Discovery and validation of plasma proteomic biomarkers relating to brain amyloid burden by SOMAscan assay

Introduction

Plasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins.

Methods

4001 plasma proteins were measured in two groups of participants (discovery group = 516, replication group = 365) selected from the European Medical Information Framework for Alzheimer's disease Multimodal Biomarker Discovery study, all of whom had measures of amyloid.

Results

A panel of proteins (n = 44), along with age and apolipoprotein E (APOE) ε4, predicted brain amyloid deposition with good performance in both the discovery group (area under the curve = 0.78) and the replication group (area under the curve = 0.68). Furthermore, a causal relationship between amyloid and tau was confirmed by Mendelian randomization.

Discussion

The results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition.

Something Is Better Than Nothing: The Value of Active Intervention in Stated Preferences for Treatments to Delay Onset of Alzheimer's Disease Symptoms

BACKGROUND

The objective of the study was to understand respondents' willingness to accept hypothetical treatment-related risks in return for the benefit of additional time with normal memory from potential Alzheimer's disease interception therapies.

METHODS

A US web-based discrete-choice survey was administered to respondents ages 60 to 85 years with no Alzheimer's disease diagnosis and no cognitive symptoms. Choice questions required respondents to indicate whether they preferred a constant, no-treatment condition described as 4 years of normal memory followed by 3 years of cognitive impairment and 5 years of dementia or an interception treatment with chosen risks of disabling stroke and death, but with increased duration of normal memory. The study design included internal validity tests to verify data quality.

RESULTS

On average, respondents were willing to accept a 5% to 13% risk of stroke or death in the first year for treatments that could provide 1 or more additional years with normal memory. Nevertheless, 30% of respondents failed a simple internal-validity test question where the treatment alternative offered no improvement in disease progression but had significant side effects. These respondents also were more likely to choose active treatment in the subsequent series of choice questions. This unexpected finding is consistent with hopeful attitudes of patients with debilitating and potentially fatal conditions.

CONCLUSION

Pro-treatment attitudes are clinically relevant and can affect the analysis and interpretation of stated-preference data. Internal-validity tests generally are underutilized in preference research. This study demonstrated how analysis of apparent validity failures can yield important insights about patient preferences.

Inflammatory biomarkers in Alzheimer's disease plasma

Introduction

Plasma 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.

Methods

A 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.

Results

Ten 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).

Discussion

Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.

Cerebrospinal fluid biomarkers of neurodegeneration, synaptic integrity, and astroglial activation across the clinical Alzheimer's disease spectrum

INTRODUCTION

We investigated relations between amyloid-β (Aβ) status, apolipoprotein E (APOE) ε4, and cognition, with cerebrospinal fluid markers of neurogranin (Ng), neurofilament light (NFL), YKL-40, and total tau (T-tau).

METHODS

We included 770 individuals with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD)-type dementia from the EMIF-AD Multimodal Biomarker Discovery study. We tested the association of Ng, NFL, YKL-40, and T-tau with Aβ status (Aβ- vs. Aβ+), clinical diagnosis APOE ε4 carriership, baseline cognition, and change in cognition.

RESULTS

Ng and T-tau distinguished between Aβ+ from Aβ- individuals in each clinical group, whereas NFL and YKL-40 were associated with Aβ+ in nondemented individuals only. APOE ε4 carriership did not influence NFL, Ng, and YKL-40 in Aβ+ individuals. NFL was the best predictor of cognitive decline in Aβ+ individuals across the cognitive spectrum.

DISCUSSION

Axonal degeneration, synaptic dysfunction, astroglial activation, and altered tau metabolism are involved already in preclinical AD. NFL may be a useful prognostic marker.

Normative data from linear and nonlinear quantile regression in CANTAB: Cognition in mid-to-late life in an epidemiological sample

Introduction

Normative cognitive data can help to distinguish pathological decline from normal aging. This study presents normative data from the Cambridge Neuropsychological Test Automated Battery, using linear regression and nonlinear quantile regression approaches.

Methods

Heinz Nixdorf Recall study participants completed Cambridge Neuropsychological Test Automated Battery tests: paired-associate learning, spatial working memory, and reaction time. Data were available for 1349-1529 healthy adults aged 57-84 years. Linear and nonlinear quantile regression analyses examined age-related changes, adjusting for sex and education. Quantile regression differentiated seven performance bands (percentiles: 97.7, 93.3, 84.1, 50, 15.9, 6.7, and 2.3).

Results

Normative data show age-related cognitive decline across all tests, but with quantile regression revealing heterogeneous trajectories of cognitive aging, particularly for the test of episodic memory function (paired-associate learning).

Discussion

This study presents normative data from Cambridge Neuropsychological Test Automated Battery in mid-to-late life. Quantile regression can model heterogeneity in age-related cognitive trajectories as seen in the paired-associate learning episodic memory measure.

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The study presents normative cognitive data from the Cambridge Neuropsychological Test Automated Battery in mid-to-late life.

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Most tasks showed similar decline across performance bands with increasing age.

