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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. 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. 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. The results suggest that high-dimensional plasma protein testing could be a useful and reproducible approach for measuring brain amyloid deposition. This research was conducted as part of the EMIF-AD project, which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007-2013), and EFPIA companies’ in-kind contribution. The DESCRIPA study was funded by the European Commission within the 5th framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the 5th framework program (contract # 37670). The Leuven cohort was funded by the Stichting voor Alzheimer Onderzoek (grant numbers #11020, #13007, and #15005). R.V. is a senior clinical investigator of the Flemish Research Foundation (FWO). J.S. is currently an employee of UCB, Braine-l’Alleud, Belgium. The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01.41142.001.H). The authors acknowledge the contribution of the personnel of the Genomic Service Facility at the VIB-U Antwerp Center for Molecular Neurology. The research at VIBCMN is funded in part by the University of Antwerp Research Fund. F.B. is supported by the NIHR biomedical research centre at UCLH. L.S. is funded by DPUK through MRC (grant no. MR/L023784/2) and the UK Medical Research Council Award to the University of Oxford (grant no. MC_PC_17215).

© 2020 – IOS Press and the authors., [Background]: Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer’s disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown. [Objective]: We aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes. [Methods]: We first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677). [Results]: We identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T–N–, A+T+N–, A+T–N+, and A+T+N+) from no AD pathology (A–T–N–) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts. [Conclusions]: Our results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo., This research was conducted as part of the EMIF-AD project which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n° 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in-kind contribution. The DESCRIPA study was funded by the European Commission within the 5th framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the 5th framework program (contract # 37670). The Leuven cohort was funded by the Stichting voor Alzheimer Onderzoek (grant numbers #11020, #13007 and #15005). The Lausanne cohort study was supported by a grant from the Swiss National Research Foundation to JP (SNF 320030_141179). RV is a senior clinical investigator of the Flemish Research Foundation (FWO). JS is currently an employee of UCB, Braine-l’Alleud, Belgium. The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01.41142.001.H). We acknowledge the contribution of the personnel of the Genomic Service Facility at the VIB-U Antwerp Center for Molecular Neurology. The research at VIB-CMN is funded in part by the University of Antwerp Research Fund. FB is supported by the NIHR biomedical research centre at UCLH. RD is supported by Health Data Research UK, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London, Maudsley NHS Foundation Trust, King’s College London and the NIHR University College London Hospitals Biomedical Research Centre. LS is funded by DPUK through MRC (grant no. MR/L023784/2) and the UK Medical Research Council Award to the University of Oxford (grant no. MC_PC_17215). Also support was received from the NIHR Biomedical Research Centre at Oxford Health NHS Foundation Trust.

We have previously investigated, discovered, and replicated plasma protein biomarkers for use to triage potential trials participants for PET or cerebrospinal fluid measures of Alzheimer's disease (AD) pathology. This study sought to undertake validation of these candidate plasma biomarkers in a large, multi-center sample collection. Targeted plasma analyses of 34 proteins with prior evidence for prediction of in vivo pathology were conducted in up to 1,000 samples from cognitively healthy elderly individuals, people with mild cognitive impairment, and in patients with AD-type dementia, selected from the EMIF-AD catalogue. Proteins were measured using Luminex xMAP, ELISA, and Meso Scale Discovery assays. Seven proteins replicated in their ability to predict in vivo amyloid pathology. These proteins form a biomarker panel that, along with age, could significantly discriminate between individuals with high and low amyloid pathology with an area under the curve of 0.74. The performance of this biomarker panel remained consistent when tested in apolipoprotein E ɛ4 non-carrier individuals only. This blood-based panel is biologically relevant, measurable using practical immunocapture arrays, and could significantly reduce the cost incurred to clinical trials through screen failure. ispartof: JOURNAL OF ALZHEIMERS DISEASE vol:74 issue:1 pages:213-225 ispartof: location:Netherlands status: published

