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Cruchaga C, Ali M, Shen Y, Do A, Wang L, Western D, Liu M, Beric A, Budde J, Gentsch J, Schindler S, Morris J, Holtzman D, Fernández M, Ruiz A, Alvarez I, Aguilar M, Pastor P, Rutledge J, Oh H, Wilson E, Le Guen Y, Khalid R, Robins C, Pulford D, Ibanez L, Wyss-Coray T, Ju Sung Y. Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-3631708. [PMID: 38410465 PMCID: PMC10896368 DOI: 10.21203/rs.3.rs-3631708/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Changes in Amyloid-β (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).
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Affiliation(s)
| | | | | | - Anh Do
- Washington University School of Medicine
| | - Lihua Wang
- Washington University School of Medicine
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | | | | | | | | | | | | | | | - Ignacio Alvarez
- Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain
| | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
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2
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Shen Y, Ali M, Timsina J, Wang C, Do A, Western D, Liu M, Gorijala P, Budde J, Liu H, Gordon B, McDade E, Morris JC, Llibre-Guerra JJ, Bateman RJ, Joseph-Mathurin N, Perrin RJ, Maschi D, Wyss-Coray T, Pastor P, Goate A, Renton AE, Surace EI, Johnson ECB, Levey AI, Alvarez I, Levin J, Ringman JM, Allegri RF, Seyfried N, Day GS, Wu Q, Fernández MV, Ibanez L, Sung YJ, Cruchaga C. Systematic proteomics in Autosomal dominant Alzheimer's disease reveals decades-early changes of CSF proteins in neuronal death, and immune pathways. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301242. [PMID: 38260583 PMCID: PMC10802763 DOI: 10.1101/2024.01.12.24301242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background To date, there is no high throughput proteomic study in the context of Autosomal Dominant Alzheimer's disease (ADAD). Here, we aimed to characterize early CSF proteome changes in ADAD and leverage them as potential biomarkers for disease monitoring and therapeutic strategies. Methods We utilized Somascan® 7K assay to quantify protein levels in the CSF from 291 mutation carriers (MCs) and 185 non-carriers (NCs). We employed a multi-layer regression model to identify proteins with different pseudo-trajectories between MCs and NCs. We replicated the results using publicly available ADAD datasets as well as proteomic data from sporadic Alzheimer's disease (sAD). To biologically contextualize the results, we performed network and pathway enrichment analyses. Machine learning was applied to create and validate predictive models. Findings We identified 125 proteins with significantly different pseudo-trajectories between MCs and NCs. Twelve proteins showed changes even before the traditional AD biomarkers (Aβ42, tau, ptau). These 125 proteins belong to three different modules that are associated with age at onset: 1) early stage module associated with stress response, glutamate metabolism, and mitochondria damage; 2) the middle stage module, enriched in neuronal death and apoptosis; and 3) the presymptomatic stage module was characterized by changes in microglia, and cell-to-cell communication processes, indicating an attempt of rebuilding and establishing new connections to maintain functionality. Machine learning identified a subset of nine proteins that can differentiate MCs from NCs better than traditional AD biomarkers (AUC>0.89). Interpretation Our findings comprehensively described early proteomic changes associated with ADAD and captured specific biological processes that happen in the early phases of the disease, fifteen to five years before clinical onset. We identified a small subset of proteins with the potentials to become therapy-monitoring biomarkers of ADAD MCs. Funding Proteomic data generation was supported by NIH: RF1AG044546.
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Timsina J, Ali M, Do A, Wang L, Western D, Sung YJ, Cruchaga C. Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification. Neurobiol Dis 2024; 190:106373. [PMID: 38072165 DOI: 10.1016/j.nbd.2023.106373] [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: 05/25/2023] [Revised: 10/06/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.
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Affiliation(s)
- Jigyasha Timsina
- 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
| | - Muhammad Ali
- 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
| | - Anh Do
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA; Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- 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
| | - Daniel Western
- 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
| | - Yun Ju 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; Division of Biostatistics, 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 Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA.
