Hope Center member publications

Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization
(2021) Scientific Reports, 11 (1), art. no. 15265, . 

Chung, Y.^a , Lee, H.^a , Weiner, M.W.^b , Aisen, P.^c , Petersen, R.^d , Jack, C.R., Jr.^d , Jagust, W.^e , Trojanowki, J.Q.^f , Toga, A.W.^g , Beckett, L.^h , Green, R.C.ⁱ , Saykin, A.J.^j , Morris, J.^k , Shaw, L.M.^f , Khachaturian, Z.^l , Sorensen, G.^m , Carrillo, M.ⁿ , Kuller, L.^o , Raichle, M.^k , Paul, S.^p , Davies, P.^q , Fillit, H.^r , Hefti, F.^s , Holtzman, D.^k , Mesulam, M.M.^t , Potter, W.^u , Snyder, P.^v , Montine, T.^w , Thomas, R.G.^c , Donohue, M.^c , Walter, S.^c , Sather, T.^c , Jiminez, G.^c , Balasubramanian, A.B.^c , Mason, J.^c , Sim, I.^c , Harvey, D.^h , Bernstein, M.^d , Fox, N.^x , Thompson, P.^y , Schuf, N.^b , DeCArli, C.^h , Borowski, B.^d , Gunter, J.^d , Senjem, M.^d , Vemuri, P.^d , Jones, D.^d , Kantarci, K.^d , Ward, C.^d , Koeppe, R.A.^z , Foster, N.^aa , Reiman, E.M.^ab , Chen, K.^ab , Mathis, C.^o , Landau, S.^e , Cairns, N.J.^k , Householder, E.^k , Taylor-Reinwald, L.^k , Lee, V.^y , Korecka, M.^y , Figurski, M.^y , Crawford, K.^g , Neu, S.^g , Foroud, T.M.^j , Potkin, S.^ac , Shen, L.^j , Faber, K.^j , Kim, S.^j , Tha, L.^m , Frank, R.^ae , Hsiao, J.^af , Kaye, J.^ag , Quinn, J.^ah , Silbert, L.^ah , Lind, B.^ah , Carter, R.^ah , Dolen, S.^ah , Ances, B.^k , Carroll, M.^k , Creech, M.L.^k , Franklin, E.^k , Mintun, M.A.^k , Schneider, S.^k , Oliver, A.^k , Schneider, L.S.^k , Pawluczyk, S.^g , Beccera, M.^g , Teodoro, L.^g , Spann, B.M.^g , Brewer, J.^c , Vanderswag, H.^c , Fleisher, A.^c , Marson, D.^ah , Grifth, R.^ah , Clark, D.^ah , Geldmacher, D.^ah , Brockington, J.^ah , Roberson, E.^ah , Love, M.N.^ad , Heidebrink, J.L.^e , Lord, J.L.^e , Mason, S.S.^d , Albers, C.S.^d , Knopman, D.^d , Johnson, K.^d , Grossman, H.^ae , Mitsis, E.^ai , Shah, R.C.^aj , deToledo-Morrell, L.^aj , Doody, R.S.^ak , Villanueva-Meyer, J.^ak , Chowdhury, M.^ak , Rountree, S.^ak , Dang, M.^ak , Duara, R.^al , Varon, D.^al , Greig, M.T.^al , Roberts, P.^al , Stern, Y.^am , Honig, L.S.^am , Bell, K.L.^am , Albert, M.^an , Onyike, C.^ad , D’Agostino, D., II^ad , Kielb, S.^ad , Galvin, J.E.^an , Cerbone, B.^an , Michel, C.A.^an , Pogorelec, D.M.^an , Rusinek, H.^an , de Leon, M.J.^an , Glodzik, L.^an , De Santi, S.^an , Womack, K.^ao , Mathews, D.^ao , Quiceno, M.^ao , Doraiswamy, P.M.^ap , Petrella, J.R.^ap , Borges-Neto, S.^ap , Wong, T.Z.^ap , Coleman, E.^ap , Levey, A.I.^aq , Lah, J.J.^aq , Cella, J.S.^aq , Burns, J.M.^ar , Swerdlow, R.H.^ar , Brooks, W.M.^ar , Arnold, S.E.^f , Karlawish, J.H.^f , Wolk, D.^f , Clark, C.M.^f , Apostolova, L.^y , Tingus, K.^y , Woo, E.^y , Silverman, D.H.S.