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Zhao R, Lu Y, Wan Z, Qiao P, Yang L, Zhang Y, Huang S, Chen X. Identification and validation of an anoikis-related genes signature for prognostic implication in papillary thyroid cancer. Aging (Albany NY) 2024; 16:7405-7425. [PMID: 38663918 PMCID: PMC11087102 DOI: 10.18632/aging.205766] [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: 11/22/2023] [Accepted: 03/03/2024] [Indexed: 05/08/2024]
Abstract
Thyroid cancer, notably papillary thyroid cancer (PTC), is a global health concern with increasing incidence. Anoikis, a regulator of programmed cell death, is pivotal in normal physiology and, when dysregulated, can drive cancer progression and metastasis. This study explored the impact of anoikis on PTC prognosis. Analyzing data from GEO, TCGA, and GeneCards, we identified a prognostic signature consisting of six anoikis-related genes (ARGs): EZH2, PRKCQ, CD36, INHBB, TDGF1, and MMP9. This signature independently predicted patient outcomes, with high-risk scores associated with worse prognoses. A robust predictive ability was confirmed via ROC analysis, and a nomogram achieved a C-index of 0.712. Differences in immune infiltration levels were observed between high- and low-risk groups. Importantly, the high-risk group displayed reduced drug sensitivity and poor responses to immunotherapy. This research provides insights into anoikis in PTC, offering a novel ARG signature for predicting patient prognosis and guiding personalized treatment strategies.
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Affiliation(s)
- Runyu Zhao
- Postgraduate Training Base at Shanghai Gongli Hospital, Ningxia Medical University, Shanghai 200135, China
| | - Yingying Lu
- School of Medicine, Shanghai University, Shanghai 200444, China
| | - Zhihan Wan
- Department of Endocrinology, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
| | - Peipei Qiao
- Department of Otolaryngology Head and Neck Surgery, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
| | - Liyun Yang
- Department of Otolaryngology Head and Neck Surgery, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
| | - Yi Zhang
- Department of Otolaryngology Head and Neck Surgery, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
| | - Shuixian Huang
- Department of Otolaryngology Head and Neck Surgery, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
| | - Xiaoping Chen
- Department of Otolaryngology Head and Neck Surgery, Gongli Hospital of Shanghai Pudong New Area, Shanghai 200135, China
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Dressman D, Tasaki S, Yu L, Schneider J, Bennett DA, Elyaman W, Vardarajan B. Polygenic risk associated with Alzheimer's disease and other traits influences genes involved in T cell signaling and activation. Front Immunol 2024; 15:1337831. [PMID: 38590520 PMCID: PMC10999606 DOI: 10.3389/fimmu.2024.1337831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/22/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction T cells, known for their ability to respond to an enormous variety of pathogens and other insults, are increasingly recognized as important mediators of pathology in neurodegeneration and other diseases. T cell gene expression phenotypes can be regulated by disease-associated genetic variants. Many complex diseases are better represented by polygenic risk than by individual variants. Methods We first compute a polygenic risk score (PRS) for Alzheimer's disease (AD) using genomic sequencing data from a cohort of Alzheimer's disease (AD) patients and age-matched controls, and validate the AD PRS against clinical metrics in our cohort. We then calculate the PRS for several autoimmune disease, neurological disorder, and immune function traits, and correlate these PRSs with T cell gene expression data from our cohort. We compare PRS-associated genes across traits and four T cell subtypes. Results Several genes and biological pathways associated with the PRS for these traits relate to key T cell functions. The PRS-associated gene signature generally correlates positively for traits within a particular category (autoimmune disease, neurological disease, immune function) with the exception of stroke. The trait-associated gene expression signature for autoimmune disease traits was polarized towards CD4+ T cell subtypes. Discussion Our findings show that polygenic risk for complex disease and immune function traits can have varying effects on T cell gene expression trends. Several PRS-associated genes are potential candidates for therapeutic modulation in T cells, and could be tested in in vitro applications using cells from patients bearing high or low polygenic risk for AD or other conditions.
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Affiliation(s)
- Dallin Dressman
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
| | - Shinya Tasaki
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Lei Yu
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Julie Schneider
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
- Department of Pathology, Rush University Medical Center, Chicago, IL, United States
| | - David A. Bennett
- Rush University Medical Center, Rush Alzheimer’s Disease Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Wassim Elyaman
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
| | - Badri Vardarajan
- Department of Neurology, Columbia University, New York, NY, United States
- The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, United States
- College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, The Gertrude H. Sergievsky Center, New York, NY, United States
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Almeida MC, Eger SJ, He C, Audouard M, Nikitina A, Glasauer SMK, Han D, Mejía-Cupajita B, Acosta-Uribe J, Villalba-Moreno ND, Littau JL, Elcheikhali M, Rivera EK, Carrettiero DC, Villegas-Lanau CA, Sepulveda-Falla D, Lopera F, Kosik KS. Single-nucleus RNA sequencing demonstrates an autosomal dominant Alzheimer's disease profile and possible mechanisms of disease protection. Neuron 2024:S0896-6273(24)00093-X. [PMID: 38417436 DOI: 10.1016/j.neuron.2024.02.009] [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: 05/16/2023] [Revised: 01/07/2024] [Accepted: 02/09/2024] [Indexed: 03/01/2024]
Abstract
Highly penetrant autosomal dominant Alzheimer's disease (ADAD) comprises a distinct disease entity as compared to the far more prevalent form of AD in which common variants collectively contribute to risk. The downstream pathways that distinguish these AD forms in specific cell types have not been deeply explored. We compared single-nucleus transcriptomes among a set of 27 cases divided among PSEN1-E280A ADAD carriers, sporadic AD, and controls. Autophagy genes and chaperones clearly defined the PSEN1-E280A cases compared to sporadic AD. Spatial transcriptomics validated the activation of chaperone-mediated autophagy genes in PSEN1-E280A. The PSEN1-E280A case in which much of the brain was spared neurofibrillary pathology and harbored a homozygous APOE3-Christchurch variant revealed possible explanations for protection from AD pathology including overexpression of LRP1 in astrocytes, increased expression of FKBP1B, and decreased PSEN1 expression in neurons. The unique cellular responses in ADAD and sporadic AD require consideration when designing clinical trials.
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Affiliation(s)
- Maria Camila Almeida
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Center for Natural and Humans Sciences, Federal University of ABC, Sao Bernardo do Campo, SP 09608020, Brazil
| | - Sarah J Eger
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Caroline He
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Morgane Audouard
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Arina Nikitina
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Stella M K Glasauer
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Dasol Han
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Barbara Mejía-Cupajita
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia
| | - Juliana Acosta-Uribe
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia
| | - Nelson David Villalba-Moreno
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Jessica Lisa Littau
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Megan Elcheikhali
- Department of Statistics and Applied Probability, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Erica Keane Rivera
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Daniel Carneiro Carrettiero
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA; Center for Natural and Humans Sciences, Federal University of ABC, Sao Bernardo do Campo, SP 09608020, Brazil
| | | | - Diego Sepulveda-Falla
- Molecular Neuropathology of Alzheimer's Disease, Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, School of Medicine, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Kenneth S Kosik
- Neuroscience Research Institute and Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
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Gorijala P, Aslam MM, Dang LT, Xicota L, Fernandez MV, Sung YJ, Fan K, Feingold E, Surace EI, Chhatwal JP, Hom CL, Hartley SL, Hassenstab J, Perrin RJ, Mapstone M, Zaman SH, Ances BM, Kamboh MI, Lee JH, Cruchaga C. Alzheimer's polygenic risk scores are associated with cognitive phenotypes in Down syndrome. Alzheimers Dement 2024; 20:1038-1049. [PMID: 37855447 PMCID: PMC10916941 DOI: 10.1002/alz.13506] [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: 05/03/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/20/2023]
Abstract
INTRODUCTION This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS AD polygenic risk scores (PRS) were tested for association with DS-related traits. RESULTS The AD risk PRS was associated with disease status in several cohorts of sporadic late- and early-onset and familial late-onset AD, but not in familial early-onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE , p = 2.84 × 10-4 ; PRS excluding APOE, PRSnonAPOE , p = 1.60 × 10-2 ). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. HIGHLIGHTS Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults.
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Affiliation(s)
- Priyanka Gorijala
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- Neurogenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
| | - M. Muaaz Aslam
- Department of Human GeneticsUniversity of PittsburghSchool of Public HealthPittsburghPennsylvaniaUSA
| | - Lam‐Ha T. Dang
- Department of EpidemiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Sergievsky CenterTaub Institute for Research on Alzheimer's Disease and the Aging Brainand Department of NeurologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - L. Xicota
- Sergievsky CenterTaub Institute for Research on Alzheimer's Disease and the Aging Brainand Department of NeurologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Maria V. Fernandez
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- Neurogenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
| | - Yun Ju Sung
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- Neurogenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Division of BiostatisticsWashington University School of MedicineSt. LouisMissouriUSA
| | - Kang‐Hsien Fan
- Department of Human GeneticsUniversity of PittsburghSchool of Public HealthPittsburghPennsylvaniaUSA
| | - Eleanor Feingold
- Department of Human GeneticsUniversity of PittsburghSchool of Public HealthPittsburghPennsylvaniaUSA
| | - Ezequiel I. Surace
- Laboratory of Neurodegenerative Diseases ‐ Institute of Neurosciences (INEU‐Fleni‐ CONICET)Buenos AiresArgentina
| | - Jasmeer P Chhatwal
- Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Christy L. Hom
- Dept. of Psychiatry and Human BehaviorUniversity of CaliforniaIrvine School of MedicineCaliforniaUSA
| | | | | | - Sigan L. Hartley
- Waisman Center and School of Human EcologyUniversity of Wisconsin‐ MadisonMadisonWisconsinUSA
| | - Jason Hassenstab
- Department of Neurology and Psychological & Brain SciencesWashington UniversitySt. LouisMissouriUSA
| | - Richard J. Perrin
- Hope Center for Neurologic DiseasesWashington UniversitySt. LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSt. LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Mark Mapstone
- Department of NeurologyUniversity of California‐IrvineIrvineCaliforniaUSA
| | - Shahid H Zaman
- Cambridge Intellectual and Developmental Disabilities Research GroupDepartment of PsychiatryUniversity of CambridgeDouglas HouseCambridgeUK
- Cambridgeshire and Peterborough NHS Foundation TrustElizabeth HouseFulbourn HospitalFulbournCambridgeUK
| | - Beau M Ances
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - M. Ilyas Kamboh
- Department of Human GeneticsUniversity of PittsburghSchool of Public HealthPittsburghPennsylvaniaUSA
| | - Joseph H Lee
- Department of EpidemiologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Sergievsky CenterTaub Institute for Research on Alzheimer's Disease and the Aging Brainand Department of NeurologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Carlos Cruchaga
- Department of PsychiatryWashington University School of MedicineSt. LouisMissouriUSA
- Neurogenomics and Informatics CenterWashington University School of MedicineSt. LouisMissouriUSA
- Hope Center for Neurologic DiseasesWashington UniversitySt. LouisMissouriUSA
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Vivek S, Faul J, Thyagarajan B, Guan W. Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia. J Biomed Inform 2023; 148:104536. [PMID: 37926392 PMCID: PMC11106718 DOI: 10.1016/j.jbi.2023.104536] [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: 01/31/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) and AD related dementias (ADRD) are complex multifactorial neurodegenerative diseases. The associations between genetic variants obtained from genome wide association studies (GWAS) are the most widely available and well documented variants associated with ADRD. Application of deep learning methods to analyze large scale GWAS data may be a powerful approach to elucidate the biological mechanisms in ADRD compared to penalized regression models that may lead to over-fitting. METHODS We developed a deep learning frame work explainable variational autoencoder (E-VAE) classifier model using genotype (GWAS SNPs = 5474) data from 2714 study participants in the Health and Retirement Study (HRS) to classify ADRD. We validated the generalizability of this model among 234 participants in the Religious Orders Study and Memory and Aging Project (ROSMAP). Utilizing a linear decoder approach we have extracted the weights associated with latent features for biological interpretation. RESULTS We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset. CONCLUSION This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort. The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.
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Affiliation(s)
- Sithara Vivek
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States
| | - Jessica Faul
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States.
| | - Weihua Guan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis MN, United States.
