Hope Center Member Publications: August 24, 2025

“Tau PET positivity in individuals with and without cognitive impairment varies with age, amyloid-β status, APOE genotype and sex” (2025) Nature Neuroscience

Tau PET positivity in individuals with and without cognitive impairment varies with age, amyloid-β status, APOE genotype and sex
(2025) Nature Neuroscience, 28 (8), pp. 1610-1621.

Ossenkoppele, R.^a ^b ^c , Coomans, E.M.^b ^c , Apostolova, L.G.^d , Baker, S.L.^e , Barthel, H.^f , Beach, T.G.^g , Benzinger, T.L.S.^h ⁱ , Betthauser, T.^j ^k ^l , Bischof, G.N.^m ⁿ , Bottlaender, M.^o ^p , Bourgeat, P.^q , den Braber, A.^b ^c ^r , Brendel, M.^s ^t , Brickman, A.M.^u ^v , Cash, D.M.^w ^x , Carrillo, M.C.^y , Coath, W.^z , Christian, B.T.^aa , Dickerson, B.C.^ab ^ac ^ad , Dore, V.^q ^ae , Drzezga, A.^m ⁿ ^t , Feizpour, A.^ae ^af , van der Flier, W.M.^b ^c ^ag , Franzmeier, N.^ah ^ai ^aj , Frisoni, G.B.^ak , Garibotto, V.^al ^am ^an , van de Giessen, E.^c ^ao , Domingo-Gispert, J.^ap ^aq , Gnoerich, J.^s , Gu, Y.^ar , Guan, Y.^as , Hanseeuw, B.J.^at ^au ^av , Harrison, T.M.^aw , Jack, C.R.^ax , Jaeger, E.^ay , Jagust, W.J.^aw , Jansen, W.J.^az , La Joie, R.^ba , Johnson, K.A.^bb ^bc ^bd ^be , Johnson, S.C.^j ^k , Kennedy, I.A.^bf , Kim, J.P.^ar , van Laere, K.^bg ^bh , Lagarde, J.^o ^bi , Lao, P.^bj ^bk , Luchsinger, J.A.^bk , Kern, S.^bl ^bm , Kreisl, W.C.^bj , Malotaux, V.^at , Malpetti, M.^bn , Manly, J.J.^bj ^bo , Mao, X.^as , Mattsson-Carlgren, N.^a ^bp ^bq , Messerschmidt, K.^f , Minguillon, C.^ap , Mormino, E.M.^br , O’Brien, J.T.^bs , Palmqvist, S.^a ^bt , Peretti, D.E.^am , Petersen, R.C.^bu , Pijnenburg, Y.A.L.^b ^c , Pontecorvo, M.J.^bf , Poirier, J.^bv ^bw , Rabinovici, G.D.^ba ^bx , Rahmouni, N.^by ^bz , Risacher, S.L.^ca , Rosa-Neto, P.^by ^bz , Rosen, H.^ba , Rowe, C.C.^ae ^af , Rowe, J.B.^bn ^cb ^cc , Rullmann, M.^cd , Salman, Y.^at , Sarazin, M.^o ^ce , Saykin, A.J.^ca , Schneider, J.A.^cf ^cg ^ch , Schöll, M.^ci ^cj ^ck , Schott, J.M.^z ^cl , Seo, S.W.^ar , Serrano, G.E.^g , Shcherbinin, S.^bf , Shekari, M.^ap , Skoog, I.^bl , Smith, R.^a ^bt , Sperling, R.A.^bb ^bc ^bd , Spruyt, L.^cm , Stomrud, E.^a ^bt , Strandberg, O.^a , Therriault, J.^by ^bz , Xie, F.^as , Vandenberghe, R.^cm ^cn , Villemagne, V.L.^ae ^co , Villeneuve, S.^bv ^bw , Visser, P.J.^b ^c ^az , Vossler, H.^br , Young, C.B.^br , Groot, C.^b ^c , Hansson, O.^a ^bt , Mayo Clinic Study on Aging^cp , PREVENT-AD Research Group^cq

^a Clinical Memory Research Unit, Department of Clinical Sciences in Malmö, Lund University, Lund, Sweden
^b Alzheimer Center Amsterdam, Neurology, Amsterdam UMC; location VUmc, Amsterdam, The Netherlands
^c Neurodegeneration, Amsterdam Neuroscience, Amsterdam, Netherlands
^d Indiana University School of Medicine, Indianapolis, IN, United States
^e Lawrence Berkeley National Laboratory, Berkeley, CA, United States
^f Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
^g Banner Sun Health Research Institute, Sun City, AZ, United States
^h Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
ⁱ Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington, Washington University School of Medicine, St. Louis, MO, USA
^j Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
^k Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
^l Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
^m Faculty of Medicine, University of Cologne and Department of Nuclear Medicine University Hospital Cologne, Cologne, Germany
ⁿ Molecular Organization of the Brain, Institute for Neurosciences and Medicine, Jülich, Germany
^o Service Hospitalier Frédéric Joliot CEA, CNRS, INSERM, Université Paris-Saclay, Orsay, France
^p UNIACT, Neurospin, Gif-sur-Yvette, CEA, France
^q Australian eHealth Research Centre, CSIRO, Melbourne, VIC, Australia
^r Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^s Department of Nuclear Medicine, LMU Hospital, Ludwig-Maximilians-University of Munich, Munich, Germany
^t German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
^u Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
^v Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
^w Dementia Research Centre, University College London, UCL Queen Square Institute of Neurology, London, United Kingdom
^x UK Dementia Research Institute at University College London, London, United Kingdom
^y Medical & Scientific Relations, Alzheimer’s Association, Chicago, IL, United States
^z Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
^aa Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
^ab Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
^ac Thinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States
^ad Massachusetts Alzheimer’s Disease Research Center, Boston, MA, United States
^ae Department of Molecular Imaging & Therapy, Austin Health, Heidelberg, VIC, Australia
^af University of Melbourne, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
^ag Department of Epidemiology and Biostatistics, Amsterdam UMC; location VUmc, Amsterdam, The Netherlands
^ah Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^ai Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
^aj Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Gothenburg, Sweden
^ak Memory Clinic, Geneva University HospitalsGeneva, Switzerland
^al Division of Nuclear Medicine and Molecular Imaging, Geneva University HospitalsGeneva, Switzerland
^am Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of Medicine, University of GenevaGeneva, Switzerland
^an CIBM Center for Biomedical ImagingGeneva, Switzerland
^ao Department of Radiology & Nuclear Medicine, Amsterdam UMC; location VUmc, Amsterdam, The Netherlands
^ap Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
^aq Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y NanomedicinaMadrid, Spain
^ar Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of MedicineSeoul, South Korea
^as Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan UniversityShanghai, China
^at Institute of Neuroscience, UCLouvain, Brussels, Belgium
^au Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
^av Gordon Center for Medical Imaging, Department of Radiology, Mass General Brigham, Boston, MA, United States
^aw Department of Neuroscience, University of California Berkeley, Berkeley, CA, United States
^ax Department of Radiology, Mayo Clinic, Rochester, MN, United States
^ay Department of Nuclear Medicine, University of Cologne, University Hospital of Cologne, Cologne, Germany
^az Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
^ba Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, United States
^bb Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
^bc Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
^bd Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
^be Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
^bf Eli Lilly and Company, Indianapolis, IN, United States
^bg Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium
^bh Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
^bi Department of Neurology of Memory and Language, GHU Paris Psychiatrie & Neurosciences, Hôpital Sainte Anne, Paris, France
^bj Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
^bk Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
^bl Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
^bm Department of Neuropsychiatry, Sahlgrenska University Hospital, Gothenburg, Sweden
^bn Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
^bo Gertrude H. Sergievsky Center and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, USA
^bp Department of Neurology, Lund University, Skåne University Hospital, Lund, Sweden
^bq Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
^br Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
^bs Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
^bt Memory Clinic, Skåne University Hospital, Malmö, Sweden
^bu Department of Neurology, Mayo Clinic, Rochester, MN, United States
^bv Douglas Mental Health University Institute, Centre for Studies on the Prevention of Alzheimer’s Disease (StoP-AD), Montréal, Québec, Canada
^bw Department of Psychiatry, McGill University, Montréal, Québec, Canada
^bx Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, United States
^by Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Montréal, Québec, Canada
^bz Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, Québec, Canada
^ca Indiana University, Indianapolis, IN, United States
^cb Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
^cc Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
^cd Clinic for Cognitive Neurology, University Hospital of Leipzig and Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
^ce Department of Neurology of Memory and Language, GHU Paris Psychiatrie & Neurosciences, Hôpital Sainte-Anne, Paris, France
^cf Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
^cg Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
^ch Department of Pathology, Rush University Medical Center, Chicago, IL, United States
^ci Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
^cj Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
^ck Dementia Research Centre, University College London, Queen Square Institute of Neurology, London, United Kingdom
^cl UK Dementia Research Institute, London, United Kingdom
^cm Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium
^cn Neurology Service, University Hospital Leuven, Leuven, Belgium
^co Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States

Abstract
Tau positron emission tomography (PET) imaging allows in vivo detection of tau proteinopathy in Alzheimer’s disease, which is associated with neurodegeneration and cognitive decline. Understanding how demographic, clinical and genetic factors relate to tau PET positivity will facilitate its use for clinical practice and research. Here we conducted an analysis of 42 cohorts worldwide (N = 12,048), including 7,394 cognitively unimpaired (CU) participants, 2,177 participants with mild cognitive impairment (MCI) and 2,477 participants with dementia. We found that from age 60 years to 80 years, tau PET positivity in a temporal composite region increased from 1.1% to 4.4% among CU amyloid-β (Aβ)-negative participants and from 17.4% to 22.2% among CU Aβ-positive participants. Across the same age span, tau PET positivity decreased from 68.0% to 52.9% in participants with MCI and from 91.5% to 74.6% in participants with dementia. Age, Aβ status, APOE ε4 carriership and female sex were all associated with a higher prevalence of tau PET positivity across groups. APOE ε4 carriership in CU individuals lowered the age at onset of both Aβ positivity and tau positivity by decades. Finally, we replicated these associations in an independent autopsy dataset (N = 5,072 from 3 cohorts). © 2025. The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus

“Shared and disease-specific pathways in frontotemporal dementia and Alzheimer’s and Parkinson’s diseases” (2025) Nature Medicine

Shared and disease-specific pathways in frontotemporal dementia and Alzheimer’s and Parkinson’s diseases
(2025) Nature Medicine, 31 (8), pp. 2567-2577.

Ali, M.^a ^b , Erabadda, B.^c , Chen, Y.^a ^b , Xu, Y.^a ^b , Gong, K.^a ^b , Liu, M.^a ^b , Pichet Binette, A.^d , Timsina, J.^a ^b , Western, D.^a ^b , Yang, C.^a ^b , Heo, G.^a ^b , Vogel, J.W.^e , Tijms, B.M.^f ^g , Krish, V.^h , Imam, F.^h , Hansson, O.^d ⁱ , Winchester, L.^c , Cruchaga, C.^a ^b ^j ^k ^l , Global Neurodegeneration Proteomics Consortium (GNPC)^m

^a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^b NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Psychiatry, University of Oxford, Oxford, United Kingdom
^d Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
^e Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden
^f Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^g Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
^h Seattle, WA, United States
ⁱ Eli LillyStockholm, Sweden
^j Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^k Hope Center for Neurological Disorders, Washington University, St. Louis, MO, United States
^l Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and frontotemporal dementia (FTD), exhibit distinct yet overlapping pathological mechanisms. Leveraging large-scale plasma proteomics data from the Global Neurodegeneration Proteomics Consortium, we analyzed 10,527 plasma samples (1,936 AD, 525 PD, 163 FTD, 1,638 dementia and 6,265 controls) to identify disease-specific and shared proteins across NDs. We identified 5,187 proteins significantly associated with AD, 3,748 with PD and 2,380 with FTD that revealed both common and divergent proteomic signatures, which were confirmed by multiple analytical approaches and orthogonal validation. PD and FTD showed the highest overlap (r2 = 0.44) and AD and PD the least (r2 = 0.04). Immune system, glycolysis, and matrisome-related pathways were enriched across all NDs, while disease-specific pathways included apoptotic processes in AD, endoplasmic reticulum-phagosome impairment in PD and platelet dysregulation in FTD. Network analysis identified key upstream regulators (RPS27A in AD, IRAK4 in PD and MAPK1 in FTD) potentially driving these proteomic changes. These findings reveal distinct and shared mechanisms across NDs, highlighting potential regulatory proteins and pathways for diagnostic and therapeutic strategies in neurodegeneration. © 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.

Document Type: Article
Publication Stage: Final
Source: Scopus

“The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging” (2025) Nature Medicine

The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging
(2025) Nature Medicine, 31 (8), pp. 2556-2566.

Imam, F.^a , Saloner, R.^b , Vogel, J.W.^c , Krish, V.^a , Abdel-Azim, G.^d , Ali, M.^e ^f , An, L.^c , Anastasi, F.^g ^h ⁱ , Bennett, D.^j , Pichet Binette, A.^k ^l ^m , Boxer, A.L.^b , Bringmann, M.^d , Burns, J.M.ⁿ ^o , Cruchaga, C.^e ^f ^p , Dage, J.L.^q ^r , Farinas, A.^s ^t ^u , Ferrucci, L.^v , Finney, C.A.^w ^x , Frasier, M.^y , Hansson, O.^k , Hohman, T.J.^z ^aa , Johnson, E.C.B.^ab ^ac , Kivimaki, M.^ad ^ae , Korologou-Linden, R.^af , Ruiz Laza, A.^ag ^ah ^ai , Levey, A.I.^ab ^ac , Liepelt-Scarfone, I.^aj ^ak ^al , Lu, L.^k , Mattsson-Carlgren, N.^k ^am , Middleton, L.T.^af , Nho, K.^an , Oh, H.S.-H.^t ^u ^ao , Petersen, R.C.^ap , Reiman, E.M.^aq , Robinson, O.^af ^ar , Rothstein, J.D.^as , Saykin, A.J.^q ^r , Shvetcov, A.^w ^x , Slawson, C.ⁿ ^at , Smets, B.^au , Suárez-Calvet, M.^g ^h ^av , Tijms, B.M.^aw ^ax , Timmers, M.^au , Vieira, F.^ay , Vilor-Tejedor, N.^g ⁱ ^az , Visser, P.J.^aw ^ax ^ba , Walker, K.A.^bb , Winchester, L.M.^bc , Wyss-Coray, T.^t ^u ^bd , Yang, C.^e ^f , Bose, N.^a , Lovestone, S.^be , Global Neurodegeneration Proteomics Consortium (GNPC)^bf

^a Seattle, WA, United States
^b Department of Neurology, University of California, San Francisco, CA, United States
^c Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden
^d Johnson & Johnson, Spring HousePA, United States
^e Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^f NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
^g Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
^h Hospital del Mar Research Institute, Barcelona, Spain
ⁱ Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
^j Department of Neurological Sciences, Rush Alzheimer’s Disease Center, Chicago, IL, United States
^k Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
^l Department of Physiology and Pharmacology, Université de Montréal, Montreal, QC, Canada
^m Montreal Geriatrics Institute Research Center, Montreal, QC, Canada
ⁿ University of Kansas Alzheimer’s Disease Research Center, Kansas City, KS, United States
^o Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States
^p Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^q Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, United States
^r Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States
^s Graduate Program in Neuroscience, Stanford University, Stanford, CA, United States
^t Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, United States
^u Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, United States
^v Translational Gerontology Branch, National Institute on Aging, Bethesda, MD, United States
^w Neurodegeneration and Precision Medicine Research Group, Westmead Institute for Medical Research, WestmeadNSW, Australia
^x Faculty of Medicine and Health, University of Sydney School of Medical Sciences, WestmeadNSW, Australia
^y Michael J. Fox Foundation, New York, NY, USA
^z Vanderbilt Memory & Alzheimer’s Disease, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
^aa Vanderbilt Genetics Institute, Vanderbilt Medical Center, Nashville, TN, United States
^ab Emory University School of Medicine, Atlanta, GA, United States
^ac Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
^ad UCL Brain Sciences, University College London, London, United Kingdom
^ae University of Helsinki, Clinicum, Helsinki, Finland
^af Ageing & Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, United Kingdom
^ag Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain
^ah Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), National Institute of Health Carlos IIIMadrid, Spain
^ai Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases and Department of Microbiology, Immunology and Molecular Genetics, Long School of Medicine, University of Texas Health Science Center, San Antonio, TX, United States
^aj Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, Tübingen, Germany
^ak Department of Neurodegenerative Diseases, German Center of Neurodegenerative Diseases, Tübingen, Germany
^al IB Hochschule für Gesundheit und Soziales, Standort Stuttgart, Germany
^am Memory Clinic, Skåne University Hospital, Malmö, Sweden
^an Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
^ao Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
^ap Department of Neurology, Mayo Clinic, Rochester, MN, United States
^aq Banner Alzheimer’s Institute, Phoenix, AZ, United States
^ar Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
^as Robert Packard Center for ALS Research, Johns Hopkins University, Baltimore, MD, United States
^at Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, KS, United States
^au Johnson & Johnson, Beerse, Belgium
^av Department of Neurology, Hospital del Mar, Barcelona, Spain
^aw Department of Neurology, Alzheimer Center Amsterdam, Amsterdam, Netherlands
^ax Amsterdam Neuroscience, Amsterdam, Netherlands
^ay ALS Therapy Development Institute, Cambridge, MA, United States
^az Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
^ba Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
^bb Laboratory of Behavioral Neuroscience, National Institute on Aging, Bethesda, MD, United States
^bc Department of Psychiatry, Oxford University, Oxford, United Kingdom
^bd Department of Neurology and Neurological Sciences, Stanford University School of Medicine, StandfordCA, United States
^be Johnson & Johnson, London, United Kingdom

Abstract
More than 57 million people globally suffer from neurodegenerative diseases, a figure expected to double every 20 years. Despite this growing burden, there are currently no cures, and treatment options remain limited due to disease heterogeneity, prolonged preclinical and prodromal phases, poor understanding of disease mechanisms, and diagnostic challenges. Identifying novel biomarkers is crucial for improving early detection, prognosis, staging and subtyping of these conditions. High-dimensional molecular studies in biofluids (‘omics’) offer promise for scalable biomarker discovery, but challenges in assembling large, diverse datasets hinder progress. To address this, the Global Neurodegeneration Proteomics Consortium (GNPC)-a public-private partnership-established one of the world’s largest harmonized proteomic datasets. It includes approximately 250 million unique protein measurements from multiple platforms from more than 35,000 biofluid samples (plasma, serum and cerebrospinal fluid) contributed by 23 partners, alongside associated clinical data spanning Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). This dataset is accessible to GNPC members via the Alzheimer’s Disease Data Initiative’s AD Workbench, a secure cloud-based environment, and will be available to the wider research community on 15 July 2025. Here we present summary analyses of the plasma proteome revealing disease-specific differential protein abundance and transdiagnostic proteomic signatures of clinical severity. Furthermore, we describe a robust plasma proteomic signature of APOE ε4 carriership, reproducible across AD, PD, FTD and ALS, as well as distinct patterns of organ aging across these conditions. This work demonstrates the power of international collaboration, data sharing and open science to accelerate discovery in neurodegeneration research. © 2025. The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus

“Disruption of the cerebrospinal fluid-plasma protein balance in cognitive impairment and aging” (2025) Nature Medicine

Disruption of the cerebrospinal fluid-plasma protein balance in cognitive impairment and aging
(2025) Nature Medicine, 31 (8), pp. 2578-2589.

