The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10 −4 ) or temporal stage (p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference / A.L. Young, R.V. Marinescu, N.P. Oxtoby, M. Bocchetta, K. Yong, N.C. Firth, D.M. Cash, D.L. Thomas, K.M. Dick, J. Cardoso, J. van Swieten, B. Borroni, D. Galimberti, M. Masellis, M.C. Tartaglia, J.B. Rowe, C. Graff, F. Tagliavini, G.B. Frisoni, R. Laforce, E. Finger, A. de Mendonça, S. Sorbi, J.D. Warren, S. Crutch, N.C. Fox, S. Ourselin, J.M. Schott, J.D. Rohrer, D.C. Alexander, C. Andersson, S. Archetti, A. Arighi, L. Benussi, G. Binetti, S. Black, M. Cosseddu, M. Fallström, C. Ferreira, C. Fenoglio, M. Freedman, G.G. Fumagalli, S. Gazzina, R. Ghidoni, M. Grisoli, V. Jelic, L. Jiskoot, R. Keren, G. Lombardi, C. Maruta, L. Meeter, S. Mead, R. van Minkelen, B. Nacmias, L. Öijerstedt, A. Padovani, J. Panman, M. Pievani, C. Polito, E. Premi, S. Prioni, R. Rademakers, V. Redaelli, E. Rogaeva, G. Rossi, M. Rossor, E. Scarpini, D. Tang-Wai, H. Thonberg, P. Tiraboschi, A. Verdelho, M.W. Weiner, P. Aisen, R. Petersen, C.R. Jack, W. Jagust, J.Q. Trojanowki, A.W. Toga, L. Beckett, R.C. Green, A.J. Saykin, J. Morris, L.M. Shaw, Z. Khachaturian, G. Sorensen, L. Kuller, M. Raichle, S. Paul, P. Davies, H. Fillit, F. Hefti, D. Holtzman, M.M. Mesulam, W. Potter, P. Snyder, A. Schwartz, T. Montine, R.G. Thomas, M. Donohue, S. Walter, D. Gessert, T. Sather, G. Jiminez, D. Harvey, M. Bernstein, P. Thompson, N. Schuff, B. Borowski, J. Gunter, M. Senjem, P. Vemuri, D. Jones, K. Kantarci, C. Ward, R.A. Koeppe, N. Foster, E.M. Reiman, K. Chen, C. Mathis, S. Landau, N.J. Cairns, E. Householder, L. Taylor-Reinwald, V. Lee, M. Korecka, M. Figurski, K. Crawford, S. Neu, T.M. Foroud, S. Potkin, L. Shen, K. Faber, S. Kim, K. Nho, L. Thal, N. Buckholtz, M. Albert, R. Frank, J. Hsiao, J. Kaye, J. Quinn, B. Lind, R. Carter, S. Dolen, L.S. Schneider, S. Pawluczyk, M. Beccera, L. Teodoro, B.M. Spann, J. Brewer, H. Vanderswag, A. Fleisher, J.L. Heidebrink, J.L. Lord, S.S. Mason, C.S. Albers, D. Knopman, K. Johnson, R.S. Doody, J. Villanueva-Meyer, M. Chowdhury, S. Rountree, M. Dang, Y. Stern, L.S. Honig, K.L. Bell, B. Ances, M. Carroll, S. Leon, M.A. Mintun, S. Schneider, A. Oliver, D. Marson, R. Griffith, D. Clark, D. Geldmacher, J. Brockington, E. Roberson, H. Grossman, E. Mitsis, L. de Toledo-Morrell, R.C. Shah, R. Duara, D. Varon, M.T. Greig, P. Roberts, M. Albert, C. Onyike, D. D’Agostino, S. Kielb, J.E. Galvin, B. Cerbone, C.A. Michel, H. Rusinek, M.J. de Leon, L. Glodzik, S. De