Publications by authors named "Simon Lovestone"

292 Publications

TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels.

Alzheimers Dement 2021 May 14. Epub 2021 May 14.

Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium.

Introduction: Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively.

Methods: We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates.

Results: We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1.

Discussion: Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.
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http://dx.doi.org/10.1002/alz.12330DOI Listing
May 2021

Comorbidity between Alzheimer's disease and major depression: a behavioural and transcriptomic characterization study in mice.

Alzheimers Res Ther 2021 04 2;13(1):73. Epub 2021 Apr 2.

Neurobiology of Behaviour Research Group (GReNeC-NeuroBio), Department of Experimental and Health Science, Universitat Pompeu Fabra, Carrer Dr Aiguader 88, 08003, Barcelona, Spain.

Background: Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer's disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown.

Methods: We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before β-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis.

Results: We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart, Grin3b, Nr1d1 and Mc4r, and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD.

Conclusions: The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.
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http://dx.doi.org/10.1186/s13195-021-00810-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017643PMC
April 2021

Replication study of plasma proteins relating to Alzheimer's pathology.

Alzheimers Dement 2021 Mar 31. Epub 2021 Mar 31.

Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.

Introduction: This study sought to discover and replicate plasma proteomic biomarkers relating to Alzheimer's disease (AD) including both the "ATN" (amyloid/tau/neurodegeneration) diagnostic framework and clinical diagnosis.

Methods: Plasma proteins from 972 subjects (372 controls, 409 mild cognitive impairment [MCI], and 191 AD) were measured using both SOMAscan and targeted assays, including 4001 and 25 proteins, respectively.

Results: Protein co-expression network analysis of SOMAscan data revealed the relation between proteins and "N" varied across different neurodegeneration markers, indicating that the ATN variants are not interchangeable. Using hub proteins, age, and apolipoprotein E ε4 genotype discriminated AD from controls with an area under the curve (AUC) of 0.81 and MCI convertors from non-convertors with an AUC of 0.74. Targeted assays replicated the relation of four proteins with the ATN framework and clinical diagnosis.

Discussion: Our study suggests that blood proteins can predict the presence of AD pathology as measured in the ATN framework as well as clinical diagnosis.
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http://dx.doi.org/10.1002/alz.12322DOI Listing
March 2021

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders.

Genome Biol 2021 Mar 26;22(1):90. Epub 2021 Mar 26.

Centre for Clinical Research, The University of Queensland, Brisbane, QLD, 4019, Australia.

Background: People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer's disease, amyotrophic lateral sclerosis, and Parkinson's disease.

Results: We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson's disease (and none with Alzheimer's disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights.

Conclusions: We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.
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http://dx.doi.org/10.1186/s13059-021-02275-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8004462PMC
March 2021

The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer's disease.

Alzheimers Dement 2021 Jan 25. Epub 2021 Jan 25.

Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.

Introduction: This study investigated the diagnostic and disease-monitoring potential of plasma biomarkers in mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and cognitively unimpaired (CU) individuals.

Methods: Plasma was analyzed using Simoa assays from 99 CU, 107 MCI, and 103 AD dementia participants.

Results: Phosphorylated-tau181 (P-tau181), neurofilament light, amyloid-β (Aβ42/40), Total-tau and Glial fibrillary acidic protein were altered in AD dementia but P-tau181 significantly outperformed all biomarkers in differentiating AD dementia from CU (area under the curve [AUC] = 0.91). P-tau181 was increased in MCI converters compared to non-converters. Higher P-tau181 was associated with steeper cognitive decline and gray matter loss in temporal regions. Longitudinal change of P-tau181 was strongly associated with gray matter loss in the full sample and with Aβ measures in CU individuals.

Discussion: P-tau181 detected AD at MCI and dementia stages and was strongly associated with cognitive decline and gray matter loss. These findings highlight the potential value of plasma P-tau181 as a non-invasive and cost-effective diagnostic and prognostic biomarker in AD.
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http://dx.doi.org/10.1002/alz.12283DOI Listing
January 2021

Pathophysiological subtypes of Alzheimer's disease based on cerebrospinal fluid proteomics.