Farinas, A.^a ^b ^c , Rutledge, J.^b ^c , Bot, V.A.^b ^c ^d , Western, D.^e ^f ^g , Ying, K.^b ^c ^h ⁱ , Lawrence, K.A.^j , Oh, H.S.-H.^b ^c ^k ^l ^m ⁿ , Yoon, S.^b ^c , Ding, D.Y.^b ^c , Tsai, A.P.^b ^c , Moran-Losada, P.^b ^c , Timsina, J.^e ^f , Le Guen, Y.^o , Montgomery, S.B.^j ^p ^q , Baker, D.^h ⁱ ^r , Poston, K.L.^b ^c ^s , Wagner, A.D.^c ^t , Mormino, E.^s , Cruchaga, C.^e ^f ^u ^v ^w ^x , Wyss-Coray, T.^y ^z ^aa , Global Neurodegeneration Proteomics Consortium (GNPC)^ab

^a Graduate Program in Neuroscience, Stanford University, Stanford, CA, United States
^b Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, United States
^c Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, United States
^d Graduate Program in Biomedical Engineering, Stanford University, Stanford, CA, United States
^e Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^f NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
^g Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, United States
^h Department of Biochemistry, University of Washington, Seattle, WA, United States
ⁱ Institute for Protein Design, University of Washington, Seattle, WA, United States
^j Department of Genetics, Stanford University School of Medicine, Stanford, CA, United States
^k Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
^l Brain and Body Research Center of the Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
^m Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
ⁿ Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
^o Quantitative Services Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
^p Department of Pathology, Stanford University School of Medicine, Stanford, CA, United States
^q Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United States
^r Howard Hughes Medical Institute, University of Washington, Seattle, WA, United States
^s Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
^t Department of Psychology, Stanford University, Stanford, CA, United States
^u Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^v Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^w Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, United States
^x Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
^y Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, United States
^z Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, United States
^aa Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States

Abstract
The brain barrier system, including the choroid plexus, meninges and brain vasculature, regulates substrate transport and maintains differential protein concentrations between blood and cerebrospinal fluid (CSF). Aging and neurodegeneration disrupt brain barrier function, but proteomic studies of the effects on blood-CSF protein balance are limited. Here we used SomaScan proteomics to characterize paired CSF and plasma samples from 2,171 healthy or cognitively impaired older individuals from multiple cohorts, including the Global Neurodegeneration Proteomics Consortium. We identified proteins with correlated CSF and plasma levels that are produced primarily outside the brain and are enriched for structural domains that may enable their transport across brain barriers. CSF to plasma ratios of 848 proteins increased with aging in healthy control individuals, including complement and coagulation proteins, chemokines and proteins linked to neurodegeneration, whereas 64 protein ratios decreased with age, suggesting substrate-specific barrier regulation. Notably, elevated CSF to plasma ratios of peripherally derived or vascular-associated proteins, including DCUN1D1, MFGE8 and VEGFA, were associated with preserved cognitive function. Genome-wide association studies identified genetic loci associated with CSF to plasma ratios of 241 proteins, many of which have known disease associations, including FCN2, the collagen-like domain of which may facilitate blood-CSF transport. Overall, this work provides molecular insight into the human brain barrier system and its disruption with age and disease, with implications for the development of brain-permeable therapeutics. © 2025. The Author(s).

Document Type: Article
Publication Stage: Final
Source: Scopus

“Longitudinal magnetic resonance imaging reveals differences in cortical expansion in fetuses with congenital heart defects” (2025) Cerebral Cortex

Longitudinal magnetic resonance imaging reveals differences in cortical expansion in fetuses with congenital heart defects
(2025) Cerebral Cortex, 35 (8), art. no. bhaf220, .

Garcia, K.E.^a ^b , Taylor, K.^c , Bhaskara, M.^d , Velasco-Annis, C.^e , Vieth, J.^f , Garrett, J.^g , Patel, J.^g , Pointer, B.^g , Cao, S.^h , Newburger, J.W.ⁱ ^j , Gholipour, A.^k ^l , Rollins, C.K.^m ⁿ , Ortinau, C.M.^g

^a Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Evansville, IN, United States
^b Department of Mechanical Engineering and Materials Science, Washington University, St. Louis, MO, United States
^c Department of Neurology, Washington University, St. Louis, MO, United States
^d Indiana University School of Medicine, Evansville, IN, United States
^e Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
^f University of Kentucky College of Medicine, Lexington, KY, United States
^g Department of Pediatrics, Washington University, St. Louis, MO, United States
^h Department of Biostatistics and Health Data Science, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
ⁱ Department of Cardiology, Boston Children’s Hospital, Boston, MA, United States
^j Department of Pediatrics, Harvard Medical School, Boston, MA, United States
^k Department of Radiological Sciences, School of Medicine, University of California Irvine, Irvine, CA, United States
^l Department of Electrical Engineering and Computer Science, School of Engineering, University of California Irvine, Irvine, CA, United States
^m Department of Neurology, Harvard Medical School, Boston, MA, United States
ⁿ Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States

Abstract
For children with congenital heart disease (CHD), differences in brain structure are already present at birth. Cortical surface area and gyrification increase dramatically over the third trimester, and cortical expansion has been hypothesized to drive brain folding. This study sought to quantify differences in cortical expansion in fetuses with CHD and typically developing controls. Fetal magnetic resonance imaging was conducted at early (26-31 weeks) and late (34-39 weeks) gestational time points, and cortical surfaces were reconstructed using a high resolution, motion-corrected pipeline. For fetuses with reconstructions at both time points (36 CHD, 24 control), anatomically-constrained multimodal surface matching (aMSM) was used to generate individualized maps of cortical surface expansion. Global analysis revealed significant reductions in total cortical expansion and gyrification index among CHD fetuses. Furthermore, expansion maps revealed high expansion in the lateral temporal lobes of control fetuses that was reduced in fetuses with CHD, consistent with previous reports of atypical folding in this region. This study is the first to reveal spatiotemporal patterns of cortical expansion in typical and atypical fetal development. This detailed understanding of cortical growth trajectory may improve understanding of functional deficits associated with specific cortical areas and inform clinical interventions for patients with CHD. © 2025 The Author(s). Published by Oxford University Press. All rights reserved.