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Li C, Lin J, Jiang Q, Yang T, Xiao Y, Huang J, Hou Y, Wei Q, Cui Y, Wang S, Zheng X, Ou R, Liu K, Chen X, Song W, Zhao B, Shang H. Genetic Modifiers of Age at Onset for Amyotrophic Lateral Sclerosis: A Genome-Wide Association Study. Ann Neurol 2023; 94:933-941. [PMID: 37528491 DOI: 10.1002/ana.26752] [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: 02/10/2023] [Revised: 07/03/2023] [Accepted: 07/12/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVE Age at onset (AAO) is an essential clinical feature associated with disease progression and mortality in amyotrophic lateral sclerosis (ALS). Identification of genetic variants and environmental risk factors influencing AAO of ALS could help better understand the disease's biological mechanism and provide clinical guidance. However, most genetic studies focused on the risk of ALS, while the genetic background of AAO is less explored. This study aimed to identify genetic and environmental determinants for AAO of ALS. METHODS We performed a genome-wide association analysis using a Cox proportional hazards model on AAO of ALS in 10,068 patients. We further conducted colocalization analysis and in-vitro functional exploration for the target variants, as well as Mendelian randomization analysis to identify risk factors influencing AAO of ALS. RESULTS The total heritability of AAO of ALS was ~0.16 (standard error [SE] = 0.03). One novel locus rs2046243 (CTIF) was significantly associated with earlier AAO by ~1.29 years (p = 1.68E-08, beta = 0.10, SE = 0.02). Functional exploration suggested this variant was associated with increased expression of CTIF in multiple tissues including the brain. Colocalization analysis detected a colocalization signal at the locus between AAO of ALS and expression of CTIF. Causal inference indicated higher education level was associated with later AAO. INTERPRETATION These findings improve the current knowledge of the genetic and environmental etiology of AAO of ALS, and provide a novel target CTIF for further research on ALS pathogenesis and potential therapeutic options to delay the disease onset. ANN NEUROL 2023;94:933-941.
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Affiliation(s)
- Chunyu Li
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Junyu Lin
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Qirui Jiang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Tianmi Yang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Xiao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Jingxuan Huang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Yanbing Hou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Qianqian Wei
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Yiyuan Cui
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Shichan Wang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoting Zheng
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Kuncheng Liu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Xueping Chen
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Song
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Bi Zhao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
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Li C, Wei Q, Hou Y, Lin J, Ou R, Zhang L, Jiang Q, Xiao Y, Liu K, Chen X, Yang T, Song W, Zhao B, Wu Y, Shang H. Genome-wide analyses identify NEAT1 as genetic modifier of age at onset of amyotrophic lateral sclerosis. Mol Neurodegener 2023; 18:77. [PMID: 37872557 PMCID: PMC10594666 DOI: 10.1186/s13024-023-00669-6] [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: 02/07/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023] Open
Abstract
BACKGROUND Patients with amyotrophic lateral sclerosis (ALS) demonstrate great heterogeneity in the age at onset (AAO), which is closely related to the course of disease. However, most genetic studies focused on the risk of ALS, while the genetic background underlying AAO of ALS is still unknown. METHODS To identify genetic determinants influencing AAO of ALS, we performed genome-wide association analysis using a Cox proportional hazards model in 2,841 patients with ALS (Ndiscovery = 2,272, Nreplication = 569) in the Chinese population. We further conducted colocalization analysis using public cis-eQTL dataset, and Mendelian randomization analysis to identify risk factors for AAO of ALS. Finally, functional experiments including dual-luciferase reporter assay and RT-qPCR were performed to explore the regulatory effect of the target variant. RESULTS The total heritability of AAO of ALS was ~ 0.24. One novel locus rs10128627 (FRMD8) was significantly associated with earlier AAO by ~ 3.15 years (P = 1.54E-08, beta = 0.31, SE = 0.05). This locus was cis-eQTL of NEAT1 in multiple brain tissues and blood. Colocalization analysis detected association signals at this locus between AAO of ALS and expression of NEAT1. Furthermore, functional exploration supported the variant rs10128627 was associated with upregulated expression of NEAT1 in cell models and patients with ALS. Causal inference suggested higher total cholesterol, low-density lipoprotein, and eosinophil were nominally associated with earlier AAO of ALS, while monocyte might delay the AAO. CONCLUSIONS Collective evidence from genetic, bioinformatic, and functional results suggested NEAT1 as a key player in the disease progression of ALS. These findings improve the current understanding of the genetic role in AAO of ALS, and provide a novel target for further research on the pathogenesis and therapeutic options to delay the disease onset.
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Affiliation(s)
- Chunyu Li
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Qianqian Wei
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Yanbing Hou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Junyu Lin
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Ruwei Ou
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Lingyu Zhang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Qirui Jiang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Yi Xiao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Kuncheng Liu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Xueping Chen
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - TianMi Yang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Wei Song
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Bi Zhao
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Ying Wu
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China
| | - Huifang Shang
- Department of Neurology, Laboratory of Neurodegenerative Disorders, West China Hospital, National Clinical Research Center for Geriatric, Sichuan University, Chengdu, China.
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ZHAO M, LUO Y, WANG H, CAO Y, MA L, PEI H, LI H. Guilingji capsule for Alzheimer's disease: secondary analysis of a randomized non-inferiority controlled trial. J TRADIT CHIN MED 2023; 43:1019-1025. [PMID: 37679990 PMCID: PMC10465832 DOI: 10.19852/j.cnki.jtcm.20230404.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 09/14/2022] [Indexed: 09/09/2023]
Abstract
OBJECTIVE To investigate the effectiveness and safety of Guilingji capsule (, GLJC) in treatment of Alzheimer's disease (AD) patients with kidney-marrow deficiency pattern (KMDP) compared with gingko extract tablets. METHODS This is a secondary analysis of a large-scale multicenter randomized non-inferiority clinical trial. A total of 120 AD patients with KMDP were enrolled in this study. The participants were randomly categorized into two groups: (a) GLJC group ( = 60) and (b) gingko group ( = 60). The GLJC group was treated with GLJC and gingko extract mimetic tablets, whereas the gingko group received gingko extract tablets and mimetic GLJC. The data on the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Activities of Daily Living (ADL), and Chinese Medicine Symptom Scale (CM-SS) was evaluated at 0, 12, and 24 weeks of treatment. The serum levels of acetylcholine (Ach), acetylcholinesterase (AchE), B-cell lymphoma-2 (Bcl-2), and Bcl-2-associated X protein (Bax) in the participants were measured before and after 24 weeks of treatment. The safety was based on the incidence of adverse events. RESULTS Both interventions significantly increased the MMSE scores of the participants and decreased their ADAS-Cog, ADL, and CM-SS scores ( < 0.01). Compared with the gingko group, the GLJC group had a higher effective rate of improvement in the symptoms of "amnesia" and "dull expression and slow thinking" at the 12th week and 24th week ( < 0.05, < 0.01). In the GLJC group, serum Bcl-2 levels were significantly increased at the 24th week ( < 0.05). Serum Bax and AchE levels of the two groups were significantly decreased at the 24th week ( < 0.01). No treatment-related adverse events were reported in the two groups. CONCLUSIONS GLJC is equivalent to the gingko extract tablets in terms of improving cognitive function and the quality of life in AD patients with KMDP and has good clinical efficacy and safety. When it comes to improving TCM symptoms and anti-aging, GLJC is even more advantageous.
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Affiliation(s)
- Ming ZHAO
- 1 Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yimiao LUO
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Huichan WANG
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Yu CAO
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Lina MA
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Hui PEI
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
| | - Hao LI
- 2 Department of Geriatrics, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing 100091, China
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9
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Ikonnikova A, Morozova A, Antonova O, Ochneva A, Fedoseeva E, Abramova O, Emelyanova M, Filippova M, Morozova I, Zorkina Y, Syunyakov T, Andryushchenko A, Andreuyk D, Kostyuk G, Gryadunov D. Evaluation of the Polygenic Risk Score for Alzheimer's Disease in Russian Patients with Dementia Using a Low-Density Hydrogel Oligonucleotide Microarray. Int J Mol Sci 2023; 24:14765. [PMID: 37834213 PMCID: PMC10572681 DOI: 10.3390/ijms241914765] [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: 09/04/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The polygenic risk score (PRS), together with the ɛ4 allele of the APOE gene (APOE-ɛ4), has shown high potential for Alzheimer's disease (AD) risk prediction. The aim of this study was to validate the model of polygenic risk in Russian patients with dementia. A microarray-based assay was developed to identify 21 markers of polygenic risk and ɛ alleles of the APOE gene. This case-control study included 348 dementia patients and 519 cognitively normal volunteers. Cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau protein levels were assessed in 57 dementia patients. PRS and APOE-ɛ4 were significant genetic risk factors for dementia. Adjusted for APOE-ɛ4, individuals with PRS corresponding to the fourth quartile had an increased risk of dementia compared to the first quartile (OR 1.85; p-value 0.002). The area under the curve (AUC) was 0.559 for the PRS model only, and the inclusion of APOE-ɛ4 improved the AUC to 0.604. PRS was positively correlated with tTau and pTau181 and inversely correlated with Aβ42/Aβ40 ratio. Carriers of APOE-ɛ4 had higher levels of tTau and pTau181 and lower levels of Aβ42 and Aβ42/Aβ40. The developed assay can be part of a strategy for assessing individuals for AD risk, with the purpose of assisting primary preventive interventions.
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Affiliation(s)
- Anna Ikonnikova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Anna Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Olga Antonova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Alexandra Ochneva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Elena Fedoseeva
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Olga Abramova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Marina Emelyanova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Marina Filippova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Irina Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Yana Zorkina
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Timur Syunyakov
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- International Centre for Education and Research in Neuropsychiatry (ICERN), Samara State Medical University, 443016 Samara, Russia
| | - Alisa Andryushchenko
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Denis Andreuyk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Economy Faculty, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Georgy Kostyuk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Psychiatry, Federal State Budgetary Educational Institution of Higher Education “Moscow State University of Food Production”, Volokolamskoye Highway 11, 125080 Moscow, Russia
| | - Dmitry Gryadunov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
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10
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Bradley J, Gorijala P, Schindler SE, Sung YJ, Ances B, Fernandez MV, Cruchaga C. Genetic architecture of plasma Alzheimer disease biomarkers. Hum Mol Genet 2023; 32:2532-2543. [PMID: 37208024 PMCID: PMC10360384 DOI: 10.1093/hmg/ddad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 05/21/2023] Open
Abstract
Genome-wide association studies (GWAS) of cerebrospinal fluid (CSF) Alzheimer's Disease (AD) biomarker levels have identified novel genes implicated in disease risk, onset and progression. However, lumbar punctures have limited availability and may be perceived as invasive. Blood collection is readily available and well accepted, but it is not clear whether plasma biomarkers will be informative for genetic studies. Here we perform genetic analyses on concentrations of plasma amyloid-β peptides Aβ40 (n = 1,467) and Aβ42 (n = 1,484), Aβ42/40 (n = 1467) total tau (n = 504), tau phosphorylated (p-tau181; n = 1079) and neurofilament light (NfL; n = 2,058). GWAS and gene-based analysis was used to identify single variant and genes associated with plasma levels. Finally, polygenic risk score and summary statistics were used to investigate overlapping genetic architecture between plasma biomarkers, CSF biomarkers and AD risk. We found a total of six genome-wide significant signals. APOE was associated with plasma Aβ42, Aβ42/40, tau, p-tau181 and NfL. We proposed 10 candidate functional genes on the basis of 12 single nucleotide polymorphism-biomarker pairs and brain differential gene expression analysis. We found a significant genetic overlap between CSF and plasma biomarkers. We also demonstrate that it is possible to improve the specificity and sensitivity of these biomarkers, when genetic variants regulating protein levels are included in the model. This current study using plasma biomarker levels as quantitative traits can be critical to identification of novel genes that impact AD and more accurate interpretation of plasma biomarker levels.
