A blood-based signature of cerebrospinal fluid Aβ1–42 status
Authors: Goudey Benjamin , Fung Bowen J. , Schieber Christine , Faux Noel G. ,   Alzheimer’s Disease Metabolomics Consortium, Weiner Michael W. , Aisen Paul , Petersen Ronald , Jack Clifford R. , Jagust William , Trojanowki John Q. , Toga Arthur W. , Beckett Laurel , Green Robert C. , Saykin Andrew J. , Morris John , Shaw Leslie M. , Kaye Jeffrey , Quinn Joseph , Silbert Lisa , Lind Betty , Carter Raina , Dolen Sara , Schneider Lon S. , Pawluczyk Sonia , Beccera Mauricio , Teodoro Liberty , Spann Bryan M. , Brewer James , Vanderswag Helen , Fleisher Adam , Heidebrink Judith L. , Lord Joanne L. , Mason Sara S. , Albers Colleen S. , Knopman David , Johnson Kris , Doody Rachelle S. , Villanueva-Meyer Javier , Chowdhury Munir , Rountree Susan , Dang Mimi , Stern Yaakov , Honig Lawrence S. , Bell Karen L. , Ances Beau , Morris John C. , Carroll Maria , Creech Mary L. , Franklin Erin , Mintun Mark A. , Schneider Stacy , Oliver Angela , Marson Daniel , Griffth Randall , Clark David , Geldmacher David , Brockington John , Roberson Erik , Love Marissa Natelson , Grossman Hillel , Mitsis Effie , Shah Raj C. , deToledo-Morrell Leyla , Duara Ranjan , Varon Daniel , Greig Maria T. , Roberts Peggy , Albert Marilyn , Onyike Chiadi , D’Agostino Daniel , Kielb Stephanie , Galvin James E. , Cerbone Brittany , Michel Christina A. , Pogorelec Dana M. , Rusinek Henry , de Leon Mony J. , Glodzik Lidia , De Santi Susan , Doraiswamy P. Murali , Petrella Jeffrey R. , Borges-Neto Salvador , Wong Terence Z. , Coleman Edward , Smith Charles D. , Jicha Greg , Hardy Peter , Sinha Partha , Oates Elizabeth , Conrad Gary , Porsteinsson Anton P. , Goldstein Bonnie S. , Martin Kim , Makino Kelly M. , Ismail M. Saleem , Brand Connie , Mulnard Ruth A. , Thai Gaby , Mc-Adams-Ortiz Catherine , Womack Kyle , Mathews Dana , Quiceno Mary , Levey Allan I. , Lah James J. , Cellar Janet S. , Burns Jeffrey M. , Swerdlow Russell H. , Brooks William M. , Apostolova Liana , Tingus Kathleen , Woo Ellen , Silverman Daniel H. S. , Lu Po H. , Bartzokis George , Graff-Radford Neill R. , Parftt Francine , Kendall Tracy , Johnson Heather , Farlow Martin R. , Hake Ann Marie , Matthews Brandy R. , Brosch Jared R. , Herring Scott , Hunt Cynthia , van Dyck Christopher H. , Carson Richard E. , MacAvoy Martha G. , Varma Pradeep , Chertkow Howard , Bergman Howard , Hosein Chris , Black Sandra , Stefanovic Bojana , Caldwell Curtis , Hsiung Ging-Yuek Robin , Feldman Howard , Mudge Benita , Assaly Michele , Finger Elizabeth , Pasternack Stephen , Rachisky Irina , Trost Dick , Kertesz Andrew , Bernick Charles , Munic Donna , Mesulam Marek-Marsel , Lipowski Kristine , Weintraub Sandra , Bonakdarpour Borna , Kerwin Diana , Wu Chuang-Kuo , Johnson Nancy , Sadowsky Carl , Villena Teresa , Turner Raymond Scott , Johnson Kathleen , Reynolds Brigid , Sperling Reisa A. , Johnson Keith A. , Marshall Gad , Yesavage Jerome , Taylor Joy L. , Lane Barton , Rosen Allyson , Tinklenberg Jared , Sabbagh Marwan N. , Belden Christine M. , Jacobson Sandra A. , Sirrel Sherye A. , Kowall Neil , Killiany Ronald , Budson Andrew E. , Norbash Alexander , Johnson Patricia Lynn , Obisesan Thomas O. , Wolday Saba , Allard Joanne , Lerner Alan , Ogrocki Paula , Tatsuoka Curtis , Fatica Parianne , Fletcher Evan , Maillard Pauline , Olichney John , DeCarli Charles , Carmichael Owen , Kittur Smita , Borrie Michael , Lee T.-Y. , Bartha Rob , Johnson Sterling , Asthana Sanjay , Carlsson Cynthia M. , Potkin Steven G. , Preda Adrian , Nguyen Dana , Tariot Pierre , Burke Anna , Trncic Nadira , Fleisher Adam , Reeder Stephanie , Bates Vernice , Capote Horacio , Rainka Michelle , Scharre Douglas W. , Kataki Maria , Adeli Anahita , Zimmerman Earl A. , Celmins Dzintra , Brown Alice D. , Pearlson Godfrey D. , Blank Karen , Anderson Karen , Flashman Laura A. , Seltzer Marc , Hynes Mary L. , Santulli Robert B. , Sink Kaycee M. , Gordineer Leslie , Williamson Je D. , Garg Pradeep , Watkins Franklin , Ott Brian R. , Querfurth Henry , Tremont Geffrey , Salloway Stephen , Malloy Paul , Correia Stephen , Rosen Howard J. , Miller Bruce L. , Perry David , Mintzer Jacobo , Spicer Kenneth , Bachman David , Pomara Nunzio , Hernando Raymundo , Sarrael Antero , Relkin Norman , Chaing Gloria , Lin Michael , Ravdin Lisa , Smith Amanda , Raj Balebail Ashok , Fargher Kristin ,   Alzheimer’s Disease Neuroimaging Initiative, Saykin Andrew , Nho Kwangsik , Kling Mitchel , Toledo John , Shaw Leslie , Trojanowski John , Farrer Lindsay , Kastsenmüller Gabi , Arnold Matthias , Wishart David , Würtz Peter , Bhattcharyya Sudeepa , van Duijin Cornelia , Mangravite Lara , Han Xianlin , Hankemeier Thomas , Fiehn Oliver , Barupal Dinesh , Thiele Ines , Heinken Almut , Meikle Peter , Price Nathan , Funk Cory , Jia Wei , Kueider-Paisley Alexandra , Doraiswamy P. Murali , Tenebaum Jessica , Black Colette , Moseley Arthur , Thompson Will , Mahmoudiandehkorki Siam , Baillie Rebecca , Welsh-Bohmer Kathleen , Plassman Brenda 
Published: March 2019

It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy.