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Puerta R, de Rojas I, García-González P, Olivé C, Sotolongo-Grau O, García-Sánchez A, García-Gutiérrez F, Montrreal L, Tartari JP, Sanabria Á, Pytel V, Lage C, Quintela I, Aguilera N, Rodriguez-Rodriguez E, Alarcón-Martín E, Orellana A, Pastor P, Pérez-Tur J, Piñol-Ripoll G, López de Munian A, García-Alberca JM, Royo JL, Bullido MJ, Álvarez V, Real LM, Anchuelo AC, Gómez-Garre D, Martínez Larrad MT, Franco-Macías E, Mir P, Medina M, Sánchez-Valle R, Icardo OD, Sáez ME, Carracedo Á, Tárraga L, Alegret M, Valero S, Marquié M, Boada M, Juan PS, Cavazos JE, Cabrera A, Cano A, Ruiz A. Connecting genomic and proteomic signatures of amyloid burden in the brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.06.24313124. [PMID: 39281766 PMCID: PMC11398581 DOI: 10.1101/2024.09.06.24313124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
Background Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease. Methods We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses. Results After filtering the meta-GWAS, we observed genome-wide significance in the rs429358- APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis. Conclusions The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.
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Moody JN, Howard E, Nolan KE, Prieto S, Logue MW, Hayes JP. Traumatic Brain Injury and Genetic Risk for Alzheimer's Disease Impact Cerebrospinal Fluid β-Amyloid Levels in Vietnam War Veterans. Neurotrauma Rep 2024; 5:760-769. [PMID: 39184178 PMCID: PMC11342050 DOI: 10.1089/neur.2024.0048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2024] Open
Abstract
Traumatic brain injuries (TBIs) may increase the risk for Alzheimer's disease (AD) and its neuropathological correlates, although the mechanisms of this relationship are unclear. The current study examined the synergistic effects of TBI and genetic risk for AD on β-amyloid (Aβ) levels among Vietnam War Veterans. We hypothesized that the combination of TBI and higher polygenic risk score (PRS) for AD would be associated with lower cerebrospinal fluid (CSF) Aβ42/40. Data were obtained from the Department of Defense Alzheimer's Disease Neuroimaging Initiative. Participants included Vietnam War Veterans without dementia who identified as White non-Hispanic/Latino and had available demographic, clinical assessment, genetic, and CSF biomarker data. Lifetime TBI history was assessed using The Ohio State University TBI Identification Method. Participants were categorized into those with and without TBI. Among those with a prior TBI, injury severity was defined as either mild or moderate/severe. CSF Aβ42/40 ratios were calculated. Genetic propensity for AD was assessed using PRSs. Hierarchical linear regression models examined the interactive effects of TBI and PRS for AD on Aβ42/40. Exploratory analyses examined the interaction between TBI severity and PRS. The final sample included 88 male Vietnam War Veterans who identified as White non-Hispanic/Latino (M age = 68.3 years), 49 of whom reported a prior TBI. There was a significant interaction between TBI and PRS, such that individuals with TBI and higher PRS for AD had lower Aβ42/40 (B = -0.45, 95% CI: -0.86 to -0.05, p = 0.03). This relationship may be stronger with increasing TBI severity (p = 0.05). Overall, TBI was associated with lower Aβ42/40, indicating greater amyloid deposition in the brain, in the context of greater polygenic risk for AD. These findings highlight who may be at increased risk for AD neuropathology following TBI.
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Affiliation(s)
- Jena N. Moody
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Erica Howard
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Kate E. Nolan
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Sarah Prieto
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
| | - Mark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, Massachusetts, USA
- Psychiatry and Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Jasmeet P. Hayes
- Department of Psychology, The Ohio State University, Columbus, Ohio, USA
- Chronic Brain Injury Initiative, The Ohio State University, Columbus, Ohio, USA
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Altmann A, Aksman LM, Oxtoby NP, Young AL, Alexander DC, Barkhof F, Shoai M, Hardy J, Schott JM. Towards cascading genetic risk in Alzheimer's disease. Brain 2024; 147:2680-2690. [PMID: 38820112 PMCID: PMC11292901 DOI: 10.1093/brain/awae176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024] Open
Abstract
Alzheimer's disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimer's disease. Here, we investigate whether genetic risk for Alzheimer's disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimer's disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimer's disease, in addition to opening therapeutic windows for targeted interventions.
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Affiliation(s)
- Andre Altmann
- UCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
| | - Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1E 6BT, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, 1081 HV, The Netherlands
| | - Maryam Shoai
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute, University College London, London, WC1E 6BT, UK
| | - John Hardy
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- UK Dementia Research Institute, University College London, London, WC1E 6BT, UK
| | - Jonathan M Schott
- UK Dementia Research Institute, University College London, London, WC1E 6BT, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
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Duggan MR, Gomez GT, Joynes CM, Bilgel M, Chen J, Fattorelli N, Hohman TJ, Mancuso R, Cordon J, Castellano T, Koran MEI, Candia J, Lewis A, Moghekar A, Ashton NJ, Kac PR, Karikari TK, Blennow K, Zetterberg H, Martinez-Muriana A, De Strooper B, Thambisetty M, Ferrucci L, Gottesman RF, Coresh J, Resnick SM, Walker KA. Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression. Brain Behav Immun 2024; 120:604-619. [PMID: 38977137 DOI: 10.1016/j.bbi.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/07/2024] [Accepted: 07/04/2024] [Indexed: 07/10/2024] Open
Abstract
While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-β (Aβ) progression - a central feature of AD - remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aβ accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aβ progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aβ-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aβ accumulation, including causal relationships with Aβ PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aβ accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.
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Affiliation(s)
- Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Gabriela T Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Cassandra M Joynes
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nicola Fattorelli
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Renzo Mancuso
- Microglia and Inflammation in Neurological Disorders Laboratory, Center for Molecular Neurology, Flanders Institute for Biotechnology, Antwerp, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Jenifer Cordon
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Tonnar Castellano
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mary Ellen I Koran
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julián Candia
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK; NIHR Biomedical Research Center for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK; Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Przemysław R Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; ICM Institute, Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France; First Affiliated Hospital, University of Science and Technology of China, Anhui, PR China
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK; UK Dementia Research Institute, University College London, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong Special Administrative Region; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna Martinez-Muriana
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Bart De Strooper
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium; UK Dementia Research Institute, University College London, London, UK
| | - Madhav Thambisetty
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Josef Coresh
- Departments of Population Health and Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Lorenzini L, Collij LE, Tesi N, Vilor-Tejedor N, Ingala S, Blennow K, Foley C, Frisoni GB, Haller S, Holstege H, van der van der Lee S, Martinez-Lage P, Marioni RE, McCartney DL, O' Brien J, Oliveira TG, Payoux P, Reinders M, Ritchie C, Scheltens P, Schwarz AJ, Sudre CH, Waldman AD, Wolz R, Chatelat G, Ewers M, Wink AM, Mutsaerts HJMM, Gispert JD, Visser PJ, Tijms BM, Altmann A, Barkhof F. Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals. Alzheimers Dement 2024. [PMID: 39073684 DOI: 10.1002/alz.14096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/20/2024] [Accepted: 06/03/2024] [Indexed: 07/30/2024]
Abstract
INTRODUCTION Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine. METHODS We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity. RESULTS CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication. DISCUSSION This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies. HIGHLIGHTS Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.
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Affiliation(s)
- Luigi Lorenzini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Niccoló Tesi
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Natàlia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Silvia Ingala
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Cerebriu A/S, Copenhagen, Denmark
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | | | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- University Hospitals and University of Geneva, Geneva, Switzerland
| | - Sven Haller
- CIMC - Centre d'Imagerie Médicale de Cornavin, Geneva, Switzerland
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
| | - Henne Holstege
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sven van der van der Lee
- Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pablo Martinez-Lage
- Centro de Investigación y Terapias Avanzadas, Neurología, CITA-Alzheimer Foundation, San Sebastián, Spain
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - John O' Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tiago Gil Oliveira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | - Pierre Payoux
- Department of Nuclear Medicine, Toulouse University Hospital, Toulouse, France
- ToNIC, Toulouse NeuroImaging Center, University of Toulouse, Inserm, Toulouse, France
| | - Marcel Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Craig Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain Sciences, Outpatient Department 2, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Brain Health Scotland, Edinburgh, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adam J Schwarz
- Takeda Pharmaceuticals Ltd., Cambridge, Massachusetts, USA
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Department of Medicine, Imperial College London, London, UK
| | | | - Gael Chatelat
- Université de Normandie, Unicaen, Inserm, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", institut Blood-and-Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Henk J M M Mutsaerts
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Alzheimer Center Limburg, Department of Psychiatry & Neuropsychology, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
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Narasimhan S, Holtzman DM, Apostolova LG, Cruchaga C, Masters CL, Hardy J, Villemagne VL, Bell J, Cho M, Hampel H. Apolipoprotein E in Alzheimer's disease trajectories and the next-generation clinical care pathway. Nat Neurosci 2024; 27:1236-1252. [PMID: 38898183 DOI: 10.1038/s41593-024-01669-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/18/2024] [Indexed: 06/21/2024]
Abstract
Alzheimer's disease (AD) is a complex, progressive primary neurodegenerative disease. Since pivotal genetic studies in 1993, the ε4 allele of the apolipoprotein E gene (APOE ε4) has remained the strongest single genome-wide associated risk variant in AD. Scientific advances in APOE biology, AD pathophysiology and ApoE-targeted therapies have brought APOE to the forefront of research, with potential translation into routine AD clinical care. This contemporary Review will merge APOE research with the emerging AD clinical care pathway and discuss APOE genetic risk as a conduit to genomic-based precision medicine in AD, including ApoE's influence in the ATX(N) biomarker framework of AD. We summarize the evidence for APOE as an important modifier of AD clinical-biological trajectories. We then illustrate the utility of APOE testing and the future of ApoE-targeted therapies in the next-generation AD clinical-diagnostic pathway. With the emergence of new AD therapies, understanding how APOE modulates AD pathophysiology will become critical for personalized AD patient care.
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Affiliation(s)
| | - David M Holtzman
- Department of Neurology, Hope Center for Neurological Disorders, Knight ADRC, Washington University in St. Louis, St. Louis, MO, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Neurosciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Colin L Masters
- Florey Institute and the University of Melbourne, Parkville, Victoria, Australia
| | - John Hardy
- Department of Neurodegenerative Disease and Dementia Research Institute, Reta Lila Weston Research Laboratories, UCL Institute of Neurology, Queen Square, London, UK
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Lemche E, Killick R, Mitchell J, Caton PW, Choudhary P, Howard JK. Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis. Neurobiol Dis 2024; 196:106485. [PMID: 38643861 DOI: 10.1016/j.nbd.2024.106485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 03/18/2024] [Accepted: 03/23/2024] [Indexed: 04/23/2024] Open
Abstract
Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.
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Affiliation(s)
- Erwin Lemche
- Section of Cognitive Neuropsychiatry and Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, United Kingdom.
| | - Richard Killick
- Section of Old Age Psychiatry, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, United Kingdom
| | - Jackie Mitchell
- Department of Basic and Clinical Neurosciences, Maurice Wohl CIinical Neurosciences Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 125 Coldharbour Lane, London SE5 9NU, United Kingdom
| | - Paul W Caton
- Diabetes Research Group, School of Life Course Sciences, King's College London, Hodgkin Building, Guy's Campus, London SE1 1UL, United Kingdom
| | - Pratik Choudhary
- Diabetes Research Group, Weston Education Centre, King's College London, 10 Cutcombe Road, London SE5 9RJ, United Kingdom
| | - Jane K Howard
- School of Cardiovascular and Metabolic Medicine & Sciences, Hodgkin Building, Guy's Campus, King's College London, Great Maze Pond, London SE1 1UL, United Kingdom
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8
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Saadmaan G, Dalmasso MC, Ramirez A, Hiltunen M, Kemppainen N, Lehtisalo J, Mangialasche F, Ngandu T, Rinne J, Soininen H, Stephen R, Kivipelto M, Solomon A. Alzheimer's disease genetic risk score and neuroimaging in the FINGER lifestyle trial. Alzheimers Dement 2024; 20:4345-4350. [PMID: 38647197 PMCID: PMC11180864 DOI: 10.1002/alz.13843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION We assessed a genetic risk score for Alzheimer's disease (AD-GRS) and apolipoprotein E (APOE4) in an exploratory neuroimaging substudy of the FINGER trial. METHODS 1260 at-risk older individuals without dementia were randomized to multidomain lifestyle intervention or health advice. N = 126 participants underwent magnetic resonance imaging (MRI), and N = 47 positron emission tomography (PET) scans (Pittsburgh Compund B [PiB], Fluorodeoxyglucose) at baseline; N = 107 and N = 38 had repeated 2-year scans. RESULTS The APOE4 allele, but not AD-GRS, was associated with baseline lower hippocampus volume (β = -0.27, p = 0.001), greater amyloid deposition (β = 0.48, p = 0.001), 2-year decline in hippocampus (β = -0.27, p = 0.01), total gray matter volume (β = -0.25, p = 0.01), and cortical thickness (β = -0.28, p = 0.003). In analyses stratified by AD-GRS (below vs above median), the PiB composite score increased less in intervention versus control in the higher AD-GRS group (β = -0.60, p = 0.03). DISCUSSION AD-GRS and APOE4 may have different impacts on potential intervention effects on amyloid, that is, less accumulation in the higher-risk group (AD-GRS) versus lower-risk group (APOE). HIGHLIGHTS First study of neuroimaging and AD genetics in a multidomain lifestyle intervention. Possible intervention effect on brain amyloid deposition may rely on genetic risk. AD-GRS and APOE4 allele may have different impacts on amyloid during intervention.
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Affiliation(s)
- Gazi Saadmaan
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
| | - Maria Carolina Dalmasso
- Studies in Neuroscience and Complex Systems Unit (ENyS)CONICET‐HEC‐UNAJFlorencio VarelaArgentina
- Division of Neurogenetics and Molecular PsychiatryDepartment of Psychiatry and PsychotherapyUniversity of CologneMedical FacultyCologneGermany
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular PsychiatryDepartment of Psychiatry and PsychotherapyUniversity of CologneMedical FacultyCologneGermany
- Department of Neurodegenerative Diseases and Geriatric PsychiatryUniversity Hospital BonnBonnGermany
- German Center for Neurodegenerative DiseasesDZNE BonnBonnGermany
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTexasUSA
- Excellence Cluster on Cellular Stress Responses in Aging‐Associated Diseases (CECAD)University of CologneCologneGermany
| | - Mikko Hiltunen
- Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
| | - Nina Kemppainen
- Turku PET CentreUniversity of TurkuTurkuFinland
- Division of Clinical NeurosciencesTurku University HospitalTurkuFinland
| | - Jenni Lehtisalo
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
- Population Health UnitFinnish Institute for Health and WelfareHelsinkiFinland
| | - Francesca Mangialasche
- Division of Clinical GeriatricsCenter for Alzheimer ResearchDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Tiia Ngandu
- Population Health UnitFinnish Institute for Health and WelfareHelsinkiFinland
- Division of Clinical GeriatricsCenter for Alzheimer ResearchDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Juha Rinne
- Turku PET CentreUniversity of TurkuTurkuFinland
- Division of Clinical NeurosciencesTurku University HospitalTurkuFinland
| | - Hilkka Soininen
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
| | - Ruth Stephen
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
| | - Miia Kivipelto
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
- Division of Clinical GeriatricsCenter for Alzheimer ResearchDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
- Institute of Public Health and Clinical NutritionUniversity of Eastern FinlandKuopioFinland
- Ageing Epidemiology Research UnitSchool of Public Health, Imperial College LondonLondonUK
| | - Alina Solomon
- Department of NeurologyInstitute of Clinical MedicineUniversity of Eastern FinlandKuopioFinland
- Division of Clinical GeriatricsCenter for Alzheimer ResearchDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
- Ageing Epidemiology Research UnitSchool of Public Health, Imperial College LondonLondonUK
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9
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Kikuchi M, Miyashita A, Hara N, Kasuga K, Saito Y, Murayama S, Kakita A, Akatsu H, Ozaki K, Niida S, Kuwano R, Iwatsubo T, Nakaya A, Ikeuchi T. Polygenic effects on the risk of Alzheimer's disease in the Japanese population. Alzheimers Res Ther 2024; 16:45. [PMID: 38414085 PMCID: PMC10898021 DOI: 10.1186/s13195-024-01414-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. METHODS In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. RESULTS The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. CONCLUSIONS We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.
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Affiliation(s)
- Masataka Kikuchi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan.
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Akinori Miyashita
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Yuko Saito
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
| | - Shigeo Murayama
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
- Brain Bank for Neurodevelopmental, Neurological and Psychiatric Disorders, United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiroyasu Akatsu
- Department of General Medicine & General Internal Medicine, Nagoya City University Graduate School of Medicine, Nagoya, Japan
| | - Kouichi Ozaki
- Medical Genome Center, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shumpei Niida
- Core Facility Administration, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
| | - Ryozo Kuwano
- Social Welfare Corporation Asahigawaso, Asahigawaso Research Institute, Okayama, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akihiro Nakaya
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan.
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10
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Hardy J, Schott JM. Identifying Genetic Risk for Amyloid-Related Imaging Abnormalities. Neurology 2024; 102:e208096. [PMID: 38165303 DOI: 10.1212/wnl.0000000000208096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
In the last 2 years, there have been 3 successful trials of antiamyloid antibodies in Alzheimer disease (AD): aducanemab, now controversially US Food and Drug Administration-approved under the accelerated approval pathway1; lecanemab, now FDA-approved2; and donanemab, now going through the approval process.3 All 3 share a common broad mechanism, that is, antibody-mediated removal of β-amyloid (Aβ) from the brain, and this is almost certainly the basis of their therapeutic action.4 When used in the earliest symptomatic stages of AD, all have modest clinical effects, all clear Aβ from the brain, and all show evidence for some changes in molecular markers believed to be downstream of Aβ accumulation in keeping with disease modification.4 However, all these drugs-and several other antiamyloid immunotherapies that failed to show positive effects in clinical trials (e.g. bapineuzemab and gantenerumab)5,6-have the troubling adverse event of antibody-related imaging abnormalities (ARIA). ARIA can take the form of vasogenic edema or sulcal effusion (ARIA-E) or haemosiderin deposition due to hemorrhage (ARIA-H).7 In vivo, ARIA is detected using MRI: ARIA-E is visible on fluid attenuation inversion recovery sequences; ARIA-H is best seen on iron-sensitive (T2* or susceptibility-weighted imaging) as microbleeds and/or superficial hemosiderin deposition. The pathophysiology of ARIA has yet to be fully determined but may result from antibody-mediated breakdown of amyloid plaques releasing Aβ which is deposited in vessels leading to increased cerebral amyloid angiopathy or alterations in perivascular clearance or inflammation, possibly through complement activation.8.
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Affiliation(s)
- John Hardy
- From the Department of Neurodegenerative Disease (J.H., J.M.S.), UCL Institute of Neurology, London, UK
| | - Jonathan M Schott
- From the Department of Neurodegenerative Disease (J.H., J.M.S.), UCL Institute of Neurology, London, UK
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11
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Rubido N, Riedel G, Vuksanović V. Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer's disease. Brain Commun 2023; 6:fcad320. [PMID: 38173803 PMCID: PMC10763534 DOI: 10.1093/braincomms/fcad320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/14/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Genetic associations with macroscopic brain networks can provide insights into healthy and aberrant cortical connectivity in disease. However, associations specific to dynamic functional connectivity in Alzheimer's disease are still largely unexplored. Understanding the association between gene expression in the brain and functional networks may provide useful information about the molecular processes underlying variations in impaired brain function. Given the potential of dynamic functional connectivity to uncover brain states associated with Alzheimer's disease, it is interesting to ask: How does gene expression associated with Alzheimer's disease map onto the dynamic functional brain connectivity? If genetic variants associated with neurodegenerative processes involved in Alzheimer's disease are to be correlated with brain function, it is essential to generate such a map. Here, we investigate how the relation between gene expression in the brain and dynamic functional connectivity arises from nodal interactions, quantified by their role in network centrality (i.e. the drivers of the metastability), and the principal component of genetic co-expression across the brain. Our analyses include genetic variations associated with Alzheimer's disease and also genetic variants expressed within the cholinergic brain pathways. Our findings show that contrasts in metastability of functional networks between Alzheimer's and healthy individuals can in part be explained by the two combinations of genetic co-variations in the brain with the confidence interval between 72% and 92%. The highly central nodes, driving the brain aberrant metastable dynamics in Alzheimer's disease, highly correlate with the magnitude of variations from two combinations of genes expressed in the brain. These nodes include mainly the white matter, parietal and occipital brain regions, each of which (or their combinations) are involved in impaired cognitive function in Alzheimer's disease. In addition, our results provide evidence of the role of genetic associations across brain regions in asymmetric changes in ageing. We validated our findings on the same cohort using alternative brain parcellation methods. This work demonstrates how genetic variations underpin aberrant dynamic functional connectivity in Alzheimer's disease.
