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Beydoun MA, Beydoun HA, Hu YH, Maino Vieytes CA, Noren Hooten N, Song M, Georgescu MF, Fanelli-Kuczmarski MT, Meirelles O, Launer LJ, Evans MK, Zonderman AB. Plasma proteomic biomarkers and the association between poor cardiovascular health and incident dementia: The UK Biobank study. Brain Behav Immun 2024; 119:995-1007. [PMID: 38710337 DOI: 10.1016/j.bbi.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/04/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND The study examined how plasma proteome indicators may explain the link between poor cardiovascular health (CVH) and dementia risk. METHODS The present study involved 28,974 UK Biobank participants aged 50-74y at baseline (2006-2010) who were followed-up for ≤ 15 y for incidence of dementia. CVH was calculated using Life's Essential 8 (LE8) total scores. The scores were standardized and reverse coded to reflect poor CVH (LE8z_rev). OLINK proteomics was available on this sample (k = 1,463 plasma proteins). The study primarily tested the mediating effects of the plasma proteome in LE8z_rev-dementia effect. The total effect was decomposed into "mediation only" or pure indirect effect (PIE), "interaction only" or interaction referent (INTREF), "neither mediation nor interaction" or controlled direct effect (CDE), and "both mediation and interaction" or mediated interaction (INTMED). RESULTS The study found poorer CVH assessed by LE8z_rev increased the risk of all-cause dementia by 11 % [per 1 SD, hazard ratio, (HR) = 1.11, 95 % CI: 1.03-1.20, p = 0.005). The study identified 11 plasma proteins with strong mediating effects, with GDF15 having the strongest association with dementia risk (per 1 SD, HR = 1.24, 95 % CI: 1.16, 1.33, P < 0.001 when LE8z_rev is set at its mean value) and the largest proportion mediated combining PIE and INTMED (62.6 %; 48 % of TE is PIE), followed by adrenomedullin or ADM. A first principal component with 10 top mediators (TNFRSF1A, GDF15, FSTL3, COL6A3, PLAUR, ADM, GFRAL, ACVRL1, TNFRSF6B, TGFA) mediated 53.6 % of the LE8z_rev-dementia effect. Using all the significant PIE (k = 526) proteins, we used OLINK Insight pathway analysis to identify key pathways, which revealed the involvement of the immune system, signal transduction, metabolism, disease, protein metabolism, hemostasis, membrane trafficking, extracellular matrix organization, developmental biology, and gene expression among others. STRING analysis revealed that five top consistent proteomic mediators were represented in two larger clusters reflecting numerous interconnected biological gene ontology pathways, most notably cytokine-mediated signaling pathway for GDF15 cluster (GO:0019221) and regulation of peptidyl-tyrosine phosphorylation for the ADM cluster (GO:0050730). CONCLUSION Dementia is linked to poor CVH mediated by GDF15 and ADM among several key proteomic markers which collectively explained ∼ 54 % of the total effect.
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Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States.
| | - Hind A Beydoun
- VA National Center on Homelessness Among Veterans, U.S. Department of Veterans Affairs, Washington, DC 20420, United States; Department of Management, Policy, and Community Health, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Christian A Maino Vieytes
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Minkyo Song
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Michael F Georgescu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Marie T Fanelli-Kuczmarski
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Osorio Meirelles
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, United States
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Beuthner BE, Elkenani M, Evert K, Mustroph J, Jacob CF, Paul NB, Beißbarth T, Zeisberg EM, Schnelle M, Puls M, Hasenfuß G, Toischer K. Histological assessment of cardiac amyloidosis in patients undergoing transcatheter aortic valve replacement. ESC Heart Fail 2024; 11:1636-1646. [PMID: 38407567 PMCID: PMC11098657 DOI: 10.1002/ehf2.14709] [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: 06/27/2023] [Revised: 11/28/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