Author Keywords
congenital heart disease; cortical expansion; fetal development; Gyrification; magnetic resonance imaging

Funding details
Mend a Heart Foundation
National Institutes of HealthNIH
National Heart, Lung, and Blood InstituteNHLBIK23 HL141602, R01 NS106030, R01 EB018988
National Heart, Lung, and Blood InstituteNHLBI
National Institute of Neurological Disorders and StrokeNINCDSR01 NS133116, R01 NS121334, K23 NS101120
National Institute of Neurological Disorders and StrokeNINCDS

Document Type: Article
Publication Stage: Final
Source: Scopus

“Plasma proteomic analysis identifies proteins and pathways related to Alzheimer’s risk” (2025) Alzheimer’s and Dementia

Plasma proteomic analysis identifies proteins and pathways related to Alzheimer’s risk
(2025) Alzheimer’s and Dementia, 21 (8), art. no. e70579, .

Huang, Y.-N.^a ^b , Liu, S.^a ^b , Park, T.^a ^b , Chaudhuri, S.^a ^b ^c , Kuchenbecker, L.A.^d ^e , Carrasquillo, M.M.^d ^e , Ertekin-Taner, N.^d , Bice, P.J.^a ^b , Zetterberg, H.^f ^g ^h ⁱ ^j ^k , Blennow, K.^f ^g ^l ^m , Russ, K.^c ⁿ , Dage, J.L.^b ^c ⁿ ^o , Nudelman, K.N.H.^b ^o , Cruchaga, C.^p ^q , Brosch, J.R.^a ^b ⁿ , Farlow, M.R.^a ^b ^r , Clark, D.G.^a ^b ⁿ , Mathew, S.^a ^b , Unverzagt, F.^a ^b ^r , Gao, S.^a ^b ^s , Wang, S.^a ^b ^r , Apostolova, L.G.^a ^b ⁿ , Wilcock, D.M.^c ⁿ , Foroud, T.^o , Risacher, S.L.^a ^b , Saykin, A.J.^a ^b ^o , Nho, K.^a ^b ^t

^a Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
^b Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, United States
^c Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States
^d Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
^e Center for Clinical and Translational Science, Mayo Clinic, Rochester, MN, United States
^f Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
^g Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
^h UK Dementia Research Institute at UCL, London, United Kingdom
ⁱ Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, United Kingdom
^j Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong
^k Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
^l Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
^m Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, China
ⁿ Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States
^o Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
^p Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^q NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, United States
^r Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
^s Department of Biostatistics & Health Data Science, Indiana University, Indianapolis, IN, United States
^t School of Informatics and Computing, Indiana University, Indianapolis, IN, United States

Abstract
INTRODUCTION: We investigated associations of plasma proteins with blood-based amyloid/tau/neurodegeneration/inflammation (A/T/N/I) biomarkers for Alzheimer’s disease (AD). METHODS: Plasma proteomics and clinical data from the Indiana AD Research Center (N = 498) were used. Association analysis of plasma proteins with blood A/T/N/I biomarkers as well as diagnosis was performed, followed by replication in an independent cohort (N = 323), network analysis, pathway enrichment, and machine learning classification to identify proteins and pathways related to AD risk. RESULTS: We identified 35 proteins associated with AD, 20 of which were replicated in the independent cohort. We identified 150, 448, and 219 proteins associated with T/N/I biomarkers, respectively, revealing biomarker-specific pathways. Network analysis identified two modules associated with T/N/I biomarkers, preserved in cerebrospinal fluid (CSF), and their enriched pathways. The classification model of proteins effectively differentiated AD (area under the curve [AUC] = 0.930). CONCLUSION: Our findings suggest dysregulated plasma proteins and pathways in AD, enhancing our understanding of molecular mechanisms and diagnostic strategies for AD. Highlights: Plasma proteins were identified as being associated with Alzheimer’s disease (AD) and plasma biomarkers. The identified proteins were replicated in both plasma and cerebrospinal fluid (CSF) proteomics. The identified proteins were associated with AD biomarker-specific pathways. The identified proteins improved the performance of the AD classification. Protein network analysis identified network modules and their enriched pathways. © 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association.

Author Keywords
Alzheimer’s disease; amyloid; biomarker; inflammation; machine learning; network analysis; neurodegeneration; plasma; proteomics; SomaScan; tau

Funding details
Alzheimer’s AssociationAA
Alzheimer’s Disease Neuroimaging InitiativeADNI
BioClinica
National Institute of Biomedical Imaging and BioengineeringNIBIB
AbbVie
Biogen
Gates Ventures
National Institute on AgingNIA
Alzheimer’s Drug Discovery FoundationADDF
U.S. Department of DefenseUSDODW81XWH‐12‐2‐0012
U.S. Department of DefenseUSDOD
National Institutes of HealthNIHU19 AG024904, P30 AG072976, T32 AG071444, R01 AG019771, P30 AG010133, U01 AG072177, R01 AG057739, R01 LM013463, U19 AG074879, R01 LM012535, U01 AG068057, R01 AG068193
National Institutes of HealthNIH
U24 AG021886
VetenskapsrådetVR2022‐01018, 2019‐02397, 2023‐00356
VetenskapsrådetVR
U19AG074879, 71320
101053962

Document Type: Article
Publication Stage: Final
Source: Scopus

“Pilot Study of [11C]HY-2-15: A Mixed Alpha-Synuclein and Tau PET Radiotracer” (2025) Cells

Pilot Study of [11C]HY-2-15: A Mixed Alpha-Synuclein and Tau PET Radiotracer
(2025) Cells, 14 (15), art. no. 1157, .

Hsieh, C.-J.^a , Saturnino Guarino, D.^a , Young, A.J.^a , Siderowf, A.D.^b , Nasrallah, I.^a , Schmitz, A.^a , Garcia, C.^a , Kim, H.Y.^a , Schubert, E.K.^a , Lee, H.^a , Perlmutter, J.S.^c , Mach, R.H.^a

^a Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
^b Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
^c Department of Neurology, School of Medicine, Washington University, Saint Louis, MO 63110, United States

Abstract
A novel brain positron emission tomography (PET) radioligand, [11C]HY-2-15, has potential for imaging alpha-synuclein aggregations in multiple system atrophy and misfolded tau proteins in tauopathies, based on its high binding affinity in disease brain tissue homogenates. Here, we demonstrate that [3H]HY-2-15 has the capability to bind to aggregated alpha-synuclein in multiple system atrophy brain and tau aggregations in progressive supranuclear palsy and corticobasal degeneration brain tissues via in vitro autoradiography study. A first-in-human pilot multicenter clinical study recruited a total of 10 subjects including healthy controls and patients with Parkinson’s disease, multiple system atrophy, or progressive supranuclear palsy. The study revealed that [11C]HY-2-15 has a relatively higher specific uptake in the pallidum and midbrain of patients with progressive supranuclear palsy. Total-body scans performed on the PennPET Explorer showed the radiotracer was cleared by renal excretion. However, the rapid metabolism and low brain uptake resulted in a limited signal of [11C]HY-2-15 in brain. © 2025 by the authors.