^y , Lu, P.H.^y , Bartzokis, G.^y , Smith, C.D.^as , Jicha, G.^as , Hardy, P.^as , Sinha, P.^as , Oates, E.^as , Conrad, G.^as , Graf-Radford, N.R.^at , Parftt, F.^at , Kendall, T.^at , Johnson, H.^at , Lopez, O.L.^o , Oakley, M.A.^o , Simpson, D.M.^o , Farlow, M.R.^j , Hake, A.M.^j , Matthews, B.R.^j , Brosch, J.R.^j , Herring, S.^j , Hunt, C.^j , Porsteinsson, A.P.^au , Goldstein, B.S.^au , Martin, K.^au , Makino, K.M.^au , Ismail, M.S.^au , Brand, C.^au , Mulnard, R.A.^au , Thai, G.^au , Mc-Adams-Ortiz, C.^au , van Dyck, C.H.^av , Carson, R.E.^av , MacAvoy, M.G.^av , Varma, P.^av , Chertkow, H.^aw , Bergman, H.^aw , Hosein, C.^aw , Black, S.^ax , Stefanovic, B.^ax , Caldwell, C.^ax , Hsiung, G.-Y.R.^ay , Feldman, H.^ay , Mudge, B.^ay , Assaly, M.^ay , Finger, E.^az , Pasternack, S.^az , Rachisky, I.^az , Trost, D.^az , Kertesz, A.^az bi , Bernick, C.^ba , Munic, D.^ba , Lipowski, K.^t , Weintraub, M.^t , Bonakdarpour, B.^t , Kerwin, D.^t , Wu, C.-K.^t , Johnson, N.^t , Sadowsky, C.^bb , Villena, T.^bb , Turner, R.S.^bc , Johnson, K.^bc , Reynolds, B.^bc , Sperling, R.A.ⁱ , Johnson, K.A.ⁱ , Marshall, G.ⁱ , Yesavage, J.^bd , Taylor, J.L.^bd , Lane, B.^bd , Rosen, A.^bd , Tinklenberg, J.^bd , Sabbagh, M.N.^ab , Belden, C.M.^ab , Jacobson, S.A.^ab , Sirrel, S.A.^ab , Kowall, N.^be , Killiany, R.^be , Budson, A.E.^be , Norbash, A.^be , Johnson, P.L.^be , Obisesan, T.O.^bf , Wolday, S.^bf , Allard, J.^bf , Lerner, A.^bg , Ogrocki, P.^bg , Tatsuoka, C.^bg , Fatica, P.^bg , Fletcher, E.^h , Maillard, P.^h , Olichney, J.^h , Carmichael, O.^h , Kittur, S.^bh , Borrie, M.^bi , Lee, T.-Y.^bi , Bartha, R.^bi , Johnson, S.^bi , Asthana, S.^bi , Carlsson, C.M.^bi , Preda, A.^ac , Nguyen, D.^ac , Tariot, P.^ab , Burke, A.^ab , Trncic, N.^ab , Fleisher, A.^ab , Reeder, S.^ab , Bates, V.^bj , Capote, H.^bj , Rainka, M.^bj , Scharre, D.W.^bk , Kataki, M.^bk , Adeli, A.^bk , Zimmerman, E.A.^bl , Celmins, D.^bl , Brown, A.D.^bl , Pearlson, G.D.^bm , Blank, K.^bm , Anderson, K.^bm , Flashman, L.A.^bn , Seltzer, M.^bn , Hynes, M.L.^bn , Santulli, R.B.^bn , Sink, K.M.^bo , Gordineer, L.^bo , Williamson, J.D.^bo , Garg, P.^bo , Watkins, F.^bo , Ott, B.R.^v , Querfurth, H.^v , Tremont, G.^v , Salloway, S.^v , Malloy, P.^v , Correia, S.^v , Rosen, H.J.^b , Miller, B.L.^b , Perry, D.^b , Mintzer, J.^bp , Spicer, K.^bp , Bachman, D.^bp , Finger, E.^bi , Pasternak, S.^bi , Rachinsky, I.^bi , Rogers, J.^bi , Drost, D.^bi , Pomara, N.^bq , Hernando, R.^bq , Sarrael, A.^bq , Schultz, S.K.^br , Ponto, L.L.B.^br , Shim, H.^br , Smith, K.E.^br , Relkin, N.^p , Chaing, G.^p , Lin, M.^p , Ravdin, L.^p , Smith, A.^bs , Raj, B.A.^bs , Fargher, K.^bs , the Alzheimer’s Disease Neuroimaging Initiative^bt

^a School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
^b UC San Francisco, San Francisco, CA 94107, United States
^c UC San Diego, La Jolla, CA 92093, United States
^d Mayo Clinic, Rochester, MN, United States
^e UC Berkeley, San Francisco, Berkeley, United States
^f University of Pennsylvania, Philadelphia, PA 19104, United States
^g USC, Los Angeles, CA 90032, United States
^h UC Davis, Sacramento, CA, United States
ⁱ Brigham and Women’s Hospital/Harvard Medical School, Boston, MA 02215, United States
^j Indiana University, Bloomington, IN 47405, United States
^k Washington University St. Louis, St. Loui, MO 63110, United States
^l Prevent Alzheimer’s Disease, Rockville, MD 20850, United States
^m Siemens, Erlangen, Germany
ⁿ Alzheimer’s Association, Chicago, IL 60631, United States
^o University of Pittsburg, Pittsburgh, PA 15213, United States
^p Cornell University, Ithaca, NY 14853, United States
^q Albert Einstein College of Medicine, Yeshiva University, Bronx, NY 10461, United States
^r AD Drug Discovery Foundation, New York, NY 10019, United States
^s Acumen Pharmaceuticals, Livermore, CA 94551, United States
^t Northwestern University, Chicago, IL 60611, United States
^u National Institute of Mental Health, Bethesda, MD 20892, United States
^v Brown University, Providence, RI 02912, United States
^w University of Washington, Seattle, WA 98195, United States
^x University of London, London, United Kingdom
^y UCLA, Torrance, CA 90509, United States
^z University of Michigan, Ann Arbor, MI 48109-2800, United States
^aa University of Utah, Salt Lake City, UT 84112, United States
^ab Banner Alzheimer’s Institute, Phoenix, AZ 85006, United States
^ac UUC Irvine, Orange, CA 92868, United States
^ad Johns Hopkins University, Baltimore, MD 21205, United States
^ae Richard Frank Consulting, New York, United States
^af National Institute on Aging, Baltimore, MD, United States
^ag Oregon Health and Science University, Portland, OR 97239, United States
^ah University of Alabama, Birmingham, AL, United States
^ai Mount Sinai School of Medicine, New York, NY, United States
^aj Rush University Medical Center, Chicago, IL 60612, United States
^ak Baylor College of Medicine, Houston, TX, United States
^al Wien Center, Miami Beach, FL 33140, United States
^am Columbia University Medical Center, New York, NY, United States
^an New York University, New York, NY, United States
^ao University of Texas Southwestern Medical School, Galveston, TX 77555, United States
^ap Duke University Medical Center, Durham, NC, United States
^aq Emory University, Atlanta, GA 30307, United States
^ar Medical Center, University of Kansas, Kansas City, KS, United States
^as University of Kentucky, Lexington, KY, United States
^at Mayo Clinic, Jacksonville, FL, United States
^au University of Rochester Medical Center, Rochester, NY 14642, United States
^av Yale University School of Medicine, New Haven, CT, United States
^aw McGill Univ. Montreal-Jewish General Hospital, Montreal, PQ H3A 2A7, Canada
^ax Sunnybrook Health Sciences, Toronto, ON, Canada
^ay U.B.C. Clinic for AD & Related Disorders, Vancouver, BC, Canada
^az Cognitive Neurology – St. Joseph’s, London, ON, Canada
^ba Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, United States
^bb Premiere Research Inst (Palm Beach Neurology), W Palm Beach, FL, United States
^bc Georgetown University Medical Center, Washington, DC 20007, United States
^bd Stanford University, Stanford, CA 94305, United States
^be Boston University, Boston, MA, United States
^bf Howard University, Washington, DC 20059, United States
^bg Case Western Reserve University, Cleveland, OH 44106, United States
^bh Neurological Care of CNY, Liverpool, NY 13088, United States
^bi St. Joseph’s Health Care, London, ON N6A 4H1, Canada
^bj Dent Neurologic Institute, Amherst, NY 14226, United States
^bk Ohio State University, Columbus, OH 43210, United States
^bl Albany Medical College, Albany, NY 12208, United States
^bm Hartford Hospital Olin Neuropsychiatry Research Center, Hartford, CT 06114, United States
^bn Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States
^bo Wake Forest University Health Sciences, Winston-Salem, NC, United States
^bp Medical University South Carolina, Charleston, SC 29425, United States
^bq Nathan Kline Institute, Orangeburg, NY, United States
^br University of Iowa College of Medicine, Iowa City, IA 52242, United States
^bs USF Health Byrd Alzheimer’s Institute, University of South Florida, Tampa, FL 33613, United States

Abstract
Alzheimer’s disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments. © 2021, The Author(s).

