Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects

Objective

Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.

Methods

Amyloid‐β 1 to 42 peptide (Aβ_1–42), total tau (t‐tau), and tau phosphorylated at the threonine 181 were measured in (1) cerebrospinal fluid (CSF) samples obtained during baseline evaluation of 100 mild AD, 196 mild cognitive impairment, and 114 elderly cognitively normal (NC) subjects in ADNI; and (2) independent 56 autopsy‐confirmed AD cases and 52 age‐matched elderly NCs using a multiplex immunoassay. Detection of an AD CSF profile for t‐tau and Aβ_1–42 in ADNI subjects was achieved using receiver operating characteristic cut points and logistic regression models derived from the autopsy‐confirmed CSF data.

Results

CSF Aβ_1–42 was the most sensitive biomarker for AD in the autopsy cohort of CSF samples: receiver operating characteristic area under the curve of 0.913 and sensitivity for AD detection of 96.4%. In the ADNI cohort, a logistic regression model for Aβ_1–42, t‐tau, and APOε4 allele count provided the best assessment delineation of mild AD. An AD‐like baseline CSF profile for t‐tau/Aβ_1–42 was detected in 33 of 37 ADNI mild cognitive impairment subjects who converted to probable AD during the first year of the study.

Interpretation

The CSF biomarker signature of AD defined by Aβ_1–42 and t‐tau in the autopsy‐confirmed AD cohort and confirmed in the cohort followed in ADNI for 12 months detects mild AD in a large, multisite, prospective clinical investigation, and this signature appears to predict conversion from mild cognitive impairment to AD. Ann Neurol 2009     

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.     

Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease

Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18^F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R² = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R² = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two‐way interaction models. The best performing model included an interaction between amyloid‐β‐PET and P‐tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker ‘space', providing information for personalised or stratified healthcare or clinical trial design.     

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.     

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: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [¹⁸F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.     

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.     

Recommendations for cerebrospinal fluid Alzheimer's disease biomarkers in the diagnostic evaluation of mild cognitive impairment

This article presents recommendations, based on the Grading of Recommendations, Assessment, Development, and Evaluation method, for the clinical application of cerebrospinal fluid (CSF) amyloid-β_1–42, tau, and phosphorylated tau in the diagnostic evaluation of patients with mild cognitive impairment (MCI). The recommendations were developed by a multidisciplinary working group and based on the available evidence and consensus from focused group discussions for 1) prediction of clinical progression to Alzheimer's disease (AD) dementia, 2) cost-effectiveness, 3) interpretation of results, and 4) patient counseling. The working group recommended using CSF AD biomarkers in the diagnostic workup of MCI patients, after prebiomarker counseling, as an add-on to clinical evaluation to predict functional decline or conversion to AD dementia and to guide disease management. Because of insufficient evidence, it was uncertain whether CSF AD biomarkers outperform imaging biomarkers. Furthermore, the working group provided recommendations for interpretation of ambiguous CSF biomarker results and for pre- and post-biomarker counseling.     

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.     

Value of CSF beta-amyloid1-42 and tau as predictors of Alzheimer's disease in patients with mild cognitive impairment

Subjects with mild cognitive impairment (MCI) are at a high risk of developing clinical Alzheimer's disease (AD). We asked to what extent the core biomarker candidates cerebro-spinal fluid (CSF) beta-amyloid(1-42) (Abeta(1-42)) and CSF tau protein concentrations predict conversion from MCI to AD. We studied 52 patients with MCI, 93 AD patients, and 10 healthy controls (HC). The MCI group was composed of 29 patients who had converted to AD during follow-up, and of 23 patients who showed no cognitive decline. CSF Abeta(1-42) and tau protein levels were assessed at baseline in all subjects, using enzyme-linked immunosorbent assays. For assessment of sensitivity and specificity, we used independently established reference values for CSF Abeta(1-42) and CSF tau. The levels of CSF tau were increased, whereas levels of Abeta(1-42) were decreased in MCI subjects. Abeta(1-42) predicted AD in converted MCI with a sensitivity of 59% and a specificity of 100% compared to HC. Tau yielded a greater sensitivity of 83% and a specificity of 90%. In a multiple Cox regression analysis within the MCI group, low baseline levels of Abeta(1-42), but not other predictor variables (tau protein, gender, age, apolipoprotein E epsilon4 carrier status, Mini Mental Status Examination score, observation time, antidementia therapy), correlated with conversion status (P<0.05). Our findings support the notion that CSF tau and Abeta(1-42) may be useful biomarkers in the early identification of AD in MCI subjects.     