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Luthra NS, Christou DD, Clow A, Corcos DM. Targeting neuroendocrine abnormalities in Parkinson's disease with exercise. Front Neurosci 2023; 17:1228444. [PMID: 37746149 PMCID: PMC10514367 DOI: 10.3389/fnins.2023.1228444] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 09/26/2023] Open
Abstract
Parkinson's Disease (PD) is a prevalent and complex age-related neurodegenerative condition for which there are no disease-modifying treatments currently available. The pathophysiological process underlying PD remains incompletely understood but increasing evidence points to multiple system dysfunction. Interestingly, the past decade has produced evidence that exercise not only reduces signs and symptoms of PD but is also potentially neuroprotective. Characterizing the mechanistic pathways that are triggered by exercise and lead to positive outcomes will improve understanding of how to counter disease progression and symptomatology. In this review, we highlight how exercise regulates the neuroendocrine system, whose primary role is to respond to stress, maintain homeostasis and improve resilience to aging. We focus on a group of hormones - cortisol, melatonin, insulin, klotho, and vitamin D - that have been shown to associate with various non-motor symptoms of PD, such as mood, cognition, and sleep/circadian rhythm disorder. These hormones may represent important biomarkers to track in clinical trials evaluating effects of exercise in PD with the aim of providing evidence that patients can exert some behavioral-induced control over their disease.
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Affiliation(s)
- Nijee S. Luthra
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Demetra D. Christou
- Department of Applied Physiology and Kinesiology, College of Health and Human Performance, University of Florida, Gainesville, FL, United States
| | - Angela Clow
- Department of Psychology, School of Social Sciences, University of Westminster, London, United Kingdom
| | - Daniel M. Corcos
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, McCormick School of Engineering, Northwestern University, Chicago, IL, United States
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5
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Ray NR, Ayodele T, Jean-Francois M, Baez P, Fernandez V, Bradley J, Crane PK, Dalgard CL, Kuzma A, Nicaretta H, Sims R, Williams J, Cuccaro ML, Pericak-Vance MA, Mayeux R, Wang LS, Schellenberg GD, Cruchaga C, Beecham GW, Reitz C. The Early-Onset Alzheimer's Disease Whole-Genome Sequencing Project: Study design and methodology. Alzheimers Dement 2023; 19:4187-4195. [PMID: 37390458 PMCID: PMC10527497 DOI: 10.1002/alz.13370] [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] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/02/2023]
Abstract
INTRODUCTION Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations, resulting in a lack of understanding of its molecular etiology. METHODS Whole-genome sequencing and harmonization of clinical, neuropathological, and biomarker data of over 5000 EOAD cases of diverse ancestries. RESULTS A publicly available genomics resource for EOAD with extensive harmonized phenotypes. Primary analysis will (1) identify novel EOAD risk loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. DISCUSSION This novel resource complements over 50,000 control and late-onset AD samples generated through the Alzheimer's Disease Sequencing Project (ADSP). The harmonized EOAD/ADSP joint call will be available through upcoming ADSP data releases and will allow for additional analyses across the full onset range. HIGHLIGHTS Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations. This results in a significant lack of understanding of the molecular etiology of this devastating form of the disease. The Early-Onset Alzheimer's Disease Whole-genome Sequencing Project is a collaborative initiative to generate a large-scale genomics resource for early-onset Alzheimer's disease with extensive harmonized phenotype data. Primary analyses are designed to (1) identify novel EOAD risk and protective loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. The harmonized genomic and phenotypic data from this initiative will be available through NIAGADS.