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Quantile regression is sensitive for evaluating diverging trajectories with age.

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Episodic memory showed accelerated decline in the average performance range.

Assessment of cognitive-driven activity of daily living impairment in non-demented Parkinson's patients

The core criterion for Parkinson's disease dementia (PDD) is the impairment in activities of daily living (ADL) function primarily caused by cognitive, not motor symptoms. There is evidence to assume that mild ADL impairments in mild cognitive impairment (PD-MCI) characterize those patients at high risk for dementia. Data of 216 Parkinson's disease (PD) patients assessed with comprehensive motor and neuropsychological assessments were analysed. Based on linear regression models, subscores of the Functional Activities Questionnaire (FAQ) primarily reflecting patients' global cognitive status (FAQ_C ) or PD-related motor severity (FAQ_M ) were developed. A quotient (FAQ_Q ) of both scores was calculated, with values >1 indicating more cognitive- compared to motor-driven ADL impairment. Both FAQ_C and FAQ_M scores were higher in PD-MCI than cognitively normal (PD-CN) patients, indicating more severe cognitive- and motor-driven ADL impairments in this group. One third (31.6%) of the PD-MCI group had a FAQ_Q score >1, which was significantly different from patients with PD-CN (p = .02). PD-MCI patients with an FAQ_Q score >1 were more impaired on tests assessing attention (p = .019) and language (p = .033) compared to PD-MCI patients with lower FAQ_Q values. The differentiation between cognitive- and motor-driven ADL is important, as the loss of functional capacity is the defining factor for a diagnosis of PDD. We were able to differentiate the cognitive-driven from the motor-driven ADL impairments for the FAQ. PD-MCI patients with more cognitive- compared to motor-driven ADL impairments may pose a risk group for conversion to PDD and can be targeted for early treatments.

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

BACKGROUND

With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification.

METHODS

We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects.

RESULTS

In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures.

CONCLUSIONS

Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies.

The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics

BACKGROUND

There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer's disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants.

METHODS

Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling.

RESULTS

We included 1221 individuals (NC n = 492, MCI n = 527, AD-type dementia n = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals (p < 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p < 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC (p < 0.001) and MCI (p < 0.001) groups.

CONCLUSIONS

The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog.

Evaluating Potential QT Effects of JNJ-54861911, a BACE Inhibitor in Single- and Multiple-Ascending Dose Studies, and a Thorough QT Trial With Additional Retrospective Confirmation, Using Concentration-QTc Analysis

Nonclinical assays with JNJ-54861911, a β-secretase 1 inhibitor have indicated that at high concentrations, it may delay cardiac repolarization. A 4-way crossover thorough QT (TQT) study was performed in 64 healthy subjects with 50 and 150 mg JNJ-54861911 once daily for 7 days, placebo, and 400 mg moxifloxacin. Retrospective high-precision QT (HPQT) analysis was performed on serial elecrocardiograms extracted from first-in-human single-ascending dose (SAD) and multiple-ascending dose (MAD) studies to evaluate if early studies could detect and predict QT effect. In the TQT study, a high therapeutic 50 mg dose did not cause QT prolongation, and an effect >10 milliseconds could be excluded at all postdose timepoints. QT prolongation with peak effect on placebo-corrected change from baseline QTcF of 15.5 milliseconds (90%CI, 12.9-18.1 milliseconds) was observed following a supratherapeutic dose (150 mg). No clinically relevant QT changes were observed in earlier studies. However, with SAD/MAD findings by HPQT, the slope of the exposure-response (ER) relationship in the SAD study (doses up to 150 mg) was similar to the TQT study slope, and the estimated QT effect was comparable at high plasma levels. In the MAD study, doses up to 90 mg once daily for 7 days resulted in JNJ-54861911 peak plasma concentrations (C_max ) comparable to those in the SAD study (∼750 ng/mL), but ER by HPQT failed to detect a QT effect and resulted in negative estimations. Adding a higher dose cohort (150 mg; C_max , 1125 ng/mL) demonstrated a QT effect, with a slightly lower ER slope than the TQT study. JNJ-54861911 (up to 50 mg) did not cause QT prolongation at clinically relevant plasma concentrations in any studies. Provided sufficiently high plasma concentrations were captured, mild QT prolongation observed postdose with a supratherapeutic dose could be detected (TQT study) and estimated in SAD/MAD studies. Based on population pharmacokinetic modeling and simulation, 5 and 25 mg doses are currently considered for further phase 3 studies and are expected not to cause any relevant QT prolongation.

Biomarker pattern of ARIA-E participants in phase 3 randomized clinical trials with bapineuzumab

OBJECTIVE

To evaluate whether amyloid-related imaging abnormalities with edema/effusion (ARIA-E) observed in bapineuzumab clinical trials was associated with specific biomarker patterns.

METHODS

Bapineuzumab, an anti-β-amyloid monoclonal antibody, was evaluated in patients with mild to moderate Alzheimer disease. Amyloid PET imaging, CSF biomarkers, or volumetric MRI (vMRI) were assessed.