AIMS Studies have reported a strongly varying co-prevalence of aortic stenosis (AS) and cardiac amyloidosis (CA). We sought to histologically determine the co-prevalence of AS and CA in patients undergoing transcatheter aortic valve replacement (TAVR). Consequently, we aimed to derive an algorithm to identify cases in which to suspect the co-prevalence of AS and CA. METHODS AND RESULTS In this prospective, monocentric study, endomyocardial biopsies of 162 patients undergoing TAVR between January 2017 and March 2021 at the University Medical Centre Göttingen were analysed by one pathologist blinded to clinical data using haematoxylin-eosin staining, Elastica van Gieson staining, and Congo red staining of endomyocardial biopsies. CA was identified in only eight patients (4.9%). CA patients had significantly higher N-terminal pro-brain natriuretic peptide (NT-proBNP) levels (4356.20 vs. 1938.00 ng/L, P = 0.034), a lower voltage-to-mass ratio (0.73 vs. 1.46 × 10-2 mVm2/g, P = 0.022), and lower transaortic gradients (Pmean 17.5 vs. 38.0 mmHg, P = 0.004) than AS patients. Concomitant CA was associated with a higher prevalence of post-procedural acute kidney injury (50.0% vs. 13.1%, P = 0.018) and sudden cardiac death [SCD; P (log-rank test) = 0.017]. Following propensity score matching, 184 proteins were analysed to identify serum biomarkers of concomitant CA. CA patients expressed lower levels of chymotrypsin (P = 0.018) and carboxypeptidase 1 (P = 0.027). We propose an algorithm using commonly documented parameters-stroke volume index, ejection fraction, NT-proBNP levels, posterior wall thickness, and QRS voltage-to-mass ratio-to screen for CA in AS patients, reaching a sensitivity of 66.6% with a specificity of 98.1%. CONCLUSIONS The co-prevalence of AS and CA was lower than expected, at 4.9%. Despite excellent 1 year mortality, AS + CA patients died significantly more often from SCD. We propose a multimodal algorithm to facilitate more effective screening for CA containing parameters commonly documented during clinical routine. Proteomic biomarkers may yield additional information in the future.
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Affiliation(s)
- Bo Eric Beuthner
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Manar Elkenani
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Katja Evert
- Institute of PathologyUniversity of RegensburgRegensburgGermany
| | - Julian Mustroph
- Department of Internal Medicine IIUniversity Medical Centre RegensburgRegensburgGermany
| | - Christoph Friedemann Jacob
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Niels Benjamin Paul
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- Department of Medical BioinformaticsUniversity Medical Centre Göttingen, Georg August University of GöttingenGöttingenGermany
| | - Tim Beißbarth
- Department of Medical BioinformaticsUniversity Medical Centre Göttingen, Georg August University of GöttingenGöttingenGermany
| | - Elisabeth Maria Zeisberg
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Moritz Schnelle
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
- Department of Clinical ChemistryUniversity Medical Centre Göttingen, Georg August University of GöttingenGöttingenGermany
| | - Miriam Puls
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Gerd Hasenfuß
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
| | - Karl Toischer
- Department of Cardiology and PneumologyUniversity Medical Centre Göttingen, Georg August University of GöttingenRobert‐Koch‐Straße 4037075GöttingenGermany
- German Centre for Cardiovascular Research (DZHK), partner site GöttingenGöttingenGermany
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Beydoun HA, Beydoun MA, Noren Hooten N, Weiss J, Li Z, Georgescu MF, Maino Vieytes CA, Meirelles O, Launer LJ, Evans MK, Zonderman AB. Mediating and moderating effects of plasma proteomic biomarkers on the association between poor oral health problems and incident dementia: The UK Biobank study. GeroScience 2024:10.1007/s11357-024-01202-3. [PMID: 38809392 DOI: 10.1007/s11357-024-01202-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/12/2024] [Indexed: 05/30/2024] Open
Abstract
The plasma proteome can mediate poor oral health problems (POHP)'s link to incident dementia. We screened 37,269 UK Biobank participants 50-74 years old (2006-2010) for prevalent POHP, further tested against 1463 plasma proteins and incident dementia over up to 15 years of follow-up. Total effect (TE) of POHP-dementia through plasma proteomic markers was decomposed into pure indirect effect (PIE), interaction referent (INTREF), controlled direct effect (CDE), or mediated interaction (INTMED). POHP increased the risk of all-cause dementia by 17% (P < 0.05). Growth differentiation factor 15 (GDF15) exhibited the strongest mediating effects (PIE > 0, P < 0.001), explaining 28% the total effect of POHP on dementia, as a pure indirect effect. A first principal component encompassing top 4 mediators (GDF15, IL19, MMP12, and ACVRL1), explained 11% of the POHP-dementia effect as a pure indirect effect. Pathway analysis including all mediators (k = 173 plasma proteins) revealed the involvement of the immune system, signal transduction, metabolism, disease, and gene expression, while STRING analysis indicated that top mediators within the first principal component were also represented in the two largest proteomic clusters. The dominant biological GO pathway for the GDF15 cluster was GO:0007169 labeled as "transmembrane receptor protein tyrosine kinase signaling pathway." Dementia is linked to POHP mediated by GDF15 among several proteomic markers.
