Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects

Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1-42 and total tau (T-tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F-18 fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.     

Biological heterogeneity in ADNI amnestic mild cognitive impairment

BackgroundPrevious work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.MethodsADNI subjects diagnosed with amnestic MCI (n = 138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.ResultsFour clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.ConclusionsSubjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.     

Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data

BackgroundThis study examined the predictive value of different classes of markers in the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) over an extended 4-year follow-up in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.MethodsMCI patients were assessed for clinical, cognitive, magnetic resonance imaging (MRI), positron emission tomography–fluorodeoxyglucose (PET-FDG), and cerebrospinal fluid (CSF) markers at baseline and were followed on a yearly basis for 4 years to ascertain progression to AD. Logistic regression models were fitted in clusters, including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF, amyloid-β, and tau).ResultsThe predictive model at 4 years revealed that two cognitive measures, an episodic memory measure and a Clock Drawing screening test, were the best predictors of conversion (area under the curve = 0.78).ConclusionsThis model of prediction is consistent with the previous model at 2 years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers.     

The Alzheimer's Disease Neuroimaging Initiative: Progress report and future plans

The Alzheimer's Disease Neuroimaging Initiative (ADNI) beginning in October 2004, is a 6-year research project that studies changes of cognition, function, brain structure and function, and biomarkers in elderly controls, subjects with mild cognitive impairment, and subjects with Alzheimer's disease (AD). A major goal is to determine and validate MRI, PET images, and cerebrospinal fluid (CSF)/blood biomarkers as predictors and outcomes for use in clinical trials of AD treatments. Structural MRI, FDG PET, C-11 Pittsburgh compound B (PIB) PET, CSF measurements of amyloid β (Aβ) and species of tau, with clinical/cognitive measurements were performed on elderly controls, subjects with mild cognitive impairment, and subjects with AD. Structural MRI shows high rates of brain atrophy, and has high statistical power for determining treatment effects. FDG PET, C-11 Pittsburgh compound B PET, and CSF measurements of Aβ and tau were significant predictors of cognitive decline and brain atrophy. All data are available at UCLA/LONI/ADNI, without embargo. ADNI-like projects started in Australia, Europe, Japan, and Korea. ADNI provides significant new information concerning the progression of AD.     

The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium

BackgroundIn North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI).MethodsSeven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales.ResultsRecruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects.ConclusionsAcademic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.     

Semi-Supervised Multimodal Relevance Vector Regression Improves Cognitive Performance Estimation from Imaging and Biological Biomarkers

Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer’s diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies.     

CSF α-synuclein improves diagnostic and prognostic performance of CSF tau and Aβ in Alzheimer’s disease

Alzheimer’s disease (AD) and Lewy body diseases (LBD), e.g., Parkinson’s disease (PD) dementia and dementia with Lewy bodies (DLB), are common causes of geriatric cognitive impairments. In addition, AD and LBD are often found in the same patients at autopsy; therefore, biomarkers that can detect the presence of both pathologies in living subjects are needed. In this investigation, we report the assessment of α-synuclein (α-syn) in cerebrospinal fluid (CSF) and its association with CSF total tau (t-tau), phosphorylated tau₁₈₁ (p-tau₁₈₁), and amyloid beta_1-42 (Aβ_1-42) in subjects of the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 389), with longitudinal clinical assessments. A strong correlation was noted between α-syn and t-tau in controls, as well as in patients with AD and mild cognitive impairment (MCI). However, the correlation is not specific to subjects in the ADNI cohort, as it was also seen in PD patients and controls enrolled in the Parkinson’s Progression Markers Initiative (PPMI; n = 102). A bimodal distribution of CSF α-syn levels was observed in the ADNI cohort, with high levels of α-syn in the subjects with abnormally increased t-tau values. Although a correlation was also noted between α-syn and p-tau₁₈₁, there was a mismatch (α-syn–p-tau₁₈₁-Mis), i.e., higher p-tau₁₈₁ levels accompanied by lower α-syn levels in a subset of ADNI patients. We hypothesize that this α-syn–p-tau₁₈₁-Mis is a CSF signature of concomitant LBD pathology in AD patients. Hence, we suggest that inclusion of measures of CSF α-syn and calculation of α-syn–p-tau₁₈₁-Mis improves the diagnostic sensitivity/specificity of classic CSF AD biomarkers and better predicts longitudinal cognitive changes.     

