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.     

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.     

Cerebrospinal fluid Aβ42 is the best predictor of clinical progression in patients with subjective complaints

BackgroundThe need to recognize Alzheimer’s disease (AD) as early as possible led us to evaluate the predictive value of amyloid β(1-42) (Aβ42), total tau (tau), and phosphorylated tau (ptau) in cerebrospinal fluid (CSF) for clinical progression in patients with subjective complaints.MethodsWe recruited nondemented patients with subjective complaints (i.e., criteria for mild cognitive impairment [MCI] not fulfilled) from our memory clinic. We assessed the predictive value of CSF Aβ42, tau, and ptau for clinical progression using Cox proportional hazards models adjusted for age, gender, and baseline findings on the Mini-Mental State Examination (MMSE). Clinical progression was defined as progression to MCI or AD.ResultsWe included 127 patients with subjective complaints (age 60 ± 10 years, 61 [48%] females, MMSE 29 ± 1). At baseline, Aβ42 and tau were abnormal in 20 patients (both 16%), and ptau in 32 patients (25%). Thirteen patients (10%) progressed to MCI (n = 11) or AD (n = 2). Aβ42 was the strongest predictor of progression to MCI or AD with an adjusted hazard ratio (HR) of 16.0 (3.8–66.4). The adjusted HR associated with tau was 2.8 (0.9–9.2) and with ptau 2.6 (0.8–8.2). Combinations of biomarkers had a lower predictive value than Aβ42 alone.ConclusionLow Aβ42 was the strongest predictor of clinical progression in patients with subjective complaints. These results are in line with the hypothesis that the cascade of pathologic events starts with deposition of Aβ42, whereas neuronal degeneration and hyperphosphorylation of tau are more downstream events, closer to clinical manifestation of AD.     

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.     

Primary fatty amides in plasma associated with brain amyloid burden,hippocampal volume,and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort

IntroductionA critical and as-yet unmet need in Alzheimer's disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.MethodsThis study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD-type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis.ResultsEight metabolites were associated with amyloid β and one with t-tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory.DiscussionPFAMs have been found increased and associated with amyloid β burden in CSF and clinical measures.     

Inflammatory biomarkers in Alzheimer's disease plasma

IntroductionPlasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a “Holy Grail” of AD research and intensively sought; however, there are no well-established plasma markers.MethodsA hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed.ResultsTen analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71).DiscussionPlasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.     

Diagnostic accuracy of markers for prodromal Alzheimer's disease in independent clinical series

ObjectiveTo capitalize on data from different clinical series to compare sensitivity and specificity of individual biomarkers for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD).MethodsMedial temporal atrophy, cortical hypometabolism, and cerebrospinal fluid biomarkers were assessed in 18 patients with mild cognitive impairment (MCI) with prodromal AD (pAD; conversion time, 26 ± 12 months) and 18 stable MCI (sMCI) patients from the Translational Outpatient Memory Clinic cohort, as well as in 24 pAD patients (conversion time, 36 ± 12 months) and 33 sMCI patients from the Alzheimer's Disease Neuroimaging Initiative cohort. Medial temporal atrophy was measured by manual, semi-automated, and automated hippocampal volumetry; cortical hypometabolism was measured using several indices of AD-related hypometabolism pattern; and cerebrospinal fluid markers were amyloid β (Aβ)42 and total tau protein concentrations. For each biomarker, sensitivity for pAD, specificity for sMCI, and diagnostic accuracy were computed.ResultsSensitivity to predict MCI conversion to AD in the Alzheimer's Disease Neuroimaging Initiative and Translational Outpatient Memory Clinic cohorts was 79% and 94% based on Aβ42, 46% and 28% based on hippocampal volumes, 33% to 66% and 56% to 78% based on different hypometabolism indices, and 46% and 61% based on total tau levels, respectively. Specificity to exclude sMCI was 27% and 50% based on Aβ42, 76% and 94% based on hippocampal volumes, 58% to 67% and 55% to 83% based on different hypometabolism indices, and 61% and 83% based on total tau levels, respectively.ConclusionsCurrent findings suggest that Aβ42 concentrations and hippocampal volumes may be used in combination to best identify pAD.     