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Affiliation(s)
- Hind A Beydoun
- US Department of Veterans Affairs, VA National Center On Homelessness Among Veterans, Washington, DC, 20420, USA
- Department of Management, Policy, and Community Health, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - May A Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA.
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Jordan Weiss
- Stanford Center on Longevity, Stanford University, Palo Alto, CA, 94305, USA
| | - Zhiguang Li
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Michael F Georgescu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Christian A Maino Vieytes
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Osorio Meirelles
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, 251 Bayview Blvd, Suite 100, Baltimore, MD, 21224, USA
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Guo Y, You J, Zhang Y, Liu WS, Huang YY, Zhang YR, Zhang W, Dong Q, Feng JF, Cheng W, Yu JT. Plasma proteomic profiles predict future dementia in healthy adults. NATURE AGING 2024; 4:247-260. [PMID: 38347190 DOI: 10.1038/s43587-023-00565-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 12/22/2023] [Indexed: 02/22/2024]
Abstract
The advent of proteomics offers an unprecedented opportunity to predict dementia onset. We examined this in data from 52,645 adults without dementia in the UK Biobank, with 1,417 incident cases and a follow-up time of 14.1 years. Of 1,463 plasma proteins, GFAP, NEFL, GDF15 and LTBP2 consistently associated most with incident all-cause dementia (ACD), Alzheimer's disease (AD) and vascular dementia (VaD), and ranked high in protein importance ordering. Combining GFAP (or GDF15) with demographics produced desirable predictions for ACD (area under the curve (AUC) = 0.891) and AD (AUC = 0.872) (or VaD (AUC = 0.912)). This was also true when predicting over 10-year ACD, AD and VaD. Individuals with higher GFAP levels were 2.32 times more likely to develop dementia. Notably, GFAP and LTBP2 were highly specific for dementia prediction. GFAP and NEFL began to change at least 10 years before dementia diagnosis. Our findings strongly highlight GFAP as an optimal biomarker for dementia prediction, even more than 10 years before the diagnosis, with implications for screening people at high risk for dementia and for early intervention.
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Affiliation(s)
- Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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Kerminen H, Marzetti E, D’Angelo E. Biological and Physical Performance Markers for Early Detection of Cognitive Impairment in Older Adults. J Clin Med 2024; 13:806. [PMID: 38337499 PMCID: PMC10856537 DOI: 10.3390/jcm13030806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/12/2024] Open
Abstract
Dementia is a major cause of poor quality of life, disability, and mortality in old age. According to the geroscience paradigm, the mechanisms that drive the aging process are also involved in the pathogenesis of chronic degenerative diseases, including dementia. The dissection of such mechanisms is therefore instrumental in providing biological targets for interventions and new sources for biomarkers. Within the geroscience paradigm, several biomarkers have been discovered that can be measured in blood and that allow early identification of individuals at risk of cognitive impairment. Examples of such markers include inflammatory biomolecules, markers of neuroaxonal damage, extracellular vesicles, and DNA methylation. Furthermore, gait speed, measured at a usual and fast pace and as part of a dual task, has been shown to detect individuals at risk of future dementia. Here, we provide an overview of available biomarkers that may be used to gauge the risk of cognitive impairment in apparently healthy older adults. Further research should establish which combination of biomarkers possesses the highest predictive accuracy toward incident dementia. The implementation of currently available markers may allow the identification of a large share of at-risk individuals in whom preventive interventions should be implemented to maintain or increase cognitive reserves, thereby reducing the risk of progression to dementia.