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Affiliation(s)
- Joseph Bradley
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E Schindler
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yun J Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Beau Ances
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maria V Fernandez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO 63110, USA
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11
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Ferreiro AL, Choi J, Ryou J, Newcomer EP, Thompson R, Bollinger RM, Hall-Moore C, Ndao IM, Sax L, Benzinger TLS, Stark SL, Holtzman DM, Fagan AM, Schindler SE, Cruchaga C, Butt OH, Morris JC, Tarr PI, Ances BM, Dantas G. Gut microbiome composition may be an indicator of preclinical Alzheimer's disease. Sci Transl Med 2023; 15:eabo2984. [PMID: 37315112 PMCID: PMC10680783 DOI: 10.1126/scitranslmed.abo2984] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/26/2023] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) pathology is thought to progress from normal cognition through preclinical disease and ultimately to symptomatic AD with cognitive impairment. Recent work suggests that the gut microbiome of symptomatic patients with AD has an altered taxonomic composition compared with that of healthy, cognitively normal control individuals. However, knowledge about changes in the gut microbiome before the onset of symptomatic AD is limited. In this cross-sectional study that accounted for clinical covariates and dietary intake, we compared the taxonomic composition and gut microbial function in a cohort of 164 cognitively normal individuals, 49 of whom showed biomarker evidence of early preclinical AD. Gut microbial taxonomic profiles of individuals with preclinical AD were distinct from those of individuals without evidence of preclinical AD. The change in gut microbiome composition correlated with β-amyloid (Aβ) and tau pathological biomarkers but not with biomarkers of neurodegeneration, suggesting that the gut microbiome may change early in the disease process. We identified specific gut bacterial taxa associated with preclinical AD. Inclusion of these microbiome features improved the accuracy, sensitivity, and specificity of machine learning classifiers for predicting preclinical AD status when tested on a subset of the cohort (65 of the 164 participants). Gut microbiome correlates of preclinical AD neuropathology may improve our understanding of AD etiology and may help to identify gut-derived markers of AD risk.
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Affiliation(s)
- Aura L. Ferreiro
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - JooHee Choi
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jian Ryou
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Erin P. Newcomer
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Regina Thompson
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rebecca M. Bollinger
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carla Hall-Moore
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - I. Malick Ndao
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Laurie Sax
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Susan L. Stark
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Omar H. Butt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Phillip I. Tarr
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Beau M. Ances
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gautam Dantas
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
- Department of Pathology and Immunology, Division of Laboratory and Genomic Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
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12
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Wright CA, Taylor JW, Cochran M, Lawlor JM, Moyers BA, Amaral MD, Bonnstetter ZT, Carter P, Solomon V, Myers RM, Love MN, Geldmacher DS, Cooper SJ, Roberson ED, Cochran JN. Contributions of rare and common variation to early-onset and atypical dementia risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.06.23285383. [PMID: 36798301 PMCID: PMC9934786 DOI: 10.1101/2023.02.06.23285383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
We collected and analyzed genomic sequencing data from individuals with clinician- diagnosed early-onset or atypical dementia. Thirty-two patients were previously described, with sixty-eight newly described in this report. Of those sixty-eight, sixty-two patients reported Caucasian, non-Hispanic ethnicity and six reported as African American, non-Hispanic. Fifty-three percent of patients had a returnable variant. Five patients harbored a pathogenic variant as defined by the American College of Medical Genetics criteria for pathogenicity. A polygenic risk score was calculated for Alzheimer's patients in the total cohort and compared to the scores of a late-onset Alzheimer's cohort and a control set. Patients with early-onset Alzheimer's had higher non- APOE polygenic risk scores than patients with late onset Alzheimer's, supporting the conclusion that both rare and common genetic variation associate with early-onset neurodegenerative disease risk.
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Affiliation(s)
- Carter A. Wright
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA,University of Alabama in Huntsville, Huntsville, Alabama 35899, USA
| | - Jared W. Taylor
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Meagan Cochran
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - James M.J. Lawlor
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Belle A. Moyers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | | | | | - Princess Carter
- Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
| | - Veronika Solomon
- Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
| | - Richard M. Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Marissa Natelson Love
- Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
| | - David S. Geldmacher
- Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
| | - Sara J. Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA
| | - Erik D. Roberson
- Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
| | - J. Nicholas Cochran
- HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA,Alzheimer’s Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
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13
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Panyard DJ, Deming YK, Darst BF, Van Hulle CA, Zetterberg H, Blennow K, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Asthana S, Engelman CD, Lu Q. Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:395-409. [PMID: 36744333 PMCID: PMC10050104 DOI: 10.3233/jad-220599] [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: 02/05/2023]
Abstract
BACKGROUND Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. OBJECTIVE We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. METHODS We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. RESULTS The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5). CONCLUSION These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.
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Affiliation(s)
- Daniel J. Panyard
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Yuetiva K. Deming
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Burcu F. Darst
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States of America
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
| | - Henrik Zetterberg
- 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
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - 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
| | | | | | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison, 610 Walnut Street, 9 Floor, Madison, WI 53726, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison, 600 Highland Avenue, J5/1 Mezzanine, Madison, WI 53792, United States of America
- Department of Medicine, University of Wisconsin-Madison, 1685 Highland Avenue, 5158 Medical Foundation Centennial Building, Madison, WI 53705, United States of America
- William S. Middleton Memorial Veterans Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States of America
| | - Corinne D. Engelman
- Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, 707 WARF Building, Madison, WI 53726, United States of America
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WARF Room 201, 610 Walnut Street, Madison, WI 53726, United States of America
- Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, United States of America
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14
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Gorina YV, Vlasova OL, Bolshakova AV, Salmina AB. Alzheimer’s Disease: a Search for the Best Experimental Models to Decode Cellular and Molecular Mechanisms of Its Development. J EVOL BIOCHEM PHYS+ 2023. [DOI: 10.1134/s0022093023010106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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15
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Long JM, Coble DW, Xiong C, Schindler SE, Perrin RJ, Gordon BA, Benzinger TLS, Grant E, Fagan AM, Harari O, Cruchaga C, Holtzman DM, Morris JC. Preclinical Alzheimer's disease biomarkers accurately predict cognitive and neuropathological outcomes. Brain 2022; 145:4506-4518. [PMID: 35867858 PMCID: PMC10200309 DOI: 10.1093/brain/awac250] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 05/30/2022] [Accepted: 07/20/2022] [Indexed: 01/24/2023] Open
Abstract
Alzheimer's disease biomarkers are widely accepted as surrogate markers of underlying neuropathological changes. However, few studies have evaluated whether preclinical Alzheimer's disease biomarkers predict Alzheimer's neuropathology at autopsy. We sought to determine whether amyloid PET imaging or CSF biomarkers accurately predict cognitive outcomes and Alzheimer's disease neuropathological findings. This study included 720 participants, 42-91 years of age, who were enrolled in longitudinal studies of memory and aging in the Washington University Knight Alzheimer Disease Research Center and were cognitively normal at baseline, underwent amyloid PET imaging and/or CSF collection within 1 year of baseline clinical assessment, and had subsequent clinical follow-up. Cognitive status was assessed longitudinally by Clinical Dementia Rating®. Biomarker status was assessed using predefined cut-offs for amyloid PET imaging or CSF p-tau181/amyloid-β42. Subsequently, 57 participants died and underwent neuropathologic examination. Alzheimer's disease neuropathological changes were assessed using standard criteria. We assessed the predictive value of Alzheimer's disease biomarker status on progression to cognitive impairment and for presence of Alzheimer's disease neuropathological changes. Among cognitively normal participants with positive biomarkers, 34.4% developed cognitive impairment (Clinical Dementia Rating > 0) as compared to 8.4% of those with negative biomarkers. Cox proportional hazards modelling indicated that preclinical Alzheimer's disease biomarker status, APOE ɛ4 carrier status, polygenic risk score and centred age influenced risk of developing cognitive impairment. Among autopsied participants, 90.9% of biomarker-positive participants and 8.6% of biomarker-negative participants had Alzheimer's disease neuropathological changes. Sensitivity was 87.0%, specificity 94.1%, positive predictive value 90.9% and negative predictive value 91.4% for detection of Alzheimer's disease neuropathological changes by preclinical biomarkers. Single CSF and amyloid PET baseline biomarkers were also predictive of Alzheimer's disease neuropathological changes, as well as Thal phase and Braak stage of pathology at autopsy. Biomarker-negative participants who developed cognitive impairment were more likely to exhibit non-Alzheimer's disease pathology at autopsy. The detection of preclinical Alzheimer's disease biomarkers is strongly predictive of future cognitive impairment and accurately predicts presence of Alzheimer's disease neuropathology at autopsy.
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Affiliation(s)
- Justin M Long
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Dean W Coble
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Chengjie Xiong
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Suzanne E Schindler
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Richard J Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Brian A Gordon
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Elizabeth Grant
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Anne M Fagan
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Oscar Harari
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - Carlos Cruchaga
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - David M Holtzman
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
| | - John C Morris
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
- Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, MO 63110, USA
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16
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Tomassen J, den Braber A, van der Lee SJ, Reus LM, Konijnenberg E, Carter SF, Yaqub M, van Berckel BN, Collij LE, Boomsma DI, de Geus EJ, Scheltens P, Herholz K, Tijms BM, Visser PJ. Amyloid-β and APOE genotype predict memory decline in cognitively unimpaired older individuals independently of Alzheimer's disease polygenic risk score. BMC Neurol 2022; 22:484. [PMID: 36522743 PMCID: PMC9753236 DOI: 10.1186/s12883-022-02925-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND What combination of risk factors for Alzheimer's disease (AD) are most predictive of cognitive decline in cognitively unimpaired individuals remains largely unclear. We studied associations between APOE genotype, AD-Polygenic Risk Scores (AD-PRS), amyloid-β pathology and decline in cognitive functioning over time in a large sample of cognitively unimpaired older individuals. METHODS We included 276 cognitively unimpaired older individuals (75 ± 10 years, 63% female) from the EMIF-AD PreclinAD cohort. An AD-PRS was calculated including 83 genome-wide significant variants. The APOE gene was not included in the PRS and was analyzed separately. Baseline amyloid-β status was assessed by visual read of [18F]flutemetamol-PET standardized uptake value images. At baseline and follow-up (2.0 ± 0.4 years), the cognitive domains of memory, attention, executive function, and language were measured. We used generalized estimating equations corrected for age, sex and center to examine associations between APOE genotype and AD-PRS with amyloid-β status. Linear mixed models corrected for age, sex, center and education were used to examine associations between APOE genotype, AD-PRS and amyloid-β status, and their interaction on changes in cognitive functioning over time. RESULTS Fifty-two participants (19%) had abnormal amyloid-β, and 84 participants (31%) carried at least one APOE ε4 allele. APOE genotype and AD-PRS were both associated with abnormal amyloid-β status. Increasingly more risk-full APOE genotype, a high AD-PRS and an abnormal amyloid-β status were associated with steeper decline in memory functioning in separate models (all p ≤ 0.02). A model including 4-way interaction term (APOE×AD-PRS×amyloid-β×time) was not significant. When modelled together, both APOE genotype and AD-PRS predicted steeper decline in memory functioning (APOE β(SE)=-0.05(0.02); AD-PRS β(SE)=-0.04(0.01)). Additionally, when modelled together, both amyloid-β status and AD-PRS predicted a steeper decline in memory functioning (amyloid-β β(SE)=-0.07(0.04); AD-PRS β(SE)=-0.04(0.01)). Modelling both APOE genotype and amyloid-β status, we observed an interaction, in which APOE genotype was related to steeper decline in memory and language functioning in amyloid-β abnormal individuals only (β(SE)=-0.13(0.06); β(SE)=-0.22(0.07), respectively). CONCLUSION Our results suggest that APOE genotype is related to steeper decline in memory and language functioning in individuals with abnormal amyloid-β only. Furthermore, independent of amyloid-β status other genetic risk variants contribute to memory decline in initially cognitively unimpaired older individuals.
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Affiliation(s)
- Jori Tomassen
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands ,grid.16872.3a0000 0004 0435 165XAlzheimer Center Amsterdam, Neurology, Amsterdam UMC location VUmc, 1007 MB Amsterdam, PO Box 7057, The Netherlands
| | - Anouk den Braber
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sven J. van der Lee
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Genomics of Neurodegenerative Diseases and Aging, Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Lianne M. Reus
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elles Konijnenberg
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Stephen F. Carter
- grid.5379.80000000121662407Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK ,grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Maqsood Yaqub
- grid.12380.380000 0004 1754 9227Department of Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Bart N.M. van Berckel
- grid.12380.380000 0004 1754 9227Department of Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Lyduine E. Collij
- grid.12380.380000 0004 1754 9227Department of Radiology & Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eco J.C. de Geus
- grid.12380.380000 0004 1754 9227Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Scheltens
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Karl Herholz
- grid.5379.80000000121662407Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Betty M. Tijms
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- grid.12380.380000 0004 1754 9227Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands ,grid.484519.5Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands ,grid.4714.60000 0004 1937 0626Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden
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17
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Mol MO, van der Lee SJ, Hulsman M, Pijnenburg YAL, Scheltens P, Seelaar H, van Swieten JC, Kaat LD, Holstege H, van Rooij JGJ. Mapping the genetic landscape of early-onset Alzheimer’s disease in a cohort of 36 families. Alzheimers Res Ther 2022; 14:77. [PMID: 35650585 PMCID: PMC9158156 DOI: 10.1186/s13195-022-01018-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/16/2022] [Indexed: 11/10/2022]
Abstract
Abstract
Background
Many families with clinical early-onset Alzheimer’s disease (EOAD) remain genetically unexplained. A combination of genetic factors is not standardly investigated. In addition to monogenic causes, we evaluated the possible polygenic architecture in a large series of families, to assess if genetic testing of familial EOAD could be expanded.