Blood-based biomarkers represent a less invasive and potentially cheaper approach for aiding Alzheimer's disease (AD) detection compared with cerebrospinal fluid and some neuroimaging biomarkers. Acknowledging that many in the field have made great progress, here we review some of the work that our group has pursued to identify and validate blood-based proteomic biomarkers through both case control and AD pathology endophenotype-based approaches. Our focus is primarily to identify a minimally invasive and hopefully cost-effective blood-based biomarker to reduce screen failure in clinical trials where participants have prodromal or even pre-clinical disease. We summarize some of the key findings and approaches taken in these biomarker studies, while addressing the main challenges, including that of limited replication in the field, and discuss opportunities for biomarker development.

Introduction: A 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.Methods: This 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.Results: Eight metabolites were associated with amyloid b 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.Discussion: PFAMs have been found increased and associated with amyloid b burden in CSF and clinical measures. Crown Copyright (C) 2019 Published by Elsevier Inc. on behalf of the Alzheimer's Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders. The present study was conducted as part of the EMIF-AD project, which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ in-kind contribution. The DESCRIPA study was funded by the European Commission within the fifth framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the fifth framework program (contract no. 37670). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01. 41142.001.H). Kristel Sleegers is supported by the Research Fund of the University of Antwerp. Daniel Stamate is supported by the Alzheimer’s Research UK (ARUK-PRRF2017-012).

There is a critical unmet need for reliable markers of disease and disease course in mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The growing appreciation of the importance of inflammation in early AD has focused attention on inflammatory biomarkers in cerebrospinal fluid or plasma; however, non-specific inflammation markers have disappointed to date. We have adopted a targeted approach, centered on an inflammatory pathway already implicated in the disease. Complement, a core system in innate immune defense and potent driver of inflammation, has been implicated in pathogenesis of AD based on a confluence of genetic, histochemical, and model data. Numerous studies have suggested that measurement of individual complement proteins or activation products in cerebrospinal fluid or plasma is useful in diagnosis, prediction, or stratification, but few have been replicated. Here we apply a novel multiplex assay to measure five complement proteins and four activation products in plasma from donors with MCI, AD, and controls. Only one complement analyte, clusterin, differed significantly between control and AD plasma (controls, 295 mg/l; AD, 388 mg/l: p - 5). A model combining clusterin with relevant co-variables was highly predictive of disease. Three analytes (clusterin, factor I, terminal complement complex) were significantly different between MCI individuals who had converted to dementia one year later compared to non-converters; a model combining these three analytes with informative co-variables was highly predictive of conversion. The data confirm the relevance of complement biomarkers in MCI and AD and build the case for using multi-parameter models for disease prediction and stratification.

Background Biomarkers of Alzheimer’s disease (AD) pathology and progression have now been identified across various modalities. The aims of the two studies presented (ANM-MMB and EMIF-AD biomarker discovery) are to discover and replicate previously identified biomarkers of disease pathology and progression, and moreover to determine whether a multimodal biomarker signature may add value in comparison to a biomarker of a single modality. Methods The ANM-MMB cohort is comprised of 718 AD, MCI converters and non-converters, and control subjects selected from the AddNeuroMed, Alzheimer’s research trust and Dementia case register cohorts. Cognitive measures, serum and urine metabolomics, structural MRI, genomics, whole blood transcriptomics and plasma proteomics data was available. The EMIF-AD biomarker discovery cohort consists of 1221 AD, MCI and control subjects, selected from the EMIF catalogue. All subjects had existing amyloid measures (CSF Aβ or amyloid-PET), structural MRI and clinical data, and furthermore plasma proteomics (targeted and untargeted), CSF proteomics (targeted), metabolomics, genomics and epigenetics data were generated. For both studies univariate and multivariate statistics were utilised to identify candidate biomarkers of AD pathology (neurodegeneration and/or brain amyloid burden), rates of cognitive decline, and MCI progression to dementia. pQTL-eQTL-mQTL analyses, network/pathway analysis, and multimodal classifiers were employed to detect multimodal signatures. Results Initial analyses indicate that in the ANM-MMB study a serum and urine derived 15 metabolite classifier predicts MCI progression to AD with 72% accuracy, and the biological significance of the metabolites included in the biomarker panel was identified. Further analyses will examine whether a multimodal classifier is able to predict with even greater accuracy. We will then seek to replicate this in the EMIF-AD biomarker discovery study. Further analyses will also examine single and multimodal biomarker classifiers of other endophenotypes. Conclusions These two studies could be used to identify novel and replicate previously identified single modality biomarker findings. Furthermore the impact of combining the additional modalities with these findings will be discussed. Computational and technical challenges encountered and the bioinformatics pipeline devised in the multimodal analysis of the ANM-MMB cohort will be used to inform the analysis pipeline of the EMIF-AD biomarker discovery study as a replication.