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Affiliation(s)
- Hanna Kerminen
- Faculty of Medicine and Health Technology, Gerontology Research Center (GEREC), Tampere University, Arvo Ylpön katu 34, 33520 Tampere, Finland;
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy
| | - Emanuele Marzetti
- Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy;
| | - Emanuela D’Angelo
- Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy;
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Roberts JA, Basu-Roy S, Shin J, Varma VR, Williamson A, Blackshear C, Griswold ME, Candia J, Elango P, Karikkineth AC, Tanaka T, Ferrucci L, Thambisetty M. Serum Proteomic Signatures of Common Health Outcomes among Older Adults. Gerontology 2024; 70:269-278. [PMID: 38219723 DOI: 10.1159/000534753] [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: 04/30/2023] [Accepted: 10/09/2023] [Indexed: 01/16/2024] Open
Abstract
INTRODUCTION In aging populations, the coexistence of multiple health comorbidities represents a significant challenge for clinicians and researchers. Leveraging advances in omics techniques to characterize these health conditions may provide insight into disease pathogenesis as well as reveal biomarkers for monitoring, prognostication, and diagnosis. Researchers have previously established the utility of big data approaches with respect to comprehensive health outcome measurements in younger populations, identifying protein markers that may provide significant health information with a single blood sample. METHODS Here, we employed a similar approach in two cohorts of older adults, the Baltimore Longitudinal Study of Aging (mean age = 76.12 years) and InCHIANTI Study (mean age = 66.05 years), examining the relationship between levels of serum proteins and 5 key health outcomes: kidney function, fasting glucose, physical activity, lean body mass, and percent body fat. RESULTS Correlations between proteins and health outcomes were primarily shared across both older adult cohorts. We further identified that most proteins associated with health outcomes in the older adult cohorts were not associated with the same outcomes in a prior study of a younger population. A subset of proteins, adiponectin, MIC-1, and NCAM-120, were associated with at least three health outcomes in both older adult cohorts but not in the previously published younger cohort, suggesting that they may represent plausible markers of general health in older adult populations. CONCLUSION Taken together, these findings suggest that comprehensive protein health markers have utility in aging populations and are distinct from those identified in younger adults, indicating unique mechanisms of disease with aging.
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Affiliation(s)
- Jackson A Roberts
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA,
- Columbia University Vagelos College of Physicians and Surgeons, New York, New York, USA,
| | - Sayantani Basu-Roy
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jong Shin
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Vijay R Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Andrew Williamson
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Chad Blackshear
- University of Mississippi Medical Center, Jackson, Mississippi, USA
| | | | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Palchamy Elango
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Ajoy C Karikkineth
- Clinical Research Core, National Institute on Aging, National Institutes of Health Intramural Research Program, Baltimore, Maryland, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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7
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You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT. Plasma proteomic profiles predict individual future health risk. Nat Commun 2023; 14:7817. [PMID: 38016990 PMCID: PMC10684756 DOI: 10.1038/s41467-023-43575-7] [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: 06/16/2023] [Accepted: 11/13/2023] [Indexed: 11/30/2023] Open
Abstract
Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
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Affiliation(s)
- Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Lin-Bo Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- School of Data Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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8
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Wang W, Zhong Y, Zhou Y, Yu Y, Li J, Kang S, Ma Z, Fan X, Sun L, Tang L. Low-intensity pulsed ultrasound mitigates cognitive impairment by inhibiting muscle atrophy in hindlimb unloaded mice. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2023; 154:1427-1438. [PMID: 37672304 DOI: 10.1121/10.0020835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023]
Abstract
Microgravity leads to muscle loss, usually accompanied by cognitive impairment. Muscle reduction was associated with the decline of cognitive ability. Our previous studies showed that low-intensity pulsed ultrasound (LIPUS) promoted muscle hypertrophy and prevented muscle atrophy. This study aims to verify whether LIPUS can improve cognitive impairment by preventing muscle atrophy in hindlimb unloaded mice. In this study, mice were randomly divided into normal control (NC), hindlimb unloading (HU), hindlimb unloading + LIPUS (HU+LIPUS) groups. The mice in the HU+LIPUS group received a 30 mW/cm2 LIPUS irradiation on gastrocnemius for 20 min/d. After 21 days, LIPUS significantly prevented the decrease in muscle mass and strength caused by tail suspension. The HU+LIPUS mice showed an enhanced desire to explore unfamiliar environments and their spatial learning and memory abilities, enabling them to quickly identify differences between different objects, as well as their social discrimination abilities. MSTN is a negative regulator of muscle growth and also plays a role in regulating cognition. LIPUS significantly inhibited MSTN expression in skeletal muscle and serum and its receptor ActRIIB expression in brain, upregulated AKT and BDNF expression in brain. Taken together, LIPUS may improve the cognitive dysfunction in hindlimb unloaded rats by inhibiting muscle atrophy through MSTN/AKT/BDNF pathway.