Funding details
U24 AG21886
National Institutes of HealthNIHU01 AG024904
U.S. Department of DefenseDODW81XWH-12-2-0012
National Institute on AgingNIA
National Institute of Biomedical Imaging and BioengineeringNIBIB
Alzheimer’s AssociationAA
Alzheimer’s Drug Discovery FoundationADDF
Genentech
Johnson and JohnsonJ&J
Merck
Janssen Research and DevelopmentJRD
GE Healthcare
F. Hoffmann-La Roche
Medpace
Alzheimer’s Disease Neuroimaging InitiativeADNI
Takeda Pharmaceutical CompanyTPC
Ministry of Health and WelfareMOHWHI18C0460
Korea Health Industry Development InstituteKHIDI
Ministry of Science and ICT, South KoreaMSITNRF-2018M3C7A1054935
IXICO

Document Type: Article
Publication Stage: Final
Source: Scopus

A multicenter validation of the condylar-C2 sagittal vertical alignment in Chiari malformation type I: A study using the Park-Reeves Syringomyelia Research Consortium
(2021) Journal of Neurosurgery: Pediatrics, 28 (2), pp. 176-182.

Ravindra, V.M.^a b c , Iyer, R.R.^a , Yahanda, A.T.^d , Bollo, R.J.^a , Zhu, H.^e , Joyce, E.^a , Bethel-Anderson, T.^d , Meehan, T.^d , Smyth, M.D.^d , Strahle, J.M.^d , Park, T.S.^d , Limbrick, D.D., Jr.^d , Brockmeyer, D.L.^a , the Park-Reeves Syringomyelia Research Consortium^f 

^a Division of Pediatric Neurosurgery, Primary Children’s Hospital, University of Utah, Salt Lake City, UT, United States
^b Division of Neurosurgery, University of California, San Diego, CA, United States
^c Department of Neurosurgery, Naval Medical Center, San Diego, CA, United States
^d Department of Neurological Surgery, Washington University, School of Medicine, St. Louis, MO, United States
^e Division of Pediatric Neurosurgery, Texas Children’s Hospital, Houston, TX, United States

Abstract
OBJECTIVE The condylar-C2 sagittal vertical alignment (C-C2SVA) describes the relationship between the occipitoatlantal joint and C2 in patients with Chiari malformation type I (CM-I). It has been suggested that a C-C2SVA ≥ 5 mm is predictive of the need for occipitocervical fusion (OCF) or ventral brainstem decompression (VBD). The authors’ objective was to validate the predictive utility of the C-C2SVA by using a large, multicenter cohort of patients. METHODS This validation study used a cohort of patients derived from the Park-Reeves Syringomyelia Research Consortium; patients < 21 years old with CM-I and syringomyelia treated from June 2011 to May 2016 were identified. The primary outcome was the need for OCF and/or VBD. After patients who required OCF and/or VBD were identified, 10 age- and sex-matched controls served as comparisons for each OCF/VBD patient. The C-C2SVA (defined as the position of a plumb line from the midpoint of the O-C1 joint relative to the posterior aspect of the C2-3 disc space), pBC2 (a line perpendicular to a line from the basion to the posteroinferior aspect of the C2 body), and clival-axial angle (CXA) were measured on sagittal MRI. The secondary outcome was the need for ≥ 2 CM-related operations. RESULTS Of the 206 patients identified, 20 underwent OCF/VBD and 14 underwent repeat posterior fossa decompression. A C-C2SVA ≥ 5 mm was 100% sensitive and 86% specific for requiring OCF/VBD, with a 12.6% misclassification rate, whereas CXA < 125° was 55% sensitive and 99% specific, and pBC2 ≥ 9 was 20% sensitive and 88% specific. Kaplan-Meier analysis demonstrated that there was a significantly shorter time to second decompression in children with C-C2SVA ≥ 5 mm (p = 0.0039). The mean C-C2SVA was greater (6.13 ± 1.28 vs 3.13 ± 1.95 mm, p < 0.0001), CXA was lower (126° ± 15.4° vs 145° ± 10.7°, p < 0.05), and pBC2 was similar (7.65 ± 1.79 vs 7.02 ± 1.26 mm, p = 0.31) among those who underwent OCF/VBD versus decompression only. The intraclass correlation coefficient for the continuous measurement of C-C2SVA was 0.52; the kappa value was 0.47 for the binary categorization of C-C2SVA ≥ 5 mm. CONCLUSIONS These results validated the C-C2SVA using a large, multicenter, external cohort with 100% sensitivity, 86% specificity, and a 12.6% misclassification rate. A C-C2SVA ≥ 5 mm is highly predictive of the need for OCF/VBD in patients with CM-I. The authors recommend that this measurement be considered among the tools to identify the “high-risk” CM-I phenotype. © AANS 2021, except where prohibited by US copyright law