Author Keywords
multiple system atrophy; Parkinson’s disease; PET; progressive supranuclear palsy; radioligand; tau; α-synuclein

Funding details
National Institutes of HealthNIHU19-NS110456
National Institutes of HealthNIH

Document Type: Article
Publication Stage: Final
Source: Scopus

“Baseline and longitudinal changes in cortical thickness and hippocampal volume predict cognitive decline” (2025) Journal of Alzheimer’s Disease

Baseline and longitudinal changes in cortical thickness and hippocampal volume predict cognitive decline
(2025) Journal of Alzheimer’s Disease, 106 (4), pp. 1452-1462.

Chen, G.^a ^b , McKay, N.S.^a ^b , Gordon, B.A.^a ^b ^c , Joseph-Mathurin, N.^a ^b , Liu, J.^d , Schindler, S.E.^e , Hassenstab, J.^e , Doering, S.^a ^b , Aschenbrenner, A.J.^b ^e , Wang, Q.^a ^b , LaMontagne, P.J.^a ^b , Keefe, S.J.^a ^b , Massoumzadeh, P.^a ^b , Cruchaga, C.^f , Xiong, C.^g , Morris, J.C.^a ^b ^c , Benzinger, T.L.S.^a ^b

^a Mallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St Louis, MO, United States
^b Knight Alzheimer’s Disease Research Center, Washington University in St Louis School of Medicine, St Louis, MO, United States
^c Hope Center for Neurological Disorders, Washington University in St Louis School of Medicine, St Louis, MO, United States
^d Department of Surgery, Washington University in St Louis School of Medicine, St Louis, MO, United States
^e Department of Neurology, Washington University in St Louis School of Medicine, St Louis, MO, United States
^f Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, United States
^g Divison of Biostatistics, Washington University in St Louis School of Medicine, St Louis, MO, United States

Abstract
Background: As we transition to disease-modifying treatment for Alzheimer’s disease (AD), identifying individuals most at risk for future cognitive decline is crucial. Amyloid PET, cerebrospinal fluid and more recently blood-based biomarkers can identify the first stage of AD. However, changes detectable by PiB-PET may precede the onset of the dementia by 20–30 years. MRI is a widely available tool for detecting longitudinal changes in brain structure, such as cortical thickness and hippocampal volume and may provide additional insight into which patients are at greatest risk to develop cognitive decline. Objective: To determine how well the hippocampal volume and cortical thickness, without specific AD biomarkers, can predict cognitive decline. Methods: MRI data from 344 participants (cognitively unimpaired or mild cognitive impairment, age 50–86) were used to evaluate if changes in cortical thickness and hippocampal volume predict cognitive decline, measured by a global cognitive composite score. A random coefficient model was employed to calculate longitudinal changes in cortical thickness and hippocampal volume and assess their ability to predict cognitive decline. Results: Baseline cortical thickness as well as hippocampal volume predicted cognitive decline, regardless of baseline cognitive status. In individuals unimpaired at baseline, decreases in cortical thickness and hippocampal volume independently predicted cognitive decline. For participants with baseline mild impairment, decreases in hippocampal volume predicted further cognitive decline. Conclusions: These findings indicate that MRI could serve as an effective tool for identifying individuals at elevated risk of cognitive decline, a growing public health concern as global populations continue to age. © The Author(s) 2025

Author Keywords
Alzheimer’s disease; cognition; longitudinal study; magnetic resonance imaging

Funding details
Alzheimer’s AssociationAAAARFD20681815, P50 AG00561, U24 RR021382, P01 AG026276, P01 AG003991, P20 MH071616, P30 NS09857781, R01 AG043434, R01 EB009352, UL1 TR000448, R01 AG021910
Alzheimer’s AssociationAA
National Institutes of HealthUSNIHR01AG043434, R01EB009352, P01AG003991, UL1TR002345, UL1TR000448, P30NS09857781, P01AG026276, P30AG066444
National Institutes of HealthUSNIH
Foundation for Barnes-Jewish HospitalFBJHNCRR 1S10RR022984-01A1
Foundation for Barnes-Jewish HospitalFBJH

Document Type: Article
Publication Stage: Final
Source: Scopus

“Lifestyle Composite and Resilience to Alzheimer’s Disease Pathology in Down Syndrome” (2025) Journal of Applied Research in Intellectual Disabilities

Lifestyle Composite and Resilience to Alzheimer’s Disease Pathology in Down Syndrome
(2025) Journal of Applied Research in Intellectual Disabilities, 38 (4), art. no. e70109, .

Schworer, E.K.^a , Zammit, M.D.^a , Handen, B.L.^b , Piro-Gambetti, B.^a , Jenkins, M.R.^a , Brothers, C.^a , Okonkwo, O.C.^c , Hom, C.L.^d , Ances, B.M.^e , Christian, B.T.^a ^c , Hartley, S.L.^a

^a Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
^b Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
^c Alzheimer’s Disease Research Center, University of Wisconsin-Madison, Madison, WI, United States
^d Department of Psychiatry and Human Behavior, University of California, Irvine, Orange, CA, United States
^e Department of Neurology, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Background: People with Down syndrome (DS) have a high risk for Alzheimer’s disease (AD). Identifying resiliency factors for AD is of critical importance to the DS community. Method: Participants were 63 adults with DS. Measures included amyloid-beta PET scans (amyloid age), National Task Group-Early Detection Screen for Dementia (NTG-EDSD), and Down Syndrome Mental Status Examination (DSMSE). Lifestyle composites were created by assessing time spent in leisure, employment, and physical activity across 7 days through informant reports and accelerometry. Results: There was a significant moderation effect of the lifestyle composite on the association between amyloid age and the NTG-EDSD and DSMSE. Participants with a higher lifestyle composite (higher leisure, employment engagement, and physical activity) had fewer dementia symptoms than those with a lower lifestyle composite score of a similar amyloid age. Conclusions: Modifiable lifestyle factors may allow adults with DS to maintain cognitive functioning for longer in the face of AD pathology. © 2025 The Author(s). Journal of Applied Research in Intellectual Disabilities published by John Wiley & Sons Ltd.