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Affiliation(s)
- Nicolás Rubido
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
| | - Gernot Riedel
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - Vesna Vuksanović
- Health Data Science, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK
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12
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Rosewood TJ, Nho K, Risacher SL, Gao S, Shen L, Foroud T, Saykin AJ. Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions. Genes (Basel) 2023; 14:2010. [PMID: 38002954 PMCID: PMC10671827 DOI: 10.3390/genes14112010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-β, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.
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Affiliation(s)
- Thea J. Rosewood
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kwangsik Nho
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA
| | - Shannon L. Risacher
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sujuan Gao
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, Philadelphia, PA 19104, USA;
| | - Tatiana Foroud
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN 46202, USA; (T.J.R.); (S.L.R.); (S.G.); (T.F.)
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
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13
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Lee WP, Wang H, Dombroski B, Cheng PL, Tucci A, Si YQ, Farrell J, Tzeng JY, Leung YY, Malamon J, Wang LS, Vardarajan B, Farrer L, Schellenberg G. Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects. RESEARCH SQUARE 2023:rs.3.rs-3353179. [PMID: 37886469 PMCID: PMC10602095 DOI: 10.21203/rs.3.rs-3353179/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,905) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). We found a significant burden of deletions and duplications in AD cases, particularly for singletons and homozygous events. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in complete LD with rs143080277 in NCK2. We also identified 16 SVs associated with AD and 13 SVs linked to AD-related pathological/cognitive endophenotypes. This study highlights the pivotal role of SVs in shaping our understanding of AD genetics.
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14
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Wang H, Dombroski BA, Cheng PL, Tucci A, Si YQ, Farrell JJ, Tzeng JY, Leung YY, Malamon JS, Wang LS, Vardarajan BN, Farrer LA, Schellenberg GD, Lee WP. Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.13.23295505. [PMID: 37745545 PMCID: PMC10516060 DOI: 10.1101/2023.09.13.23295505] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics.
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Affiliation(s)
- Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Po-Liang Cheng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Albert Tucci
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - Ya-Qin Si
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - John J Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, MA 02118, USA
| | - Jung-Ying Tzeng
- Bioinformatics Research Center, North Carolina State University, NC 27695, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - John S Malamon
- Department of Surgery, Scholl of Medicine, University of Colorado, CO 80045, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, NY 10032, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, NY 10032, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, MA 02118, USA
- Department of Ophthalmology, Boston University School of Medicine, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, MA 02118, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
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Yuan S, Huang X, Zhang L, Ling Y, Tan S, Peng M, Xu A, Lyu J. Associations of air pollution with all-cause dementia, Alzheimer's disease, and vascular dementia: a prospective cohort study based on 437,932 participants from the UK biobank. Front Neurosci 2023; 17:1216686. [PMID: 37600021 PMCID: PMC10436530 DOI: 10.3389/fnins.2023.1216686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/25/2023] [Indexed: 08/22/2023] Open
Abstract
Objective To prospectively assess whether air pollution, including PM2.5, PM10, and NOx, is associated with the risk of all-cause dementia, Alzheimer's disease (AD), and vascular dementia, and to investigate the potential relationship between air pollution and genetic susceptibility in the development of AD. Methods and results Our study included 437,932 participants from the UK Biobank with a median follow-up period of over 10 years. Using a Cox proportional hazards model, we found that participants exposed to PM2.5 levels of ≥10 μg/m3 had a higher risk of developing all-cause dementia (HR = 1.1; 95% CI: 1.05-1.28; p < 0.05) compared to the group exposed to PM2.5 levels of <10 μg/m3. However, there was no significant association between PM10 levels of ≥15 μg/m3 and the risk of all-cause dementia, AD, or vascular dementia when compared to the group exposed to PM10 levels of <15 μg/m3. On the other hand, participants exposed to NOx levels of ≥50 μg/m3 had a significantly higher risk of all-cause dementia (HR = 1.14; 95% CI: 1.02-1.26; p < 0.05) and AD (HR = 1.26; 95% CI: 1.08-1.48; p < 0.05) compared to the group exposed to NOx levels of <50 μg/m3. Furthermore, we examined the combined effect of air pollution (PM2.5, PM10, and NOx) and Alzheimer's disease genetic risk score (AD-GRS) on the development of AD using a Cox proportional hazards model. Among participants with a high AD-GRS, those exposed to NOx levels of ≥50 μg/m3 had a significantly higher risk of AD compared to those in the group exposed to NOx levels of <50 μg/m3 (HR = 1.36; 95% CI: 1.03-1.18; p < 0.05). Regardless of air pollutant levels (PM2.5, PM10, or NOx), participants with a high AD-GRS had a significantly increased risk of developing AD. Similar results were obtained when assessing multiple variables using inverse probability of treatment weighting (IPTW). Conclusion Our findings indicate that individuals living in areas with PM2.5 levels of ≥10 μg/m3 or NOx levels of ≥50 μg/m3 are at a higher risk of developing all-cause dementia. Moreover, individuals with a high AD-GRS demonstrated an increased risk of developing AD, particularly in the presence of NOx ≥ 50 μg/m3.
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Affiliation(s)
- Shiqi Yuan
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaxuan Huang
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Luming Zhang
- Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Yitong Ling
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shanyuan Tan
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Min Peng
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Anding Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, Guangdong, China
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Stocker H, Trares K, Beyer L, Perna L, Rujescu D, Holleczek B, Beyreuther K, Gerwert K, Schöttker B, Brenner H. Alzheimer's polygenic risk scores, APOE, Alzheimer's disease risk, and dementia-related blood biomarker levels in a population-based cohort study followed over 17 years. Alzheimers Res Ther 2023; 15:129. [PMID: 37516890 PMCID: PMC10386275 DOI: 10.1186/s13195-023-01277-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/21/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND In order to utilize polygenic risk scores (PRSs) for Alzheimer's disease (AD) in a meaningful way, influential factors (i.e. training set) and prediction across groups such as APOE e4 (APOE4) genotype as well as associations to dementia-related biomarkers should be explored. Therefore, we examined the association of APOE4 and various PRSs, based on training sets that utilized differing AD definitions, with incident AD and all-cause dementia (ACD) within 17 years, and with levels of phosphorylated tau181 (P-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) in blood. Secondarily, effect modification by APOE4 status and sex was examined. METHODS In this prospective, population-based cohort study and nested case-control study, 9,940 participants in Germany were enrolled between 2000 and 2002 by their general practitioners and followed for up to 17 years. Participants were included in this study if dementia status and genetic data were available. A subsample of participants additionally had measurements of P-tau181, NfL, and GFAP obtained from blood samples. Cox and logistic regression analyses were used to assess the association of genetic risk (APOE genotype and PRSnoAPOE) with incident ACD/AD and log-transformed blood levels of P-tau181, NfL, and GFAP. RESULTS Five thousand seven hundred sixty-five participants (54% female, aged 50-75years at baseline) were included in this study, of whom 464 received an all-cause dementia diagnosis within 17 years. The PRSs were not more predictive of dementia than APOE4. An APOE4 specific relationship was apparent with PRSs only exhibiting associations to dementia among APOE4 carriers. In the nested case-control study including biomarkers (n = 712), APOE4 status and polygenic risk were significantly associated to levels of GFAP in blood. CONCLUSIONS The use of PRSs may be beneficial for increased precision in risk estimates among APOE4 carriers. While APOE4 may play a crucial etiological role in initial disease processes such as Aβ deposition, the PRS may be an indicator of further disease drivers as well as astrocyte activation. Further research is necessary to confirm these findings, especially the association to GFAP.
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Affiliation(s)
- Hannah Stocker
- Network Aging Research, Heidelberg University, Heidelberg, Germany.
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
| | - Kira Trares
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Léon Beyer
- Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, Bochum, Germany
- Department of Biophysics, Ruhr-University Bochum, Bochum, Germany
| | - Laura Perna
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Dan Rujescu
- Department of Psychiatry, Medical University of Vienna, Vienna, Austria
| | | | | | - Klaus Gerwert
- Center for Protein Diagnostics (ProDi), Ruhr-University Bochum, Bochum, Germany
- Department of Biophysics, Ruhr-University Bochum, Bochum, Germany
| | - Ben Schöttker
- Network Aging Research, Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Hermann Brenner
- Network Aging Research, Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
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Juul Rasmussen I, Frikke-Schmidt R. Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia. Eur Heart J 2023; 44:2526-2543. [PMID: 37224508 PMCID: PMC10481783 DOI: 10.1093/eurheartj/ehad293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/22/2023] [Accepted: 05/04/2023] [Indexed: 05/26/2023] Open
Abstract
Dementia is a major global challenge for health and social care in the 21st century. A third of individuals >65 years of age die with dementia, and worldwide incidence numbers are projected to be higher than 150 million by 2050. Dementia is, however, not an inevitable consequence of old age; 40% of dementia may theoretically be preventable. Alzheimer's disease (AD) accounts for approximately two-thirds of dementia cases and the major pathological hallmark of AD is accumulation of amyloid-β. Nevertheless, the exact pathological mechanisms of AD remain unknown. Cardiovascular disease and dementia share several risk factors and dementia often coexists with cerebrovascular disease. In a public health perspective, prevention is crucial, and it is suggested that a 10% reduction in prevalence of cardiovascular risk factors could prevent more than nine million dementia cases worldwide by 2050. Yet this assumes causality between cardiovascular risk factors and dementia and adherence to the interventions over decades for a large number of individuals. Using genome-wide association studies, the entire genome can be scanned for disease/trait associated loci in a hypothesis-free manner, and the compiled genetic information is not only useful for pinpointing novel pathogenic pathways but also for risk assessments. This enables identification of individuals at high risk, who likely will benefit the most from a targeted intervention. Further optimization of the risk stratification can be done by adding cardiovascular risk factors. Additional studies are, however, highly needed to elucidate dementia pathogenesis and potential shared causal risk factors between cardiovascular disease and dementia.
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Affiliation(s)
- Ida Juul Rasmussen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Rollo J, Crawford J, Hardy J. A dynamical systems approach for multiscale synthesis of Alzheimer's pathogenesis. Neuron 2023; 111:2126-2139. [PMID: 37172582 DOI: 10.1016/j.neuron.2023.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/15/2022] [Accepted: 04/13/2023] [Indexed: 05/15/2023]
Abstract
Alzheimer's disease (AD) is a spatially dynamic pathology that implicates a growing volume of multiscale data spanning genetic, cellular, tissue, and organ levels of the organization. These data and bioinformatics analyses provide clear evidence for the interactions within and between these levels. The resulting heterarchy precludes a linear neuron-centric approach and necessitates that the numerous interactions are measured in a way that predicts their impact on the emergent dynamics of the disease. This level of complexity confounds intuition, and we propose a new methodology that uses non-linear dynamical systems modeling to augment intuition and that links with a community-wide participatory platform to co-create and test system-level hypotheses and interventions. In addition to enabling the integration of multiscale knowledge, key benefits include a more rapid innovation cycle and a rational process for prioritization of data campaigns. We argue that such an approach is essential to support the discovery of multilevel-coordinated polypharmaceutical interventions.