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Affiliation(s)
- Wanzhao Wang
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Yi Zhong
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Yaling Zhou
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Yanan Yu
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Jiaxiang Li
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Sufang Kang
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Zhanke Ma
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Xiushan Fan
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Lijun Sun
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
| | - Liang Tang
- Institute of Sports Biology, Shaanxi Normal University, Xi'an, 710119, China
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9
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Duperron MG, Knol MJ, Le Grand Q, Evans TE, Mishra A, Tsuchida A, Roshchupkin G, Konuma T, Trégouët DA, Romero JR, Frenzel S, Luciano M, Hofer E, Bourgey M, Dueker ND, Delgado P, Hilal S, Tankard RM, Dubost F, Shin J, Saba Y, Armstrong NJ, Bordes C, Bastin ME, Beiser A, Brodaty H, Bülow R, Carrera C, Chen C, Cheng CY, Deary IJ, Gampawar PG, Himali JJ, Jiang J, Kawaguchi T, Li S, Macalli M, Marquis P, Morris Z, Muñoz Maniega S, Miyamoto S, Okawa M, Paradise M, Parva P, Rundek T, Sargurupremraj M, Schilling S, Setoh K, Soukarieh O, Tabara Y, Teumer A, Thalamuthu A, Trollor JN, Valdés Hernández MC, Vernooij MW, Völker U, Wittfeld K, Wong TY, Wright MJ, Zhang J, Zhao W, Zhu YC, Schmidt H, Sachdev PS, Wen W, Yoshida K, Joutel A, Satizabal CL, Sacco RL, Bourque G, Lathrop M, Paus T, Fernandez-Cadenas I, Yang Q, Mazoyer B, Boutinaud P, Okada Y, Grabe HJ, Mather KA, Schmidt R, Joliot M, Ikram MA, Matsuda F, Tzourio C, Wardlaw JM, Seshadri S, Adams HHH, Debette S. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease. Nat Med 2023; 29:950-962. [PMID: 37069360 PMCID: PMC10115645 DOI: 10.1038/s41591-023-02268-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 02/15/2023] [Indexed: 04/19/2023]
Abstract
Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.
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Affiliation(s)
- Marie-Gabrielle Duperron
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Department of Neurology, Institute of Neurodegenerative Diseases, Bordeaux University Hospital, Bordeaux, France
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Quentin Le Grand
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Tavia E Evans
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Aniket Mishra
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Ami Tsuchida
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
| | - Gennady Roshchupkin
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Takahiro Konuma
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - David-Alexandre Trégouët
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Jose Rafael Romero
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | | | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Mathieu Bourgey
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Nicole D Dueker
- John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Pilar Delgado
- Institut de Recerca Vall d'hebron, Neurovascular Research Lab, Universitat Autònoma de Barcelona, Barcelona, Spain
- Hospital Universitari Vall d'Hebron, Neurology Department, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Saima Hilal
- Memory Aging and Cognition Center, National University Health System, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rick M Tankard
- Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia
| | - Florian Dubost
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Yasaman Saba
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Nicola J Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, Western Australia, Australia
| | - Constance Bordes
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alexa Beiser
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Dementia Collaborative Research Centre Assessment and Better Care, UNSW, Sydney, New South Wales, Australia
| | - Robin Bülow
- Institute for Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Caty Carrera
- Stroke Pharmacogenomics and Genetics Group, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
| | - Christopher Chen
- Memory Aging and Cognition Center, National University Health System, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Ian J Deary
- School of Psychology, University of Edinburgh, Edinburgh, UK
| | - Piyush G Gampawar
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Jayandra J Himali
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuo Li
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Melissa Macalli
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Pascale Marquis
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Zoe Morris