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Affiliation(s)
- Nicholas R. Ray
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
| | - Temitope Ayodele
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
| | - Melissa Jean-Francois
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Penelope Baez
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
| | - Victoria Fernandez
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Joseph Bradley
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Paul K. Crane
- Division of General Internal Medicine, University of
Washington, Seattle, WA 98195, USA
| | - Clifton L. Dalgard
- Department of Anatomy, Physiology & Genetics,
Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
- The American Genome Center, Uniformed Services University
of the Health Sciences, Bethesda, MD 20814, USA
| | - Amanda Kuzma
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical
Neurosciences, School of Medicine, Cardiff University, Cardiff CF10 3AT, UK
| | - Julie Williams
- UK Dementia Research Institute, Cardiff University,
Cardiff CF10 3AT, UK
- Division of Psychological Medicine and Clinical
Neurosciences, School of Medicine, Cardiff University, Cardiff CF10 3AT, UK
| | - Michael L. Cuccaro
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Margaret A. Pericak-Vance
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Richard Mayeux
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
- Department of Neurology, Columbia University, New York, NY
10032, USA
- Department of Epidemiology, Columbia University, New York,
NY 10032, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Gerard D. Schellenberg
- Penn Neurodegeneration Genomics Center, Department of
Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of
Medicine, Philadelphia, PA 19104, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Neurology and Genetics,
Washington University School of Medicine, St. Louis, MO 63130, USA
- Neurogenomics and Informatic (NGI) Center, Washington
University School of Medicine, St. Louis, MO 63130, USA
| | - Gary W. Beecham
- The John P. Hussman Institute for Human Genomics,
University of Miami, Miami, FL 33136, USA
- Dr. John T. MacDonald Foundation Department of Human
Genetics, University of Miami, Coral Gables, FL 33146, USA
| | - Christiane Reitz
- Gertrude H. Sergievsky Center, Columbia University, New
York, NY 10032, USA
- Taub Institute for Research on Alzheimer’s Disease
and the Aging Brain, Columbia University, New York, NY 10032, USA
- Department of Neurology, Columbia University, New York, NY
10032, USA
- Department of Epidemiology, Columbia University, New York,
NY 10032, USA
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Castner SA, Gupta S, Wang D, Moreno AJ, Park C, Chen C, Poon Y, Groen A, Greenberg K, David N, Boone T, Baxter MG, Williams GV, Dubal DB. Longevity factor klotho enhances cognition in aged nonhuman primates. NATURE AGING 2023; 3:931-937. [PMID: 37400721 PMCID: PMC10432271 DOI: 10.1038/s43587-023-00441-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 05/23/2023] [Indexed: 07/05/2023]
Abstract
Cognitive dysfunction in aging is a major biomedical challenge. Whether treatment with klotho, a longevity factor, could enhance cognition in human-relevant models such as in nonhuman primates is unknown and represents a major knowledge gap in the path to therapeutics. We validated the rhesus form of the klotho protein in mice showing it increased synaptic plasticity and cognition. We then found that a single administration of low-dose, but not high-dose, klotho enhanced memory in aged nonhuman primates. Systemic low-dose klotho treatment may prove therapeutic in aging humans.
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Affiliation(s)
- Stacy A Castner
- Department of Psychiatry and VA Connecticut Healthcare System, Yale School of Medicine, West Haven, CT, USA
| | - Shweta Gupta
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Dan Wang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Arturo J Moreno
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Cana Park
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Chen Chen
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Yan Poon
- Unity Biotechnology, Brisbane, CA, USA
| | | | | | | | - Tom Boone
- Tom Boone Consulting, Newbury Park, CA, USA
| | - Mark G Baxter
- Section on Comparative Medicine, Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Graham V Williams
- Department of Psychiatry and VA Connecticut Healthcare System, Yale School of Medicine, West Haven, CT, USA
| | - Dena B Dubal
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
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7
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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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8
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Timsina J, Ali M, Do A, Wang L, Sung YJ, Cruchaga C. Harmonization of CSF and imaging biomarkers for Alzheimer's disease biomarkers: need and practical applications for genetics studies and preclinical classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542118. [PMID: 37292823 PMCID: PMC10245826 DOI: 10.1101/2023.05.24.542118] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
INTRODUCTION In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers which leads to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. METHODS We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS result using this method with currently accepted methods. We also used a generalized mixture modelling to calculate the threshold for biomarker-positivity. RESULTS Z-scores method performed as well as meta-analysis and did not lead to any spurious results. Cutoffs calculated with this approach were found to be very similar to those reported previously. DISCUSSION This approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.