CSF Abeta42, tau and phosphorylated tau correlate with medial temporal lobe atrophy

Cerebrospinal fluid (CSF) biomarkers and medial temporal lobe (MTL) atrophy predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). We investigated the association between the CSF biomarkers and MTL atrophy and the ability of these measures to predict AD in MCI patients in the same study population. The study included 21 MCI patients of whom eight progressed to AD during the study. CSF biomarkers were measured by using ELISA method and volumes of MTL structures were assessed by magnetic resonance imaging (MRI). Abeta42 levels were lower and tau and phospho-tau levels were higher in progressive subjects. The progressive subjects had lower volumes in all MRI measures. Tau and phospho-tau correlated inversely with hippocampal volumes and left entorhinal cortex volume in the whole study group. In the stable group, tau correlated with hippocampal volumes. Abeta42 had a negative correlation whereas phospho-tau exhibited a positive correlation with left hippocampal volume in the progressive group. These results indicate that both measures may reflect the ongoing neurodegenerative process in the progressive MCI patients. However, the order of the changes in the CSF biomarkers and MTL atrophy remain unclear due to a small number of studied subjects and study design.     

Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease

BackgroundIn the earliest clinical stages of Alzheimer’s disease (AD) when symptoms are mild, clinical diagnosis can be difficult. AD pathology most likely precedes symptoms. Biomarkers can serve as early diagnostic indicators or as markers of preclinical pathologic change. Candidate biomarkers derived from structural and functional neuroimaging and those measured in cerebrospinal fluid (CSF) and plasma show the greatest promise. Unbiased exploratory approaches, eg, proteomics or cortical thickness analysis, could yield novel biomarkers. The objective of this article was to review recent progress in selected imaging and neurochemical biomarkers for early diagnosis, classification, progression, and prediction of AD.MethodsWe performed a survey of recent research, focusing on core biomarker candidates in AD.ResultsA number of in vivo neurochemistry and neuroimaging techniques, which can reliably assess aspects of physiology, pathology, chemistry, and neuroanatomy, hold promise as biomarkers. These neurobiologic measures appear to relate closely to pathophysiologic, neuropathologic, and clinical data, such as hyperphosphorylation of tau, amyloid beta (Aβ) metabolism, lipid peroxidation, pattern and rate of atrophy, loss of neuronal integrity, functional and cognitive decline, as well as risk of future decline. Current advances in the neuroimaging of mediotemporal, neocortical, and subcortical areas of the brain of mild cognitive impairment (MCI) and AD subjects are presented. CSF levels of Aβ42, tau, and hyperphosphorylated tau protein (p-tau) can distinguish subjects with MCI who are likely to progress to AD. They also show preclinical alterations that predict later development of early AD symptoms. Studies on plasma Aβ are not entirely consistent, but recent findings suggest that decreased plasma Aβ42 relative to Aβ40 might increase the risk of AD. Increased production of Aβ in aging is suggested by elevation of BACE1 protein and enzyme activity in the brain and CSF of subjects with MCI. CSF tau and p-tau are increased in MCI as well and show predictive value. Other biomarkers might indicate components of a cascade initiated by Aβ, such as oxidative stress or inflammation. These merit further study in MCI and earlier.ConclusionsA number of neuroimaging candidate markers are promising, such as hippocampus and entorhinal cortex volumes, basal forebrain nuclei, cortical thickness, deformation-based and voxel-based morphometry, structural and effective connectivity by using diffusion tensor imaging, tractography, and functional magnetic resonance imaging. CSF Aβ42, BACE1, total tau, and p-tau are substantially altered in MCI and clinical AD. Other interesting novel marker candidates derived from blood are being currently proposed (phase I). Biomarker discovery through proteomic approaches requires further research. Large-scale international controlled multicenter trials (such as the U.S., European, Australian, and Japanese Alzheimer’s Disease Neuroimaging Initiative and the German Dementia Network) are engaged in phase III development of the core feasible imaging and CSF biomarker candidates in AD. Biomarkers are in the process of implementation as primary outcome variables into regulatory guideline documents regarding study design and approval for compounds claiming disease modification.     

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

Introduction

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

Clinical core of the Alzheimer's disease neuroimaging initiative: Progress and plans

The Clinical Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has provided clinical, operational, and data management support to ADNI since its inception. This article reviews the activities and accomplishments of the core in support of ADNI aims. These include the enrollment and follow-up of more than 800 subjects in the three original cohorts: healthy controls, amnestic mild cognitive impairment (now referred to as late MCI, or LMCI), and mild Alzheimer's disease (AD) in the first phase of ADNI (ADNI 1), with baseline longitudinal, clinical, and cognitive assessments. These data, when combined with genetic, neuroimaging, and cerebrospinal fluid measures, have provided important insights into the neurobiology of the AD spectrum. Furthermore, these data have facilitated the development of novel clinical trial designs. ADNI has recently been extended with funding from an NIH Grand Opportunities (GO) award, and the new ADNI GO phase has been launched; this includes the enrollment of a new cohort, called early MCI, with milder episodic memory impairment than the LMCI group. An application for a further 5 years of ADNI funding (ADNI 2) was recently submitted. This funding would support ongoing follow-up of the original ADNI 1 and ADNI GO cohorts, as well as additional recruitment into all categories. The resulting data would provide valuable data on the earliest stages of AD, and support the development of interventions in these critically important populations.     

Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease

OBJECTIVES: While plasma biomarkers have been proposed to aid in the clinical diagnosis of Alzheimer disease (AD), few biomarkers have been validated in independent patient cohorts. Here we aim to determine plasma biomarkers associated with AD in 2 independent cohorts and validate the findings in the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI). METHODS: Using a targeted proteomic approach, we measured levels of 190 plasma proteins and peptides in 600 participants from 2 independent centers (University of Pennsylvania, Philadelphia; Washington University, St. Louis, MO), and identified 17 analytes associated with the diagnosis of very mild dementia/mild cognitive impairment (MCI) or AD. Four analytes (apoE, B-type natriuretic peptide, C-reactive protein, pancreatic polypeptide) were also found to be altered in clinical MCI/AD in the ADNI cohort (n = 566). Regression analysis showed CSF Aβ42 levels and t-tau/Aβ42 ratios to correlate with the number of APOE4 alleles and plasma levels of B-type natriuretic peptide and pancreatic polypeptide. CONCLUSION: Four plasma analytes were consistently associated with the diagnosis of very mild dementia/MCI/AD in 3 independent clinical cohorts. These plasma biomarkers may predict underlying AD through their association with CSF AD biomarkers, and the association between plasma and CSF amyloid biomarkers needs to be confirmed in a prospective study.     

Addressing population aging and Alzheimer's disease through the Australian Imaging Biomarkers and Lifestyle study: Collaboration with the Alzheimer's Disease Neuroimaging Initiative

The Australian Imaging Biomarkers and Lifestyle (AIBL) study is a longitudinal study of 1112 volunteers from healthy, mild cognitive impairment, and Alzheimer's disease (AD) populations who can be assessed and followed up for prospective research into aging and AD. AIBL aims to improve understanding of the pathogenesis, early clinical manifestation, and diagnosis of AD, and identify diet and lifestyle factors that influence the development of AD. For AIBL, the magnetic resonance imaging parameters of Alzheimer's Disease Neuroimaging Initiative (ADNI) were adopted and the Pittsuburgh compound B (¹¹C-PiB) positron emission tomography (PET) acquisition and neuropsychological tests were designed to permit comparison and pooling with ADNI data. Differences to ADNI include assessment every 18-months, imaging in 25% (magnetic resonance imaging, ¹¹C-PiB PET but no fluorodeoxyglucose PET), more comprehensive neuropsychological testing, and detailed collection of diet and lifestyle data. AIBL has completed the first 18-month follow-up and is making imaging and clinical data available through the ADNI website. Cross-sectional analysis of baseline data is revealing links between cognition, brain amyloid burden, structural brain changes, biomarkers, and lifestyle.     

Hypothesis: cerebrospinal fluid protein markers suggest a pathway toward symptomatic resilience to AD pathology

IntroductionWe sought biological pathways that explained discordance between Alzheimer's disease (AD) pathology and symptoms.MethodsIn 306 Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 participants across the AD clinical spectrum, we investigated association between cognitive outcomes and 23 cerebrospinal fluid (CSF) analytes associated with abnormalities in the AD biomarkers amyloid β_1-42 and total-tau. In a 200-person “training” set, Least Absolute Shrinkage and Selection Operator regression estimated model weights for the 23 proteins, and for the AD biomarkers themselves, as predictors of ADAS-Cog₁₁ scores. In the remaining 106 participants (“validation” set), fully adjusted regression models then tested the Least Absolute Shrinkage and Selection Operator–derived models and a related protein marker summary score as predictors of ADAS-Cog₁₁, ADNI diagnostic category, and longitudinal cognitive trajectory.ResultsAD biomarkers alone explained 26% of the variance in validation set cognitive scores. Surprisingly, the 23 AD-related proteins explained 31% of this variance. The biomarkers and protein markers appeared independent in this respect, jointly explaining 42% of test score variance. The composite protein marker score also predicted ADNI diagnosis and subsequent cognitive trajectory. Cognitive outcome prediction redounded principally to ten markers related to lipid or vascular functions or to microglial activation or chemotaxis. In each analysis, apoE protein and four markers in the latter immune-activation group portended better outcomes.DiscussionCSF markers of vascular, lipid-metabolic and immune-related functions may explain much of the disjunction between AD biomarker abnormality and symptom severity. In particular, our results suggest the hypothesis that innate immune activation improves cognitive outcomes in persons with AD pathology. This hypothesis should be tested by further study of cognitive outcomes related to CSF markers of innate immune activation.     