Attention-deficit hyperactivity disorder (ADHD) in adults is associated with impaired sleep, and it has been postulated that this impairment may contribute to the psychopathology of this common condition. One key driver of sleep/wake cycles is the circadian system, which at the molecular level consists of a series of transcriptional feedback loops of clock genes, which in turn produce endocrine, physiological and behavioural outputs with a near 24 h periodicity. We set out to examine circadian rhythms at the behavioural, endocrine and molecular levels in ADHD. Adults with ADHD as well as age- and sex-matched controls were recruited. Circadian rhythms were measured by means of actigraphy for the determination of gross motor patterns, by self-sampling of oral mucosa for assessment of rhythmic expression of the clock genes BMAL1 and PER2, and by estimation of salivary cortisol and melatonin levels. Actigraphic analysis revealed significant diurnal and nocturnal hyperactivity in the ADHD group, as well as a significant shorter period of best fit for the locomotor circadian rhythm in ADHD. BMAL1 and PER2 showed circadian rhythmicity in controls with this being lost in the ADHD group. Cortisol rhythms were significantly phase delayed in the ADHD group. These findings indicate that adult ADHD is accompanied by significant changes in the circadian system, which in turn may lead to decreased sleep duration and quality in the condition. Further, modulation of circadian rhythms may represent a novel therapeutic avenue in the management of ADHD.

Background: Genetic susceptibility to schizophrenia (SZ) has been suggested to influence the cortical systems supporting working memory (WM) and face processing. Genetic imaging studies link the SZ risk variant rs1344706 on the ZNF804A gene to psychosis via alterations in functional brain connectivity during WM, but no work has looked at the effects of ZNF804A on WM with face-processing components. Methods: We therefore investigated healthy controls that were genotyped for rs1344706 with a face WM task during functional magnetic resonance imaging. We suggested that variation at the rs1344706 locus would be associated with similar alterations as patients previously tested using the same WM task for faces. Results: The rs1344706 risk allele was indeed associated with altered activation in the right dorsolateral prefrontal (rDLPFC) cortex. We established that the rDLPFC was activated in a task-dependent manner, suggesting that the differences in activation between rs1344706 genotype groups reflected alterations in task processing. Furthermore, we demonstrated that the rDLPFC region showed significant volumetric overlap with the rDLPFC which had previously been reported to be altered during task processing for patients with SZ. Conclusions: The findings support an association between rs1344706 and alterations in DLPFC activity during WM for faces. We further suggest that WM for faces may be a useful intermediate phenotype in the investigation of genetic susceptibility to psychosis.