Brain 2020 12;143(12):3776-3792

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC - Location VUmc, The Netherlands.

Alzheimer's disease is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. CSF contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether Alzheimer's disease biological subtypes can be detected in CSF proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 911 tested) proteins differed between Alzheimer's disease (defined as having abnormal amyloid, n = 425) and controls (defined as having normal CSF amyloid and tau and normal cognition, n = 127). Using these proteins for data-driven clustering, we identified three robust pathophysiological Alzheimer's disease subtypes within each cohort showing (i) hyperplasticity and increased BACE1 levels; (ii) innate immune activation; and (iii) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals were labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, and 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for Alzheimer's disease (all P > 0.01). Additional pathological comparisons that were available for a subset in ADNI suggested that subtypes showed similar severity of Alzheimer's disease pathology, and did not differ in the frequencies of co-pathologies, providing further support that found subtypes truly reflect Alzheimer's disease heterogeneity. Compared to controls, all non-demented Alzheimer's disease individuals had increased risk of showing clinical progression (all P < 0.01). Compared to subtype 1, subtype 2 showed faster clinical progression after correcting for age, sex, level of education and tau levels (hazard ratio = 2.5; 95% confidence interval = 1.2, 5.1; P = 0.01), and subtype 3 at trend level (hazard ratio = 2.1; 95% confidence interval = 1.0, 4.4; P = 0.06). Together, these results demonstrate the value of CSF proteomics in studying the biological heterogeneity in Alzheimer's disease patients, and suggest that subtypes may require tailored therapy.
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http://dx.doi.org/10.1093/brain/awaa325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805814PMC
December 2020

Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer's disease.

Alzheimers Res Ther 2021 01 9;13(1):20. Epub 2021 Jan 9.

UK Dementia Research Institute, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK.

Background: Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer's disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls.

Methods: Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations.

Results: Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine.

Conclusions: Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.
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http://dx.doi.org/10.1186/s13195-020-00741-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797094PMC
January 2021

Metabolic phenotyping reveals a reduction in the bioavailability of serotonin and kynurenine pathway metabolites in both the urine and serum of individuals living with Alzheimer's disease.

Alzheimers Res Ther 2021 01 9;13(1):20. Epub 2021 Jan 9.

UK Dementia Research Institute, Imperial College London, Hammersmith Hospital, London, W12 0NN, UK.

Background: Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer's disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls.

Methods: Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations.

Results: Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine.

Conclusions: Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.
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http://dx.doi.org/10.1186/s13195-020-00741-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797094PMC
January 2021

Evaluating the Alzheimer's disease data landscape.

Alzheimers Dement (N Y) 2020 16;6(1):e12102. Epub 2020 Dec 16.

Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany.

Introduction: Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital.

Methods: Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies.

Results: The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de).

Discussion: Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.
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http://dx.doi.org/10.1002/trc2.12102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744022PMC
December 2020

Urinary metabolic phenotyping for Alzheimer's disease.

Sci Rep 2020 12 10;10(1):21745. Epub 2020 Dec 10.

Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.

Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer's Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer's Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer's Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer's Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer's pathology in previous studies.
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http://dx.doi.org/10.1038/s41598-020-78031-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730184PMC
December 2020

ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset.

J Alzheimers Dis 2021 ;79(1):423-431

Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.

Background: Accessible datasets are of fundamental importance to the advancement of Alzheimer's disease (AD) research. The AddNeuroMed consortium conducted a longitudinal observational cohort study with the aim to discover AD biomarkers. During this study, a broad selection of data modalities was measured including clinical assessments, magnetic resonance imaging, genotyping, transcriptomic profiling, and blood plasma proteomics. Some of the collected data were shared with third-party researchers. However, this data was incomplete, erroneous, and lacking in interoperability.

Objective: To provide the research community with an accessible, multimodal, patient-level AD cohort dataset.