Author Keywords
amyloid-beta; cognition; employment; leisure; physical activity

Funding details
Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNICHD
NIHR Cambridge Biomedical Research Centre
National Institute of Child Health and Human DevelopmentNICHDU01AG051406, U19AG068054, U01AG051412, T32HD007489
National Institute of Child Health and Human DevelopmentNICHD
National Institute on AgingNIAK99AG084738
National Institute on AgingNIA
U24AG21886
Intellectual and Developmental Disabilities Research CenterIDDRCP50HD105353, U54HD090256, U54HD087011
Intellectual and Developmental Disabilities Research CenterIDDRC
National Institutes of HealthNIHP30AG066519, P30AG062421, P50AG16537, P50AG008702, P50AG005133, P50AG005681, P30AG062715
National Institutes of HealthNIH
National Center for Advancing Translational SciencesNCATSUL1TR002373, UL1TR002345, UL1TR001857, UL1TR001873, UL1TR001414
National Center for Advancing Translational SciencesNCATS

Document Type: Article
Publication Stage: Final
Source: Scopus

“Hyperglycemia selectively increases cerebral non-oxidative glucose consumption without affecting blood flow” (2025) Journal of Cerebral Blood Flow and Metabolism

Hyperglycemia selectively increases cerebral non-oxidative glucose consumption without affecting blood flow
(2025) Journal of Cerebral Blood Flow and Metabolism, art. no. 0271678X251329714, .

Blazey, T.^a , Lee, J.J.^a , Snyder, A.Z.^a ^b , Goyal, M.S.^a ^b ^c , Hershey, T.^a ^b ^d , Arbeláez, A.M.^e , Raichle, M.E.^a ^b ^c ^f

^a Mallinckrodt Institute of Radiology, School of Medicine, Washington University, St. Louis, MO, United States
^b Department of Neurology, School of Medicine, Washington University, St. Louis, MO, United States
^c Department of Neuroscience, School of Medicine, Washington University, St. Louis, MO, United States
^d Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO, United States
^e Department of Pediatrics, School of Medicine, Washington University, St. Louis, MO, United States
^f Department of Biomedical Engineering, Washington University, St. Louis, MO, United States

Abstract
Multiple studies have shown that hyperglycemia increases the cerebral metabolic rate of glucose (CMRglc) in subcortical white matter. This observation remains unexplained. Using positron emission tomography (PET) and pancreatic glucose clamps with basal insulin replacement in twenty-nine healthy young adults (34.5 years, SD = 10.1) we found that acute hyperglycemia increases non-oxidative CMRglc (i.e., aerobic glycolysis (AG)) in subcortical white mater as well as in medial temporal lobe structures, cerebellum and brainstem, all areas with low CMRglc during euglycemia. Surprisingly, hyperglycemia did not change regional cerebral blood flow (CBF), the cerebral metabolic rate of oxygen (CMRO2), or the blood-oxygen-level-dependent (BOLD) response. Correlation with existing regional gene expression data showed that brain regions where CMRglc increased have greater expression of hexokinase 2 (HK2). Simulations of glucose transport revealed that, unlike hexokinase 1, HK2 is not saturated at euglycemia, and thus can accommodate increased AG during hyperglycemia. © The Author(s) 2025

Author Keywords
diabetes; Energy metabolism; glucose; hyperglycemia; white matter/oligodendrocytes

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Sonic Hedgehog Agonists Induce Repair Schwann Cells” (2025) Biotechnology and Bioengineering

Sonic Hedgehog Agonists Induce Repair Schwann Cells
(2025) Biotechnology and Bioengineering, .

Colchado, D.^a ^b , Schofield, J.B.^a , Hunter, D.A.^a , Xia, X.^a , Yang, M.^a , Sacks, J.M.^a , Wood, M.D.^a , Li, X.^a

^a Department of Surgery, Division of Plastic and Reconstructive Surgery, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Surgery, Division of General Surgery, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Peripheral nerve regeneration relies on repair Schwann cells (SCs) to support axonal regrowth and functional recovery. This study aimed to identify drugs that promote this repair phenotype, which is regulated by the expression of the transcription factor c-Jun. Purmorphamine (PUR) and Smoothened agonist (SAG) are both Sonic Hedgehog (SHH) agonists that have been implicated in promoting regeneration after neurological injury in animal models. Here, we have demonstrated that SHH agonists significantly increased c-Jun expression in rat primary SCs and promoted morphological and functional changes consistent with the repair SC phenotype, including an elongated bipolar morphology and increased secretion of neurotrophic factors. Notably, PUR consistently demonstrated a greater potency in driving these effects compared with SAG at the same concentrations. We also identified 2.5 µM PUR as an effective dosage producing these measurable effects in vitro. Coculturing dorsal root ganglion (DRG) neurons with PUR-treated SCs resulted in a marked increase in neurite elongation, suggesting that cell-based or contact-dependent features of repair SCs contribute to axon growth. These findings demonstrate that SHH agonists effectively reprogram SCs into a repair phenotype, which constitutes a potential therapeutic strategy for enhancing nerve regeneration and functional recovery in peripheral nerve injury treatment. © 2025 Wiley Periodicals LLC.

Author Keywords
C-Jun; neurite outgrowth; peripheral nerve injury; repair Schwann cells; Sonic Hedgehog

Funding details
Plastic Surgery FoundationPSF
University of WashingtonUW
U.S. Department of DefenseDODW81XWH‐22‐PRMRP‐DA, W81XWH‐22‐1‐0785
U.S. Department of DefenseDOD
National Heart, Lung, and Blood InstituteNHLBIR01HL168513
National Heart, Lung, and Blood InstituteNHLBI

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Circadian circuits control plasticity of group 3 innate lymphoid cells by sustaining epigenetic configuration of RORγt” (2025) Nature Immunology

Circadian circuits control plasticity of group 3 innate lymphoid cells by sustaining epigenetic configuration of RORγt
(2025) Nature Immunology, .

Bhattarai, B.^a , Antonova, A.U.^a , Fachi, J.L.^a , Hopkins, L.S.^b , McCullen, M.V.D.^b , Saini, A.^b , Oliveira, S.D.^a ^c , Beatty, W.L.^d , Musiek, E.S.^e , Kuchroo, V.K.^f , Lazar, M.A.^g , Oltz, E.M.^b ^h , Colonna, M.^a

^a Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States
^c Department of Genetics and Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, Brazil
^d Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, United States
^e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^f Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Mass General Hospital and Harvard Medical School and Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Boston, MA, United States
^g Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, and the Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^h Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, United States