Author Keywords
Cervical spine;  Chiari malformation;  Clival-axial angle;  Occipitoatlantal joint;  Park-Reeves Syringomyelia Research Consortium;  PBC2;  Sagittal vertical alignment

Document Type: Article
Publication Stage: Final
Source: Scopus

Associations between Physical Activity, Blood-Based Biomarkers of Neurodegeneration, and Cognition in Healthy Older Adults: The MAPT Study
(2021) Journals of Gerontology – Series A Biological Sciences and Medical Sciences, 76 (8), pp. 1382-1390. 

Raffin, J.^a , Rolland, Y.^a b , Aggarwal, G.^c d , Nguyen, A.D.^c d , Morley, J.E.^c , Li, Y.^e f , Bateman, R.J.^e , Vellas, B.^a b , Barreto, P.D.S.^a b

^a Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, France
^b Umr Inserm, 1027 University of Toulouse Iii, Faculté de Médecine, France
^c Division of Geriatric Medicine, Saint Louis University School of MedicineMO, United States
^d Henry and Amelia Nasrallah Center for Neuroscience, Saint Louis UniversityMO, United States
^e Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^f Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Physical activity (PA) demonstrated benefits on brain health, but its relationship with blood biomarkers of neurodegeneration remains poorly investigated. We explored the cross-sectional associations of PA with blood concentrations of neurofilament light chain (NFL) and beta amyloid (Aβ)42/40. We further examined whether the interaction between PA and these biomarkers was longitudinally related to cognition. Four-hundred and sixty-five nondemented older adults engaged in an interventional study and who had a concomitant assessment of PA levels and blood measurements of NFL (pg/mL) and Aβ42/40 were analyzed. A composite Z-score combining 4 cognitive tests was used for cognitive assessment up to a 4-year follow-up. Multiple linear regressions demonstrated that people achieving 500-999 and 2000+ MET-min/week of PA had lower (ln)NFL concentrations than their inactive peers. Logistic regressions revealed that achieving at least 90 MET-min/week of PA was associated with a lower probability of having high NFL concentrations (ie, ≥91.961 pg/mL [third quartile]). PA was not associated with (Aβ)42/40. Mixed-model linear regressions demonstrated that the reverse relationship between PA and cognitive decline tended to be more pronounced as Aβ42/40 increased, while it was dampened with increasing levels of (ln)NFL concentrations. This study demonstrates that PA is associated with blood NFL but not with Aβ42/40. Furthermore, it suggests that PA may attenuate the negative association between amyloid load and cognition, while having high NFL levels mitigates the favorable relationship between PA and cognition. More investigations on non demented older adults are required for further validation of the present findings. © 2021 The Author(s). Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.

Author Keywords
Aging;  Neurofilament light chain;  Physical exercise;  Plasma amyloid

Document Type: Article
Publication Stage: Final
Source: Scopus

Development and external validation of the KIIDS-TBI tool for managing children with mild traumatic brain injury and intracranial injuries
(2021) Academic Emergency Medicine, . 