Methods: We systematically addressed several limitations of the originally shared resources and provided additional unreleased data to enhance the dataset.

Results: In this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1,702 study participants and is accessible to the research community via a centralized portal.

Conclusion: ANMerge is an information rich patient-level data resource that can serve as a discovery and validation cohort for data-driven AD research, such as, for example, machine learning and artificial intelligence approaches.
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http://dx.doi.org/10.3233/JAD-200948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902946PMC
January 2021

Genome-wide association study of Alzheimer's disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset.

Transl Psychiatry 2020 11 22;10(1):403. Epub 2020 Nov 22.

Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures derived from quantifications of five separate amyloid-beta (Aβ) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to Aβ42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-Aβ38 and CSF-Aβ40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-Aβ and -tau-related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.
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http://dx.doi.org/10.1038/s41398-020-01074-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680793PMC
November 2020

Cerebrospinal fluid total tau levels indicate aberrant neuronal plasticity in Alzheimer's disease.

medRxiv 2020 Nov 3. Epub 2020 Nov 3.

Alzheimer's disease (AD) is characterised by abnormal amyloid beta and tau processing. Previous studies reported that cerebrospinal fluid (CSF) total tau (t-tau) levels vary between patients. Here we show that CSF t-tau variability is associated with distinct impairments in neuronal plasticity mediated by gene repression factors SUZ12 and REST. AD individuals with abnormal t-tau levels have increased CSF concentrations of plasticity proteins regulated by SUZ12 and REST. AD individuals with normal t-tau, on the contrary, have decreased concentrations of these plasticity proteins and increased concentrations in proteins associated with blood-brain and blood CSF-barrier dysfunction. Genomic analyses suggested that t-tau levels in part depend on genes involved in gene expression. The distinct plasticity abnormalities in AD as signaled by t-tau urge the need for personalised treatment.
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http://dx.doi.org/10.1101/2020.10.29.20211920DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654872PMC
November 2020

Association of blood-based transcriptional risk scores with biomarkers for Alzheimer disease.

Neurol Genet 2020 Dec 30;6(6):e517. Epub 2020 Sep 30.

Department of Radiology and Imaging Sciences (Y.H.P., A.J.S., K.N.), and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis; Department of Neurology (Y.H.P.), Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea; Institute of Psychiatry, Psychology & Neuroscience (A.H., A.S.), King's College London, United Kingdom; Department of Psychiatry (S.L.), University of Oxford, United Kingdom; Departments of Radiology, Medicine, and Psychiatry (M.W.W.), University of California-San Francisco; Department of Veterans Affairs Medical Center (M.W.W.), San Francisco, CA; Department of Medical and Molecular Genetics (A.J.S.), Indiana University School of Medicine, Indianapolis; and Center for Computational Biology and Bioinformatics (K.N.), Indiana University School of Medicine, Indianapolis.

Objective: To determine whether transcriptional risk scores (TRSs), a summation of polarized expression levels of functional genes, reflect the risk of Alzheimer disease (AD).

Methods: Blood transcriptome data were from Caucasian participants, which included AD, mild cognitive impairment, and cognitively normal controls (CN) in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 661) and AddNeuroMed (n = 674) cohorts. To calculate TRSs, we selected functional genes that were expressed under the control of the AD risk loci and were identified as being responsible for AD by using Bayesian colocalization and mendelian randomization methods. Regression was used to investigate the association of the TRS with diagnosis (AD vs CN) and MRI biomarkers (entorhinal thickness and hippocampal volume). Regression was also used to evaluate whether expression of each functional gene was associated with AD diagnosis.

Results: The TRS was significantly associated with AD diagnosis, hippocampal volume, and entorhinal cortical thickness in the ADNI. The association of the TRS with AD diagnosis and entorhinal cortical thickness was also replicated in AddNeuroMed. Among functional genes identified to calculate the TRS, and were significantly upregulated, and was significantly downregulated in patients with AD compared with CN, all of which were identified in the ADNI and replicated in AddNeuroMed.