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Affiliation(s)
- Jennifer Rollo
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - John Crawford
- Adam Smith Business School, University of Glasgow, Glasgow G12 8QQ, UK
| | - John Hardy
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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de Oliveira FF, Miraldo MC, de Castro-Neto EF, de Almeida SS, Matas SLDA, Bertolucci PHF, Naffah-Mazzacoratti MDG. Differential associations of clinical features with cerebrospinal fluid biomarkers in dementia with Lewy bodies and Alzheimer's disease. Aging Clin Exp Res 2023:10.1007/s40520-023-02452-5. [PMID: 37264166 DOI: 10.1007/s40520-023-02452-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/22/2023] [Indexed: 06/03/2023]
Abstract
AIM To explore associations of cerebrospinal fluid biomarkers of neurodegeneration and amyloidosis with caregiver burden, cognition and functionality in dementia with Lewy bodies (DLB) paired with late-onset Alzheimer's disease (AD) and healthy older people. METHODS Consecutive outpatients with DLB were matched with outpatients with AD according to sex, cognitive scores and dementia stage, and with cognitively healthy controls according to age and sex to investigate associations of cerebrospinal fluid amyloid-β (Aβ42,Aβ40,Aβ38), tau, phospho-tau Thr181, ubiquitin, α-synuclein and neurofilament light with caregiver burden, functionality, reverse digit span, a clock drawing test, Mini-Mental State Examination (MMSE) and Severe MMSE, adjusted for sex, age, education, dementia duration and APOE-ε4 alleles. RESULTS Overall, 27 patients with DLB (78.98 ± 9.0 years-old; eleven APOE-ε4 +) were paired with 27 patients with AD (81.50 ± 5.8 years-old; twelve APOE-ε4 +) and 27 controls (78.98 ± 8.7 years-old; four APOE-ε4 +); two-thirds were women. In AD, Aβ42/Aβ38 and Aβ42 were lower, while tau/Aβ42 and phospho-tau Thr181/Aβ42 were higher; α-synuclein/Aβ42 was lower in DLB and higher in AD. The following corrected associations remained significant: in DLB, instrumental functionality was inversely associated with tau/phospho-tau Thr181 and tau/Aβ42, and reverse digit span associated with α-synuclein; in AD, instrumental functionality was inversely associated with neurofilament light, clock drawing test scores inversely associated with phospho-tau Thr181/Aβ42 and α-synuclein/Aβ42, and Severe MMSE inversely associated with tau/Aβ42 and tau/phospho-tau Thr181. CONCLUSIONS Cerebrospinal fluid phospho-tau Thr181 in DLB was similar to AD, but not Aβ42. In associations with test scores, biomarker ratios were superior to isolated biomarkers, while worse functionality was associated with axonal degeneration only in AD.
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Affiliation(s)
- Fabricio Ferreira de Oliveira
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil.
| | - Marjorie Câmara Miraldo
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil
| | - Eduardo Ferreira de Castro-Neto
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil
| | - Sandro Soares de Almeida
- Department of Biophysics, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Sandro Luiz de Andrade Matas
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil
| | - Paulo Henrique Ferreira Bertolucci
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil
| | - Maria da Graça Naffah-Mazzacoratti
- Department of Neurology and Neurosurgery, Escola Paulista de Medicina, Federal University of São Paulo (UNIFESP), Rua Botucatu 740, Vila Clementino, São Paulo, SP, 04023-900, Brazil
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Rafii MS, Sperling RA, Donohue MC, Zhou J, Roberts C, Irizarry MC, Dhadda S, Sethuraman G, Kramer LD, Swanson CJ, Li D, Krause S, Rissman RA, Walter S, Raman R, Johnson KA, Aisen PS. The AHEAD 3-45 Study: Design of a prevention trial for Alzheimer's disease. Alzheimers Dement 2023; 19:1227-1233. [PMID: 35971310 PMCID: PMC9929028 DOI: 10.1002/alz.12748] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/25/2022] [Accepted: 06/10/2022] [Indexed: 11/09/2022]
Abstract
INTRODUCTION The Alzheimer's disease (AD) continuum begins with a long asymptomatic or preclinical stage, during which amyloid beta (Aβ) is accumulating for more than a decade prior to widespread cortical tauopathy, neurodegeneration, and manifestation of clinical symptoms. The AHEAD 3-45 Study (BAN2401-G000-303) is testing whether intervention with lecanemab (BAN2401), a humanized immunoglobulin 1 (IgG1) monoclonal antibody that preferentially targets soluble aggregated Aβ, initiated during this asymptomatic stage can slow biomarker changes and/or cognitive decline. The AHEAD 3-45 Study is conducted as a Public-Private Partnership of the Alzheimer's Clinical Trial Consortium (ACTC), funded by the National Institute on Aging, National Institutes of Health (NIH), and Eisai Inc. METHODS The AHEAD 3-45 Study was launched on July 14, 2020, and consists of two sister trials (A3 and A45) in cognitively unimpaired (CU) individuals ages 55 to 80 with specific dosing regimens tailored to baseline brain amyloid levels on screening positron emission tomography (PET) scans: intermediate amyloid (≈20 to 40 Centiloids) for A3 and elevated amyloid (>40 Centiloids) for A45. Both trials are being conducted under a single protocol, with a shared screening process and common schedule of assessments. A3 is a Phase 2 trial with PET-imaging end points, whereas A45 is a Phase 3 trial with a cognitive composite primary end point. The treatment period is 4 years. The study utilizes innovative approaches to enriching the sample with individuals who have elevated brain amyloid. These include recruiting from the Trial-Ready Cohort for Preclinical and Prodromal Alzheimer's disease (TRC-PAD), the Australian Dementia Network (ADNeT) Registry, and the Japanese Trial Ready Cohort (J-TRC), as well as incorporation of plasma screening with the C2N mass spectrometry platform to quantitate the Aβ 42/40 ratio (Aβ 42/40), which has been shown previously to reliably identify cognitively normal participants not likely to have elevated brain amyloid levels. A blood sample collected at a brief first visit is utilized to "screen out" individuals who are less likely to have elevated brain amyloid, and to determine the participant's eligibility to proceed to PET imaging. Eligibility to randomize into the A3 Trial or A45 Trial is based on the screening PET imaging results. RESULT The focus of this article is on the innovative design of the study. DISCUSSION The AHEAD 3-45 Study will test whether with lecanemab (BAN2401) can slow the accumulation of tau and prevent the cognitive decline associated with AD during its preclinical stage. It is specifically targeting both the preclinical and the early preclinical (intermediate amyloid) stages of AD and is the first secondary prevention trial to employ plasma-based biomarkers to accelerate the screening process and potentially substantially reduce the number of screening PET scans.
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Affiliation(s)
- Michael S. Rafii
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | - Reisa A. Sperling
- Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael C. Donohue
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | - Jin Zhou
- Eisai, Inc., Woodcliff Lake, New Jersey, USA
| | | | | | | | - Gopalan Sethuraman
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | | | | | - David Li
- Eisai, Inc., Woodcliff Lake, New Jersey, USA
| | | | - Robert A. Rissman
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
- Department of Neurosciences, UC San Diego, La Jolla, California, USA
| | - Sarah Walter
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | - Rema Raman
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
| | - Keith A. Johnson
- Brigham and Women’s Hospital, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul S. Aisen
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, California, USA
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Petrican R, Fornito A. Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing. Dev Cogn Neurosci 2023; 60:101229. [PMID: 36947895 PMCID: PMC10041470 DOI: 10.1016/j.dcn.2023.101229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9-13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer's Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool L69 7ZA, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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Schork NJ, Elman JA. Pathway-specific polygenic risk scores correlate with clinical status and Alzheimer's-related biomarkers. RESEARCH SQUARE 2023:rs.3.rs-2583037. [PMID: 36909609 PMCID: PMC10002839 DOI: 10.21203/rs.3.rs-2583037/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Background: APOE is the largest genetic risk factor for sporadic Alzheimer's disease (AD), but there is a substantial polygenic component as well. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk associated with different molecular processes and pathways. Variability at the genetic level may contribute to the extensive phenotypic heterogeneity of Alzheimer's disease (AD). Here, we examine polygenic risk impacting specific pathways associated with AD and examined its relationship with clinical status and AD biomarkers of amyloid, tau, and neurodegeneration (A/T/N). Methods: A total of 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with genotyping data were included. Sets of variants identified from a pathway analysis of AD GWAS summary statistics were combined into clusters based on their assigned pathway. We constructed pathway-specific PRSs for each participant and tested their associations with diagnostic status (AD vs cognitively normal), abnormal levels of amyloid and ptau (positive vs negative), and hippocampal volume. The APOE region was excluded from all PRSs, and analyses controlled for APOE -ε4 carrier status. Results: Thirteen pathway clusters were identified relating to categories such as immune response, amyloid precursor processing, protein localization, lipid transport and binding, tyrosine kinase, and endocytosis. Eight pathway-specific PRSs were significantly associated with AD dementia diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau positivity was additionally associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs, suggesting a strong synergistic effect of all loci contributing to the global AD PRS. Conclusions: Pathway PRS may contribute to understanding separable disease processes, but do not appear to add significant power for predictive purposes. These findings demonstrate that, although genetic risk for AD is widely distributed, AD-phenotypes may be preferentially associated with risk in specific pathways. Defining genetic risk along multiple dimensions at the individual level may help clarify the etiological heterogeneity in AD.