Biomarkers for Alzheimer's disease: after Alzheimer's disease neuroimaging initiative studies

Recent advances in biomarker studies compiled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) are summarized here. CSF Aβ42, total tau, and phosphorylated tau181 are the most sensitive biomarkers for diagnosing Alzheimer's disease (AD) and predicting the onset of AD in cases with mild cognitive impairment (MCI) due to AD. Many perspective studies on PiB-PET, FDG-PET, MRI volumetry, and some neuropsychiatric tests have provided evidence for the usefulness of these biomarkers for diagnosing AD and MCI due to AD. Basic and clinical studies have contributed considerably to the establishment of clinical evidence that supports the usefulness of these markers. Given the progress in the diagnosis of preclinical AD, discovery of therapy that is essential for the cure of AD is expected soon.     

Comparación de la capacidad diagnóstica precoz de los biomarcadores de la enfermedad de Alzheimer en resonancia magnética cerebral y líquido cefalorraquídeo

Introduction

The goals of this study were to compare the early diagnostic utility of Alzheimer disease biomarkers in the CSF with those in brain MRI in conditions found in our clinical practice, and to ascertain the diagnostic accuracy of both techniques used together.

Methods

Between 2008 and 2009, we included 30 patients with mild cognitive impairment (MCI) who were examined using 1.5 Tesla brain MRI and AD biomarker analysis in CSF. MRI studies were evaluated by 2 radiologists according to the Korf?s visual scale. CSF biomarkers were analysed using INNOTEST reagents for Aβ_1-42, total-tau and phospho-tau_181p. We evaluated clinical changes 2 years after inclusion.

Results

By 2 years after inclusion, 15 of the original 30 patients (50%) had developed AD (NINCDS-ADRA criteria). The predictive utility of AD biomarkers in CSF (RR 2.7; 95% CI, 1.1-6.7; P<.01) was greater than that of MRI (RR 1.5; 95% CI 95%, 0.7-3.4; P<.2); using both techniques together yielded a sensitivity and a negative predictive value of 100%. Normal results on both complementary tests ruled out progression to AD (100%) within 2 years of inclusion.

Conclusions

Our results show that the diagnostic accuracy of biomarkers in CSF is higher than that of biomarkers in MRI. Combined use of both techniques is highly accurate for either early diagnosis or exclusion of AD in patients with MCI.     

Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI

The close correlation between abnormally low pre-mortem cerebrospinal fluid (CSF) concentrations of amyloid-??1-42 (A??_1?C42) and plaque burden measured by amyloid imaging as well as between pathologically increased levels of CSF tau and the extent of neurodegeneration measured by MRI has led to growing interest in using these biomarkers to predict the presence of AD plaque and tangle pathology. A challenge for the widespread use of these CSF biomarkers is the high variability in the assays used to measure these analytes which has been ascribed to multiple pre-analytical and analytical test performance factors. To address this challenge, we conducted a seven-center inter-laboratory standardization study for CSF total tau (t-tau), phospho-tau (p-tau₁₈₁) and A??_1?C42 as part of the Alzheimer??s Disease Neuroimaging Initiative (ADNI). Aliquots prepared from five CSF pools assembled from multiple elderly controls (n?=?3) and AD patients (n?=?2) were the primary test samples analyzed in each of three analytical runs by the participating laboratories using a common batch of research use only immunoassay reagents (INNO-BIA AlzBio3, xMAP technology, from Innogenetics) on the Luminex analytical platform. To account for the combined effects on overall precision of CSF samples (fixed effect), different laboratories and analytical runs (random effects), these data were analyzed by mixed-effects modeling with the following results: within center %CV 95% CI values (mean) of 4.0?C6.0% (5.3%) for CSF A??_1?C42; 6.4?C6.8% (6.7%) for t-tau and 5.5?C18.0% (10.8%) for p-tau₁₈₁ and inter-center %CV 95% CI range of 15.9?C19.8% (17.9%) for A??_1?C42, 9.6?C15.2% (13.1%) for t-tau and 11.3?C18.2% (14.6%) for p-tau₁₈₁. Long-term experience by the ADNI biomarker core laboratory replicated this degree of within-center precision. Diagnostic threshold CSF concentrations for A??_1?C42 and for the ratio t-tau/A??_1?C42 were determined in an ADNI independent, autopsy-confirmed AD cohort from whom ante-mortem CSF was obtained, and a clinically defined group of cognitively normal controls (NCs) provides statistically significant separation of those who progressed from MCI to AD in the ADNI study. These data suggest that interrogation of ante-mortem CSF in cognitively impaired individuals to determine levels of t-tau, p-tau₁₈₁ and A??_1?C42, together with MRI and amyloid imaging biomarkers, could replace autopsy confirmation of AD plaque and tangle pathology as the ??gold standard?? for the diagnosis of definite AD in the near future.     