Methods
Thirty-six pedigrees (77 patients) were ascertained from a larger cohort of patients, with relationships determined by genetic data (exome sequencing data and/or SNP arrays). All families included at least one AD patient with symptom onset <70 years. We evaluated segregating rare variants in known dementia-related genes, and other genes or variants if shared by multiple families. APOE was genotyped and duplications in APP were assessed by targeted test or using SNP array data. We computed polygenic risk scores (PRS) compared with a reference population-based dataset, by imputing SNP arrays or exome sequencing data.
Results
In eight families, we identified a pathogenic variant, including the genes APP, PSEN1, SORL1, and an unexpected GRN frameshift variant. APOE-ε4 homozygosity was present in eighteen families, showing full segregation with disease in seven families. Eight families harbored a variant of uncertain significance (VUS), of which six included APOE-ε4 homozygous carriers. PRS was not higher in the families combined compared with the population mean (beta 0.05, P = 0.21), with a maximum increase of 0.61 (OR = 1.84) in the GRN family. Subgroup analyses indicated lower PRS in six APP/PSEN1 families compared with the rest (beta −0.22 vs. 0.10; P = 0.009) and lower APOE burden in all eight families with monogenic cause (beta 0.29 vs. 1.15, P = 0.010). Nine families remained without a genetic cause or risk factor identified.
Conclusion
Besides monogenic causes, we suspect a polygenic disease architecture in multiple families based on APOE and rare VUS. The risk conveyed by PRS is modest across the studied families. Families without any identified risk factor render suitable candidates for further in-depth genetic evaluation.
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18
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Mirza-Davies A, Foley S, Caseras X, Baker E, Holmans P, Escott-Price V, Jones DK, Harrison JR, Messaritaki E. The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults. Front Neurosci 2022; 16:987677. [PMID: 36532292 PMCID: PMC9748570 DOI: 10.3389/fnins.2022.987677] [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: 07/06/2022] [Accepted: 11/09/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Methods Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. Results In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036). Discussion We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.
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Affiliation(s)
- Anastasia Mirza-Davies
- School of Medicine, University Hospital Wales, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Xavier Caseras
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Judith R. Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Institute for Translational and Clinical Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- BRAIN Biomedical Research Unit, School of Medicine, Cardiff University, Cardiff, United Kingdom
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Xiang T, Qiao M, Xie J, Li Z, Xie H. Emerging Roles of the Unique Molecular Chaperone Cosmc in the Regulation of Health and Disease. Biomolecules 2022; 12:biom12121732. [PMID: 36551160 PMCID: PMC9775496 DOI: 10.3390/biom12121732] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/12/2022] [Accepted: 11/13/2022] [Indexed: 11/25/2022] Open
Abstract
The core-1 β1-3galactosyltransferase-specific chaperone 1 (Cosmc) is a unique molecular chaperone of core-1 β1-3galactosyltransferase(C1GALT1), which typically functions inside the endoplasmic reticulum (ER). Cosmc helps C1GALT1 to fold correctly and maintain activity. It also participates in the synthesis of the T antigen, O-glycan, together with C1GALT1. Cosmc is a multifaceted molecule with a wide range of roles and functions. It involves platelet production and the regulation of immune cell function. Besides that, the loss of function of Cosmc also facilitates the development of several diseases, such as inflammation diseases, immune-mediated diseases, and cancer. It suggests that Cosmc is a critical control point in diseases and that it should be regarded as a potential target for oncotherapy. It is essential to fully comprehend Cosmc's roles, as they may provide critical information about its involvement in disease development and pathogenesis. In this review, we summarize the recent progress in understanding the role of Cosmc in normal development and diseases.
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Affiliation(s)
- Ting Xiang
- Hunan Province Key Laboratory of Tumor cellular Molecular Pathology, Cancer Research Institute, Heng yang School of Medicine, University of South China, Hengyang 421009, China
| | - Muchuan Qiao
- Hunan Province Key Laboratory of Tumor cellular Molecular Pathology, Cancer Research Institute, Heng yang School of Medicine, University of South China, Hengyang 421009, China
| | - Jiangbo Xie
- The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Hunan Cancer Hospital, Changsha 410013, China
| | - Zheng Li
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi’an 710069, China
- Correspondence: (Z.L.); (H.X.)
| | - Hailong Xie
- Hunan Province Key Laboratory of Tumor cellular Molecular Pathology, Cancer Research Institute, Heng yang School of Medicine, University of South China, Hengyang 421009, China
- Correspondence: (Z.L.); (H.X.)
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20
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Romero-Molina C, Garretti F, Andrews SJ, Marcora E, Goate AM. Microglial efferocytosis: Diving into the Alzheimer's disease gene pool. Neuron 2022; 110:3513-3533. [PMID: 36327897 DOI: 10.1016/j.neuron.2022.10.015] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies and functional genomics studies have linked specific cell types, genes, and pathways to Alzheimer's disease (AD) risk. In particular, AD risk alleles primarily affect the abundance or structure, and thus the activity, of genes expressed in macrophages, strongly implicating microglia (the brain-resident macrophages) in the etiology of AD. These genes converge on pathways (endocytosis/phagocytosis, cholesterol metabolism, and immune response) with critical roles in core macrophage functions such as efferocytosis. Here, we review these pathways, highlighting relevant genes identified in the latest AD genetics and genomics studies, and describe how they may contribute to AD pathogenesis. Investigating the functional impact of AD-associated variants and genes in microglia is essential for elucidating disease risk mechanisms and developing effective therapeutic approaches.
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Affiliation(s)
- Carmen Romero-Molina
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Garretti
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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21
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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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Ramanan VK, Heckman MG, Przybelski SA, Lesnick TG, Lowe VJ, Graff-Radford J, Mielke M, 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. [DOI: 10.3233/jad-220164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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, MN, USA
| | - Michael G. Heckman
- Department of Quantitative Health Sciences, Mayo Clinic-Florida, Jacksonville, FL, USA
| | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, USA
| | | | - M. Mielke
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - David S. Knopman
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - Ronald C. Petersen
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, USA
- Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, USA
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, USA
- Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville, FL, USA
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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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24
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Tarozzi M, Bartoletti-Stella A, Dall'Olio D, Matteuzzi T, Baiardi S, Parchi P, Castellani G, Capellari S. Identification of recurrent genetic patterns from targeted sequencing panels with advanced data science: a case-study on sporadic and genetic neurodegenerative diseases. BMC Med Genomics 2022; 15:26. [PMID: 35144616 PMCID: PMC8830183 DOI: 10.1186/s12920-022-01173-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Targeted Next Generation Sequencing is a common and powerful approach used in both clinical and research settings. However, at present, a large fraction of the acquired genetic information is not used since pathogenicity cannot be assessed for most variants. Further complicating this scenario is the increasingly frequent description of a poli/oligogenic pattern of inheritance showing the contribution of multiple variants in increasing disease risk. We present an approach in which the entire genetic information provided by target sequencing is transformed into binary data on which we performed statistical, machine learning, and network analyses to extract all valuable information from the entire genetic profile. To test this approach and unbiasedly explore the presence of recurrent genetic patterns, we studied a cohort of 112 patients affected either by genetic Creutzfeldt–Jakob (CJD) disease caused by two mutations in the PRNP gene (p.E200K and p.V210I) with different penetrance or by sporadic Alzheimer disease (sAD). Results Unsupervised methods can identify functionally relevant sources of variation in the data, like haplogroups and polymorphisms that do not follow Hardy–Weinberg equilibrium, such as the NOTCH3 rs11670823 (c.3837 + 21 T > A). Supervised classifiers can recognize clinical phenotypes with high accuracy based on the mutational profile of patients. In addition, we found a similar alteration of allele frequencies compared the European population in sporadic patients and in V210I-CJD, a poorly penetrant PRNP mutation, and sAD, suggesting shared oligogenic patterns in different types of dementia. Pathway enrichment and protein–protein interaction network revealed different altered pathways between the two PRNP mutations. Conclusions We propose this workflow as a possible approach to gain deeper insights into the genetic information derived from target sequencing, to identify recurrent genetic patterns and improve the understanding of complex diseases. This work could also represent a possible starting point of a predictive tool for personalized medicine and advanced diagnostic applications. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01173-4.
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Affiliation(s)
- M Tarozzi
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - A Bartoletti-Stella
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - D Dall'Olio
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - T Matteuzzi
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - S Baiardi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - P Parchi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - G Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.
| | - S Capellari
- IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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25
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de Silva E, Sudre CH, Barnes J, Scelsi MA, Altmann A. Polygenic coronary artery disease association with brain atrophy in the cognitively impaired. Brain Commun 2022; 4:fcac314. [PMID: 36523268 PMCID: PMC9746681 DOI: 10.1093/braincomms/fcac314] [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: 02/11/2022] [Revised: 09/09/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
While a number of low-frequency genetic variants of large effect size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small effect size, which, in aggregate, are embodied by a polygenic risk score. We investigate the effect of polygenic risk for coronary artery disease on brain atrophy in Alzheimer's disease using whole-brain volume and put our findings in context with the polygenic risk for Alzheimer's disease and presumed small vessel disease as quantified by white-matter hyperintensities. We use 730 subjects from the Alzheimer's disease neuroimaging initiative database to investigate polygenic risk score effects (beyond APOE) on whole-brain volumes, total and regional white-matter hyperintensities and amyloid beta across diagnostic groups. In a subset of these subjects (N = 602), we utilized longitudinal changes in whole-brain volume over 24 months using the boundary shift integral approach. Linear regression and linear mixed-effects models were used to investigate the effect of white-matter hyperintensities at baseline as well as Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score on whole-brain atrophy and whole-brain atrophy acceleration, respectively. All genetic associations were examined under the oligogenic (P = 1e-5) and the more variant-inclusive polygenic (P = 0.5) scenarios. Results suggest no evidence for a link between the polygenic risk score and markers of Alzheimer's disease pathology at baseline (when stratified by diagnostic group). However, both Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score were associated with longitudinal decline in whole-brain volume (Alzheimer's disease-polygenic risk score t = 3.3, P FDR = 0.007 over 24 months in healthy controls) and surprisingly, under certain conditions, whole-brain volume atrophy is statistically more correlated with cardiac polygenic risk score than Alzheimer's disease-polygenic risk score (coronary artery disease-polygenic risk score t = 2.1, P FDR = 0.04 over 24 months in the mild cognitive impairment group). Further, in our regional analysis of white-matter hyperintensities, Alzheimer's disease-polygenic risk score beyond APOE is predictive of white-matter volume in the occipital lobe in Alzheimer's disease subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to Alzheimer's disease-polygenic risk beyond APOE in healthy individuals (t = 2, P = 0.04). For subjects with mild cognitive impairment, beyond APOE, a more inclusive polygenic risk score including more variants, shows coronary artery disease-polygenic risk score to be more predictive of whole-brain volume atrophy, than an oligogenic approach including fewer larger effect size variants.
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Affiliation(s)
- Eric de Silva
- Centre for Medical Image Computing, University College London, London, UK.,NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Carole H Sudre
- Centre for Medical Image Computing, University College London, London, UK.,MRC Unit for Lifelong Health and Ageing, University College London, London, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, University College London, London, UK
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
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26
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Day S, Roberts S, Launder NH, Goh AMY, Draper B, Bahar-Fuchs A, Loi SM, Laver K, Withall A, Cations M. Age of Symptom Onset and Longitudinal Course of Sporadic Alzheimer's Disease, Frontotemporal Dementia, and Vascular Dementia: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2021; 85:1819-1833. [PMID: 34958038 DOI: 10.3233/jad-215360] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Understanding how the age of dementia symptom onset affects the longitudinal course of dementia can assist with prognosis and care planning. OBJECTIVE To synthesize evidence regarding the relationship of age of symptom onset with the longitudinal course of sporadic Alzheimer's disease (AD), vascular dementia (VaD), and frontotemporal dementia (FTD). METHODS We searched Medline, CINAHL, Embase, PsycINFO, PubMed, and Scopus for longitudinal studies that examined the impact of sporadic AD, VaD, or FTD symptom onset age on measures of cognition, function, or behavioral symptoms. Studies that examined age at diagnosis only were excluded. Quantitative meta-analysis was conducted where studies reported sufficient data for pooling. RESULTS Thirty studies met all inclusion criteria (people with AD (n = 26), FTD (n = 4)) though no studies examined VaD. Earlier onset of AD was associated with more rapid annual cognitive decline (estimate = -0.07; 95% CI -0.14 to 0.00; p = 0.045). Most studies that stratified their sample reported that younger AD onset (usually < 65 years) was associated with more rapid cognitive decline. Other evidence was inconclusive. CONCLUSION Younger people with AD appear to have a poorer prognosis in terms of faster cognitive decline than older people with AD. More research is required to determine the impact of symptom onset age in VaD and FTD, and on functional decline in all dementias.