LTP-like cortical plasticity predicts conversion to dementia in patients with memory impairment

BackgroundNew diagnostic criteria consider Alzheimer’s disease (AD) as a clinico-biological entity identifiable in vivo on the presence of specific patterns of CSF biomarkers.ObjectiveHere we used transcranial magnetic stimulation to investigate the mechanisms of cortical plasticity and sensory-motor integration in patients with hippocampal-type memory impairment admitted for the first time in the memory clinic stratified according to CSF biomarkers profile.MethodsSeventy-three patients were recruited and divided in three groups according to the new diagnostic criteria: 1) Mild Cognitive Impaired (MCI) patients (n = 21); Prodromal AD (PROAD) patients (n = 24); AD with manifest dementia (ADD) patients (n = 28). At time of recruitment all patients underwent CSF sampling for diagnostic purposes. Repetitive and paired-pulse transcranial magnetic stimulation protocols were performed to investigate LTP-like and LTD-like cortical plasticity, short intracortical inhibition (SICI) and short afferent inhibition (SAI). Patients were the followed up during three years to monitor the clinical progression or the conversion to dementia.ResultsMCI patients showed a moderate but significant impairment of LTP-like cortical plasticity, while ADD and PROAD groups showed a more severe loss of LTP-like cortical plasticity. No differences were observed for LTD-like cortical plasticity, SICI and SAI protocols. Kaplan-Meyer analyses showed that PROAD and MCI patients converting to dementia had weaker LTP-like plasticity at time of first evaluation.ConclusionLTP-like cortical plasticity could be a novel biomarker to predict the clinical progression to dementia in patients with memory impairment at prodromal stages of AD identifiable with the new diagnostic criteria based on CSF biomarkers.     

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

IntroductionIt is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.MethodsA deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.ResultsThe deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures.DiscussionOur method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.     

Plasma ceramides are altered in mild cognitive impairment and predict cognitive decline and hippocampal volume loss

BackgroundA blood-based biomarker of Alzheimer's disease (AD) would be superior to cerebrospinal fluid (CSF) and neuroimaging measures in terms of cost, invasiveness, and feasibility for repeated measures. We previously reported that blood ceramides varied in relation to timing of memory impairment in a population-based study. The present objective was to examine whether plasma ceramides varied by AD severity in a well-characterized clinic sample and were associated with cognitive decline and hippocampal volume loss over 1 year.MethodsParticipants included 25 normal controls (NC), 17 amnestic Mild Cognitive Impairment (MCI), and 21 early probable AD. A thorough neuropsychological battery and neuroimaging with hippocampal volume determination were conducted at baseline and 1 year later. Plasma ceramides were assayed at baseline using high performance liquid chromatography coupled electrospray ionization tandem mass spectrometry.ResultsAlthough all saturated ceramides were lower in MCI compared with AD at baseline, ceramides C22:0 and C24:0 were significantly lower in the MCI group compared with both NC and AD groups (P < .01). Ceramide levels did not differ (P > .05) in AD versus NC. There were no cross-sectional associations between ceramides C22:0 and C24:0 and either cognitive performance or hippocampal volume among any group. However, among the MCI group, higher baseline ceramide C22:0 and C24:0 levels were predictive of cognitive decline and hippocampal volume loss 1 year later.ConclusionResults suggest that very long-chain plasma ceramides C22:0 and C24:0 are altered in MCI and predict memory loss and right hippocampal volume loss among subjects with MCI. These plasma ceramides may be early indicators of AD progression.     

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.     

A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer's disease

BackgroundOur objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD).MethodsSubjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model.ResultsThe final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale—cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points).ConclusionsA point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.     