- Neuroimaging, Department of Clinical Neurosciences, Royal Infirmary of Edinburgh, Edinburgh, UK
| | - Susana Muñoz Maniega
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
| | | | - Masakazu Okawa
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthew Paradise
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Pedram Parva
- The Framingham Heart Study, Framingham, MA, USA
- Radiology Department, Boston University School of Medicine, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Evelyn F. McKnight Brain Institute, Department of Neurology, University of Miami, Miami, FL, USA
| | | | - Sabrina Schilling
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Kazuya Setoh
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Omar Soukarieh
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Julian N Trollor
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Developmental Disability Neuropsychiatry, UNSW, Sydney, New South Wales, Australia
| | - Maria C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Row Fogo Centre for Research into Ageing and the Brain, University of Edinburgh, Edinburgh, UK
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
| | - Junyi Zhang
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Wanting Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- The Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Helena Schmidt
- Institute for Molecular Biology & Biochemistry, Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Neuropsychiatric Institute, the Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Anne Joutel
- Institut de Psychiatrie et Neurosciences de Paris, Université Paris Cité, Inserm, France
| | - Claudia L Satizabal
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Ralph L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
- Evelyn F. McKnight Brain Institute, Department of Neurology, University of Miami, Miami, FL, USA
- Department of Public Health Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Neurosurgery, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
- Canadian Centre for Computational Genomics, McGill University, Montreal, Quebec, Canada
| | - Mark Lathrop
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, Montreal, Quebec, Canada
| | - Tomas Paus
- University of Montreal, Faculty of Medicine, Departments of Psychiatry and Neuroscience, Montreal, Quebec, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Centre Hospitalier Universitaire Sainte Justine, Montreal, Quebec, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Israel Fernandez-Cadenas
- Stroke Pharmacogenomics and Genetics Group, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain
- Stroke Pharmacogenomics and Genetics Group, Fundació per la Docència i la Recerca Mutua Terrassa, Terrassa, Spain
| | - Qiong Yang
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
- Bordeaux University Hospital, Bordeaux, France
| | | | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, University of New South Wales, Sydney, New South Wales, Australia
- Neuroscience Research Australia, Sydney, New South Wales, Australia
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Marc Joliot
- Groupe d'Imagerie Neurofonctionelle - Institut des maladies neurodégénératives (GIN-IMN), UMR 5293, University of Bordeaux, CNRS, CEA, Bordeaux, France
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Christophe Tzourio
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France
- Department of Medical Informatics, Bordeaux University Hospital, Bordeaux, France
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute Centre at the University of Edinburgh, Edinburgh, UK
- Row Fogo Centre for Research into Ageing and the Brain, University of Edinburgh, Edinburgh, UK
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX, USA
| | - Hieab H H Adams
- Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, UMR 1219, University of Bordeaux, Inserm, Bordeaux, France.
- Department of Neurology, Institute of Neurodegenerative Diseases, Bordeaux University Hospital, Bordeaux, France.
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10
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Short MI, Fohner AE, Skjellegrind HK, Beiser A, Gonzales MM, Satizabal CL, Austin TR, Longstreth W, Bis JC, Lopez O, Hveem K, Selbæk G, Larson MG, Yang Q, Aparicio HJ, McGrath ER, Gerszten RE, DeCarli CS, Psaty BM, Vasan RS, Zare H, Seshadri S. Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging. J Alzheimers Dis 2023; 96:1767-1780. [PMID: 38007645 PMCID: PMC10741337 DOI: 10.3233/jad-230145] [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] [Accepted: 10/04/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults. OBJECTIVE To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome. METHODS Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up). RESULTS Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities. CONCLUSIONS Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.