Relations between personality changes and cerebrospinal fluid biomarkers of Alzheimer's disease pathology

Specific changes in personality profiles may represent early non-cognitive symptoms of Alzheimer's disease (AD). Evaluating the subject's personality changes may add significant clinical information, as well as help to better understand the interaction between personality change, cognitive decline, and cerebral pathology. With this study we aimed to describe the relationship between personality changes and cerebrospinal fluid (CSF) markers of AD pathology at early clinical stages of the disease. One hundred and ten subjects, of whom 66 cognitively impaired patients (57 with mild cognitive impairment (MCI), and 9 with mild dementia) and 44 healthy controls, had neuropsychological examination as well as lumbar puncture to determine concentrations of CSF biomarkers of AD pathology (amyloid beta_1-42 (Aβ_1-42), phosphorylated tau (ptau-181), and total-tau (tau)). The Revised NEO Personality Inventory (NEO-PI-R) was administered twice, once to evaluate subjects' current personality and once to assess personality traits retrospectively 5 years before evaluation. Subjects with an AD CSF biomarker profile showed significant increase in neuroticism and decrease in conscientiousness over time as compared to non-AD CSF biomarker group. In regression analysis controlling for global cognition as measured by the MMSE score, increasing neuroticism and decreasing extraversion, openness to experience and conscientiousness were associated with lower Aβ_1-42 concentrations but not with tau and ptau-181 concentrations. Our findings suggest that early and specific changes in personality are associated with cerebral AD pathology. Concentrations of CSF biomarkers, additionally to severity of the cognitive impairment, significantly contribute in predicting specific personality changes.     

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.     

Maternal Family History is Associated with Alzheimer's Disease Biomarkers

A family history of Alzheimer's disease (AD) increases one's risk of developing late-onset AD (LOAD), and a maternal family history of LOAD influences risk more than a paternal family history. Accumulating evidence suggests that a family history of dementia associates with AD-typical biomarker changes. We analyzed cross-sectional data from non-demented, mild cognitive impairment (MCI), and LOAD participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with PET imaging using Pittsburgh Compound B (PiB, n = 99) and cerebrospinal fluid (CSF) analysis (n = 403) for amyloid-β peptide (Aβ) and total tau. We assessed the relationship of CSF and PiB biomarkers and family history of dementia, as well as parent gender effects. In the larger analysis of CSF biomarkers, we assessed diagnosis groups individually. In the overall sample, CSF Aβ, tau/Aβ ratio, and global PiB uptake were significantly different between family history positive and negative groups, with markers of increased AD burden associated with a positive maternal family history of dementia. Moreover, a maternal family history of dementia was associated with significantly greater PiB Aβ load in the brain in the parietal cortex, precuneus, and sensorimotor cortex. Individuals with MCI positive for a maternal family history of dementia had significantly more markers of AD pathophysiology than individuals with no family history of dementia. A family history of dementia is associated with AD-typical biomarker changes. These biomarker associations are most robust in individuals with a maternal family history, suggesting that a maternally inherited factor influences AD risk.     

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.     

Supporting evidence for using biomarkers in the diagnosis of MCI due to AD

The aim of this study is to support the use of biomarkers in the diagnosis of mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) according to the revised NIA-AA diagnostic criteria. We compared clinical features and conversion to AD and other dementias among groups of MCI patients with different abnormal biomarker profiles. In this study, we enrolled 58 patients with MCI, and for each of them AD biomarkers (CSF Abeta42 and tau, temporoparietal hypometabolism on 18F-FDG PET, and hippocampal volume) were collected. Patients were divided into three groups: (i) no abnormal biomarker, (ii) AD biomarker pattern (including three subgroups of early = only abnormal Abeta42, intermediate = abnormal Abeta42 and FDG PET or tau, and late = abnormal Abeta42, FDG PET or tau, and HV), and (iii) any other biomarker combination. MCI patients with AD biomarker pattern had lower behavioural disturbances than patients with any other biomarker combination (p < 0.0005). This group also showed lower performance on verbal and non-verbal memory than the other two groups (p = 0.07 and p = 0.004, respectively). Within the three subgroups with AD biomarker patterns we observed a significant trend toward a higher rate of conversion to dementia (p for trend = 0.006). With regard to dementia conversion, 100 % of patients with an AD biomarker pattern developed AD, but none of the patients with no abnormal biomarker and 27 % of patients with any other biomarker combination (p = 0.002) did so. We also described some clinical cases representative for each of these three groups. The results of this study provide evidence in favour of the use of biomarkers for the diagnosis of MCI due to AD, in line with recently published research criteria.     

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.     