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Crawford K, Leonenko G, Baker E, Grozeva D, Lan-Leung B, Holmans P, Williams J, O'Donovan MC, Escott-Price V, Ivanov DK. Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated in Alzheimer's disease through polygenic risk and RNA sequencing. Mol Psychiatry 2023; 28:1327-1336. [PMID: 36577842 PMCID: PMC10005937 DOI: 10.1038/s41380-022-01926-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/29/2022]
Abstract
Polygenic risk scores (PRS) have been widely adopted as a tool for measuring common variant liability and they have been shown to predict lifetime risk of Alzheimer's disease (AD) development. However, the relationship between PRS and AD pathogenesis is largely unknown. To this end, we performed a differential gene-expression and associated disrupted biological pathway analyses of AD PRS vs. case/controls in human brain-derived cohort sample (cerebellum/temporal cortex; MayoRNAseq). The results highlighted already implicated mechanisms: immune and stress response, lipids, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death, being disrupted biological pathways in both case/controls and PRS, as well as previously less well characterised processes such as cellular structures, mitochondrial respiration and secretion. Despite heterogeneity in terms of differentially expressed genes in case/controls vs. PRS, there was a consensus of commonly disrupted biological mechanisms. Glia and microglia-related terms were also significantly disrupted, albeit not being the top disrupted Gene Ontology terms. GWAS implicated genes were significantly and in their majority, up-regulated in response to different PRS among the temporal cortex samples, suggesting potential common regulatory mechanisms. Tissue specificity in terms of disrupted biological pathways in temporal cortex vs. cerebellum was observed in relation to PRS, but limited tissue specificity when the datasets were analysed as case/controls. The largely common biological mechanisms between a case/control classification and in association with PRS suggests that PRS stratification can be used for studies where suitable case/control samples are not available or the selection of individuals with high and low PRS in clinical trials.
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Affiliation(s)
- Karen Crawford
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, School of Medicine, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Ganna Leonenko
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Emily Baker
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Detelina Grozeva
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
- Centre for Trials Research, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Benoit Lan-Leung
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, School of Medicine, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Julie Williams
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, School of Medicine, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Valentina Escott-Price
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, School of Medicine, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK
| | - Dobril K Ivanov
- UK Dementia Research Institute (UKDRI) at Cardiff University, College of Biomedical and Life Sciences, Hadyn Ellis Building, Cardiff, CF24 4HQ, UK.
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Petrican R, Paine AL, Escott-Price V, Shelton KH. Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer's disease and/or major depressive disorder. Sci Rep 2023; 13:984. [PMID: 36653486 PMCID: PMC9849214 DOI: 10.1038/s41598-023-28057-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Early life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both major depressive disorder (MDD) and Alzheimer's disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.
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Affiliation(s)
- Raluca Petrican
- Institute of Population Health, Department of Psychology, University of Liverpool, Bedford Street South, Liverpool, L69 7ZA, UK.
| | - Amy L Paine
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Katherine H Shelton
- School of Psychology, Cardiff University, 70 Park Place, Cardiff, CF10 3AT, UK
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Schork NJ, Elman JA. Pathway-Specific Polygenic Risk Scores Correlate with Clinical Status and Alzheimer's Disease-Related Biomarkers. J Alzheimers Dis 2023; 95:915-929. [PMID: 37661888 PMCID: PMC10697039 DOI: 10.3233/jad-230548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
BACKGROUND APOE is the largest genetic risk factor for Alzheimer's disease (AD), but there is a substantial polygenic component. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk across molecular processes and pathways that contribute to heterogeneity of disease presentation. OBJECTIVE We examined polygenic risk impacting specific AD-associated pathways and its relationship with clinical status and biomarkers of amyloid, tau, and neurodegeneration (A/T/N). METHODS We analyzed data from 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied pathway analysis and clustering to identify AD-associated "pathway clusters" and construct pathway-specific PRSs (excluding the APOE region). We tested associations with diagnostic status, abnormal levels of amyloid and ptau, and hippocampal volume. RESULTS Thirteen pathway clusters were identified, and eight pathway-specific PRSs were significantly associated with AD diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau-positivity was also associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs. CONCLUSIONS Pathway PRS may contribute to understanding separable disease processes, but do not add significant power for predictive purposes. These findings demonstrate that AD-phenotypes may be preferentially associated with risk in specific pathways, and defining genetic risk along multiple dimensions may clarify etiological heterogeneity in AD. This approach to delineate pathway-specific PRS can be used to study other complex diseases.
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Affiliation(s)
- Nicholas J. Schork
- The Translational Genomics Research Institute, Quantitative Medicine and Systems Biology, Phoenix, AZ, USA
- Department of Psychiatry University of California, San Diego, La Jolla, CA, USA
| | - Jeremy A. Elman
- Department of Psychiatry University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
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26
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Wu BS, Zhang YR, Yang L, Zhang W, Deng YT, Chen SD, Feng JF, Cheng W, Yu JT. Polygenic Liability to Alzheimer's Disease Is Associated with a Wide Range of Chronic Diseases: A Cohort Study of 312,305 Participants. J Alzheimers Dis 2023; 91:437-447. [PMID: 36442194 DOI: 10.3233/jad-220740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) patients rank among the highest levels of comorbidities compared to persons with other diseases. However, it is unclear whether the conditions are caused by shared pathophysiology due to the genetic pleiotropy for AD risk genes. OBJECTIVE To figure out the genetic pleiotropy for AD risk genes in a wide range of diseases. METHODS We estimated the polygenic risk score (PRS) for AD and tested the association between PRS and 16 ICD10 main chapters, 136 ICD10 level-1 chapters, and 377 diseases with cases more than 1,000 in 312,305 individuals without AD diagnosis from the UK Biobank. RESULTS After correction for multiple testing, AD PRS was associated with two main ICD10 chapters: Chapter IV (endocrine, nutritional and metabolic diseases) and Chapter VII (eye and adnexa disorders). When narrowing the definition of the phenotypes, positive associations were observed between AD PRS and other types of dementia (OR = 1.39, 95% CI [1.34, 1.45], p = 1.96E-59) and other degenerative diseases of the nervous system (OR = 1.18, 95% CI [1.13, 1.24], p = 7.74E-10). In contrast, we detected negative associations between AD PRS and diabetes mellitus, obesity, chronic bronchitis, other retinal disorders, pancreas diseases, and cholecystitis without cholelithiasis (ORs range from 0.94 to 0.97, FDR < 0.05). CONCLUSION Our study confirms several associations reported previously and finds some novel results, which extends the knowledge of genetic pleiotropy for AD in a range of diseases. Further mechanistic studies are necessary to illustrate the molecular mechanisms behind these associations.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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27
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Eissman JM, Wells G, Khan OA, Liu D, Petyuk VA, Gifford KA, Dumitrescu L, Jefferson AL, Hohman TJ. Polygenic resilience score may be sensitive to preclinical Alzheimer's disease changes. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:449-460. [PMID: 36540999 PMCID: PMC9888419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Late-onset Alzheimer's disease (LOAD) is a polygenic disorder with a long prodromal phase, making early diagnosis challenging. Twin studies estimate LOAD as 60-80% heritable, and while common genetic variants can account for 30% of this heritability, nearly 70% remains "missing". Polygenic risk scores (PRS) leverage combined effects of many loci to predict LOAD risk, but often lack sensitivity to preclinical disease changes, limiting clinical utility. Our group has built and published on a resilience phenotype to model better-than-expected cognition give amyloid pathology burden and hypothesized it may assist in preclinical polygenic risk prediction. Thus, we built a LOAD PRS and a resilience PRS and evaluated both in predicting cognition in a dementia-free cohort (N=254). The LOAD PRS had a significant main effect on baseline memory (β=-0.18, P=1.68E-03). Both the LOAD PRS (β=-0.03, P=1.19E-03) and the resilience PRS (β=0.02, P=0.03) had significant main effects on annual memory decline. The resilience PRS interacted with CSF Aβ on baseline memory (β=-6.04E-04, P=0.02), whereby it predicted baseline memory among Aβ+ individuals (β=0.44, P=0.01) but not among Aβ- individuals (β=0.06, P=0.46). Excluding APOE from PRS resulted in mainly LOAD PRS associations attenuating, but notably the resilience PRS interaction with CSF Aβ and selective prediction among Aβ+ individuals was consistent. Although the resilience PRS is currently somewhat limited in scope from the phenotype's cross-sectional nature, our results suggest that the resilience PRS may be a promising tool in assisting in preclinical disease risk prediction among dementia-free and Aβ+ individuals, though replication and fine-tuning are needed.
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Affiliation(s)
- Jaclyn M. Eissman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Greyson Wells
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Omair A. Khan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Dandan Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Vladislav A. Petyuk
- Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest, National Laboratory, Richland, WA 99354, USA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN 37212, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37212, USA,
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28
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Bracher-Smith M, Leonenko G, Baker E, Crawford K, Graham AC, Salih DA, Howell BW, Hardy J, Escott-Price V. Whole genome analysis in APOE4 homozygotes identifies the DAB1-RELN pathway in Alzheimer's disease pathogenesis. Neurobiol Aging 2022; 119:67-76. [PMID: 35977442 PMCID: PMC9548409 DOI: 10.1016/j.neurobiolaging.2022.07.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/04/2022] [Accepted: 07/23/2022] [Indexed: 11/20/2022]
Abstract
The APOE-ε4 allele is known to predispose to amyloid deposition and consequently is strongly associated with Alzheimer's disease (AD) risk. There is debate as to whether the APOE gene accounts for all genetic variation of the APOE locus. Another question which remains is whether APOE-ε4 carriers have other genetic factors influencing the progression of amyloid positive individuals to AD. We conducted a genome-wide association study in a sample of 5,390 APOE-ε4 homozygous (ε4ε4) individuals (288 cases and 5102 controls) aged 65 or over in the UK Biobank. We found no significant associations of SNPs in the APOE locus with AD in the sample of ε4ε4 individuals. However, we identified a novel genome-wide significant locus associated to AD, mapping to DAB1 (rs112437613, OR = 2.28, CI = 1.73-3.01, p = 5.4 × 10-9). This identification of DAB1 led us to investigate other components of the DAB1-RELN pathway for association. Analysis of the DAB1-RELN pathway indicated that the pathway itself was associated with AD, therefore suggesting an epistatic interaction between the APOE locus and the DAB1-RELN pathway.
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Affiliation(s)
- Matthew Bracher-Smith
- Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK; Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Ganna Leonenko
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Emily Baker
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Karen Crawford
- Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | | | - Dervis A Salih
- Dementia Research Institute, University College London, UK
| | - Brian W Howell
- Neuroscience and Physiology, State University of New York, Albany, NY, USA
| | - John Hardy
- Dementia Research Institute, University College London, UK.
| | - Valentina Escott-Price
- Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK.
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29
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Luckett ES, Abakkouy Y, Reinartz M, Adamczuk K, Schaeverbeke J, Verstockt S, De Meyer S, Van Laere K, Dupont P, Cleynen I, Vandenberghe R. Association of Alzheimer’s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults. Alzheimers Res Ther 2022; 14:138. [PMID: 36151568 PMCID: PMC9508733 DOI: 10.1186/s13195-022-01079-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 09/08/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Early detection of individuals at risk for Alzheimer’s disease (AD) is highly important. Amyloid accumulation is an early pathological AD event, but the genetic association with known AD risk variants beyond the APOE4 effect is largely unknown. We investigated the association between different AD polygenic risk scores (PRS) and amyloid accumulation in the Flemish Prevent AD Cohort KU Leuven (F-PACK).