The Alzheimer's Disease Neuroimaging Initiative: Annual change in biomarkers and clinical outcomes

BackgroundThe Alzheimer's Disease Neuroimaging Initiative Phase 1 (ADNI-1) is a multisite prospective study designed to examine potential cerebrospinal fluid and imaging markers of Alzheimer's disease (AD) and their relationship to cognitive change. The objective of this study was to provide a global summary of the overall results and patterns of change observed in candidate markers and clinical measures over the first 2 years of follow-up.MethodsChange was summarized for 210 normal controls, 357 mild cognitive impairment, and 162 AD subjects, with baseline and at least one cognitive follow-up assessment. Repeated measures and survival models were used to assess baseline biomarker levels as predictors. Potential for improving clinical trials was assessed by comparison of precision of markers for capturing change in hypothetical trial designs.ResultsThe first 12 months of complete data on ADNI participants demonstrated the potential for substantial advances in characterizing trajectories of change in a range of biomarkers and clinical outcomes, examining their relationship and timing, and assessing the potential for improvements in clinical trial design. Reduced metabolism and greater brain atrophy in the mild cognitive impairment at baseline are associated with more rapid cognitive decline and a higher rate of conversion to AD. Use of biomarkers as study entry criteria or as outcomes could reduce the number of participants required for clinical trials.ConclusionsAnalyses and comparisons of ADNI data strongly support the hypothesis that measurable change occurs in cerebrospinal fluid, positron emission tomography, and magnetic resonance imaging well in advance of the actual diagnosis of AD.     

Limited agreement between biomarkers of neuronal injury at different stages of Alzheimer's disease

New diagnostic criteria for Alzheimer's disease (AD) treat different biomarkers of neuronal injury as equivalent. Here, we quantified the degree of agreement between hippocampal volume on structural magnetic resonance imaging, regional glucose metabolism on positron emission tomography, and levels of phosphorylated tau in cerebrospinal fluid (CSF) in 585 subjects from all phases of the AD Neuroimaging Initiative. The overall chance-corrected agreement was poor (Cohen κ, 0.24–0.34), in accord with a high rate of conflicting findings (26%–41%). Neither diagnosis nor APOE ε4 status significantly influenced the distribution of agreement between the biomarkers. The degree of agreement tended to be higher in individuals with abnormal versus normal CSF β-amyloid (Aβ_1-42) levels. Prospective diagnostic criteria for AD should address the relative importance of markers of neuronal injury and elaborate a way of dealing with conflicting biomarker findings.     

Cortical signatures of cognition and their relationship to Alzheimer’s disease

Recent changes in diagnostic criteria for Alzheimer’s disease (AD) state that biomarkers can enhance certainty in a diagnosis of AD. In the present study, we combined cognitive function and brain morphology, a potential imaging biomarker, to predict conversion from mild cognitive impairment to AD. We identified four biomarkers, or cortical signatures of cognition (CSC), from regressions of cortical thickness on neuropsychological factors representing memory, executive function/processing speed, language, and visuospatial function among participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neuropsychological factor scores were created from a previously validated multidimensional factor structure of the neuropsychological battery in ADNI. Mean thickness of each CSC at the baseline study visit was used to evaluate risk of conversion to clinical AD among participants with mild cognitive impairment (MCI) and rate of decline on the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score. Of 307 MCI participants, 119 converted to AD. For all domain-specific CSC, a one standard deviation thinner cortical thickness was associated with an approximately 50 % higher hazard of conversion and an increase of approximately 0.30 points annually on the CDR-SB. In combined models with a domain-specific CSC and neuropsychological factor score, both CSC and factor scores predicted conversion to AD and increasing clinical severity. The present study indicated that factor scores and CSCs for memory and language both significantly predicted risk of conversion to AD and accelerated deterioration in dementia severity. We conclude that predictive models are best when they utilize both neuropsychological measures and imaging biomarkers.