RESULTS

A total of 1,512 participants underwent one or more biomarker assessments; 154 developed incident ARIA-E. No differences were observed at baseline between ARIA-E and non-ARIA-E participants in brain amyloid burden by PET, the majority of vMRI measures, or CSF biomarkers, with the exception of lower baseline CSF Aβ₄₂ in APOE ε4 noncarrier ARIA-E vs non-ARIA-E groups (bapineuzumab non-ARIA-E p = 0.027; placebo non-ARIA-E p = 0.012). At week 71, bapineuzumab-treated participants with ARIA-E vs non-ARIA-E showed greater reduction in brain amyloid PET, greater reductions in CSF phosphorylated tau (p-tau) (all comparisons p < 0.01), and total tau (t-tau) (all comparisons p < 0.025), and greater hippocampal volume reduction and ventricular enlargement (all p < 0.05). Greater reduction in CSF Aβ₄₀ concentrations was observed for ARIA-E versus both non-ARIA-E groups (bapineuzumab/placebo non-ARIA-E p = 0.015/0.049). No group differences were observed at week 71 for changes in whole brain volume or CSF Aβ₄₂.

CONCLUSIONS

Baseline biomarkers largely do not predict risk for developing ARIA-E. ARIA-E was associated with significant longitudinal changes in several biomarkers, with larger reductions in amyloid PET and CSF p-tau and t-tau concentrations, and paradoxically greater hippocampal volume reduction and ventricular enlargement, suggesting that ARIA-E in bapineuzumab-treated cases may be related to increased Aβ efflux from the brain and affecting downstream pathogenic processes.

Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations

The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1-weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test-retest reliability of hippocampal subregion volumes was assessed using the intra-class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73-0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum). These results indicate that this automated segmentation method provides a robust method with which to measure hippocampal subregions, and may be useful in tracking disease progression and measuring the effects of pharmacological intervention.

NRP-1 Receptor Expression Mismatch in Skin of Subjects with Experimental and Diabetic Small Fiber Neuropathy

The in vivo cutaneous nerve regeneration model using capsaicin is applied extensively to study the regenerative mechanisms and therapeutic efficacy of disease modifying molecules for small fiber neuropathy (SFN). Since mismatches between functional and morphological nerve fiber recovery are described for this model, we aimed at determining the capability of the capsaicin model to truly mimic the morphological manifestations of SFN in diabetes. As nerve and blood vessel growth and regenerative capacities are defective in diabetes, we focused on studying the key regulator of these processes, the neuropilin-1 (NRP-1)/semaphorin pathway. This led us to the evaluation of NRP-1 receptor expression in epidermis and dermis of subjects presenting experimentally induced small fiber neuropathy, diabetic polyneuropathy and of diabetic subjects without clinical signs of small fiber neuropathy. The NRP-1 receptor was co-stained with CD31 vessel-marker using immunofluorescence and analyzed with Definiens® technology. This study indicates that capsaicin application results in significant loss of epidermal NRP-1 receptor expression, whereas diabetic subjects presenting small fiber neuropathy show full epidermal NRP-1 expression in contrast to the basal expression pattern seen in healthy controls. Capsaicin induced a decrease in dermal non-vascular NRP-1 receptor expression which did not appear in diabetic polyneuropathy. We can conclude that the capsaicin model does not mimic diabetic neuropathy related changes for cutaneous NRP-1 receptor expression. In addition, our data suggest that NRP-1 might play an important role in epidermal nerve fiber loss and/or defective regeneration and that NRP-1 receptor could change the epidermal environment to a nerve fiber repellant bed possibly through Sem3A in diabetes.

Making use of longitudinal information in pattern recognition

Longitudinal designs are widely used in medical studies as a means of observing within-subject changes over time in groups of subjects, thereby aiming to improve sensitivity for detecting disease effects. Paralleling an increased use of such studies in neuroimaging has been the adoption of pattern recognition algorithms for making individualized predictions of disease. However, at present few pattern recognition methods exist to make full use of neuroimaging data that have been collected longitudinally, with most methods relying instead on cross-sectional style analysis. This article presents a principal component analysis-based feature construction method that uses longitudinal high-dimensional data to improve predictive performance of pattern recognition algorithms. The method can be applied to data from a wide range of longitudinal study designs and permits an arbitrary number of time-points per subject. We apply the method to two longitudinal datasets, one containing subjects with mild cognitive impairment along with healthy controls, the other with early dementia subjects and healthy controls. Across both datasets, we show improvements in predictive accuracy relative to cross-sectional classifiers for discriminating disease subjects from healthy controls on the basis of whole-brain structural magnetic resonance image-based voxels. In addition, we can transfer longitudinal information from one set of subjects to make disease predictions in another set of subjects. The proposed method is simple and, as a feature construction method, flexible with respect to the choice of classifier and image registration algorithm. Hum Brain Mapp 37:4385-4404, 2016. © 2016 Wiley Periodicals, Inc.