Increasingly, clinical trials for Alzheimer’s disease (AD) are being conducted earlier in the disease phase and with biomarker confirmation using in vivo amyloid PET imaging or CSF tau and Aβ measures to quantify pathology. However, making such a pre-clinical AD diagnosis is relatively costly and the screening failure rate is likely to be high. Having a blood-based marker that would reduce such costs and accelerate clinical trials through identifying potential participants with likely pre-clinical AD would be a substantial advance. In order to seek such a candidate biomarker, discovery phase proteomic analyses using 2DGE and gel-free LC-MS/MS for high and low molecular weight analytes were conducted on longitudinal plasma samples collected over a 12-year period from non-demented older individuals who exhibited a range of 11C-PiB PET measures of amyloid load. We then sought to extend our discovery findings by investigating whether our candidate biomarkers were also associated with brain amyloid burden in disease, in an independent cohort. Seven plasma proteins, including A2M, Apo-A1, and multiple complement proteins, were identified as pre-clinical biomarkers of amyloid burden and were consistent across three time points (p < 0.05). Five of these proteins also correlated with brain amyloid measures at different stages of the disease (q < 0.1). Here we show that it is possible to detect a plasma based biomarker signature indicative of AD pathology at a stage long before the onset of clinical disease manifestation. As in previous studies, acute phase reactants and inflammatory markers dominate this signature.

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.

BIOMARKERS RELATING TO CSF MEASURES OF ALZHEIMER’S DISEASE PATHOLOGY Alison L. Baird, Nicholas Ashton, Sarah Westwood, Malcolm Ward, Chantal Bazenet, Madhav Thambisetty, Philip Scheltens, Charlotte Teunissen, Simon Lovestone, Kings College London, London, United Kingdom; Kings College London, Institute of Psychiatry, London, United Kingdom; King’s College London, London, United Kingdom; 4 National Institute on Aging, Baltimore, Maryland, United States; 5 VU University Medical Centre, Amsterdam, Netherlands. Contact e-mail: alison.baird@kcl.ac.uk

We believe this is the first study to investigate associations between blood metabolites and neocortical amyloid burden (NAB) in the search for a blood-based biomarker for Alzheimer’s disease (AD). Further, we present the first multi-modal analysis of blood markers in this field. We used blood plasma samples from 91 subjects enrolled in the University of California, San Francisco Alzheimer’s Disease Research Centre. Non-targeted metabolomic analysis was used to look for associations with NAB using both single and multiple metabolic feature models. Five metabolic features identified subjects with high NAB, with 72% accuracy. We were able to putatively identify four metabolites from this panel and improve the model further by adding fibrinogen gamma chain protein measures (accuracy=79%). One of the five metabolic features was studied in the Alzheimer’s Disease Neuroimaging Initiative cohort, but results were inconclusive. If replicated in larger, independent studies, these metabolic features and proteins could form the basis of a blood test with potential for enrichment of amyloid pathology in anti-amyloid trials.

Background: Alzheimer’s disease (AD) is the most common form of dementia, at present there remains a need for accurate biomarkers that can predict disease progression. In recent years markers of prediction, early diagnosis and progression have been identified in plasma by our group. We have therefore selected a multiplexing approach using the immunobased MILLIPLEX MAP assays to validate our previously discovered biomarkers with the aim to identify a combination of markers that can predict disease progression. Methods: 430 Alzheimer’s disease subjects, 395 cognitively normal and 173 mild cognitively impairment subjects were recruited from 2 large multi-centre cohorts. EDTA plasma was collected and evaluated for 45 analytes, using immuno-based MILLIPLEX MAP assays. Samples were randomized, blinded to the clinical information and measured in duplicate. Results: The intra-assay precision was calculated as the coefficient of variation (%) for each analyte and was