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Affiliation(s)
- Sally Day
- College of Education, Psychology and Social Work, Flinders University, Adelaide SA, Australia
| | - Stefanie Roberts
- Department of Psychiatry, The University of Melbourne, Melbourne VIC, Australia.,National Ageing Research Institute, Melbourne VIC, Australia
| | - Nathalie H Launder
- Department of Psychiatry, The University of Melbourne, Melbourne VIC, Australia
| | - Anita M Y Goh
- Department of Psychiatry, The University of Melbourne, Melbourne VIC, Australia.,National Ageing Research Institute, Melbourne VIC, Australia
| | - Brian Draper
- School of Psychiatry, UNSW Sydney, New South Wales, Australia
| | - Alex Bahar-Fuchs
- Department of Psychiatry, The University of Melbourne, Melbourne VIC, Australia
| | - Samantha M Loi
- Department of Psychiatry, The University of Melbourne, Melbourne VIC, Australia.,Neuropsychiatry, Royal Melbourne Hospital, Parkville VIC, Australia
| | - Kate Laver
- College of Medicine and Public Health, Flinders University, South Australia, Australia
| | - Adrienne Withall
- School of Population Health, UNSW Sydney, New South Wales, Australia.,Ageing Futures Institute, UNSW Sydney, New South Wales, Australia
| | - Monica Cations
- College of Education, Psychology and Social Work, Flinders University, Adelaide SA, Australia
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27
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Sun Y, Wang M, Zhao Y, Hu K, Liu Y, Liu B. A Pathway-Specific Polygenic Risk Score is Associated with Tau Pathology and Cognitive Decline. J Alzheimers Dis 2021; 85:1745-1754. [DOI: 10.3233/jad-215163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: Tauopathy is a primary neuropathological hallmark of Alzheimer’s disease with a strong relationship to cognitive impairment. In the brain, tau aggregation is associated with the regulation of tau kinases and the binding ability of tau to microtubules. Objective: To explore the potential for using specific polygenic risk scores (PRSs), combining the genetic influences involved in tau-protein kinases and the tau-protein binding pathway, as predictors of tau pathology and cognitive decline in non-demented individuals. Methods: We computed a pathway-specific PRS using summary statistics from previous large-scale genome-wide association studies of dementia. We examined whether PRS is related to tau uptake in positron emission tomography (PET), tau levels, and the rate of tau level changes in cerebrospinal fluid (CSF). We further assessed whether PRS is associated with memory impairment mediated by CSF tau levels. Results: A higher PRS was related to elevated CSF tau levels and tau-PET uptake at baseline, as well as greater rates of change in CSF tau levels. Moreover, PRS was associated with memory impairment, mediated by increased CSF tau levels. The association between PRS and tau pathology was significant when APOE was excluded, even among females. However, the effect of PRS on cognitive decline appeared to be driven by the inclusion of APOE. Conclusion: The influence of genetic risk in a specific tau-related biological pathway may make an individual more susceptible to tau pathology, resulting in cognitive dysfunction in an early preclinical phase of the disease.
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Affiliation(s)
- Yuqing Sun
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Meng Wang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuxin Zhao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
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28
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Li Y, Laws SM, Miles LA, Wiley JS, Huang X, Masters CL, Gu BJ. Genomics of Alzheimer's disease implicates the innate and adaptive immune systems. Cell Mol Life Sci 2021; 78:7397-7426. [PMID: 34708251 PMCID: PMC11073066 DOI: 10.1007/s00018-021-03986-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/14/2021] [Accepted: 10/16/2021] [Indexed: 02/08/2023]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterised by cognitive impairment, behavioural alteration, and functional decline. Over 130 AD-associated susceptibility loci have been identified by genome-wide association studies (GWAS), while whole genome sequencing (WGS) and whole exome sequencing (WES) studies have identified AD-associated rare variants. These variants are enriched in APOE, TREM2, CR1, CD33, CLU, BIN1, CD2AP, PILRA, SCIMP, PICALM, SORL1, SPI1, RIN3, and more genes. Given that aging is the single largest risk factor for late-onset AD (LOAD), the accumulation of somatic mutations in the brain and blood of AD patients have also been explored. Collectively, these genetic findings implicate the role of innate and adaptive immunity in LOAD pathogenesis and suggest that a systemic failure of cell-mediated amyloid-β (Aβ) clearance contributes to AD onset and progression. AD-associated variants are particularly enriched in myeloid-specific regulatory regions, implying that AD risk variants are likely to perturbate the expression of myeloid-specific AD-associated genes to interfere Aβ clearance. Defective phagocytosis, endocytosis, and autophagy may drive Aβ accumulation, which may be related to naturally-occurring antibodies to Aβ (Nabs-Aβ) produced by adaptive responses. Passive immunisation is providing efficiency in clearing Aβ and slowing cognitive decline, such as aducanumab, donanemab, and lecanemab (ban2401). Causation of AD by impairment of the innate immunity and treatment using the tools of adaptive immunity is emerging as a new paradigm for AD, but immunotherapy that boosts the innate immune functions of myeloid cells is highly expected to modulate disease progression at asymptomatic stage.
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Affiliation(s)
- Yihan Li
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
| | - Luke A Miles
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - James S Wiley
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Xin Huang
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Colin L Masters
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia
| | - Ben J Gu
- The Florey Institute, The University of Melbourne, 30 Royal Parade, Parkville, VIC, 3052, Australia.
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29
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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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30
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Stocker H, Perna L, Weigl K, Möllers T, Schöttker B, Thomsen H, Holleczek B, Rujescu D, Brenner H. Prediction of clinical diagnosis of Alzheimer's disease, vascular, mixed, and all-cause dementia by a polygenic risk score and APOE status in a community-based cohort prospectively followed over 17 years. Mol Psychiatry 2021; 26:5812-5822. [PMID: 32404947 PMCID: PMC8758470 DOI: 10.1038/s41380-020-0764-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/08/2023]
Abstract
The strongest genetic risk factor for Alzheimer's disease (AD) is the ε4 allele of Apolipoprotein E (APOE) and recent genome-wide association meta-analyses have confirmed additional associated genetic loci with smaller effects. The aim of this study was to investigate the ability of an AD polygenic risk score (PRS) and APOE status to predict clinical diagnosis of AD, vascular (VD), mixed (MD), and all-cause dementia in a community-based cohort prospectively followed over 17 years and secondarily across age, sex, and education strata. A PRS encompassing genetic variants reaching genome-wide significant associations to AD (excluding APOE) from the most recent genome-wide association meta-analysis data was calculated and APOE status was determined in 5203 participants. During follow-up, 103, 111, 58, and 359 participants were diagnosed with AD, VD, MD, and all-cause dementia, respectively. Prediction ability of AD, VD, MD, and all-cause dementia by the PRS and APOE was assessed by multiple logistic regression and receiver operating characteristic curve analyses. The PRS per standard deviation increase in score and APOE4 positivity (≥1 ε4 allele) were significantly associated with greater odds of AD (OR, 95% CI: PRS: 1.70, 1.45-1.99; APOE4: 3.34, 2.24-4.99) and AD prediction accuracy was significantly improved when adding the PRS to a base model of age, sex, and education (ASE) (c-statistics: ASE, 0.772; ASE + PRS, 0.810). The PRS enriched the ability of APOE to discern AD with stronger associations than to VD, MD, or all-cause dementia in a prospective community-based cohort.
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Affiliation(s)
- H Stocker
- Network Aging Research, Heidelberg University, Heidelberg, Germany.
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
| | - L Perna
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - K Weigl
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - T Möllers
- Network Aging Research, Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - B Schöttker
- Network Aging Research, Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | | | - B Holleczek
- Saarland Cancer Registry, Saarbrücken, Germany
| | - D Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Halle, Halle, Germany
| | - H 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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31
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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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Osipowicz M, Wilczynski B, Machnicka MA. Careful feature selection is key in classification of Alzheimer's disease patients based on whole-genome sequencing data. NAR Genom Bioinform 2021; 3:lqab069. [PMID: 34327330 PMCID: PMC8315124 DOI: 10.1093/nargab/lqab069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/06/2021] [Accepted: 07/20/2021] [Indexed: 02/06/2023] Open
Abstract
Despite great increase of the amount of data from genome-wide association studies (GWAS) and whole-genome sequencing (WGS), the genetic background of a partially heritable Alzheimer's disease (AD) is not fully understood yet. Machine learning methods are expected to help researchers in the analysis of the large number of SNPs possibly associated with the disease onset. To date, a number of such approaches were applied to genotype-based classification of AD patients and healthy controls using GWAS data and reported accuracy of 0.65-0.975. However, since the estimated influence of genotype on sporadic AD occurrence is lower than that, these very high classification accuracies may potentially be a result of overfitting. We have explored the possibilities of applying feature selection and classification using random forests to WGS and GWAS data from two datasets. Our results suggest that this approach is prone to overfitting if feature selection is performed before division of data into the training and testing set. Therefore, we recommend avoiding selection of features used to build the model based on data included in the testing set. We suggest that for currently available dataset sizes the expected classifier performance is between 0.55 and 0.7 (AUC) and higher accuracies reported in literature are likely a result of overfitting.
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Affiliation(s)
- Marlena Osipowicz
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, 02-097, Poland
| | - Bartek Wilczynski
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, 02-097, Poland
| | - Magdalena A Machnicka
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, 02-097, Poland
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Zhang Y, Yang H, Li S, Cao Z, Li WD, Yan T, Wang Y. Association of coffee and genetic risk with incident dementia in middle-aged and elderly adults. Nutr Neurosci 2021; 25:2359-2368. [PMID: 34424144 DOI: 10.1080/1028415x.2021.1966868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Prior evidence suggests that coffee might be related to dementia, however, little is known about coffee and dementia in individuals with elevated genetic susceptibility for dementia. Additionally, most previous studies have focused on total coffee instead of examining coffee types separately. METHODS This study included 203,776 participants (60-73 years old) from the UK Biobank who were initially free of dementia. Polygenic risk scores for dementia were divided into quintile to stratify individuals into low (lowest quintile), intermediate (quintile 2-4), and high (highest quintile) genetic risk categories. Coffee intake was assessed at baseline and included total, instant, ground, and decaffeinated coffee. RESULTS During a median follow-up of 11.4 years, 4405 cases of dementia occurred (1856 Alzheimer's disease [AD], 1105 vascular dementia). Compared to non-coffee drinking, heavy instant coffee drinking (> 6 cups/day) and moderate decaffeinated coffee drinking (1-3 cups/day) were associated with a higher risk of dementia (hazard ratio [HR] 1.19-1.34) and AD (HR 1.41-1.51), while moderate ground coffee drinking was associated with a lower risk of dementia (HR, 0.78; P = 0.001) and vascular dementia (HR, 0.58; P < 0.001). Among participants at high genetic risk, heavy coffee drinking was associated with a 95% (HR; 1.95, 95% CI, 1.21-3.16) higher risk of AD than non-coffee drinking. We found an interaction between coffee and genetic risk in relation to AD (P = 0.038). CONCLUSION The association of dementia and coffee varied by coffee types. Heavy coffee consumption was associated with a higher risk of AD in individuals with high genetic risk for dementia.