Conclusions: The blood-based TRS is significantly associated with AD diagnosis and neuroimaging biomarkers. In blood, and were known to be associated with uptake of β-amyloid and herpes simplex virus 1 infection, respectively, both of which may play a role in the pathogenesis of AD.

Classification Of Evidence: The study is rated Class III because of the case control design and the risk of spectrum bias.
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http://dx.doi.org/10.1212/NXG.0000000000000517DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577551PMC
December 2020

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Med Image Anal 2020 12 1;66:101714. Epub 2020 May 1.

Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.
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http://dx.doi.org/10.1016/j.media.2020.101714DOI Listing
December 2020

Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice.

EPMA J 2020 Sep 22;11(3):367-376. Epub 2020 Jun 22.

Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany.

Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.
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http://dx.doi.org/10.1007/s13167-020-00216-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429672PMC
September 2020

Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer's Disease Pathology.

J Alzheimers Dis 2020 ;77(3):1353-1368

University of Geneva, Geneva, Switzerland.

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.
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http://dx.doi.org/10.3233/JAD-200208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683080PMC
January 2020

Genome-wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer's pathology.

Alzheimers Dement 2020 09 5;16(9):1213-1223. Epub 2020 Aug 5.

Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.

Introduction: Abnormal gene expression patterns may contribute to the onset and progression of late-onset Alzheimer's disease (LOAD).

Methods: We performed transcriptome-wide meta-analysis (N = 1440) of blood-based microarray gene expression profiles as well as neuroimaging and cerebrospinal fluid (CSF) endophenotype analysis.

Results: We identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid beta (Aβ) accumulation, especially in the entorhinal cortex region. cis-expression quantitative trait loci mapping analysis of CREB5 detected five significant associations (P < 5 × 10 ), where rs56388170 (most significant) was also significantly associated with global cortical Aβ deposition measured by [ F]Florbetapir positron emission tomography and CSF Aβ .

Discussion: RNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to Alzheimer's disease. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity, and learning and memory.
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http://dx.doi.org/10.1002/alz.12092DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541709PMC
September 2020

An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene.

Neurobiol Aging 2020 11 3;95:26-45. Epub 2020 Jul 3.

College of Medicine and Health, University of Exeter, Exeter, UK. Electronic address:

A growing number of epigenome-wide association studies have demonstrated a role for DNA methylation in the brain in Alzheimer's disease. With the aim of exploring peripheral biomarker potential, we have examined DNA methylation patterns in whole blood collected from 284 individuals in the AddNeuroMed study, which included 89 nondemented controls, 86 patients with Alzheimer's disease, and 109 individuals with mild cognitive impairment, including 38 individuals who progressed to Alzheimer's disease within 1 year. We identified significant differentially methylated regions, including 12 adjacent hypermethylated probes in the HOXB6 gene in Alzheimer's disease, which we validated using pyrosequencing. Using weighted gene correlation network analysis, we identified comethylated modules of genes that were associated with key variables such as APOE genotype and diagnosis. In summary, this study represents the first large-scale epigenome-wide association study of Alzheimer's disease and mild cognitive impairment using blood. We highlight the differences in various loci and pathways in early disease, suggesting that these patterns relate to cognitive decline at an early stage.
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http://dx.doi.org/10.1016/j.neurobiolaging.2020.06.023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649340PMC
November 2020

APOE ε4 genotype-dependent cerebrospinal fluid proteomic signatures in Alzheimer's disease.

Alzheimers Res Ther 2020 05 27;12(1):65. Epub 2020 May 27.

Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands.

Background: Aggregation of amyloid β into plaques in the brain is one of the earliest pathological events in Alzheimer's disease (AD). The exact pathophysiology leading to dementia is still uncertain, but the apolipoprotein E (APOE) ε4 genotype plays a major role. We aimed to identify the molecular pathways associated with amyloid β aggregation using cerebrospinal fluid (CSF) proteomics and to study the potential modifying effects of APOE ε4 genotype.