Santi, P.M. Doraiswamy, J.R. Petrella, T.Z. Wong, S.E. Arnold, J.H. Karlawish, D. Wolk, C.D. Smith, G. Jicha, P. Hardy, P. Sinha, E. Oates, G. Conrad, O.L. Lopez, M. Oakley, D.M. Simpson, A.P. Porsteinsson, B.S. Goldstein, K. Martin, K.M. Makino, M.S. Ismail, C. Brand, R.A. Mulnard, G. Thai, C. Mc-Adams-Ortiz, K. Womack, D. Mathews, M. Quiceno, R. Diaz-Arrastia, R. King, M. Weiner, K. Martin-Cook, M. Devous, A.I. Levey, J.J. Lah, J.S. Cellar, J.M. Burns, H.S. Anderson, R.H. Swerdlow, L. Apostolova, K. Tingus, E. Woo, D.H. Silverman, P.H. Lu, G. Bartzokis, N.R. Graff-Radford, F. Parfitt, T. Kendall, H. Johnson, M.R. Farlow, A.M. Hake, B.R. Matthews, S. Herring, C. Hunt, C.H. van Dyck, R.E. Carson, M.G. Macavoy, H. Chertkow, H. Bergman, C. Hosein, B. Stefanovic, C. Caldwell, G.R. Hsiung, H. Feldman, B. Mudge, M. Assaly, A. Kertesz, J. Rogers, C. Bernick, D. Munic, D. Kerwin, M. Mesulam, K. Lipowski, C. Wu, N. Johnson, C. Sadowsky, W. Martinez, T. Villena, R.S. Turner, K. Johnson, B. Reynolds, R.A. Sperling, K.A. Johnson, G. Marshall, M. Frey, B. Lane, A. Rosen, J. Tinklenberg, M.N. Sabbagh, C.M. Belden, S.A. Jacobson, S.A. Sirrel, N. Kowall, R. Killiany, A.E. Budson, A. Norbash, P.L. Johnson, J. Allard, A. Lerner, P. Ogrocki, L. Hudson, E. Fletcher, O. Carmichael, J. Olichney, C. Decarli, S. Kittur, M. Borrie, T.-. Lee, R. Bartha, S. Johnson, S. Asthana, C.M. Carlsson, S.G. Potkin, A. Preda, D. Nguyen, P. Tariot, S. Reeder, V. Bates, H. Capote, M. Rainka, D.W. Scharre, M. Kataki, A. Adeli, E.A. Zimmerman, D. Celmins, A.D. Brown, G.D. Pearlson, K. Blank, K. Anderson, R.B. Santulli, T.J. Kitzmiller, E.S. Schwartz, K.M. Sink, J.D. Williamson, P. Garg, F. Watkins, B.R. Ott, H. Querfurth, G. Tremont, S. Salloway, P. Malloy, S. Correia, H.J. Rosen, B.L. Miller, J. Mintzer, K. Spicer, D. Bachman, S. Pasternak, I. Rachinsky, D. Drost, N. Pomara, R. Hernando, A. Sarrael, S.K. Schultz, L.L.B. Ponto, H. Shim, K.E. Smith, N. Relkin, G. Chaing, L. Raudin, A. Smith, K. Fargher, B.A. Raj, T. Neylan, J. Grafman, M. Davis, R. Morrison, J. Hayes, S. Finley, K. Friedl, D. Fleischman, K. Arfanakis, O. James, D. Massoglia, J.J. Fruehling, S. Harding, E.R. Peskind, E.C. Petrie, G. Li, J.A. Yesavage, J.L. Taylor, A.J. Furst. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 9:1(2018 Dec 01). [10.1038/s41467-018-05892-0]

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

D. Galimberti;A. Arighi
Membro del Collaboration Group
;
C. Fenoglio
Membro del Collaboration Group
;
E. Scarpini
Membro del Collaboration Group
;
2018

Abstract

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10 −4 ) or temporal stage (p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.
Alzheimer Disease; Frontotemporal Dementia; Genotype; Humans; Models, Neurological; Neurodegenerative Diseases; Phenotype; Reproducibility of Results; Time Factors; Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Physics and Astronomy (all)
Settore BIO/13 - Biologia Applicata
Settore MED/26 - Neurologia
1-dic-2018
15-ott-2018
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/625633
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