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Affiliation(s)
- Yuan Zhang
- School of Public Health, Tianjin Medical University, Tianjin, People's Republic of China
| | - Hongxi Yang
- School of Public Health, Tianjin Medical University, Tianjin, People's Republic of China
| | - Shu Li
- School of Public Health, Tianjin Medical University, Tianjin, People's Republic of China
| | - Zhi Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Wei-Dong Li
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, People's Republic of China
| | - Tao Yan
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin Neurological Institute, Tianjin, People's Republic of China
| | - Yaogang Wang
- School of Public Health, Tianjin Medical University, Tianjin, People's Republic of China
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Ibanez L, Cruchaga C, Fernández MV. Advances in Genetic and Molecular Understanding of Alzheimer's Disease. Genes (Basel) 2021; 12:1247. [PMID: 34440421 PMCID: PMC8394321 DOI: 10.3390/genes12081247] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/05/2021] [Accepted: 08/10/2021] [Indexed: 01/19/2023] Open
Abstract
Alzheimer's disease (AD) has become a common disease of the elderly for which no cure currently exists. After over 30 years of intensive research, we have gained extensive knowledge of the genetic and molecular factors involved and their interplay in disease. These findings suggest that different subgroups of AD may exist. Not only are we starting to treat autosomal dominant cases differently from sporadic cases, but we could be observing different underlying pathological mechanisms related to the amyloid cascade hypothesis, immune dysfunction, and a tau-dependent pathology. Genetic, molecular, and, more recently, multi-omic evidence support each of these scenarios, which are highly interconnected but can also point to the different subgroups of AD. The identification of the pathologic triggers and order of events in the disease processes are key to the design of treatments and therapies. Prevention and treatment of AD cannot be attempted using a single approach; different therapeutic strategies at specific disease stages may be appropriate. For successful prevention and treatment, biomarker assays must be designed so that patients can be more accurately monitored at specific points during the course of the disease and potential treatment. In addition, to advance the development of therapeutic drugs, models that better mimic the complexity of the human brain are needed; there have been several advances in this arena. Here, we review significant, recent developments in genetics, omics, and molecular studies that have contributed to the understanding of this disease. We also discuss the implications that these contributions have on medicine.
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Affiliation(s)
- Laura Ibanez
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA; (L.I.); (C.C.)
- Neurogenomics and Informatics Center, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA; (L.I.); (C.C.)
- Neurogenomics and Informatics Center, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, 660 S. Euclid Ave. B8111, St. Louis, MO 63110, USA
| | - Maria Victoria Fernández
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA; (L.I.); (C.C.)
- Neurogenomics and Informatics Center, Washington University School of Medicine, 660 S. Euclid Ave. B8134, St. Louis, MO 63110, USA
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Niu F, Sharma A, Wang Z, Feng L, Muresanu DF, Sahib S, Tian ZR, Lafuente JV, Buzoianu AD, Castellani RJ, Nozari A, Menon PK, Patnaik R, Wiklund L, Sharma HS. Nanodelivery of oxiracetam enhances memory, functional recovery and induces neuroprotection following concussive head injury. PROGRESS IN BRAIN RESEARCH 2021; 265:139-230. [PMID: 34560921 DOI: 10.1016/bs.pbr.2021.06.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Military personnel are the most susceptible to concussive head injury (CHI) caused by explosion, blast or missile or blunt head trauma. Mild to moderate CHI could induce lifetime functional and cognitive disturbances causing significant decrease in quality of life. Severe CHI leads to instant death and lifetime paralysis. Thus, further exploration of novel therapeutic agents or new features of known pharmacological agents are needed to enhance quality of life of CHI victims. Previous reports from our laboratory showed that mild CHI induced by weight drop technique causing an impact of 0.224N results in profound progressive functional deficit, memory impairment and brain pathology from 5h after trauma that continued over several weeks of injury. In this investigation we report that TiO2 nanowired delivery of oxiracetam (50mg/kg, i.p.) daily for 5 days after CHI resulted in significant improvement of functional deficit on the 8th day. This was observed using Rota Rod treadmill, memory improvement assessed by the time spent in finding hidden platform under water. The motor function improvement is seen in oxiracetam treated CHI group by placing forepaw on an inclined mesh walking and foot print analysis for stride length and distance between hind feet. TiO2-nanowired oxiracetam also induced marked improvements in the cerebral blood flow, reduction in the BBB breakdown and edema formation as well as neuroprotection of neuronal, glial and myelin damages caused by CHI at light and electron microscopy on the 7th day after 5 days TiO2 oxiracetam treatment. Adverse biochemical events such as upregulation of CSF nitrite and nitrate, IL-6, TNF-a and p-Tau are also reduced significantly in oxiracetam treated CHI group. On the other hand post treatment of 100mg/kg dose of normal oxiracetam in identical conditions after CHI is needed to show slight but significant neuroprotection together with mild recovery of memory function and functional deficits on the 8th day. These observations are the first to point out that nanowired delivery of oxiracetam has superior neuroprotective ability in CHI. These results indicate a promising clinical future of TiO2 oxiracetam in treating CHI patients for better quality of life and neurorehabilitation, not reported earlier.
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Affiliation(s)
- Feng Niu
- CSPC NBP Pharmaceutical Medicine, Shijiazhuang, China
| | - Aruna Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
| | - Zhenguo Wang
- CSPC NBP Pharmaceutical Medicine, Shijiazhuang, China
| | - Lianyuan Feng
- Department of Neurology, Bethune International Peace Hospital, Shijiazhuang, China
| | - Dafin F Muresanu
- Department of Clinical Neurosciences, University of Medicine & Pharmacy, Cluj-Napoca, Romania; "RoNeuro" Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
| | - Seaab Sahib
- Department of Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, United States
| | - Z Ryan Tian
- Department of Chemistry & Biochemistry, University of Arkansas, Fayetteville, AR, United States
| | - José Vicente Lafuente
- LaNCE, Department of Neuroscience, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Anca D Buzoianu
- Department of Clinical Pharmacology and Toxicology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Rudy J Castellani
- Department of Pathology, University of Maryland, Baltimore, MD, United States
| | - Ala Nozari
- Anesthesiology & Intensive Care, Massachusetts General Hospital, Boston, MA, United States
| | - Preeti K Menon
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Ranjana Patnaik
- Department of Biomaterials, School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India
| | - Lars Wiklund
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden
| | - Hari Shanker Sharma
- International Experimental Central Nervous System Injury & Repair (IECNSIR), Department of Surgical Sciences, Anesthesiology & Intensive Care Medicine, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.
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Improving the Utility of Polygenic Risk Scores as a Biomarker for Alzheimer's Disease. Cells 2021; 10:cells10071627. [PMID: 34209762 PMCID: PMC8305482 DOI: 10.3390/cells10071627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/06/2021] [Accepted: 06/25/2021] [Indexed: 12/28/2022] Open
Abstract
The treatment of complex and multifactorial diseases constitutes a big challenge in day-to-day clinical practice. As many parameters influence clinical phenotypes, accurate diagnosis and prompt therapeutic management is often difficult. Significant research and investment focuses on state-of-the-art genomic and metagenomic analyses in the burgeoning field of Precision (or Personalized) Medicine with genome-wide-association-studies (GWAS) helping in this direction by linking patient genotypes at specific polymorphic sites (single-nucleotide polymorphisms, SNPs) to the specific phenotype. The generation of polygenic risk scores (PRSs) is a relatively novel statistical method that associates the collective genotypes at many of a person’s SNPs to a trait or disease. As GWAS sample sizes increase, PRSs may become a powerful tool for prevention, early diagnosis and treatment. However, the complexity and multidimensionality of genetic and environmental contributions to phenotypes continue to pose significant challenges for the clinical, broad-scale use of PRSs. To improve the value of PRS measures, we propose a novel pipeline which might better utilize GWAS results and improve the utility of PRS when applied to Alzheimer’s Disease (AD), as a paradigm of multifactorial disease with existing large GWAS datasets that have not yet achieved significant clinical impact. We propose a refined approach for the construction of AD PRS improved by (1), taking into consideration the genetic loci where the SNPs are located, (2) evaluating the post-translational impact of SNPs on coding and non-coding regions by focusing on overlap with open chromatin data and SNPs that are expression quantitative trait loci (QTLs), and (3) scoring and annotating the severity of the associated clinical phenotype into the PRS. Open chromatin and eQTL data need to be carefully selected based on tissue/cell type of origin (e.g., brain, excitatory neurons). Applying such filters to traditional PRS on GWAS studies of complex diseases like AD, can produce a set of SNPs weighted according to our algorithm and a more useful PRS. Our proposed methodology may pave the way for new applications of genomic machine and deep learning pipelines to GWAS datasets in an effort to identify novel clinically useful genetic biomarkers for complex diseases like AD.
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Yashin AI, Wu D, Arbeev K, Bagley O, Akushevich I, Duan M, Yashkin A, Ukraintseva S. Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data. Mech Ageing Dev 2021; 196:111477. [PMID: 33798591 PMCID: PMC8173104 DOI: 10.1016/j.mad.2021.111477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 01/05/2023]
Abstract
Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.
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Affiliation(s)
| | - Deqing Wu
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | | | - Olivia Bagley
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Igor Akushevich
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Matt Duan
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit, Duke University SSRI, USA
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Riaz M, Huq A, Ryan J, Orchard SG, Tiller J, Lockery J, Woods RL, Wolfe R, Renton AE, Goate AM, Sebra R, Schadt E, Brodtmann A, Shah RC, Storey E, Murray AM, McNeil JJ, Lacaze P. Effect of APOE and a polygenic risk score on incident dementia and cognitive decline in a healthy older population. Aging Cell 2021; 20:e13384. [PMID: 34041846 PMCID: PMC8208779 DOI: 10.1111/acel.13384] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/15/2021] [Accepted: 04/26/2021] [Indexed: 01/09/2023] Open
Abstract
Few studies have measured the effect of genetic factors on dementia and cognitive decline in healthy older individuals followed prospectively. We studied cumulative incidence of dementia and cognitive decline, stratified by APOE genotypes and polygenic risk score (PRS) tertiles, in 12,978 participants of the ASPirin in Reducing Events in the Elderly (ASPREE) trial. At enrolment, participants had no history of diagnosed dementia, cardiovascular disease, physical disability or cognitive impairment. Dementia (adjudicated trial endpoint) and cognitive decline, defined as a >1.5 standard deviation decline in test score for either global cognition, episodic memory, language/executive function or psychomotor speed, versus baseline scores. Cumulative incidence for all‐cause dementia and cognitive decline was calculated with mortality as a competing event, stratified by APOE genotypes and tertiles of a PRS based on 23 common non‐APOE variants. During a median 4.5 years of follow‐up, 324 participants developed dementia, 503 died. Cumulative incidence of dementia to age 85 years was 7.4% in all participants, 12.6% in APOE ε3/ε4 and 26.6% in ε4/ε4. APOE ε4 heterozygosity/homozygosity was associated with a 2.5/6.3‐fold increased dementia risk and 1.4/1.8‐fold cognitive decline risk, versus ε3/ε3 (p < 0.001 for both). High PRS tertile was associated with a 1.4‐fold dementia risk versus low (CI 1.04–1.76, p = 0.02), but was not associated with cognitive decline (CI 0.96–1.22, p = 0.18). Incidence of dementia among healthy older individuals is low across all genotypes; however, APOE ε4 and high PRS increase relative risk. APOE ε4 is associated with cognitive decline, but PRS is not.
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Affiliation(s)
- Moeen Riaz
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Aamira Huq
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
- Department of Genomic Medicine Royal Melbourne Hospital University of Melbourne Melbourne Vic Australia
- Department of Medicine Royal Melbourne Hospital University of Melbourne Melbourne Vic Australia
| | - Joanne Ryan
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Suzanne G Orchard
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Jane Tiller
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Jessica Lockery
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Robyn L. Woods
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Alan E. Renton
- Nash Family Department of Neuroscience and Ronald Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai New York NY USA
- Departments of Neurology and Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA
| | - Alison M. Goate
- Nash Family Department of Neuroscience and Ronald Loeb Center for Alzheimer’s Disease Icahn School of Medicine at Mount Sinai New York NY USA
- Departments of Neurology and Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY USA
| | - Amy Brodtmann
- Florey Institute of Neuroscience and Mental Health Melbourne Vic Australia
- Melbourne Dementia Research Centre University of Melbourne Melbourne Vic Australia
| | - Raj C. Shah
- Department of Family Medicine and Rush Alzheimer's Disease Center Rush University Medical Center Chicago Illinois USA
| | - Elsdon Storey
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Anne M Murray
- Berman Center for Outcomes and Clinical Research Hennepin Healthcare Research Institute University of Minnesota Minneapolis MN USA
| | - John J. McNeil
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Melbourne Vic Australia
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Vélez JI, Samper LA, Arcos-Holzinger M, Espinosa LG, Isaza-Ruget MA, Lopera F, Arcos-Burgos M. A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease. Diagnostics (Basel) 2021; 11:887. [PMID: 34067584 PMCID: PMC8156402 DOI: 10.3390/diagnostics11050887] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022] Open
Abstract
Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer's disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.