Methods: We tested 243 proteins and protein fragments in CSF comparing 193 subjects with AD across the cognitive spectrum (65% APOE ε4 carriers, average age 75 ± 7 years) against 60 controls with normal CSF amyloid β, normal cognition, and no APOE ε4 allele (average age 75 ± 6 years).

Results: One hundred twenty-nine proteins (53%) were associated with aggregated amyloid β. APOE ε4 carriers with AD showed altered concentrations of proteins involved in the complement pathway and glycolysis when cognition was normal and lower concentrations of proteins involved in synapse structure and function when cognitive impairment was moderately severe. APOE ε4 non-carriers with AD showed lower expression of proteins involved in synapse structure and function when cognition was normal and lower concentrations of proteins that were associated with complement and other inflammatory processes when cognitive impairment was mild. Repeating analyses for 114 proteins that were available in an independent EMIF-AD MBD dataset (n = 275) showed that 80% of the proteins showed group differences in a similar direction, but overall, 28% effects reached statistical significance (ranging between 6 and 87% depending on the disease stage and genotype), suggesting variable reproducibility.

Conclusions: These results imply that AD pathophysiology depends on APOE genotype and that treatment for AD may need to be tailored according to APOE genotype and severity of the cognitive impairment.
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http://dx.doi.org/10.1186/s13195-020-00628-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254647PMC
May 2020

The European medical information framework: A novel ecosystem for sharing healthcare data across Europe.

Learn Health Syst 2020 Apr 25;4(2):e10214. Epub 2019 Dec 25.

Neurodegeneration, Janssen R&D, Janssen Pharmaceutica, Beerse, Belgium.

Introduction: The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET).

Methods: The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR.

Results: Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention.

Conclusions: EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.
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http://dx.doi.org/10.1002/lrh2.10214DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156868PMC
April 2020

Tau pathology in early Alzheimer's disease is linked to selective disruptions in neurophysiological network dynamics.

Neurobiol Aging 2020 08 17;92:141-152. Epub 2020 Mar 17.

Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.

Understanding the role of Tau protein aggregation in the pathogenesis of Alzheimer's disease is critical for the development of new Tau-based therapeutic strategies to slow or prevent dementia. We tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks. We used electro-magnetoencephalography with [F]AV-1451 PET scanning to quantify Tau-dependent network changes. Using a graph theoretical approach to brain connectivity, we quantified nodal measures of functional segregation, centrality, and the efficiency of information transfer and tested them against levels of [F]AV-1451. Higher Tau burden in early Alzheimer's disease was associated with a shift away from the optimal small-world organization and a more fragmented network in the beta and gamma bands, whereby parieto-occipital areas were disconnected from the anterior parts of the network. Similarly, higher Tau burden was associated with decreases in both local and global efficiency, especially in the gamma band. The results support the translational development of neurophysiological "signatures" of Alzheimer's disease, to understand disease mechanisms in humans and facilitate experimental medicine studies.
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http://dx.doi.org/10.1016/j.neurobiolaging.2020.03.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269692PMC
August 2020

Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: a multi-national multi-database case-control study.

Alzheimers Res Ther 2020 04 6;12(1):38. Epub 2020 Apr 6.

Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK.

Background: Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk.

Methods: A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from electronic health records in the UK, Spain, Denmark and the Netherlands. Conditional logistic regression models were fitted to estimate the risk of dementia.

Results: Prior methotrexate use was associated with a lower risk of dementia (OR 0.71, 95% CI 0.52-0.98). Furthermore, methotrexate use with therapy longer than 4 years had the lowest risk of dementia (odds ratio 0.37, 95% CI 0.17-0.79). Sulfasalazine use was not associated with dementia (odds ratio 0.88, 95% CI 0.57-1.37).

Conclusions: Further studies are still required to clarify the relationship between prior methotrexate use and duration as well as biological treatments with dementia risk.
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http://dx.doi.org/10.1186/s13195-020-00606-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7137292PMC
April 2020

Correction to: Wnt3a induces exosome secretion from primary cultured rat microglia.