Methods
We calculated PRS with and without the APOE region in 90 cognitively healthy F-PACK participants (baseline age 67.8 (52–80) years, 41 APOE4 carriers), with baseline and follow-up amyloid-PET (time interval 6.1 (3.4–10.9) years). Individuals were genotyped using Illumina GSA and imputed. PRS were calculated using three p-value thresholds (pT) for variant inclusion: 5 × 10−8, 1 × 10−5, and 0.1, based on the stage 1 summary statistics from Kunkle et al. (Nat Genet 51:414–30, 2019). Linear regression models determined if these PRS predicted amyloid accumulation.
Results
A score based on PRS excluding the APOE region at pT = 5 × 10−8 plus the weighted sum of the two major APOE variants (rs429358 and rs7412) was significantly associated with amyloid accumulation (p = 0.0126). The two major APOE variants were also significantly associated with amyloid accumulation (p = 0.0496). The other PRS were not significant.
Conclusions
Specific PRS are associated with amyloid accumulation in the asymptomatic phase of AD.
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30
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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31
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Page ML, Vance EL, Cloward ME, Ringger E, Dayton L, Ebbert MTW, Miller JB, Kauwe JSK. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores. Commun Biol 2022; 5:899. [PMID: 36056235 PMCID: PMC9438378 DOI: 10.1038/s42003-022-03795-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
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Affiliation(s)
- Madeline L Page
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Elizabeth L Vance
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | | | - Ed Ringger
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Louisa Dayton
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Mark T W Ebbert
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | | | - Justin B Miller
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA.
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32
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Xicota L, Gyorgy B, Grenier-Boley B, Lecoeur A, Fontaine G, Danjou F, Gonzalez JS, Colliot O, Amouyel P, Martin G, Levy M, Villain N, Habert MO, Dubois B, Lambert JC, Potier MC. Association of APOE-Independent Alzheimer Disease Polygenic Risk Score With Brain Amyloid Deposition in Asymptomatic Older Adults. Neurology 2022; 99:e462-e475. [PMID: 35606148 PMCID: PMC9421597 DOI: 10.1212/wnl.0000000000200544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 03/02/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Brain amyloid deposition, a major risk factor for Alzheimer disease (AD), is currently estimated by measuring CSF or plasma amyloid peptide levels or by PET imaging. Assessing genetic risks relating to amyloid deposition before any accumulation has occurred would allow for earlier intervention in persons at increased risk for developing AD. Previous work linking amyloid burden and genetic risk relied almost exclusively on APOE, a major AD genetic risk factor. Here, we ask whether a polygenic risk score (PRS) that incorporates an optimized list of common variants linked to AD and excludes APOE is associated with brain amyloid load in cognitively unimpaired older adults. METHODS We included 291 asymptomatic older participants from the INveStIGation of AlzHeimer's PredicTors (INSIGHT pre-AD) cohort who underwent amyloid imaging, including 83 amyloid-positive (+) participants. We used an Alzheimer's (A) PRS composed of 33 AD risk variants excluding APOE and selected the 17 variants that showed the strongest association with amyloid positivity to define an optimized (oA) PRS. Participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study (228 participants, 90 amyloid [+]) were tested as a validation cohort. Finally, 2,300 patients with AD and 6,994 controls from the European Alzheimer's Disease Initiative (EADI) were evaluated. RESULTS A-PRS was not significantly associated with amyloid burden in the INSIGHT or ADNI cohorts with or without correction for the APOE genotype. However, oA-PRS was significantly associated with amyloid status independently of APOE adjustment (INSIGHT odds ratio [OR]: 5.26 [1.71-16.88]; ADNI OR: 3.38 [1.02-11.63]). Of interest, oA-PRS accurately discriminated amyloid (+) and (-) APOE ε4 carriers (INSIGHT OR: 181.6 [7.53-10,674.6]; ADNI OR: 44.94 [3.03-1,277]). A-PRS and oA-PRS showed a significant association with disease status in the EADI cohort (OR: 1.68 [1.53-1.85] and 2.06 [1.73-2.45], respectively). Genes assigned to oA-PRS variants were enriched in ontologies related to β-amyloid metabolism and deposition. DISCUSSION PRSs relying on AD genetic risk factors excluding APOE may improve risk prediction for brain amyloid, allowing stratification of cognitively unimpaired individuals at risk of AD independent of their APOE status.
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Affiliation(s)
- Laura Xicota
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Beata Gyorgy
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Benjamin Grenier-Boley
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Alexandre Lecoeur
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Gaëlle Fontaine
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Fabrice Danjou
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Jorge Samper Gonzalez
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Olivier Colliot
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Philippe Amouyel
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Garance Martin
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Marcel Levy
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Nicolas Villain
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Marie-Odile Habert
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Bruno Dubois
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Jean-Charles Lambert
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
| | - Marie-Claude Potier
- From the ICM Paris Brain Institute (L.X., B.G., A.L., G.«F., F.D., J.S.G., O.C., N.V., B.D., M.-C.P.), CNRS UMR7225, INSERM U1127, Sorbonne University, Hôpital de la Pitié-Salpêtrière; Univ. Lille (B.G.-B., P.A., J.-C.L.), Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement; Inria (J.S.G., O.C.), Aramis-Project Team, Paris; Centre d'Acquisition et Traitement des Images (CATI platform) (G.M., M.-O.H.), cati-neuroimaging.com, Paris; Centre des Maladies Cognitives et Comportementales (M.L., M.-O.H., B.D.), IM2A, AP-HP, Sorbonne Université, Hôpital de la Salpêtrière; Department of Neurology (N.V., B.D.), Hôpital Pitié-Salpêtrière, AP-HP Sorbonne Université; Sorbonne Université (M.-O.H.), CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, LIB; and AP-HP (M.-O.H.), Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France.
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Xie YL, Li JX, Ji WZ, Yao YL. Distribution characteristics of ApoE gene polymorphism in the Tibetan population of Qinghai. EUR J INFLAMM 2022. [DOI: 10.1177/1721727x221095381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Objective: To understand the distribution characteristics of the relative frequencies of apolipoprotein E (APOE) alleles in Tibetans of Qinghai province, to provide a basis for subsequent research. Method: Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to determine the APOE genotypes and analyze the distribution characteristics in 96 indigenous Tibetans randomly selected from the Medical Examination Center of Qinghai Provincial People’s Hospital, and the results of this study were compared with those of other ethnic groups in China. Results: The frequencies of E2, E3, and E4 alleles in the 96 subjects were 1.563%, 89.062%, and 9.375%, respectively, and the genotype frequencies were E2/E2 (0%), E2/E3 (3.125%), E2/E4 (0%), E3/E3 (78.125%), E3/E4 (18.750%), and E4/E4 (0%), respectively. The frequency distribution of the ε2 allele in the Tibetan population was lower than that of the Northern Han, Southern Hakka, Hui, Mongolian, and Dai populations of China. The frequency distribution of the ε4 allele in the Tibetan population was of no significant difference compared with that of the Northern Han, Southern Hakka, Hui, and Mongolian populations, but was higher than that of the Dai population. The frequency distribution of the ε3 allele in the Tibetan population was of no significant difference compared with that of the Northern Han, Mongolian, and Dai populations, but higher than that of the Southern Hakka and Mongolian populations. Conclusion: There are ethnic differences in the frequency distribution of the three common alleles of APOE.
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Affiliation(s)
- Yan-Ling Xie
- Department of Endocrinology, Qinghai Provincial People’s Hospital, China
| | - Jian-Xun Li
- Department of Endocrinology, Qinghai Provincial People’s Hospital, China
| | - Wei-Zhong Ji
- Department of Neurology, Qinghai Provincial People’s Hospital, China
| | - Yong-Li Yao
- Department of Endocrinology, Qinghai Provincial People’s Hospital, China
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Ramanan VK, Heckman MG, Lesnick TG, Przybelski SA, Cahn EJ, Kosel ML, Murray ME, Mielke MM, Botha H, Graff-Radford J, Jones DT, Lowe VJ, Machulda MM, Jack CR, Knopman DS, Petersen RC, Ross OA, Vemuri P. Tau polygenic risk scoring: a cost-effective aid for prognostic counseling in Alzheimer's disease. Acta Neuropathol 2022; 143:571-583. [PMID: 35412102 PMCID: PMC9109940 DOI: 10.1007/s00401-022-02419-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 11/28/2022]
Abstract
Tau deposition is one of two hallmark features of biologically defined Alzheimer's disease (AD) and is more closely related to cognitive decline than amyloidosis. Further, not all amyloid-positive individuals develop tauopathy, resulting in wide heterogeneity in clinical outcomes across the population with AD. We hypothesized that a polygenic risk score (PRS) based on tau PET (tau PRS) would capture the aggregate inherited susceptibility/resistance architecture influencing tau accumulation, beyond solely the measurement of amyloid-β burden. Leveraging rich multimodal data from a population-based sample of older adults, we found that this novel tau PRS was a strong surrogate of tau PET deposition and captured a significant proportion of the variance in tau PET levels as compared with amyloid PET burden, APOE (apolipoprotein E) ε4 (the most common risk allele for AD), and a non-APOE PRS of clinical case-control AD risk variants. In independent validation samples, the tau PRS was associated with cerebrospinal fluid phosphorylated tau levels in one cohort and with postmortem Braak neurofibrillary tangle stage in another. We also observed an association of the tau PRS with longitudinal cognitive trajectories, including a statistical interaction of the tau PRS with amyloid burden on cognitive decline. Although additional study is warranted, these findings demonstrate the potential utility of a tau PRS for capturing the collective genetic background influencing tau deposition in the general population. In the future, a tau PRS could be leveraged for cost-effective screening and risk stratification to guide trial enrollment and clinical interventions in AD.
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Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Michael G Heckman
- Department of Quantitative Health Sciences, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Elliot J Cahn
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Matthew L Kosel
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Jonathan Graff-Radford
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
- Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA
| | - Prashanthi Vemuri
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, 32224, USA.
- Department of Radiology, Mayo Clinic-Minnesota, 200 First Street SW, Rochester, MN, 55905, USA.