Discriminatory and predictive capabilities of enzyme-linked immunosorbent assay and multiplex platforms in a longitudinal Alzheimer’s disease study

BackgroundMultiplex assays such as xMAP have been proposed for the assessment of Alzheimer’s disease (AD) biomarkers amyloid β 42 (Aβ₄₂), tau (Tau), and phosphorylated tau (pTau) in cerebrospinal fluid (CSF). Here, we compared the traditional enzyme-linked immunosorbent assay (ELISA) and xMAP with respect to their: (1) absolute biomarker concentration, (2) ability to distinguish AD from nondemented subjects, (3) ability to monitor AD longitudinally, and (4) ability to predict progression from mild cognitive impairment (MCI) to AD.MethodsWe selected 68 AD, 62 MCI, and 24 nondemented subjects, performed clinical examinations, and obtained CSF at baseline and 2 years later. Aβ₄₂, Tau, and pTau were measured with both ELISA and xMAP.ResultsBiomarker levels differed considerably between the two assays, and the differences were concentration dependent. No differences were observed in ability to distinguish nondemented subjects from AD patients between ELISA (area under curve of 0.84 for Aβ₄₂, 0.79 for Tau, and 0.75 for pTau) and xMAP (area under curve of 0.82 for Aβ₄₂, 0.75 for Tau, and 0.73 for pTau), all P < .05. Increased Aβ₄₂ levels of AD patients at follow-up compared with baseline were detected with ELISA, whereas increased Tau levels for nondemented subjects and MCI patients were only detected with xMAP. The hazard ratios for progression from MCI to AD did not differ between the assays.ConclusionBoth ELISA and multiplex assays can be used to measure AD biomarker levels in CSF to support clinical diagnosis and predict progression from MCI to AD with similar accuracy. Importantly, the assays’ output in absolute biomarker concentrations is remarkably different, and this discrepancy cannot be reconciled with simple correction factors.     

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.     

Blood biomarkers in mild cognitive impairment patients: Relationship between analytes and progression to Alzheimer disease dementia

Background and purpose

Blood-based biomarkers are promising tools for the diagnosis of Alzheimer disease (AD) at prodromal stages (mild cognitive impairment [MCI]) and are hoped to be implemented as screening tools for patients with cognitive complaints. In this work, we evaluated the potential of peripheral neurological biomarkers to predict progression to AD dementia and the relation between blood and cerebrospinal fluid (CSF) AD markers in MCI patients referred from a general neurological department.

Methods

A group of 106 MCI patients followed at the Neurology Department of Coimbra University Hospital was included. Data regarding baseline neuropsychological evaluation, CSF levels of amyloid β 42 (Aβ42), Aβ40, total tau (t-Tau), and phosphorylated tau 181 (p-Tau181) were available for all the patients. Aβ42, Aβ40, t-Tau, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) levels were determined in baseline stored serum and plasma samples by commercial SiMoA (Single Molecule Array) assays. Progression from MCI to AD dementia was assessed at follow-up (mean = 5.8 ± 3.4 years).

Results

At baseline, blood markers NfL, GFAP, and p-Tau181 were significantly increased in patients who progressed to AD at follow-up (p < 0.001). In contrast, plasma Aβ42/40 ratio and t-Tau showed no significant differences between groups. NfL, GFAP, and p-Tau181 demonstrated good diagnostic accuracy to identify progression to AD dementia (area under the curve [AUC] = 0.81, 0.80, and 0.76, respectively), which improved when combined (AUC = 0.89). GFAP and p-Tau181 were correlated with CSF Aβ42. Association of p-Tau181 with NfL was mediated by GFAP, with a significant indirect association of 88% of the total effect.

Conclusions

Our findings highlight the potential of combining blood-based GFAP, NfL, and p-Tau181 to be applied as a prognostic tool in MCI.     