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Affiliation(s)
- Meghan I. Short
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Department of Medicine, Tufts University School of Medicine, Boston, MA, USA
| | - Alison E. Fohner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Håvard K. Skjellegrind
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Alexa Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Mitzi M. Gonzales
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Neurology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Claudia L. Satizabal
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - W.T. Longstreth
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Neurology, University of Washington, Seattle, WA, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Oscar Lopez
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kristian Hveem
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Levanger, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Geir Selbæk
- Norwegian National Centre for Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway
| | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Hugo J. Aparicio
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Emer R. McGrath
- Framingham Heart Study, Framingham, MA, USA
- School of Medicine, National University of Ireland Galway, Galway, Ireland
- HRB Clinical Research Facility, National University of Ireland Galway, Galway, Ireland
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Charles S. DeCarli
- Department of Neurology, School of Medicine and Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California, Davis, Sacramento, CA, USA
| | - Bruce M. Psaty
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Boston University Center for Computing and Data Science, Boston, MA, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
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11
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Hillary RF, Gadd DA, McCartney DL, Shi L, Campbell A, Walker RM, Ritchie CW, Deary IJ, Evans KL, Nevado‐Holgado AJ, Hayward C, Porteous DJ, McIntosh AM, Lovestone S, Robinson MR, Marioni RE. Genome- and epigenome-wide studies of plasma protein biomarkers for Alzheimer's disease implicate TBCA and TREM2 in disease risk. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12280. [PMID: 35475137 PMCID: PMC9019629 DOI: 10.1002/dad2.12280] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 11/24/2022]
Abstract
Introduction The levels of many blood proteins are associated with Alzheimer's disease (AD) or its pathological hallmarks. Elucidating the molecular factors that control circulating levels of these proteins may help to identify proteins associated with disease risk mechanisms. Methods Genome-wide and epigenome-wide studies (nindividuals ≤1064) were performed on plasma levels of 282 AD-associated proteins, identified by a structured literature review. Bayesian penalized regression estimated contributions of genetic and epigenetic variation toward inter-individual differences in plasma protein levels. Mendelian randomization (MR) and co-localization tested associations between proteins and disease-related phenotypes. Results Sixty-four independent genetic and 26 epigenetic loci were associated with 45 proteins. Novel findings included an association between plasma triggering receptor expressed on myeloid cells 2 (TREM2) levels and a polymorphism and cytosine-phosphate-guanine (CpG) site within the MS4A4A locus. Higher plasma tubulin-specific chaperone A (TBCA) and TREM2 levels were significantly associated with lower AD risk. Discussion Our data inform the regulation of biomarker levels and their relationships with AD.
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Affiliation(s)
- Robert F. Hillary
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | - Danni A. Gadd
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | - Daniel L. McCartney
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | - Liu Shi
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Archie Campbell
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | - Rosie M. Walker
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
- Centre for Clinical Brain Sciences, Chancellor's Building, 49 Little France CrescentUniversity of EdinburghEdinburghUK
| | - Craig W. Ritchie
- Edinburgh Dementia Prevention, Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Ian J. Deary
- Lothian Birth Cohorts, Department of PsychologyUniversity of EdinburghEdinburghUK
| | - Kathryn L. Evans
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | | | - Caroline Hayward
- MRC Human Genetics UnitInstitute of Genetics and CancerUniversity of EdinburghEdinburghUK
| | - David J. Porteous
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
- Division of Psychiatry, Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
| | - Simon Lovestone
- Department of PsychiatryUniversity of OxfordOxfordUK
- NeurodegenerationJohnson and Johnson Medical LtdWokinghamUK
| | - Matthew R. Robinson
- Medical Genomics GroupInstitute of Science and Technology AustriaKlosterneuburgAustria
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Cancer, University of EdinburghEdinburghUK
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12
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Dansson HV, Stempfle L, Egilsdóttir H, Schliep A, Portelius E, Blennow K, Zetterberg H, Johansson FD. Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:151. [PMID: 34488882 PMCID: PMC8422748 DOI: 10.1186/s13195-021-00886-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/08/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. METHODS A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. RESULTS A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. CONCLUSION Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.
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Affiliation(s)
- Hákon Valur Dansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Lena Stempfle
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
| | - Hildur Egilsdóttir
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Alexander Schliep
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Erik Portelius
- 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
| | - 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
| | - 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, UCL, London, UK
| | - Fredrik D Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
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13
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Proteomic Analysis of Hydromethylthionine in the Line 66 Model of Frontotemporal Dementia Demonstrates Actions on Tau-Dependent and Tau-Independent Networks. Cells 2021; 10:cells10082162. [PMID: 34440931 PMCID: PMC8391171 DOI: 10.3390/cells10082162] [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: 05/25/2021] [Revised: 08/16/2021] [Accepted: 08/19/2021] [Indexed: 12/21/2022] Open
Abstract
Abnormal aggregation of tau is the pathological hallmark of tauopathies including frontotemporal dementia (FTD). We have generated tau-transgenic mice that express the aggregation-prone P301S human tau (line 66). These mice present with early-onset, high tau load in brain and FTD-like behavioural deficiencies. Several of these behavioural phenotypes and tau pathology are reversed by treatment with hydromethylthionine but key pathways underlying these corrections remain elusive. In two proteomic experiments, line 66 mice were compared with wild-type mice and then vehicle and hydromethylthionine treatments of line 66 mice were compared. The brain proteome was investigated using two-dimensional electrophoresis and mass spectrometry to identify protein networks and pathways that were altered due to tau overexpression or modified by hydromethylthionine treatment. Overexpression of mutant tau induced metabolic/mitochondrial dysfunction, changes in synaptic transmission and in stress responses, and these functions were recovered by hydromethylthionine. Other pathways, such as NRF2, oxidative phosphorylation and protein ubiquitination were activated by hydromethylthionine, presumably independent of its function as a tau aggregation inhibitor. Our results suggest that hydromethylthionine recovers cellular activity in both a tau-dependent and a tau-independent fashion that could lead to a wide-spread improvement of homeostatic function in the FTD brain.