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Upadhya S, Liu H, Luo S, Lutz MW, Chiba-Falek O. Polygenic Risk Score Effectively Predicts Depression Onset in Alzheimer’s Disease Based on Major Depressive Disorder Risk Variants. Front Neurosci 2022; 16:827447. [PMID: 35350557 PMCID: PMC8957806 DOI: 10.3389/fnins.2022.827447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer’s Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, these previous studies suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD, and the various genetic etiologies predisposing depression in LOAD, are largely unknown. Methods Major Depressive Disorder (MDD) genome-wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP, n = 1,708) and National Alzheimer’s Coordinating Center (NACC, n = 10,256) datasets served as discovery and validation cohorts, respectively, to assess the PRS performance in predicting depression onset in LOAD patients. Results The PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC = 0.540) and NACC (AUC = 0.527). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.581). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (P = 0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P < 0.001 for all). Conclusion This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.
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Affiliation(s)
- Suraj Upadhya
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States
| | - Hongliang Liu
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, United States
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, United States
| | - Michael W. Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, United States
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, United States
- *Correspondence: Ornit Chiba-Falek,
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Ramanan VK, Heckman MG, Przybelski SA, Lesnick TG, Lowe VJ, Graff-Radford J, Mielke MM, Jack CR, Knopman DS, Petersen RC, Ross OA, Vemuri P. Polygenic Scores of Alzheimer's Disease Risk Genes Add Only Modestly to APOE in Explaining Variation in Amyloid PET Burden. J Alzheimers Dis 2022; 88:1615-1625. [PMID: 35811524 PMCID: PMC9534315 DOI: 10.3233/jad-220164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Brain accumulation of amyloid-β is a hallmark event in Alzheimer's disease (AD) whose underlying mechanisms are incompletely understood. Case-control genome-wide association studies have implicated numerous genetic variants in risk of clinically diagnosed AD dementia. OBJECTIVE To test for associations between case-control AD risk variants and amyloid PET burden in older adults, and to assess whether a polygenic measure encompassing these factors would account for a large proportion of the unexplained variance in amyloid PET levels in the wider population. METHODS We analyzed data from the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Global cortical amyloid PET burden was the primary outcome. The 38 gene variants from Wightman et al. (2021) were analyzed as predictors, with PRSice-2 used to assess the collective phenotypic variance explained. RESULTS Known AD risk variants in APOE, PICALM, CR1, and CLU were associated with amyloid PET levels. In aggregate, the AD risk variants were strongly associated with amyloid PET levels in the MCSA (p = 1.51×10-50) and ADNI (p = 3.21×10-64). However, in both cohorts the non-APOE variants uniquely contributed only modestly (MCSA = 2.1%, ADNI = 4.4%) to explaining variation in amyloid PET levels. CONCLUSION Additional case-control AD risk variants added only modestly to APOE in accounting for individual variation in amyloid PET burden, results which were consistent across independent cohorts with distinct recruitment strategies and subject characteristics. Our findings suggest that advancing precision medicine for dementia may require integration of strategies complementing case-control approaches, including biomarker-specific genetic associations, gene-by-environment interactions, and markers of disease progression and heterogeneity.
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Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Michael G. Heckman
- Department of Quantitative Health Sciences, Mayo Clinic-Florida, Jacksonville, Florida, 32224, USA
| | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | | | - Michelle M. Mielke
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - David S. Knopman
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Ronald C. Petersen
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, Florida, 32224, USA
- Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville, Florida, 32224, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, Minnesota, 55905, USA
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Escott-Price V, Hardy J. Genome-wide association studies for Alzheimer's disease: bigger is not always better. Brain Commun 2022; 4:fcac125. [PMID: 35663382 PMCID: PMC9155614 DOI: 10.1093/braincomms/fcac125] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/15/2022] [Accepted: 05/13/2022] [Indexed: 11/25/2022] Open
Abstract
As the size of genome-wide association studies increase, the number of associated trait loci identified inevitably increase. One welcomes this if it allows the better delineation of the pathways to disease and increases the accuracy of genetic prediction of disease risk through polygenic risk score analysis. However, there are several problems in the continuing increase in the genome-wide analysis of 'Alzheimer's disease'. In this review, we have systematically assessed the history of Alzheimer's disease genome-wide association studies, including their sample sizes, age and selection/assessment criteria of cases and controls and heritability explained by these disease genome-wide association studies. We observe that nearly all earlier disease genome-wide association studies are now part of all current disease genome-wide association studies. In addition, the latest disease genome-wide association studies include (i) only a small fraction (∼10%) of clinically screened controls, substituting for them population-based samples which are systematically younger than cases, and (ii) around 50% of Alzheimer's disease cases are in fact 'proxy dementia cases'. As a consequence, the more genes the field finds, the less the heritability they explain. We highlight potential caveats this situation creates and discuss some of the consequences occurring when translating the newest Alzheimer's disease genome-wide association study results into basic research and/or clinical practice.
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Affiliation(s)
- Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Dementia Research Institute at Cardiff, Cardiff University, Cardiff, UK
- Correspondence to: Valentina Escott-Price Cardiff University, Hadyn Ellis Building Maindy Road, Cardiff CF24 4HQ, UK E-mail:
| | - John Hardy
- UCL Institute of Neurology, Queen Square, London, UK
- UCL Dementia Research Institute, UCL, London, UK
- Correspondence may also be addressed to: John Hardy UCL Institute of Neurology, Queen Square London WC1N 3BG, UK E-mail:
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Complement as a powerful "influencer" in the brain during development, adulthood and neurological disorders. Adv Immunol 2021; 152:157-222. [PMID: 34844709 DOI: 10.1016/bs.ai.2021.09.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The complement system was long considered as only a powerful effector arm of the immune system that, while critically protective, could lead to inflammation and cell death if overactivated, even in the central nervous system (CNS). However, in the past decade it has been recognized as playing critical roles in key physiological processes in the CNS, including neurogenesis and synaptic remodeling in the developing and adult brain. Inherent in these processes are the interactions with cells in the brain, and the cascade of interactions and functional consequences that ensue. As a result, investigations of therapeutic approaches for both suppressing excessive complement driven neurotoxicity and aberrant sculpting of neuronal circuits, require broad (and deep) knowledge of the functional activities of multiple components of this highly evolved and regulated system to avoid unintended negative consequences in the clinic. Advances in basic science are beginning to provide a roadmap for translation to therapeutics, with both small molecule and biologics. Here, we present examples of the critical roles of proper complement function in the development and sculpting of the nervous system, and in enabling rapid protection from infection and clearance of dying cells. Microglia are highlighted as important command centers that integrate signals from the complement system and other innate sensors that are programed to provide support and protection, but that direct detrimental responses to aberrant activation and/or regulation of the system. Finally, we present promising research areas that may lead to effective and precision strategies for complement targeted interventions to promote neurological health.
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Liu H, Lutz M, Luo S. Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer's Disease. J Alzheimers Dis 2021; 84:1323-1335. [PMID: 34657885 DOI: 10.3233/jad-210700] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer's disease (AD). OBJECTIVE In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. METHODS We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. RESULTS We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, SE = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, SE = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PiB-PET and AV45-PET), but decreased hippocampus and entorhinal cortex volumes (p < 0.05). Mediation analysis showed that the effect of PRS on the increased risk of AD may be mediated by Aβ42 (beta = 0.056, SE = 0.026, p = 0.036). CONCLUSION Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.
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Affiliation(s)
- Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Michael Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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Shea TB. Improvement of cognitive performance by a nutraceutical formulation: Underlying mechanisms revealed by laboratory studies. Free Radic Biol Med 2021; 174:281-304. [PMID: 34352370 DOI: 10.1016/j.freeradbiomed.2021.07.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 12/28/2022]
Abstract
Cognitive decline, decrease in neuronal function and neuronal loss that accompany normal aging and dementia are the result of multiple mechanisms, many of which involve oxidative stress. Herein, we review these various mechanisms and identify pharmacological and non-pharmacological approaches, including modification of diet, that may reduce the risk and progression of cognitive decline. The optimal degree of neuronal protection is derived by combinations of, rather than individual, compounds. Compounds that provide antioxidant protection are particularly effective at delaying or improving cognitive performance in the early stages of Mild Cognitive Impairment and Alzheimer's disease. Laboratory studies confirm alleviation of oxidative damage in brain tissue. Lifestyle modifications show a degree of efficacy and may augment pharmacological approaches. Unfortunately, oxidative damage and resultant accumulation of biomarkers of neuronal damage can precede cognitive decline by years to decades. This underscores the importance of optimization of dietary enrichment, antioxidant supplementation and other lifestyle modifications during aging even for individuals who are cognitively intact.
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Affiliation(s)
- Thomas B Shea
- Laboratory for Neuroscience, Department of Biological Sciences, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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Ebenau JL, van der Lee SJ, Hulsman M, Tesi N, Jansen IE, Verberk IM, van Leeuwenstijn M, Teunissen CE, Barkhof F, Prins ND, Scheltens P, Holstege H, van Berckel BN, van der Flier WM. Risk of dementia in APOE ε4 carriers is mitigated by a polygenic risk score. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12229. [PMID: 34541285 PMCID: PMC8438688 DOI: 10.1002/dad2.12229] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/09/2021] [Accepted: 06/28/2021] [Indexed: 12/22/2022]
Abstract
INTRODUCTION We investigated relationships among genetic determinants of Alzheimer's disease (AD), amyloid/tau/neurodegenaration (ATN) biomarkers, and risk of dementia. METHODS We studied cognitively normal individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort and SCIENCe project. We examined associations between genetic variants and ATN biomarkers, and evaluated their predictive value for incident dementia. A polygenic risk score (PRS) was calculated based on 39 genetic variants. The APOE gene was not included in the PRS and was analyzed separately. RESULTS The PRS and APOE ε4 were associated with amyloid-positive ATN profiles, and APOE ε4 additionally with isolated increased tau (A-T+N-). A high PRS and APOE ε4 separately predicted AD dementia. Combined, a high PRS increased while a low PRS attenuated the risk associated with ε4 carriers. DISCUSSION Genetic variants beyond APOE are clinically relevant and contribute to the pathophysiology of AD. In the future, a PRS might be used in individualized risk profiling.