MCI-to-normal reversion using neuropsychological criteria in the Alzheimer's Disease Neuroimaging Initiative

IntroductionThe low mild cognitive impairment (MCI) to cognitively normal (CN) reversion rate in the Alzheimer's Disease Neuroimaging Initiative (2-3%) suggests the need to examine reversion by other means. We applied comprehensive neuropsychological criteria (NP criteria) to determine the resulting MCI to CN reversion rate.MethodsParticipants with CN (n = 641) or MCI (n = 569) were classified at baseline and year 1 using NP criteria. Demographic, neuropsychological, and Alzheimer's disease biomarker variables as well as progression to dementia were examined across stable CN, reversion, and stable MCI groups.ResultsNP criteria produced a one-year reversion rate of 15.8%. Reverters had demographics, Alzheimer's disease biomarkers, and risk-of-progression most similar to the stable CN group and showed the most improvement on neuropsychological measures from baseline to year 1.DiscussionNP criteria produced a reversion rate that is consistent with, albeit modestly improved from, reversion rates in meta-analyses. Reverters’ biomarker profiles and progression rates suggest that NP criteria accurately tracked with underlying pathophysiologic status.     

Plasma proteins predict conversion to dementia from prodromal disease

BackgroundThe study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia.MethodsThree multicenter cohorts of cognitively healthy elderly, mild cognitive impairment (MCI), and AD participants with standardized clinical assessments and structural neuroimaging measures were used. Twenty-six candidate proteins were quantified in 1148 subjects using multiplex (xMAP) assays.ResultsSixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85%, and specificity 88%).ConclusionsWe have identified 10 plasma proteins strongly associated with disease severity and disease progression. Such markers may be useful for patient selection for clinical trials and assessment of patients with predisease subjective memory complaints.     

Aβ and cognitive change: Examining the preclinical and prodromal stages of Alzheimer's disease

BackgroundHigh β-amyloid (Aβ) is associated with faster memory decline in healthy individuals and adults with mild cognitive impairment (MCI). However, longer prospective studies are required to determine if Aβ-related memory decline continues and whether it is associated with increased rate of disease progression.MethodsHealthy controls (HCs; n = 177) and adults with MCI (n = 48) underwent neuroimaging for Aβ and cognitive assessment at baseline. Cognition was reassessed 18 and 36 months later.ResultsCompared with low-Aβ HCs, high-Aβ HC and MCI groups showed moderate decline in episodic and working memory over 36 months. Those with MCI with low Aβ did not show any cognitive decline. Rates of disease progression were increased in the high-Aβ HC and MCI groups.ConclusionsIn healthy individuals, high Aβ likely indicates that Alzheimer's disease (AD)-related neurodegeneration has begun. Once commenced, the rate of decline in cognitive function remains constant across the preclinical and prodromal stages of AD.     

Longitudinal cerebrospinal fluid biomarker trajectories along the Alzheimer's disease continuum in the BIOMARKAPD study

IntroductionWithin-person trajectories of cerebrospinal fluid (CSF) biomarkers in Alzheimer's disease (AD) are not well defined.MethodsWe included 467 subjects from the BIOMARKAPD study with at least two serial CSF samples. Diagnoses were subjective cognitive decline (n = 75), mild cognitive impairment (n = 128), and AD dementia (n = 110), and a group of cognitively unimpaired subjects (n = 154) were also included. We measured baseline and follow-up CSF levels of total tau (t-tau), phosphorylated tau (p-tau), YKL-40, and neurofilament light (NfL). Median CSF sampling interval was 2.1 years.ResultsCSF levels of t-tau, p-tau, NfL, and YKL-40 were 2% higher per each year of baseline age in controls (P <.001). In AD, t-tau levels were 1% lower (P <.001) and p-tau levels did not change per each year of baseline age. Longitudinally, only NfL (P <.001) and YKL-40 (P <.02) increased during the study period.DiscussionAll four CSF biomarkers increase with age, but this effect deviates in AD for t-tau and p-tau.     

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.