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14
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Higgins-Chen AT, Thrush KL, Levine ME. Aging biomarkers and the brain. Semin Cell Dev Biol 2021; 116:180-193. [PMID: 33509689 PMCID: PMC8292153 DOI: 10.1016/j.semcdb.2021.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/15/2022]
Abstract
Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.
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Affiliation(s)
- Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, 300 George St, Suite 901, New Haven, CT 06511, USA.
| | - Kyra L Thrush
- Program in Computational Biology and Bioinformatics, Yale University, 300 George St, Suite 501, New Haven, CT 06511, USA.
| | - Morgan E Levine
- Department of Pathology, Yale University School of Medicine, 310 Cedar Street, Suite LH 315A, New Haven, CT 06520, USA.
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15
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Ubaida-Mohien C, Moaddel R, Moore AZ, Kuo PL, Faghri F, Tharakan R, Tanaka T, Nalls MA, Ferrucci L. Proteomics and Epidemiological Models of Human Aging. Front Physiol 2021; 12:674013. [PMID: 34135771 PMCID: PMC8202502 DOI: 10.3389/fphys.2021.674013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 05/03/2021] [Indexed: 12/21/2022] Open
Abstract
Human aging is associated with a decline of physical and cognitive function and high susceptibility to chronic diseases, which is influenced by genetics, epigenetics, environmental, and socio-economic status. In order to identify the factors that modulate the aging process, established measures of aging mechanisms are required, that are both robust and feasible in humans. It is also necessary to connect these measures to the phenotypes of aging and their functional consequences. In this review, we focus on how this has been addressed from an epidemiologic perspective using proteomics. The key aspects of epidemiological models of aging can be incorporated into proteomics and other omics which can provide critical detailed information on the molecular and biological processes that change with age, thus unveiling underlying mechanisms that drive multiple chronic conditions and frailty, and ideally facilitating the identification of new effective approaches for prevention and treatment.
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Affiliation(s)
- Ceereena Ubaida-Mohien
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Ruin Moaddel
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Ann Zenobia Moore
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Pei-Lun Kuo
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Faraz Faghri
- Center for Alzheimer's and Related Dementias, National Institute on Aging, Bethesda, MD, United States.,Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States.,Data Tecnica International, Glen Echo, MD, United States
| | - Ravi Tharakan
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Toshiko Tanaka
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias, National Institute on Aging, Bethesda, MD, United States.,Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States.,Data Tecnica International, Glen Echo, MD, United States
| | - Luigi Ferrucci
- Biomedical Research Center, National Institute on Aging, National Institute of Health, Baltimore, MD, United States
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16
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Hillary RF, Marioni RE. MethylDetectR: a software for methylation-based health profiling. Wellcome Open Res 2021; 5:283. [PMID: 33969230 PMCID: PMC8080939 DOI: 10.12688/wellcomeopenres.16458.2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/01/2021] [Indexed: 12/23/2022] Open
Abstract
DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called 'MethylDetectR' which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.
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Affiliation(s)
- Robert F. Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK
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17
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Hillary RF, Marioni RE. MethylDetectR: a software for methylation-based health profiling. Wellcome Open Res 2020; 5:283. [PMID: 33969230 PMCID: PMC8080939 DOI: 10.12688/wellcomeopenres.16458.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/26/2020] [Indexed: 04/02/2024] Open
Abstract
DNA methylation is an important biological process which involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this, we created a secure, web-based interactive platform called 'MethylDetectR' which calculates estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits, high-density lipoprotein cholesterol and the levels of 27 blood proteins related to inflammatory and neurological processes and disease. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The 'MethylDetectR' platform allows for the fast and secure calculation of DNA methylation-derived estimates for many human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.
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Affiliation(s)
- Robert F. Hillary
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, Midlothian, EH4 2XU, UK
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