There have been proposals for REM to have a function of emotional memory consolidation, and also for REM sleep to be involved in the promotion of attachment behaviour. The hormones cortisol and oxytocin, respectively, may be involved in these proposed REM sleep functions. However, there are conflicting reports on whether levels of cortisol differ between sleep stages when time since sleep onset (SSO) is controlled, and virtually no literature on whether levels of oxytocin differ between sleep stages. This study thus investigated the changes in levels of oxytocin (OT) and cortisol (CT) across the night, and whether these levels differ between REM and N2 sleep when time SSO is controlled. 20 participants (10 males, 10 females, mean age = 20.45, SD = 2.01) were awakened 10 min into REM and N2 sleep periods in the sleep laboratory and gave saliva samples which were assayed for OT and CT. Levels of OT were relatively constant across the night, whereas CT increased significantly. REM and N2 did not differ significantly neither for OT nor for CT. The study has implications for models of sleep-dependent memory consolidation that incorporate the late sleep increase in cortisol as a functional component of memory consolidation, and also for the medical diagnostic assaying of OT during sleep.

Recent GWAS identified a risk variant for Alzheimer's disease (AD) at a locus (rs11136000) of the clusterin gene (CLU). Here we use functional magnetic resonance imaging (fMRI) during working memory to probe the effect of the risk variant on brain activation in healthy individuals. Participants with the CLU risk genotype had higher activity than participants with the protective allele in frontal and posterior cingulate cortex and the hippocampus, particularly during high memory demand. These results inform pathophysiological models of the preclinical progression of AD.

OBJECTIVES: Circadian rhythms are patterns in behavioural and physiological measures that recur on a daily basis and are driven by an endogenous circadian timekeeping system whose molecular machinery consists of a number of clock genes. The typical anti-psychotic haloperidol has previously been shown to induce significant deficiencies in circadian timing in patients. In this study we examined the impact of haloperidol treatment on molecular components of the circadian clock in the mouse brain. METHODS: We examined how haloperidol treatment, either acute (both at day and night) or chronically over 14 days, alters the expression of three clock gene protein products (PER1, PER2, BMAL1) across the mouse brain by means of immunohistochemistry. RESULTS: Chronic haloperidol treatment significantly decreases the expression levels of PER1 in a number of brain areas, including the hippocampus, the prefrontal and cingulate cerebral cortex and the paraventricular nucleus of the hypothalamus. PER2 expression was only altered in the dentate gyrus and the CA3, and BMAL1 expression was only altered in the paraventricular nucleus of the hypothalamus. CONCLUSION: These data indicate that haloperidol has the potential to alter circadian rhythms via modulation of circadian clock gene expression.

Background: Dysfunctional working memory (WM) has been recognized as one of the most consistent deficits in schizophrenia. Studies that investigated the neural correlates of WM-related pathology by comparing patients with schizophrenia and control participants have produced controversial results, reporting task-related hyper- or hypoactivity in frontoparietal networks. Method: We addressed this question by comparing BOLD signals for accurate responses during a WM task for emotional faces between a homogeneous group of high-performing patients and a control group. Results: Our results confirm previous findings of left prefrontal hyperactivity contrasted with hypoactivity in right prefrontal cortex to support WM performance. We also extend previous work by reporting enhanced activity in higher visual areas of patients during encoding and maintenance. Conclusion: Our findings and those of the literature can be integrated into a model, where preserved visual cognition in high-functioning patients with hypofrontality is explained by activation of contralateral homologue areas combined with enhanced recruitment of sensory areas.

Background:Measures of neocortical amyloid burden (NAB) identify individuals who are at substantially greater risk of developing Alzheimer's disease (AD). Blood-based biomarkers predicting NAB would have great utility for the enrichment of AD clinical trials, including large-scale prevention trials.Methods:Nontargeted proteomic discovery was applied to 78 subjects from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing with a range of NAB values. Technical and independent replications were performed by immunoassay.Results:Seventeen discovery candidates were selected for technical replication. α2-Macroglobulin, fibrinogen γ-chain (FGG), and complement factor H-related protein 1 were confirmed to be associated with NAB. In an independent cohort, FGG plasma levels combined with age predicted NAB had a sensitivity of 59% and specificity of 78%.Conclusion:A single blood protein, FGG, combined with age, was shown to relate to NAB and therefore could have potential for enrichment of clinical trial populations.