Greenberg, J.K.^a , Ahluwalia, R.^b , Hill, M.^c , Johnson, G.^a , Hale, A.T.^b , Belal, A.^d , Baygani, S.^d , Olsen, M.A.^e , Foraker, R.E.^e , Carpenter, C.R.^f , Yan, Y.^g , Ackerman, L.^d , Noje, C.^h , Jackson, E.ⁱ , Burns, E.^j , Sayama, C.M.^k , Selden, N.R.^k , Vachhrajani, S.^c , Shannon, C.N.^b l , Kuppermann, N.^m , Limbrick, D.D., Jr.^a

^a Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^b Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
^c Department of Neurological Surgery, Dayton Children’s Hospital, Dayton, OH, United States
^d Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
^e Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^f Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^g Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
^h Department of Anesthesiology, Johns Hopkins School of Medicine, Baltimore, MD, United States
ⁱ Department of Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, MD, United States
^j Department of Pediatrics, Oregon Health and Science University, Portland, OR, United States
^k Department of Neurological Surgery, Oregon Health and Science University, Portland, OR, United States
^l American Society for Reproductive Medicine, University of California Davis, Davis, CA, United States
^m Department of Emergency Medicine, University of California–Davis, Davis, CA, United States

Abstract
Background: Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study’s objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. Methods: We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004–2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. Results: Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as “high risk” for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%–96.6%) but improved specificity (67.4%–81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. Conclusions: The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population. © 2021 by the Society for Academic Emergency Medicine

Funding details
Agency for Healthcare Research and QualityAHRQ1F32HS027075‐01A1
Pfizer
Merck
Thrasher Research FundTRF15024
Sanofi Pasteur

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

MAPT R406W increases tau T217 phosphorylation in absence of amyloid pathology
(2021) Annals of Clinical and Translational Neurology, . 

Sato, C.^a , Mallipeddi, N.^a , Ghoshal, N.^a b , Wright, B.A.^c , Day, G.S.^d , Davis, A.A.^a e , Kim, A.H.^e f , Zipfel, G.J.^e f , Bateman, R.J.^a e g , Gabelle, A.^h , Barthélemy, N.R.^a

^a Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^c Department of Neurosciences, University of California San Diego School of Medicine, La Jolla, CA, United States
^d Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, United States
^e Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
^f Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
^g Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
^h Department of Neurology, Memory Research and Resources Center, University Hospital of Montpellier, Neurosciences Institute of Montpellier, University of Montpellier, Montpellier, France

Abstract
Objective: Tau hyperphosphorylation at threonine 217 (pT217) in cerebrospinal fluid (CSF) has recently been linked to early amyloidosis and could serve as a highly sensitive biomarker for Alzheimer’s disease (AD). However, it remains unclear whether other tauopathies induce pT217 modifications. To determine if pT217 modification is specific to AD, CSF pT217 was measured in AD and other tauopathies. Methods: Using immunoprecipitation and mass spectrometry methods, we compared CSF T217 phosphorylation occupancy (pT217/T217) and amyloid-beta (Aβ) 42/40 ratio in cognitively normal individuals and those with symptomatic AD, progressive supranuclear palsy, corticobasal syndrome, and sporadic and familial frontotemporal dementia. Results: Individuals with AD had high CSF pT217/T217 and low Aβ42/40. In contrast, cognitively normal individuals and the majority of those with 4R tauopathies had low CSF pT217/T217 and normal Aβ 42/40. We identified a subgroup of individuals with increased CSF pT217/T217 and normal Aβ 42/40 ratio, most of whom were MAPT R406W mutation carriers. Diagnostic accuracies of CSF Aβ 42/40 and CSF pT217/T217, alone and in combination were compared. We show that CSF pT217/T217 × CSF Aβ 42/40 is a sensitive composite biomarker that can separate MAPT R406W carriers from cognitively normal individuals and those with other tauopathies. Interpretation: MAPT R406W is a tau mutation that leads to 3R+4R tauopathy similar to AD, but without amyloid neuropathology. These findings suggest that change in CSF pT217/T217 ratio is not specific to AD and might reflect common downstream tau pathophysiology common to 3R+4R tauopathies. © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association

Author Keywords
Alzheimer’s Disease;  biomarker;  cerebrospinal fluid;  mass spectrometry;  tau phosphorylation;  Tauopathies

Funding details
P41 GM103422
National Institutes of HealthNIH
National Institute on AgingNIAK01AG062796, K23AG064029
Foundation for Barnes-Jewish Hospital3945
Washington University School of Medicine in St. Louis
Hope Center for Neurological Disorders
Tau Consortium

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

Modeling Sporadic Alzheimer’s Disease in Human Brain Organoids under Serum Exposure
(2021) Advanced Science, . 