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Affiliation(s)
- Jarith L. Ebenau
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Sven J. van der Lee
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Clinical GeneticsAmsterdam UMCAmsterdamthe Netherlands
| | - Marc Hulsman
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Clinical GeneticsAmsterdam UMCAmsterdamthe Netherlands
- Delft Bioinformatics LabDelft University of TechnologyDelftthe Netherlands
| | - Niccolò Tesi
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Clinical GeneticsAmsterdam UMCAmsterdamthe Netherlands
- Delft Bioinformatics LabDelft University of TechnologyDelftthe Netherlands
| | - Iris E. Jansen
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Complex Trait GeneticsCenter for Neurogenomics and Cognitive ResearchAmsterdam NeuroscienceVU UniversityAmsterdamthe Netherlands
| | - Inge M.W. Verberk
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Neurochemistry LaboratoryDepartment of Clinical ChemistryVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Mardou van Leeuwenstijn
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryDepartment of Clinical ChemistryVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear MedicineAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Niels D. Prins
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Philip Scheltens
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Henne Holstege
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Clinical GeneticsAmsterdam UMCAmsterdamthe Netherlands
- Delft Bioinformatics LabDelft University of TechnologyDelftthe Netherlands
| | - Bart N.M. van Berckel
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Radiology & Nuclear MedicineAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
| | - Wiesje M. van der Flier
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
- Department of Epidemiology and BiostatisticsAmsterdam UMCAmsterdamthe Netherlands
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Pyun JM, Park YH, Lee KJ, Kim S, Saykin AJ, Nho K. Predictability of polygenic risk score for progression to dementia and its interaction with APOE ε4 in mild cognitive impairment. Transl Neurodegener 2021; 10:32. [PMID: 34465370 PMCID: PMC8406896 DOI: 10.1186/s40035-021-00259-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer's disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD. METHODS We analyzed 732 MCI from the Alzheimer's Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline (n = 270) and remained stable as MCI (n = 462). The predictability of PRS including and excluding the APOE region (PRS+APOE and PRS-APOE) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses. RESULTS PRS+APOE (hazard ratio [HR] 1.468, 95% CI 1.335-1.615) and PRS-APOE (HR 1.293, 95% CI 1.157-1.445) were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRS+APOE and APOE ε4 carrier status was significant with a P-value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers (PRS+APOE: HR 1.710, 95% CI 1.244-2.351; PRS-APOE: HR 1.429, 95% CI 1.182-1.728) than in APOE ε4 carriers (PRS+APOE: HR 1.167, 95% CI 1.005-1.355; PRS-APOE: HR 1.172, 95% CI 1.020-1.346). CONCLUSIONS PRS could predict the conversion of MCI to dementia with a stronger association in APOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles.
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Affiliation(s)
- Jung-Min Pyun
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Republic of Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea.
| | - Keon-Joo Lee
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital and Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Kwangsik Nho
- 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.
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Reveglia P, Paolillo C, Ferretti G, De Carlo A, Angiolillo A, Nasso R, Caputo M, Matrone C, Di Costanzo A, Corso G. Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches. Metabolomics 2021; 17:78. [PMID: 34453619 PMCID: PMC8403122 DOI: 10.1007/s11306-021-01828-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 08/06/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds. AIM OF REVIEW The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.
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Affiliation(s)
- Pierluigi Reveglia
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
| | - Carmela Paolillo
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
| | - Gabriella Ferretti
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Armando De Carlo
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy
- Policlinico Riuniti University Hospital, 71122, Foggia, Italy
| | - Antonella Angiolillo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100, Campobasso, Italy
| | - Rosarita Nasso
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Mafalda Caputo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Carmela Matrone
- Department of Neuroscience, School of Medicine, University of Naples Federico II, 80131, Napoli, Italy
| | - Alfonso Di Costanzo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100, Campobasso, Italy
| | - Gaetano Corso
- Department of Clinical and Experimental Medicine, University of Foggia, 71122, Foggia, Italy.
- Policlinico Riuniti University Hospital, 71122, Foggia, Italy.
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Leonenko G, Baker E, Stevenson-Hoare J, Sierksma A, Fiers M, Williams J, de Strooper B, Escott-Price V. Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores. Nat Commun 2021; 12:4506. [PMID: 34301930 PMCID: PMC8302739 DOI: 10.1038/s41467-021-24082-z] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 06/02/2021] [Indexed: 11/09/2022] Open
Abstract
Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.
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Affiliation(s)
- Ganna Leonenko
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | | | - Annerieke Sierksma
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
| | - Mark Fiers
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
- UK Dementia Research Institute, University College London, London, UK
| | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Bart de Strooper
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Laboratory for the Research of Neurodegenerative Diseases, Department of Neurosciences, Leuven Brain Institute (LBI), KU Leuven (University of Leuven), Leuven, Belgium
- UK Dementia Research Institute, University College London, London, UK
| | - Valentina Escott-Price
- UK Dementia Research Institute, Cardiff University, Cardiff, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
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Albright J, Ashford MT, Jin C, Neuhaus J, Rabinovici GD, Truran D, Maruff P, Mackin RS, Nosheny RL, Weiner MW. Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12207. [PMID: 34136635 PMCID: PMC8190559 DOI: 10.1002/dad2.12207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 12/16/2022]
Abstract
INTRODUCTION This study investigated the extent to which subjective and objective data from an online registry can be analyzed using machine learning methodologies to predict the current brain amyloid beta (Aβ) status of registry participants. METHODS We developed and optimized machine learning models using data from up to 664 registry participants. Models were assessed on their ability to predict Aβ positivity using the results of positron emission tomography as ground truth. RESULTS Study partner-assessed Everyday Cognition score was preferentially selected for inclusion in the models by a feature selection algorithm during optimization. DISCUSSION Our results suggest that inclusion of study partner assessments would increase the ability of machine learning models to predict Aβ positivity.
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Affiliation(s)
| | - Miriam T. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Chengshi Jin
- University of California San Francisco Department of Epidemiology and BiostatisticsSan FranciscoCaliforniaUSA
| | - John Neuhaus
- University of California San Francisco Department of Epidemiology and BiostatisticsSan FranciscoCaliforniaUSA
| | - Gil D. Rabinovici
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Diana Truran
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | | | - R. Scott Mackin
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Rachel L. Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging and Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California San FranciscoSan FranciscoCaliforniaUSA
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A machine learning approach to screen for preclinical Alzheimer's disease. Neurobiol Aging 2021; 105:205-216. [PMID: 34102381 DOI: 10.1016/j.neurobiolaging.2021.04.024] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/06/2021] [Accepted: 04/23/2021] [Indexed: 11/22/2022]
Abstract
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.
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Wang H, Bennett DA, De Jager PL, Zhang QY, Zhang HY. Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction. Alzheimers Res Ther 2021; 13:55. [PMID: 33663605 PMCID: PMC7934265 DOI: 10.1186/s13195-021-00794-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 02/22/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD. METHODS We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction. RESULTS We identified 2 candidate genetic interactions with PFDR < 0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR < 0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD. CONCLUSIONS In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.
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Affiliation(s)
- Hui Wang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - David A Bennett
- Rush University Medical Center, Rush Alzheimer's Disease Center, Chicago, IL, USA
- Rush University Medical Center, Department of Neurological Sciences, Chicago, IL, USA
| | - Philip L De Jager
- Columbia University Medical Center, Center for Translational and Computational Neuroimmunology, New York, NY, USA
- Broad Institute, Cell Circuits Program, Cambridge, MA, USA
| | - Qing-Ye Zhang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China
| | - Hong-Yu Zhang
- Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, No.1 Shizishan Street, Hongshan District, Wuhan, 430070, Hubei, China.
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Ashford MT, Veitch DP, Neuhaus J, Nosheny RL, Tosun D, Weiner MW. The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status. Alzheimers Dement 2021; 17:866-887. [PMID: 33583100 DOI: 10.1002/alz.12253] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/10/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734). METHODS We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively. RESULTS We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening. DISCUSSION Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.
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Affiliation(s)
- Miriam T Ashford
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA
| | - John Neuhaus
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA
| | - Rachel L Nosheny
- Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education, San Francisco, California, USA.,Department of Veterans Affairs Medical Center, Center for Imaging and Neurodegenerative Diseases, San Francisco, California, USA.,Department of Psychiatry, University of California San Francisco, San Francisco, California, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.,Department of Medicine, University of California San Francisco, San Francisco, California, USA.,Department of Neurology, University of California San Francisco, San Francisco, California, USA
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50
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Skoog I, Kern S, Najar J, Guerreiro R, Bras J, Waern M, Zetterberg H, Blennow K, Zettergren A. A Non-APOE Polygenic Risk Score for Alzheimer's Disease Is Associated With Cerebrospinal Fluid Neurofilament Light in a Representative Sample of Cognitively Unimpaired 70-Year Olds. J Gerontol A Biol Sci Med Sci 2021; 76:983-990. [PMID: 33512503 PMCID: PMC8140047 DOI: 10.1093/gerona/glab030] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Indexed: 01/01/2023] Open
Abstract
The effect of Alzheimer's disease (AD) polygenic risk scores (PRS) on amyloid and tau pathophysiology and neurodegeneration in cognitively unimpaired older adults is not known in detail. This study aims to investigate non-APOE AD-PRS and APOE ε4 in relation to AD pathophysiology evaluated by cerebrospinal fluid (CSF) biomarkers in a population-based sample of 70-year olds. A total of 303 dementia-free individuals from the Gothenburg H70 Birth Cohort Studies were included. Genotyping was performed using the NeuroChip, and AD-PRS were calculated. CSF levels of amyloid-β (Aβ42), total tau (t-tau), phosphorylated tau (p-tau), neurogranin (Ng), and neurofilament light (NfL) were measured with enzyme-linked immunosorbent assay. Associations were found between non-APOE PRS and both NfL (p = .001) and Aβ42 (p = .02), and between APOE ε4 and Aβ42 (p = 1e-10), t-tau (p = 5e-4), and p-tau (p = .002). Similar results were observed when only including individuals with CDR = 0, except for no evidence of an association between non-APOE PRS and Aβ42. There was an interaction between non-APOE PRS and Aβ42 pathology status in relation to NfL (p = .005); association was only present in individuals without Aβ42 pathology (p = 3e-4). In relation to Aβ42, there was a borderline interaction (p = .06) between non-APOE PRS and APOE ε4; association was present in ε4 carriers only (p = .03). Similar results were observed in individuals with CDR = 0 (n = 246). In conclusion, among cognitively healthy 70-year olds from the general population, genetic risk of AD beyond the APOE locus was associated with NfL in individuals without Aβ42 pathology, and with Aβ42 in APOE ε4 carriers, suggesting these associations are driven by different mechanisms.
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Affiliation(s)
- Ingmar Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Sweden,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Silke Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Sweden,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Jenna Najar
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Sweden,Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Rita Guerreiro
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, USA
| | - Jose Bras
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, USA
| | - Margda Waern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Sweden,Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, Gothenburg, Sweden
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom,UK Dementia Research Institute at UCL, London, United Kingdom,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Anna Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Sweden,Address correspondence to: Anna Zettergren, PhD, Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, Wallinsgatan 6, 431 41 Mölndal, Sweden. E-mail:
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