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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).

BACKGROUNDPhysiological differences between males and females could contribute to the development of AD. Here, we examined metabolic pathways that may lead to precision medicine initiatives.METHODSWe explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, CSF biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites’ discriminatory performance in AD.RESULTSIn females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (AUC = 0.83, SE = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046).CONCLUSIONSMetabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, associated to females, paving the way to personalised treatment.

© 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

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.

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.

Altres ajuts: Fundació Bancària Obra Social La Caixa, Fundació Catalana Síndrome de Down, Fundació La Marató de TV3, Fundación Española para el Fomento de la Investigación de la Esclerosis Lateral Amiotrófica, Fundación BBVA i Fundació Víctor Grífols i Lucas Background: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives. Methods: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites' discriminatory performance in AD. Results: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). Conclusions: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.

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/).

Background: Blood biomarkers may aid in recruitment to clinical trials of Alzheimer’s disease (AD) modifying therapeutics by triaging potential trials participants for amyloid positron emission tomography (PET) or cerebrospinal fluid (CSF) Aß and tau tests. Objective: To discover a plasma proteomic signature associated with CSF and PET measures of AD pathology. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics were performed in plasma from participants with subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD, recruited to the Amsterdam Dementia Cohort, stratified by CSF Tau/Aß42 (n = 50). Technical replication and independent validation were performed by immunoassay in plasma from SCD, MCI, and AD participants recruited to the Amsterdam Dementia Cohort with CSF measures (n = 100), MCI participants enrolled in the GE067-005 study with [18F]-Flutemetamol PET amyloid measures (n = 173), and AD, MCI and cognitively healthy participants from the EMIF 500 study with CSF Aß42 measurements (n = 494). Results: 25 discovery proteins were nominally associated with CSF Tau/Aß42 (P < 0.05) with associations of ficolin-2 (FCN2), apolipoprotein C-IV and fibrinogen ß chain confirmed by immunoassay (P < 0.05). In the GE067-005 cohort, FCN2 was nominally associated with PET amyloid (P < 0.05) replicating the association with CSF Tau/Aß42. There were nominally significant associations of complement component 3 with PET amyloid, and apolipoprotein(a), apolipoprotein A-I, ceruloplasmin, and PPY with MCI conversion to AD (all P < 0.05). In the EMIF 500 cohort FCN2 was trending toward a significant relationship with CSF Aß42 (P ˜ 0.05), while both A1AT and clusterin were nominally significantly associated with CSF Aß42 (both P < 0.05). Conclusion: Associations of plasma proteins with multiple measures of AD pathology and progression are demonstrated. To our knowledge this is the first study to report an association of FCN2 with AD pathology. Further testing of the proteins in larger independent cohorts will be important. © Copyright © 2018 Westwood, Baird, Hye, Ashton, Nevado-Holgado, Anand, Liu, Newby, Bazenet, Kiddle, Ward, Newton, Desai, Tan Hehir, Zanette, Galimberti, Parnetti, Lleó, Baker, Narayan, van der Flier, Scheltens, Teunissen, Visser and Lovestone.

INTRODUCTIONMachine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer’s Disease (AD). Here we set out to test the performance of metabolites in blood to categorise AD when compared to CSF biomarkers.METHODSThis study analysed 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).RESULTSOn 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.DISCUSSIONThis 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

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 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

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.

Background Insulin resistance (IR) has previously been associated with an increased risk of developing Alzheimer’s disease (AD), although the relationship between IR and AD is not yet clear. Here, we examined the influence of IR on AD using plasma and cerebrospinal fluid (CSF) biomarkers related to IR and AD in cognitively healthy men. We also aimed to characterise the shared protein signatures between IR and AD. Methods Fifty-eight cognitively healthy men, 28 IR and 30 non-IR (age and APOE ε4 matched), were drawn from the Metabolic Syndrome in Men study in Kuopio, Finland. CSF AD biomarkers (amyloid β-peptide (Aβ), total tau and tau phosphorylated at the Thr181 epitope) were examined with respect to IR. Targeted proteomics using ELISA and Luminex xMAP assays were performed to assess the influence of IR on previously identified CSF and plasma protein biomarker candidates of AD pathology. Furthermore, CSF and plasma SOMAscan was performed to discover proteins that associate with IR and CSF AD biomarkers. Results CSF AD biomarkers did not differ between IR and non-IR groups, although plasma insulin correlated with CSF Aβ/tau across the whole cohort. In total, 200 CSF and 487 plasma proteins were differentially expressed between IR and non-IR subjects, and significantly enriched pathways, many of which have been previously implicated in AD, were identified. CSF and plasma proteins significantly associated with CSF AD biomarkers were also discovered, and those sensitive to both IR and AD were identified. Conclusions These data indicate that IR is not directly related to the level of CSF AD pathology in cognitively healthy men. Proteins that associated with both AD and IR are potential markers indicative of shared pathology., published version, peerReviewed

Circadian rhythms are repeating patterns of physiological and other parameters that recur with periods of approximately 24 h, and are generated by an endogenous circadian timekeeping mechanism. Such circadian rhythms, and their underlying molecular mechanisms, are known to be altered by a number of central nervous system acting pharmacological compounds, as well as becoming perturbed in a number of common psychiatric and neurological conditions. The psychostimulant methylphenidate and the non-stimulant atomoxetine are used in the pharmacotherapy of attention deficit hyperactivity disorder, a common condition in which circadian rhythms have been reported to be altered. In the present study we have examined the effects of daily methylphenidate or atomoxetine treatment across 7 days on circadian clock gene product expression across numerous brain regions in the male mouse to test the potential impact of such compounds on circadian timing. We report drug, brain region and molecular specific effects of such treatments, including alterations in expression profiles in the suprachiasmatic nucleus, the master circadian pacemaker. These results indicate that drugs used in the clinical management of attention deficit hyperactivity disorder can alter molecular factors that are believed to underpin circadian timekeeping, and such effects may be of importance in both the therapeutic and side effect profiles of such drugs.

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.

Circadian rhythms are intrinsically generated daily patterns manifested in a wide range of physiological and behavioral parameters, which have been linked with health and well-being. Previous evidence has suggested that circadian rhythms become altered in attention deficit hyperactivity disorder (ADHD), a common neurodevelopmental condition that affects both children and adults. The psychostimulant methylphenidate is a first-line treatment for ADHD, acting through mechanisms modulating brain catecholamines. Here we discuss the impact of methylphenidate on sleep and circadian timekeeping processes, including the expression of clock genes that encode the molecular workings of the circadian system. We discuss whether such impacts may be important in the therapeutic profile of methylphenidate, and whether chronobiological principles may be deployed to increase the therapeutic efficacy of methylphenidate. Further we discuss whether circadian processes may be of importance in the misuse of methylphenidate.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterised by impulsivity, inattention and hyperactivity. Aside from these core psychopathologies, sleep disturbances are found to be highly comorbid with ADHD, and indeed dysregulated sleep may contribute to some of the symptoms of the disorder. It is not clear how sleep disturbances come to be so common in ADHD, but one putative mechanism is through the circadian timekeeping system. This system underpins the generation of near 24-hour rhythms in a host of physiological, behavioural and psychological parameters, and is a key determinant of the sleep/wake cycle. In this paper we review the evidence for sleep and circadian rhythm disturbance in ADHD, examine the possible mechanistic links between these factors and the disorder and discuss future directions through which the circadian clock can be targetted for ADHD symptom relief.

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.