Chen, X.^a , Sun, G.^a , Tian, E.^a , Zhang, M.^a , Davtyan, H.^b , Beach, T.G.^c , Reiman, E.M.^d , Blurton-Jones, M.^e , Holtzman, D.M.^f , Shi, Y.^a

^a Division of Stem Cell Biology Research, Department of Developmental and Stem Cell Biology, Beckman Research Institute of City of Hope, 1500 E. Duarte Rd, Duarte, CA 91010, United States
^b Institute for Memory Impairments & Neurological Disorders and Sue & Bill Gross Stem Cell Research Center, University of California Irvine, Irvine, CA 92697, United States
^c Banner Sun Health Research Institute, 105015 West Santa Fe Drive, Sun City, AZ 85351, United States
^d Banner Alzheimer Institute, 901 East Willetta Street, Phoenix, AZ 95006, United States
^e Department of Neurobiology & Behavior, Institute for Memory Impairments & Neurological Disorders and Sue & Bill Gross Stem Cell Research Center, University of California Irvine, Irvine, CA 92697, United States
^f Department of Neurology, Hope Center for Neurological Disorders, Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO 63110, United States

Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with no cure. Huge efforts have been made to develop anti-AD drugs in the past decades. However, all drug development programs for disease-modifying therapies have failed. Possible reasons for the high failure rate include incomplete understanding of complex pathophysiology of AD, especially sporadic AD (sAD), and species difference between humans and animal models used in preclinical studies. In this study, sAD is modeled using human induced pluripotent stem cell (hiPSC)-derived 3D brain organoids. Because the blood–brain barrier (BBB) leakage is a well-known risk factor for AD, brain organoids are exposed to human serum to mimic the serum exposure consequence of BBB breakdown in AD patient brains. The serum-exposed brain organoids are able to recapitulate AD-like pathologies, including increased amyloid beta (Aβ) aggregates and phosphorylated microtubule-associated tau protein (p-Tau) level, synaptic loss, and impaired neural network. Serum exposure increases Aβ and p-Tau levels through inducing beta-secretase 1 (BACE) and glycogen synthase kinase-3 alpha / beta (GSK3α/β) levels, respectively. In addition, single-cell transcriptomic analysis of brain organoids reveals that serum exposure reduced synaptic function in both neurons and astrocytes and induced immune response in astrocytes. The human brain organoid-based sAD model established in this study can provide a powerful platform for both mechanistic study and therapeutic development in the future. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH

Author Keywords
brain organoids;  disease modeling;  induced pluripotent stem cells;  serum exposure;  sporadic Alzheimer’s disease

Funding details
National Institutes of HealthNIHP30 AG066519, R01 AG056303, R01 AG056305, R56 AG061171, RF1 AG061794, RF1 DA048813
National Institute on AgingNIAP30 AG19610
National Cancer InstituteNCIP30CA33572
National Institute of Neurological Disorders and StrokeNINDSU24 NS072026
Michael J. Fox Foundation for Parkinson’s ResearchMJFF
Arizona Department of Health ServicesADHS211002
Arizona Biomedical Research CommissionABRC0011, 05‐901, 1001, 4001

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

Predictive modeling of spread in adult-onset isolated dystonia: Key properties and effect of tremor inclusion
(2021) European Journal of Neurology, . 

Wang, M.^a , Sajobi, T.^a , Morgante, F.^b c , Adler, C.^d , Agarwal, P.^e , Bäumer, T.^f , Berardelli, A.^g h , Berman, B.D.ⁱ , Blumin, J.^j , Borsche, M.^k , Brashear, A.^l , Deik, A.^m , Duque, K.ⁿ , Espay, A.J.ⁿ , Ferrazzano, G.^g , Feuerstein, J.^o , Fox, S.^p , Frank, S.^q , Hallett, M.^r , Jankovic, J.^s , LeDoux, M.S.^t , Leegwater-Kim, J.^u , Mahajan, A.^v , Malaty, I.A.^w , Ondo, W.^x y , Pantelyat, A.^z , Pirio-Richardson, S.^aa , Roze, E.^ab , Saunders-Pullman, R.^ac , Suchowersky, O.^ad , Truong, D.^ae af , Vidailhet, M.^ab , Shukla, A.W.^w , Perlmutter, J.S.^ag , Jinnah, H.A.^ah , Martino, D.^ai