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Affiliation(s)
- Jorge I. Vélez
- Department of Industrial Engineering, Universidad del Norte, Barranquilla 081007, Colombia
| | - Luiggi A. Samper
- Department of Public Health, Universidad del Norte, Barranquilla 081007, Colombia;
| | - Mauricio Arcos-Holzinger
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
| | - Lady G. Espinosa
- INPAC Research Group, Fundación Universitaria Sanitas, Bogotá 111321, Colombia; (L.G.E.); (M.A.I.-R.)
| | - Mario A. Isaza-Ruget
- INPAC Research Group, Fundación Universitaria Sanitas, Bogotá 111321, Colombia; (L.G.E.); (M.A.I.-R.)
| | - Francisco Lopera
- Neuroscience Research Group, University of Antioquia, Medellín 050010, Colombia;
| | - Mauricio Arcos-Burgos
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia;
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Neuroinflammation in Alzheimer's Disease. Biomedicines 2021; 9:biomedicines9050524. [PMID: 34067173 PMCID: PMC8150909 DOI: 10.3390/biomedicines9050524] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/20/2021] [Accepted: 04/28/2021] [Indexed: 12/18/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease associated with human aging. Ten percent of individuals over 65 years have AD and its prevalence continues to rise with increasing age. There are currently no effective disease modifying treatments for AD, resulting in increasingly large socioeconomic and personal costs. Increasing age is associated with an increase in low-grade chronic inflammation (inflammaging) that may contribute to the neurodegenerative process in AD. Although the exact mechanisms remain unclear, aberrant elevation of reactive oxygen and nitrogen species (RONS) levels from several endogenous and exogenous processes in the brain may not only affect cell signaling, but also trigger cellular senescence, inflammation, and pyroptosis. Moreover, a compromised immune privilege of the brain that allows the infiltration of peripheral immune cells and infectious agents may play a role. Additionally, meta-inflammation as well as gut microbiota dysbiosis may drive the neuroinflammatory process. Considering that inflammatory/immune pathways are dysregulated in parallel with cognitive dysfunction in AD, elucidating the relationship between the central nervous system and the immune system may facilitate the development of a safe and effective therapy for AD. We discuss some current ideas on processes in inflammaging that appear to drive the neurodegenerative process in AD and summarize details on a few immunomodulatory strategies being developed to selectively target the detrimental aspects of neuroinflammation without affecting defense mechanisms against pathogens and tissue damage.
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Ravona-Springer R, Sharvit-Ginon I, Ganmore I, Greenbaum L, Bendlin BB, Sternberg SA, Livny A, Domachevsky L, Sandler I, Ben Haim S, Golan S, Ben-Ami L, Lesman-Segev O, Manzali S, Heymann A, Beeri MS. The Israel Registry for Alzheimer's Prevention (IRAP) Study: Design and Baseline Characteristics. J Alzheimers Dis 2021; 78:777-788. [PMID: 33044181 DOI: 10.3233/jad-200623] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Family history of Alzheimer's disease (AD) is associated with increased dementia-risk. OBJECTIVE The Israel Registry for Alzheimer's Prevention (IRAP) is a prospective longitudinal study of asymptomatic middle-aged offspring of AD patients (family history positive; FH+) and controls (whose parents have aged without dementia; FH-) aimed to unravel the contribution of midlife factors to future cognitive decline and dementia. Here we present the study design, methods, and baseline characteristics. METHODS Participants are members of the Maccabi Health Services, 40-65 years of age, with exquisitely detailed laboratory, medical diagnoses and medication data available in the Maccabi electronic medical records since 1998. Data collected through IRAP include genetic, sociodemographic, cognitive, brain imaging, lifestyle, and health-related characteristics at baseline and every three years thereafter. RESULTS Currently IRAP has 483 participants [mean age 54.95 (SD = 6.68) and 64.8% (n = 313) women], 379 (78.5%) FH+, and 104 (21.5%) FH-. Compared to FH-, FH+ participants were younger (p = 0.011), more often males (p = 0.003) and with a higher prevalence of the APOE E4 allele carriers (32.9% FH+, 22% FH-; p = 0.040). Adjusting for age, sex, and education, FH+ performed worse than FH-in global cognition (p = 0.027) and episodic memory (p = 0.022). CONCLUSION Lower cognitive scores and higher rates of the APOE E4 allele carriers among the FH+ group suggest that FH ascertainment is good. The combination of long-term historical health-related data available through Maccabi with the multifactorial information collected through IRAP will potentially enable development of dementia-prevention strategies already in midlife, a critical period in terms of risk factor exposure and initiation of AD-neuropathology.
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Affiliation(s)
- Ramit Ravona-Springer
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Memory Clinic, Sheba Medical Center, Tel Hashomer, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Inbal Sharvit-Ginon
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel
| | - Ithamar Ganmore
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Memory Clinic, Sheba Medical Center, Tel Hashomer, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology, Sheba Medical Center, Tel Hashomer, Israel
| | - Lior Greenbaum
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel Hashomer, Israel
| | - Barbara B Bendlin
- Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Abigail Livny
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Diagnostic imaging, Seba Medical Center, Tel Hashomer, Israel
| | - Liran Domachevsky
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Diagnostic imaging, Seba Medical Center, Tel Hashomer, Israel
| | - Israel Sandler
- Department of Diagnostic imaging, Seba Medical Center, Tel Hashomer, Israel
| | - Simona Ben Haim
- Department of Medical Biophysics and Nuclear Medicine, Hadassah University Hospital, Ein Kerem, Jerusalem, Israel.,Institute of Nuclear Medicine, University College London and UCL Hospitals, NHS Trust, London, UK
| | - Sapir Golan
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel
| | - Liat Ben-Ami
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Department of Diagnostic imaging, Seba Medical Center, Tel Hashomer, Israel
| | - Orit Lesman-Segev
- Department of Diagnostic imaging, Seba Medical Center, Tel Hashomer, Israel
| | - Sigalit Manzali
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Department of Pathology, Sheba Medical Center, Tel-Hashomer, Israel
| | - Anthony Heymann
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Maccabi Healthcare Services, Israel
| | - Michal Schnaider Beeri
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel-Hashomer, Israel.,Department of Psychiatry, The Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Seto M, Weiner RL, Dumitrescu L, Hohman TJ. Protective genes and pathways in Alzheimer's disease: moving towards precision interventions. Mol Neurodegener 2021; 16:29. [PMID: 33926499 PMCID: PMC8086309 DOI: 10.1186/s13024-021-00452-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/20/2021] [Indexed: 12/29/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that is characterized by neurodegeneration, cognitive impairment, and an eventual inability to perform daily tasks. The etiology of Alzheimer's is complex, with numerous environmental and genetic factors contributing to the disease. Late-onset AD is highly heritable (60 to 80%), and over 40 risk loci for AD have been identified via large genome-wide association studies, most of which are common variants with small effect sizes. Although these discoveries have provided novel insight on biological contributors to AD, disease-modifying treatments remain elusive. Recently, the concepts of resistance to pathology and resilience against the downstream consequences of pathology have been of particular interest in the Alzheimer's field as studies continue to identify individuals who evade the pathology of the disease even into late life and individuals who have all of the neuropathological features of AD but evade downstream neurodegeneration and cognitive impairment. It has been hypothesized that a shift in focus from Alzheimer's risk to resilience presents an opportunity to uncover novel biological mechanisms of AD and to identify promising therapeutic targets for the disease. This review will highlight a selection of genes and variants that have been reported to confer protection from AD within the literature and will also discuss evidence for the biological underpinnings behind their protective effect with a focus on genes involved in lipid metabolism, cellular trafficking, endosomal and lysosomal function, synaptic function, and inflammation. Finally, we offer some recommendations in areas where the field can rapidly advance towards precision interventions that leverage the ideas of protection and resilience for the development of novel therapeutic strategies.
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Affiliation(s)
- Mabel Seto
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, 1207 17th Ave S, Nashville, TN 37212 USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN USA
| | - Rebecca L. Weiner
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, 1207 17th Ave S, Nashville, TN 37212 USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, 1207 17th Ave S, Nashville, TN 37212 USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, 1207 17th Ave S, Nashville, TN 37212 USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN USA
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Bakulski KM, Vadari HS, Faul JD, Heeringa SG, Kardia SLR, Langa KM, Smith JA, Manly JJ, Mitchell CM, Benke KS, Ware EB. Cumulative Genetic Risk and APOE ε4 Are Independently Associated With Dementia Status in a Multiethnic, Population-Based Cohort. NEUROLOGY-GENETICS 2021; 7:e576. [PMID: 33688582 PMCID: PMC7938646 DOI: 10.1212/nxg.0000000000000576] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 10/29/2020] [Indexed: 11/26/2022]
Abstract
Objective Alzheimer disease (AD) is a common and costly neurodegenerative disorder. A large proportion of AD risk is heritable, and many genetic risk factors have been identified. The objective of this study was to test the hypothesis that cumulative genetic risk of known AD markers contributed to odds of dementia in a population-based sample. Methods In the US population-based Health and Retirement Study (waves 1995–2014), we evaluated the role of cumulative genetic risk of AD, with and without the APOE ε4 alleles, on dementia status (dementia, cognitive impairment without dementia, borderline cognitive impairment without dementia, and cognitively normal). We used logistic regression, accounting for demographic covariates and genetic principal components, and analyses were stratified by European and African genetic ancestry. Results In the European ancestry sample (n = 8,399), both AD polygenic score excluding the APOE genetic region (odds ratio [OR] = 1.10; 95% confidence interval [CI]: 1.00–1.20) and the presence of any APOE ε4 alleles (OR = 2.42; 95% CI: 1.99–2.95) were associated with the odds of dementia relative to normal cognition in a mutually adjusted model. In the African ancestry sample (n = 1,605), the presence of any APOE ε4 alleles was associated with 1.77 (95% CI: 1.20–2.61) times higher odds of dementia, whereas the AD polygenic score excluding the APOE genetic region was not significantly associated with the odds of dementia relative to normal cognition 1.06 (95% CI: 0.97–1.30). Conclusions Cumulative genetic risk of AD and APOE ε4 are both independent predictors of dementia in European ancestry. This study provides important insight into the polygenic nature of dementia and demonstrates the utility of polygenic scores in dementia research.
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Affiliation(s)
- Kelly M Bakulski
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Harita S Vadari
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Jessica D Faul
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Steven G Heeringa
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Sharon L R Kardia
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Kenneth M Langa
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Jennifer A Smith
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Jennifer J Manly
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Colter M Mitchell
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Kelly S Benke
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Erin B Ware
- Department of Epidemiology (K.M.B., S.L.R.K., J.A.S.), School of Public Health, University of Michigan; Survey Research Center (H.S.V., J.D.F., S.G.H., K.M.L., C.M.M., E.B.W.), Institute for Social Research, University of Michigan; VA Center for Clinical Management Research (K.M.L.), Ann Arbor, MI; Department of Neurology (J.J.M.), Columbia University, and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain (J.J.M.), New York; and Department of Mental Health (K.S.B.), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
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Meeker KL, Wisch JK, Hudson D, Coble D, Xiong C, Babulal GM, Gordon BA, Schindler SE, Cruchaga C, Flores S, Dincer A, Benzinger TL, Morris JC, Ances BM. Socioeconomic Status Mediates Racial Differences Seen Using the AT(N) Framework. Ann Neurol 2021; 89:254-265. [PMID: 33111990 PMCID: PMC7903892 DOI: 10.1002/ana.25948] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 10/26/2020] [Accepted: 10/26/2020] [Indexed: 12/22/2022]
Abstract
OBJECTIVES African Americans are at greater risk for developing Alzheimer's disease (AD) dementia than non-Hispanic whites. In addition to biological considerations (eg, genetic influences and comorbid disorders), social and environmental factors may increase the risk of AD dementia. This paper (1) assesses neuroimaging biomarkers of amyloid (A), tau (T), and neurodegeneration (N) for potential racial differences and (2) considers mediating effects of socioeconomic status (SES) and measures of small vessel and cardiovascular disease on observed race differences. METHODS Imaging measures of AT(N) (amyloid and tau positron emission tomography [PET]) structural magnetic resonance imaging (MRI), and resting state functional connectivity (rs-fc) were collected from African American (n = 131) and white (n = 685) cognitively normal participants age 45 years and older. Measures of small vessel and cardiovascular disease (white matter hyperintensities [WMHs] on MRI, blood pressure, and body mass index [BMI]) and area-based SES were included in mediation analyses. RESULTS Compared to white participants, African American participants had greater neurodegeneration, as measured by decreased cortical volumes (Cohen's f2 = 0.05, p < 0.001). SES mediated the relationship between race and cortical volumes. There were no significant race effects for amyloid, tau, or rs-fc signature. INTERPRETATION Modifiable factors, such as differences in social contexts and resources, particularly area-level SES, may contribute to observed racial differences in AD. Future studies should emphasize collection of relevant psychosocial factors in addition to the development of intentional diversity and inclusion efforts to improve the racial/ethnic and socioeconomic representativeness of AD studies. ANN NEUROL 2021;89:254-265.
