 and increased amyloid PET tracer retention. After a lag period, which varies from patient to patient, neuronal dysfunction and neurodegeneration become the dominant pathological processes. Biomarkers of neuronal injury and neurodegeneration are increased CSF tau and structural MRI measures of cerebral atrophy. Neurodegeneration is accompanied by synaptic dysfunction, which is indicated by decreased fluorodeoxyglucose uptake on PET. We propose a model that relates disease stage to AD biomarkers in which Aβ biomarkers become abnormal first, before neurodegenerative biomarkers and cognitive symptoms, and neurodegenerative biomarkers become abnormal later, and correlate with clinical symptom severity.
3,953 citations
Mayo Clinic1, University College London2, University of California, Los Angeles3, Arizona State University4, University of California, Davis5, University of California, San Diego6, Boston University7, University of California, San Francisco8, Siemens9, General Electric10, Philips11, Johns Hopkins University12, Stanford University13, University of Virginia14
TL;DR: The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom‐based monitoring of all scanners could be used as a model for other multisite trials.
Abstract: Dementia, one of the most feared associates of increasing longevity, represents a pressing public health problem and major research priority. Alzheimer's disease (AD) is the most common form of dementia, affecting many millions around the world. There is currently no cure for AD, but large numbers of novel compounds are currently under development that have the potential to modify the course of the disease and slow its progression. There is a pressing need for imaging biomarkers to improve understanding of the disease and to assess the efficacy of these proposed treatments. Structural magnetic resonance imaging (MRI) has already been shown to be sensitive to presymptomatic disease (1-10) and has the potential to provide such a biomarker. For use in large-scale multicenter studies, however, standardized methods that produce stable results across scanners and over time are needed.
The Alzheimer's Disease Neuroimaging Initiative (ADNI) study is a longitudinal multisite observational study of elderly individuals with normal cognition, mild cognitive impairment (MCI), or AD (11,12). It is jointly funded by the National Institutes of Health (NIH) and industry via the Foundation for the NIH. The study will assess how well information (alone or in combination) obtained from MRI, (18F)-fludeoyglucose positron emission tomography (FDG PET), urine, serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical and neuropsychometric assessments, can measure disease progression in the three groups of elderly subjects mentioned above. At the 55 participating sites in North America, imaging, clinical, and biologic samples will be collected at multiple time points in 200 elderly cognitively normal, 400 MCI, and 200 AD subjects. All subjects will be scanned with 1.5 T MRI at each time point, and half of these will also be scanned with FDG PET. Subjects not assigned to the PET arm of the study will be eligible for 3 T MRI scanning. The goal is to acquire both 1.5 T and 3 T MRI studies at multiple time points in 25% of the subjects who do not undergo PET scanning [R2C1]. CSF collection at both baseline and 12 months is targeted for 50% of the subjects. Sampling varies by clinical group. Healthy elderly controls will be sampled at 0, 6, 12, 24, and 36 months. Subjects with MCI will be sampled at 0, 6, 12, 18, 24, and 36 months. AD subjects will be sampled at 0, 6, 12, and 24 months.
Major goals of the ADNI study are: to link all of these data at each time point and make this repository available to the general scientific community; to develop technical standards for imaging in longitudinal studies; to determine the optimum methods for acquiring and analyzing images; to validate imaging and biomarker data by correlating these with concurrent psychometric and clinical assessments; and to improve methods for clinical trials in MCI and AD. The ADNI study overall is divided into cores, with each core managing ADNI-related activities within its sphere of expertise: clinical, informatics, biostatistics, biomarkers, and imaging. The purpose of this report is to describe the MRI methods and decision-making process underlying the selection of the MRI protocol employed in the ADNI study.
3,611 citations
TL;DR: Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.
Abstract: If the clinical diagnosis of probable AD is imprecise with accuracy rates of approximately 90% or lower using established consensus criteria for probable AD, but definite AD requires autopsy confirmation, it is not surprising that diagnostic accuracy is lower at early and presymptomatic stages of AD.1–4 It is believed that the development of full-blown AD takes place over an approximately 20-year prodromal period, but this is difficult to determine in the absence of biomarkers that reliably signal the onset of nascent disease before the emergence of measurable cognitive impairments. Because intervention with disease-modifying therapies for AD is likely to be most efficacious before significant neurodegeneration has occurred, there is an urgent need for biomarker-based tests that enable a more accurate and early diagnosis of AD.5–7 Moreover, such tests could also improve monitoring AD progression, evaluation of new AD therapies, and enrichment of AD cohorts with specific subsets of AD subjects in clinical trials.
The defining lesions of AD are neurofibrillary tangles and senile plaques formed, respectively, by neuronal accumulations of abnormal hyperphosphorylated tau filaments and extracellular deposits of amyloid β (Aβ) fibrils, mostly the 1 to 42 peptide (Aβ1-42), the least soluble of the known Aβ peptides produced from Aβ precursor protein by the action of various peptidases.1–3 Hence, for these and other reasons summarized in consensus reports on AD biomarkers, cerebrospinal fluid (CSF), total tau (t-tau), and Aβ were identified as being among the most promising and informative AD biomarkers.5,6 Increased levels of tau in CSF are thought to occur after its release from damaged and dying neurons that harbor dystrophic tau neurites and tangles, whereas reduced CSF levels of Aβ1-42 are believed to result from large-scale accumulation of this least soluble of Aβ peptides into insoluble plaques in the AD brain. The combination of increased CSF concentrations of t-tau and phosphotau (p-tau) species and decreased concentrations of Aβ1-42 are considered to be a pathological CSF biomarker signature that is diagnostic for AD.5,6,8,9 Notably, recent studies have provided compelling preliminary data to suggest that this combination of CSF tau and Aβ biomarker changes may predict the conversion to AD in mild cognitive impairment (MCI) subjects.10 Thus, an increase in levels of CSF tau associated with a decline in levels of CSF Aβ1-42 may herald the onset of AD before it becomes clinically manifest.
However, before the utility of CSF Aβ1-42 and tau concentrations for diagnosis of AD can be established, it is critical to standardize the methodology for their measurement.5–8,10 For example, among the published studies of CSF tau and Aβ, there is considerable variability in the observed levels of these analytes, as well as their diagnostic sensitivity and specificity. This is attributable to variability in analytical methodology standardization and other factors that differ between studies of the same CSF analytes in similar but not identical cohorts.5–7
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2004 to address these and other limitations in AD biomarkers (see reviews in Shaw and colleagues7 and Mueller and coauthors,11 and the ADNI Web site [http://www.adni-info.org/index] where the ADNI grant and all ADNI data are posted for public access). To this end, the Biomarker Core of ADNI conducts studies on ADNI-derived CSF samples to measure CSF Aβ1-42, t-tau, and p-tau (tau phosphorylated at threonine181 [p-tau181p]) in standardized assays. Evaluation of CSF obtained at baseline evaluation of 416 of the 819 ADNI subjects is now complete, and we report here our findings on the performance of these tests using a standardized multiplex immunoassay system that measures the biomarkers simultaneously in the same sample aliquot in ADNI subjects and in an independent cohort of autopsy-confirmed AD cases.
1,912 citations