^a Department of Community Health Sciences, Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
^b Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George’s, University of London, London, United Kingdom
^c Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
^d Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, United States
^e Booth Gardner Parkinson’s Center, Evergreen Health, Kirkland, WA, United States
^f Institute of Systems Motor Science, Center for Rare Diseases, University Medical Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
^g Department of Human Neurosciences, University of Rome “La Sapienza”, Rome, Italy
^h IRCCS Neuromed, Pozzilli, Italy
ⁱ Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
^j Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, United States
^k Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
^l Department of Neurology, University of California, Davis, Sacramento, CA, United States
^m Disease and Movement Disorders Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
ⁿ Department of Neurology and Rehabilitation Medicine, Gardner Family Center for Parkinson’s Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, United States
^o Department of Neurology, University of Colorado, Aurora, CO, United States
^p Movement Disorder Clinic, Edmond J Safra Program in Parkinson Disease, Toronto Western Hospital, and Division of Neurology, University of Toronto, Toronto, ON, Canada
^q Beth Israel Deaconess Medical Center, Boston, MA, United States
^r Human Motor Control Section, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, United States
^s Parkinson’s Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
^t Department of Psychology and School of Health Sciences, University of Memphis, and Veracity Neuroscience, Memphis, TN, United States
^u Lahey Hospital and Medical Center, Tufts University School of Medicine, Burlington, MA, United States
^v Rush Parkinson’s disease and movement disorders program, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
^w Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
^x Houston Methodist Hospital, Houston, TX, United States
^y Weill Cornell Medical School, New York, NY, United States
^z Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
^aa Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, NM, United States
^ab Sorbonne Université, Institut du Cerveau – Paris Brain Institute – ICM, Inserm, CNRS, AP-HP, Hôpital Salpetriere, Paris, France
^ac Department of Neurology, Icahn School of Medicine at Mount Sinai and Mount Sinai Beth Israel, New York, NY, United States
^ad Department of Medicine, University of Alberta, Edmonton, AB, Canada
^ae Department of Neurosciences, UC Riverside, Riverside, CA, United States
^af The Parkinson and Movement Disorder Institute, Fountain Valley, CA, United States
^ag Departments of Neurology, Psychiatry, Radiology, Neurobiology, Physical Therapy and Occupational Therapy, Washington University School of Medicine, St. Louis, MO, United States
^ah Departments of Neurology, Human Genetics, and Pediatrics, Emory University School of Medicine, Atlanta, GA, United States
^ai Department of Clinical Neurosciences & Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada

Abstract
Background and purpose: Several clinical and demographic factors relate to anatomic spread of adult-onset isolated dystonia, but a predictive model is still lacking. The aims of this study were: (i) to develop and validate a predictive model of anatomic spread of adult-onset isolated dystonia; and (ii) to evaluate whether presence of tremor associated with dystonia influences model predictions of spread. Methods: Adult-onset isolated dystonia participants with focal onset from the Dystonia Coalition Natural History Project database were included. We developed two prediction models, one with dystonia as sole disease manifestation (“dystonia-only”) and one accepting dystonia OR tremor in any body part as disease manifestations (“dystonia OR tremor”). Demographic and clinical predictors were selected based on previous evidence, clinical plausibility of association with spread, or both. We used logistic regressions and evaluated model discrimination and calibration. Internal validation was carried out based on bootstrapping. Results: Both predictive models showed an area under the curve of 0.65 (95% confidence intervals 0.62–0.70 and 0.62–0.69, respectively) and good calibration after internal validation. In both models, onset of dystonia in body regions other than the neck, older age, depression and history of neck trauma were predictors of spread. Conclusions: This predictive modeling of spread in adult-onset isolated dystonia based on accessible predictors (demographic and clinical) can be easily implemented to inform individuals’ risk of spread. Because tremor did not influence prediction of spread, our results support the argument that tremor is a part of the dystonia syndrome, and not an independent or coincidental disorder. © 2021 European Academy of Neurology

Author Keywords
isolated dystonia;  neurological diseases;  predictive models;  spread;  tremor

Funding details
U54 NS065701, U54 NS116025
National Institute of Neurological Disorders and StrokeNINDS
Dystonia Coalition
Rare Diseases Clinical Research NetworkRDCRNU54 TR001456

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