Abstract
The gut experiences daily fluctuations in microbes and nutrients aligned with circadian rhythms that regulate nutrient absorption and immune function. Group 3 innate lymphoid cells (ILC3s) support gut homeostasis through interleukin-22 (IL-22) but can convert into interferon-γ-producing ILC1s. How circadian proteins control this plasticity remains unclear. Here we showed that the circadian proteins REV-ERBα and REV-ERBβ maintain ILC3 identity. Their combined deletion promoted ILC3-to-ILC1 conversion, reduced energy metabolism and IL-22 production, increased interferon-γ production, and heightened susceptibility to Citrobacter rodentium infection. Single-cell multiomics and gene editing revealed that REV-ERBα/REV-ERBβ deficiency upregulated the transcription factor NFIL3, which repressed the expression of RORγt via a –2-kb cis-regulatory element in the Rorc gene, shifting cells toward a T-bet-driven state. Chromatin and metabolic analyses indicated that REV-ERBα/REV-ERBβ loss reprogrammed regulatory and metabolic circuits. Thus, REV-ERBα/REV-ERBβ safeguard gut integrity by regulating clock genes that control RORγt expression and preserve ILC3 identity and resistance to intestinal inflammation. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

Funding details
Pelotonia
National Center for Research ResourcesNCRR
National Institutes of HealthUSNIHR01DK45586, R01DK132327, R01DK30292, R01AI134035, T32 AI165391, 1R01DK126969
National Institutes of HealthUSNIH
National Cancer InstituteNCIP30 CA91842
National Cancer InstituteNCI

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts” (2025) Magnetic Resonance in Medicine

Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts
(2025) Magnetic Resonance in Medicine, .

Saca, L.^a , Gaggar, R.^b ^c , Pappas, I.^d , Benzinger, T.^e ^f , Reiman, E.M.^g , Shiroishi, M.S.^h , Joe, E.B.ⁱ ^j , Ringman, J.M.ⁱ ^j , Yassine, H.N.^b ^c , Schneider, L.S.ⁱ ^j ^k , Chui, H.C.ⁱ ^j , Nation, D.A.^b ^l , Zlokovic, B.V.^b ^c , Toga, A.W.^d ⁱ , Chakhoyan, A.^b ^c , Barnes, S.^a

^a Department of Radiology, Loma Linda University, Loma Linda, CA, United States
^b Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^c Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^d Laboratory of NeuroImaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^e Mallinckrodt Institute of Radiology, Washington University in St. Louis School of Medicine, St. Louis, MO, United States
^f The Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
^g Banner Alzheimer Institute, Phoenix, AZ, United States
^h Department of Radiology, University of Southern California, Los Angeles, CA, United States
ⁱ Alzheimer’s Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^j Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^k Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, United States
^l Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States

Abstract
Purpose: Arterial input function (AIF) extraction is a crucial step in quantitative pharmacokinetic modeling of DCE-MRI. This work proposes a robust deep learning model that can precisely extract an AIF from DCE-MRI images. Methods: A diverse dataset of human brain DCE-MRI images from 289 participants, totaling 384 scans, from five different institutions with extracted gadolinium-based contrast agent curves from large penetrating arteries, and with most data collected for blood–brain barrier (BBB) permeability measurement, was retrospectively analyzed. A 3D UNet model was implemented and trained on manually drawn AIF regions. The testing cohort was compared using proposed AIF quality metric AIFitness and Ktrans values from a standard DCE pipeline. This UNet was then applied to a separate dataset of 326 participants with a total of 421 DCE-MRI images with analyzed AIF quality and Ktrans values. Results: The resulting 3D UNet model achieved an average AIFitness score of 93.9 compared to 99.7 for manually selected AIFs, and white matter Ktrans values were 0.45/min × 10−3 and 0.45/min × 10−3, respectively. The intraclass correlation between automated and manual Ktrans values was 0.89. The separate replication dataset yielded an AIFitness score of 97.0 and white matter Ktrans of 0.44/min × 10−3. Conclusion: Findings suggest a 3D UNet model with additional convolutional neural network kernels and a modified Huber loss function achieves superior performance for identifying AIF curves from DCE-MRI in a diverse multi-center cohort. AIFitness scores and DCE-MRI-derived metrics, such as Ktrans maps, showed no significant differences in gray and white matter between manually drawn and automated AIFs. © 2025 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Author Keywords
3D UNet; AIF detection; artificial intelligence; blood–brain barrier; DCE-MRI; multi-site data

Document Type: Article
Publication Stage: Article in Press
Source: Scopus

“Cannabidiol improves learning and memory deficits and alleviates anxiety in 12-month-old SAMP8 mice” (2025) PLoS One

Cannabidiol improves learning and memory deficits and alleviates anxiety in 12-month-old SAMP8 mice
(2025) PLoS One, 2025 Aug 14;20(8):e0296586.

Monica N Goodland ^{1 2}, Subhashis Banerjee ³, Michael L Niehoff ³, Benjamin J Young ¹, Heather Macarthur ^{1 2}, Andrew A Butler ^{1 2}, John E Morley ³, Susan A Farr ^{1 2 3 4}

¹ Deparment of Pharmacology and Physiology, Saint Louis University School of Medicine, St. Louis, MO, USA.
² Institute for Translational Neuroscience, Saint Louis University School of Medicine, St. Louis, Missouri, USA.
³ Division of Geriatric Medicine, Saint Louis University School of Medicine, Saint Louis, MO, USA.
⁴ Research and Development, Veterans Affairs Medical Center, St. Louis Missouri, USA.

Abstract
Cannabidiol (CBD) has gained a lot of interest in recent years for its purported medicinal properties. CBD has been investigated for the treatment of anxiety, depression, epilepsy, neuroinflammation, and pain. Recently there has been an interest in CBD as a possible treatment for age-related disorders such as Alzheimer’s disease and related disorders (ADRD). Here we tested the hypothesis that chronic CBD administration would improve learning and memory in the SAMP8 mouse model of Alzheimer’s disease. SAMP8 mice aged 11 months (at the start of the study) were administered vehicle or CBD (3 or 30 mg/Kg) daily via oral gavage for 2 months. Vehicle-treated young SAMP8 mice (age 3 months at the start of the study) served as unimpaired controls. After 30 days of treatment (4 and 12 months of age), learning and memory, activity, anxiety, strength and dexterity were assessed. High dose CBD treatment significantly improved learning and memory of the 12-month-old mice in the T maze. Novel object recognition memory was also improved by CBD in aged CBD treated mice. Aged CBD treated mice also displayed less anxiety in the elevated plus maze test compared to controls. However, activity and strength levels were similar between groups. Biochemical analysis revealed decreased markers of oxidative stress, providing a possible mechanism by which CBD treatment impacts learning, memory, and anxiety. These results highlight the potential use of CBD as a therapeutic for age related cognitive impairment and dementia. © 2025 Public Library of Science. All rights reserved.

Funding details
Saint Louis University

Document Type: Article
Source: Journal
Publication Stage: FINAL