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Affiliation(s)
- Karin L Meeker
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Darrell Hudson
- Brown School, Washington University in St. Louis, St. Louis, MO, USA
| | - Dean Coble
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Ganesh M Babulal
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Shaney Flores
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Aylin Dincer
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie L Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
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Acosta D, Llibre-Guerra JJ, Jiménez-Velázquez IZ, Llibre-Rodríguez JJ. Dementia Research in the Caribbean Hispanic Islands: Present Findings and Future Trends. Front Public Health 2021; 8:611998. [PMID: 33537283 PMCID: PMC7848137 DOI: 10.3389/fpubh.2020.611998] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/18/2020] [Indexed: 11/13/2022] Open
Abstract
During the last decade, the Caribbean Hispanic islands experienced accelerated demographic aging, representing the fastest aging region within Latin America. Age-related non-communicable diseases, including dementia, are now reported at high prevalence. The Caribbean islands share similar genetic ancestry, culture, migration patterns, and risk profiles, providing a unique setting to understand dementia in the Caribbean-Hispanics. This perspective article aimed to describe the impact of dementia in the Caribbean, at a local and regional level and reflect on research strategies to address dementia. We report on 10/66 project findings, described research projects and regional plans for the region. According to our results, the prevalence of dementia in the Caribbean is the highest in Latin America, with 11.7% in Dominican Republic, 11.6% in Puerto Rico, and 10.8% in Cuba. Preliminary data from new waves of the 10/66 study shows increasing numbers of dementia cases. Furthermore, dementia is expected to be one of the most serious medical and social issues confronted by Caribbean health systems. However, there is a scarcity of knowledge, awareness, and health services to deal with this public health crisis. In light of the new evidence, local and regional strategies are underway to better understand dementia trends for the region and develop policies aimed to decrease the impact of dementia. Implementation of our national plans is critical to deal with an aging population with high dementia rates. Current recommendations include emphasizing public health prevention campaigns to address modifiable risk factors and expand support to caregiver and family interventions.
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Affiliation(s)
- Daisy Acosta
- Department of Internal Medicine, Universidad Nacional Pedro Henriquez Urena, Santo Domingo, Dominican Republic
| | - Jorge J Llibre-Guerra
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.,National Institute of Neurology and Neurosurgery, Habana, Cuba
| | - Ivonne Z Jiménez-Velázquez
- Department of Internal Medicine, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico
| | - Juan J Llibre-Rodríguez
- Department of Internal Medicine, Universidad Nacional Pedro Henriquez Urena, Santo Domingo, Dominican Republic.,Finlay-Albarrán Medicine Faculty, Universidad de Ciencias Medicas, Habana, Cuba
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Zhou X, Li YYT, Fu AKY, Ip NY. Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications. Front Neurosci 2021; 15:650220. [PMID: 33854414 PMCID: PMC8039467 DOI: 10.3389/fnins.2021.650220] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 03/09/2021] [Indexed: 12/13/2022] Open
Abstract
The high prevalence of Alzheimer's disease (AD) among the elderly population and its lack of effective treatments make this disease a critical threat to human health. Recent epidemiological and genetics studies have revealed the polygenic nature of the disease, which is possibly explainable by a polygenic score model that considers multiple genetic risks. Here, we systemically review the rationale and methods used to construct polygenic score models for studying AD. We also discuss the associations of polygenic risk scores (PRSs) with clinical outcomes, brain imaging findings, and biochemical biomarkers from both the brain and peripheral system. Finally, we discuss the possibility of incorporating polygenic score models into research and clinical practice along with potential challenges.
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Affiliation(s)
- Xiaopu Zhou
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
| | - Yolanda Y. T. Li
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Amy K. Y. Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
| | - Nancy Y. Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, China
- *Correspondence: Nancy Y. Ip,
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Yashin AI, Wu D, Arbeev K, Yashkin AP, Akushevich I, Bagley O, Duan M, Ukraintseva S. Roles of interacting stress-related genes in lifespan regulation: insights for translating experimental findings to humans. JOURNAL OF TRANSLATIONAL GENETICS AND GENOMICS 2021; 5:357-379. [PMID: 34825130 PMCID: PMC8612394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
AIM Experimental studies provided numerous evidence that caloric/dietary restriction may improve health and increase the lifespan of laboratory animals, and that the interplay among molecules that sense cellular stress signals and those regulating cell survival can play a crucial role in cell response to nutritional stressors. However, it is unclear whether the interplay among corresponding genes also plays a role in human health and lifespan. METHODS Literature about roles of cellular stressors have been reviewed, such as amino acid deprivation, and the integrated stress response (ISR) pathway in health and aging. Single nucleotide polymorphisms (SNPs) in two candidate genes (GCN2/EIF2AK4 and CHOP/DDIT3) that are closely involved in the cellular stress response to amino acid starvation, have been selected using information from experimental studies. Associations of these SNPs and their interactions with human survival in the Health and Retirement Study data have been estimated. The impact of collective associations of multiple interacting SNP pairs on survival has been evaluated, using a recently developed composite index: the SNP-specific Interaction Polygenic Risk Score (SIPRS). RESULTS Significant interactions have been found between SNPs from GCN2/EIF2AK4 and CHOP/DDI3T genes that were associated with survival 85+ compared to survival between ages 75 and 85 in the total sample (males and females combined) and in females only. This may reflect sex differences in genetic regulation of the human lifespan. Highly statistically significant associations of SIPRS [constructed for the rs16970024 (GCN2/EIF2AK4) and rs697221 (CHOP/DDIT3)] with survival in both sexes also been found in this study. CONCLUSION Identifying associations of the genetic interactions with human survival is an important step in translating the knowledge from experimental to human aging research. Significant associations of multiple SNPxSNP interactions in ISR genes with survival to the oldest old age that have been found in this study, can help uncover mechanisms of multifactorial regulation of human lifespan and its heterogeneity.
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Hannon E, Shireby GL, Brookes K, Attems J, Sims R, Cairns NJ, Love S, Thomas AJ, Morgan K, Francis PT, Mill J. Genetic risk for Alzheimer's disease influences neuropathology via multiple biological pathways. Brain Commun 2020; 2:fcaa167. [PMID: 33376986 PMCID: PMC7750986 DOI: 10.1093/braincomms/fcaa167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/26/2022] Open
Abstract
Alzheimer’s disease is a highly heritable, common neurodegenerative disease characterized neuropathologically by the accumulation of β-amyloid plaques and tau-containing neurofibrillary tangles. In addition to the well-established risk associated with the APOE locus, there has been considerable success in identifying additional genetic variants associated with Alzheimer’s disease. Major challenges in understanding how genetic risk influences the development of Alzheimer’s disease are clinical and neuropathological heterogeneity, and the high level of accompanying comorbidities. We report a multimodal analysis integrating longitudinal clinical and cognitive assessment with neuropathological data collected as part of the Brains for Dementia Research study to understand how genetic risk factors for Alzheimer’s disease influence the development of neuropathology and clinical performance. Six hundred and ninety-three donors in the Brains for Dementia Research cohort with genetic data, semi-quantitative neuropathology measurements, cognitive assessments and established diagnostic criteria were included in this study. We tested the association of APOE genotype and Alzheimer’s disease polygenic risk score—a quantitative measure of genetic burden—with survival, four common neuropathological features in Alzheimer’s disease brains (neurofibrillary tangles, β-amyloid plaques, Lewy bodies and transactive response DNA-binding protein 43 proteinopathy), clinical status (clinical dementia rating) and cognitive performance (Mini-Mental State Exam, Montreal Cognitive Assessment). The APOE ε4 allele was significantly associated with younger age of death in the Brains for Dementia Research cohort. Our analyses of neuropathology highlighted two independent pathways from APOE ε4, one where β-amyloid accumulation co-occurs with the development of tauopathy, and a second characterized by direct effects on tauopathy independent of β-amyloidosis. Although we also detected association between APOE ε4 and dementia status and cognitive performance, these were all mediated by tauopathy, highlighting that they are a consequence of the neuropathological changes. Analyses of polygenic risk score identified associations with tauopathy and β-amyloidosis, which appeared to have both shared and unique contributions, suggesting that different genetic variants associated with Alzheimer’s disease affect different features of neuropathology to different degrees. Taken together, our results provide insight into how genetic risk for Alzheimer’s disease influences both the clinical and pathological features of dementia, increasing our understanding about the interplay between APOE genotype and other genetic risk factors.
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Affiliation(s)
- Eilis Hannon
- College of Medicine and Health, University of Exeter, Exeter, Devon, EX2 5DW, UK
| | - Gemma L Shireby
- College of Medicine and Health, University of Exeter, Exeter, Devon, EX2 5DW, UK
| | - Keeley Brookes
- School of Science & Technology, Nottingham Trent University, Nottingham, NG11 8NF, UK
| | - Johannes Attems
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Nigel J Cairns
- College of Medicine and Health, University of Exeter, Exeter, Devon, EX2 5DW, UK
| | - Seth Love
- Bristol Medical School (THS), University of Bristol, Bristol, BS2 8DZ, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Kevin Morgan
- Human Genetics Group, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Paul T Francis
- College of Medicine and Health, University of Exeter, Exeter, Devon, EX2 5DW, UK
| | - Jonathan Mill
- College of Medicine and Health, University of Exeter, Exeter, Devon, EX2 5DW, UK
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López ME, Turrero A, Cuesta P, Rodríguez-Rojo IC, Barabash A, Marcos A, Maestú F, Fernández A. A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease. GeroScience 2020; 42:1715-1732. [PMID: 32886293 PMCID: PMC7732920 DOI: 10.1007/s11357-020-00260-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 08/24/2020] [Indexed: 11/26/2022] Open
Abstract
The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
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Affiliation(s)
- María Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain.
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain.
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain.
| | - Agustín Turrero
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Statistics and Operational Research, Complutense University of Madrid, Madrid, Spain
| | - Pablo Cuesta
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
| | - Inmaculada Concepción Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Psychology Faculty, Centro Universitario Villanueva, Madrid, Spain
- Physiotherapy and Nursing Faculty, University of Castilla-La Mancha, Toledo, Spain
| | - Ana Barabash
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Laboratory of Psychoneuroendocrinology and Genetics, San Carlos University Hospital, Madrid, Spain
| | - Alberto Marcos
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Polytechnic University of Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain
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50
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Bruni AC, Bernardi L, Gabelli C. From beta amyloid to altered proteostasis in Alzheimer's disease. Ageing Res Rev 2020; 64:101126. [PMID: 32683041 DOI: 10.1016/j.arr.2020.101126] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/27/2020] [Accepted: 07/13/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is an age related neurodegenerative disorder causing severe disability and important socio-economic burden, but with no cure available to date. To disentangle this puzzling disease genetic studies represented an important way for the comprehension of pathogenic mechanisms. Abnormal processing and accumulation of amyloid-β peptide (Aβ) has been considered the main cause and trigger factor of the disease. The amyloid cascade theory has fallen into crisis because the failure of several anti-amyloid drugs trials and because of the simple equation AD = abnormal Aβ deposition is not always the case. We now know that multiple neurodegenerative diseases share common pathogenic mechanisms leading to accumulation of misfolded protein species. Genome Wide Association studies (GWAS) led to the identification of large numbers of DNA common variants (SNPs) distributed on different chromosomes and modulating the Alzheimer's risk. GWAS genes fall into several common pathways such as immune system and neuroinflammation, lipid metabolism, synaptic dysfunction and endocytosis, all of them addressing to novel routes for different pathogenic mechanisms. Other hints could be derived from epidemiological and experimental studies showing some lifestyles may have a major role in the pathogenesis of many age-associated diseases by modifying cell metabolism, proteostasis and microglia mediated neuroinflammation.
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Affiliation(s)
- Amalia C Bruni
- Regional Neurogenetic Centre, ASP Catanzaro, Lamezia Terme (CZ), Italy.
| | - Livia Bernardi
- Regional Neurogenetic Centre, ASP Catanzaro, Lamezia Terme (CZ), Italy
| | - Carlo Gabelli
- Regional Brain Aging Centre, Azienda Ospedale Università Di Padova, Padova Italy
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