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Sci Rep. 2019 Mar 11;9(1):4163. doi: 10.1038/s41598-018-37149-7.

A blood-based signature of cerebrospinal fluid Aβ1-42 status.

Collaborators (274)

Weiner MW, Aisen P, Petersen R, Jack CR, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Kaye J, Quinn J, Silbert L, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Morris JC, Carroll M, Creech ML, Franklin E, Mintun MA, Schneider S, Oliver A, Marson D, Griffth R, Clark D, Geldmacher D, Brockington J, Roberson E, Love MN, Grossman H, Mitsis E, Shah RC, deToledo-Morrell L, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D'Agostino D, Kielb S, Galvin JE, Cerbone B, Michel CA, Pogorelec DM, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy PM, Petrella JR, Borges-Neto S, Wong TZ, Coleman E, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Levey AI, Lah JJ, Cellar JS, Burns JM, Swerdlow RH, Brooks WM, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parftt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Brosch JR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Varma P, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GR, Feldman H, Mudge B, Assaly M, Finger E, Pasternack S, Rachisky I, Trost D, Kertesz A, Bernick C, Munic D, Mesulam MM, Lipowski K, Weintraub S, Bonakdarpour B, Kerwin D, Wu CK, Johnson N, Sadowsky C, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Allard J, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, DeCarli C, Carmichael O, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Burke A, Trncic N, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Flashman LA, Seltzer M, Hynes ML, Santulli RB, Sink KM, Gordineer L, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Perry D, Mintzer J, Spicer K, Bachman D, Pomara N, Hernando R, Sarrael A, Relkin N, Chaing G, Lin M, Ravdin L, Smith A, Raj BA, Fargher K, Saykin A, Nho K, Kling M, Toledo J, Shaw L, Trojanowski J, Farrer L, Kastsenmüller G, Arnold M, Wishart D, Würtz P, Bhattcharyya S, van Duijin C, Mangravite L, Han X, Hankemeier T, Fiehn O, Barupal D, Thiele I, Heinken A, Meikle P, Price N, Funk C, Jia W, Kueider-Paisley A, Doraiswamy PM, Tenebaum J, Black C, Moseley A, Thompson W, Mahmoudiandehkorki S, Baillie R, Welsh-Bohmer K, Plassman B.

Author information

1
IBM Research Australia, Carlton, Victoria, Australia.
2
Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.
3
Department of Computing and Information System, The University of Melbourne, Parkville, Victoria, Australia.
4
School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.
5
IBM Research Australia, Carlton, Victoria, Australia. noel.faux@au1.ibm.com.
6
The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia. noel.faux@au1.ibm.com.

Abstract

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.

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