BMC Neurosci 2020 03 6;21(1):10. Epub 2020 Mar 6.

King's College London, MRC Centre for Neurodegenerative Research, Institute of Psychiatry, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.

Following the publication of this article [1], it has been noted by the authors that an image of the same cell nuclei has been used in error twice, in Fig. 8, parts A and B. These images are redundant in this figure as the images in parts D and E show Wnt3a treated and control cells stained with both Hoechst 33342 (as in parts A and B) and fluorescein diacetate. The data from multiple repetitions of the Hoechst 33342 stain experiment are presented in graph C. Thus, the duplicated images (in Fig. 8A and B) add no additional data and do not change the results or conclusions reached in the article. The authors apologize for any confusion this may have caused.
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http://dx.doi.org/10.1186/s12868-020-0558-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7059358PMC
March 2020

Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort.

J Alzheimers Dis 2020 ;74(1):213-225

Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.

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.
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http://dx.doi.org/10.3233/JAD-190434DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175945PMC
May 2021

Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis.

Neurobiol Aging 2020 04 10;88:24-32. Epub 2019 Dec 10.

Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA. Electronic address:

Transcriptomics has become an important tool for identification of biological pathways dysregulated in Alzheimer's disease (AD). We performed a network-based gene expression analysis of blood-based microarray gene expression profiles using 2 independent cohorts, Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 661) and AddNeuroMed (N = 674). Weighted gene coexpression network analysis identified 17 modules from ADNI and 13 from AddNeuroMed. Four of the modules derived in ADNI were significantly related to AD; 5 modules in AddNeuroMed were significant. Gene-set enrichment analysis of the AD-related modules identified and replicated 3 biological pathways including the Fc gamma receptor-mediated phagocytosis pathway. Module-based association analysis showed the AD-related module, which has the 3 pathways, to be associated with cognitive function and neuroimaging biomarkers. Gene-based association analysis identified PRKCD in the Fc gamma receptor-mediated phagocytosis pathway as being significantly associated with cognitive function and cerebrospinal fluid biomarkers. The identification of the Fc gamma receptor-mediated phagocytosis pathway implicates the peripheral innate immune system in the pathophysiology of AD. PRKCD is known to be related to neurodegeneration induced by amyloid-β.
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http://dx.doi.org/10.1016/j.neurobiolaging.2019.12.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085455PMC
April 2020

A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort.

Alzheimers Dement (N Y) 2019 18;5:933-938. Epub 2019 Dec 18.

Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.

Introduction: 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.

Methods: 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).

Results: 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.

Discussion: 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.
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http://dx.doi.org/10.1016/j.trci.2019.11.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928349PMC
December 2019

Aiding the discovery of new treatments for dementia by uncovering unknown benefits of existing medications.

Alzheimers Dement (N Y) 2019 9;5:862-870. Epub 2019 Dec 9.

Johnson & Johnson, Scientific Innovation, South San Francisco, CA, USA.

Introduction: There is a significant need for disease-modifying therapies to treat and prevent dementia, including Alzheimer's disease. Availability of real-world observational information and new analytic techniques to analyze large volumes of data can provide a path to aid drug discovery.

Methods: Using a self-controlled study design, we examined the association between 2181 medications and incidence of dementia across four US insurance claims databases. Medications associated with ≥50% reduction in risk of dementia in ≥2 databases were examined.

Results: A total of 117,015,066 individuals were included in the analysis. Seventeen medications met our threshold criteria for a potential protective effect on dementia and fell into five classes: catecholamine modulators, anticonvulsants, antibiotics/antivirals, anticoagulants, and a miscellaneous group.

Discussion: The biological pathways of the medications identified in this analysis may be targets for further research and may aid in discovering novel therapeutic approaches to treat dementia. These data show association not causality.
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http://dx.doi.org/10.1016/j.trci.2019.07.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909196PMC
December 2019