Biomarker profiles and their relation to clinical variables in mild cognitive impairment

The aim of the study was to compare clinical variables between MCI patients at different risk for Alzheimer's disease (AD) according to their biomarker profile. Fifty-four percent out of 39 MCI patients had a low Abeta42 and high tau in cerebrospinal fluid (CSF) (high-risk), 26% either a low CSF Abeta32 or high CSF tau (intermediate-risk) and 20% a normal CSF Abeta42 and tau (low-risk). Both high-and intermediate-risk subjects differed from the low-risk group in episodic memory, executive functions and the preclinical AD scale (PAS),which combines a set of clinical parameters. Subjects at high risk did not differ from subjects with an intermediate risk. Abeta42 levels correlated with the MTA and PAS scores, tau levels with episodic memory. These correlations suggest that the biomarkers are not independent when compared to the other AD markers. Longitudinal studies are necessary to interpret the correlations between biomarkers, imaging, and neuropsychological markers.     

Spatial correlation maps of the hippocampus with cerebrospinal fluid biomarkers and cognition in Alzheimer's disease: A longitudinal study

This study is an observational study that takes the existing longitudinal data from Alzheimer''s disease Neuroimaging Initiative to examine the spatial correlation map of hippocampal subfield atrophy with CSF biomarkers and cognitive decline in the course of AD. This study included 421 healthy controls (HC), 557 patients of stable mild cognitive impairment (s‐MCI), 304 Alzheimer''s Disease (AD) patients, and 241 subjects who converted to be AD from MCI (c‐MCI), and 6,525 MRI scans in a period from 2004 to 2019. Our findings revealed that all the hippocampal subfields showed their accelerated atrophy rate from cognitively normal aging to stable MCI and AD. The presubiculum, dentate gyrus, and fimbria showed greater atrophy beyond the whole hippocampus in the HC, s‐MCI, and AD groups and corresponded to a greater decline of memory and attention in the s‐MCI group. Moreover, the higher atrophy rates of the subiculum and CA2/3, CA4 were also associated with a greater decline in attention in the s‐MCI group. Interestingly, patients with c‐MCI showed that the presubiculum atrophy was associated with CSF tau levels and corresponded to the onset age of AD and a decline in attention in patients with c‐MCI. These spatial correlation findings of the hippocampus suggested that the hippocampal subfields may not be equally impacted by normal aging, MCI, and AD, and their atrophy was selectively associated with declines in specific cognitive domains. The presubiculum atrophy was highlighted as a surrogate marker for the AD prognosis along with tau pathology and attention decline.     

Biomarker profiles and their relation to clinical variables in mild cognitive impairment

The aim of the study was to compare clinical variables between MCI patients at different risk for Alzheimer’s disease (AD) according to their biomarker profile. Fifty-four percent out of 39 MCI patients had a low Aβ42 and high tau in cerebrospinal fluid (CSF) (high-risk), 26% either a low CSF Aβ42 or high CSF tau (intermediate-risk) and 20% a normal CSF Aβ42 and tau (low-risk). Both high- and intermediate-risk subjects differed from the low-risk group in episodic memory, executive functions and the preclinical AD scale (PAS), which combines a set of clinical parameters. Subjects at high risk did not differ from subjects with an intermediate risk. Aβ42 levels correlated with the MTA and PAS scores, tau levels with episodic memory. These correlations suggest that the biomarkers are not independent when compared to the other AD markers. Longitudinal studies are necessary to interpret the correlations between biomarkers, imaging, and neuropsychological markers.     

The clinical use of cerebrospinal fluid biomarker testing for Alzheimer's disease diagnosis: A consensus paper from the Alzheimer's Biomarkers Standardization Initiative

BackgroundCerebrospinal fluid (CSF) biomarkers β-amyloid 1-42 (Aβ_1-42), also expressed as Aβ_1-42:Aβ_1-40 ratio, T-tau, and P-tau_181P, have proven diagnostic accuracy for mild cognitive impairment and Alzheimer's disease (AD). How to use, interpret, and disclose biomarker results drives the need for standardization.MethodsPrevious Alzheimer's Biomarkers Standardization Initiative meetings discussed preanalytical issues affecting Aβ_1-42 and tau in CSF. This second round of consensus meetings focused on issues related to clinical use of AD CSF biomarkers.ResultsConsensus was reached that lumbar puncture for AD CSF biomarker analysis be considered as a routine clinical test in patients with early-onset dementia, at the prodromal stage or with atypical AD. Moreover, consensus was reached on which biomarkers to use, how results should be interpreted, and potential confounding factors.ConclusionsChanges in Aβ_1-42, T-tau, and P-tau_181P allow diagnosis of AD in its prodromal stage. Conversely, having all three biomarkers in the normal range rules out AD. Intermediate conditions require further patient follow-up.