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Affiliation(s)
- Jigyasha Timsina
- 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
| | - Muhammad Ali
- 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
| | - Anh Do
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lihua Wang
- 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
| | - Yun Ju 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
- Division of Biostatistics, 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 Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110, USA
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Chen XR, Shao Y, Sadowski MJ. Interaction between KLOTHO-VS Heterozygosity and APOE ε4 Allele Predicts Rate of Cognitive Decline in Late-Onset Alzheimer's Disease. Genes (Basel) 2023; 14:917. [PMID: 37107675 PMCID: PMC10137709 DOI: 10.3390/genes14040917] [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: 03/01/2023] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
KLOTHO-VS heterozygosity (KL-VShet+) promotes longevity and protects against cognitive decline in aging. To determine whether KL-VShet+ mitigates Alzheimer's disease (AD) progression, we used longitudinal linear-mixed models to compare the rate of change in multiple cognitive measures in AD patients stratified by APOE ε4 carrier status. We aggregated data on 665 participants (208 KL-VShet-/ε4-, 307 KL-VShet-/ε4+, 66 KL-VShet+/ε4-, and 84 KL-VShet+/ε4+) from two prospective cohorts, the National Alzheimer's Coordinating Center and the Alzheimer's Disease Neuroimaging Initiative. All participants were initially diagnosed with mild cognitive impairment, later developed AD dementia during the study, and had at least three subsequent visits. KL-VShet+ conferred slower cognitive decline in ε4 non-carriers (+0.287 MMSE points/year, p = 0.001; -0.104 CDR-SB points/year, p = 0.026; -0.042 ADCOMS points/year, p < 0.001) but not in ε4 carriers who generally had faster rates of decline than non-carriers. Stratified analyses showed that the protective effect of KL-VShet+ was particularly prominent in male participants, those who were older than the median baseline age of 76 years, or those who had an education level of at least 16 years. For the first time, our study provides evidence that KL-VShet+ status has a protective effect on AD progression and interacts with the ε4 allele.
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Affiliation(s)
- Xi Richard Chen
- School of Medicine & Dentistry, University of Rochester, Rochester, NY 14642, USA
| | - Yongzhao Shao
- Department of Population Health, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Martin J. Sadowski
- Department of Neurology, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY 10016, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
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Timsina J, Gomez-Fonseca D, Wang L, Do A, Western D, Alvarez I, Aguilar M, Pastor P, Henson RL, Herries E, Xiong C, Schindler SE, Fagan AM, Bateman RJ, Farlow M, Morris JC, Perrin R, Moulder K, Hassenstab J, Chhatwal J, Mori H, Sung YJ, Cruchaga C. Comparative Analysis of Alzheimer's Disease Cerebrospinal Fluid Biomarkers Measurement by Multiplex SOMAscan Platform and Immunoassay-Based Approach. J Alzheimers Dis 2022; 89:193-207. [PMID: 35871346 PMCID: PMC9562128 DOI: 10.3233/jad-220399] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
BACKGROUND The SOMAscan assay has an advantage over immunoassay-based methods because it measures a large number of proteins in a cost-effective manner. However, the performance of this technology compared to the routinely used immunoassay techniques needs to be evaluated. OBJECTIVE We performed comparative analyses of SOMAscan and immunoassay-based protein measurements for five cerebrospinal fluid (CSF) proteins associated with Alzheimer's disease (AD) and neurodegeneration: NfL, Neurogranin, sTREM2, VILIP-1, and SNAP-25. METHODS We compared biomarkers measured in ADNI (N = 689), Knight-ADRC (N = 870), DIAN (N = 115), and Barcelona-1 (N = 92) cohorts. Raw protein values were transformed using z-score in order to combine measures from the different studies. sTREM2 and VILIP-1 had more than one analyte in SOMAscan; all available analytes were evaluated. Pearson's correlation coefficients between SOMAscan and immunoassays were calculated. Receiver operating characteristic curve and area under the curve were used to compare prediction accuracy of these biomarkers between the two platforms. RESULTS Neurogranin, VILIP-1, and NfL showed high correlation between SOMAscan and immunoassay measures (r > 0.9). sTREM2 had a fair correlation (r > 0.6), whereas SNAP-25 showed weak correlation (r = 0.06). Measures in both platforms provided similar predicted performance for all biomarkers except SNAP-25 and one of the sTREM2 analytes. sTREM2 showed higher AUC for SOMAscan based measures. CONCLUSION Our data indicate that SOMAscan performs as well as immunoassay approaches for NfL, Neurogranin, VILIP-1, and sTREM2. Our study shows promise for using SOMAscan as an alternative to traditional immunoassay-based measures. Follow-up investigation will be required for SNAP-25 and additional established biomarkers.
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Affiliation(s)
- Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Duber Gomez-Fonseca
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Anh Do
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Dan Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Ignacio Alvarez
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Miquel Aguilar
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Pau Pastor
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Rachel L. Henson
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Elizabeth Herries
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Suzanne E. Schindler
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Anne M. Fagan
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Martin Farlow
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana University Health, Indianapolis, IN, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Richard Perrin
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Krista Moulder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jasmeer Chhatwal
- Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hiroshi Mori
- Dept. of Clinical Neuroscience, Osaka City University Medical School, Nagaoka Sutoku University, Japan
| | | | | | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
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