Association of Initial β-Amyloid Levels With Subsequent Flortaucipir Positron Emission Tomography Changes in Persons Without Cognitive Impairment

David S Knopman; Emily S Lundt; Terry M Therneau; Sabrina M Albertson; Jeffrey L Gunter; Matthew L Senjem; Christopher G Schwarz; Michelle M Mielke; Mary M Machulda; Bradley F Boeve; David T Jones; Jon Graff-Radford; Prashanthi Vemuri; Kejal Kantarci; Val J Lowe; Ronald C Petersen; Clifford R Jack, Jr; and the Alzheimer’s Disease Neuroimaging Initiative

doi:10.1001/jamaneurol.2020.3921

. 2020 Oct 19;78(2):1–12. doi: 10.1001/jamaneurol.2020.3921

Association of Initial β-Amyloid Levels With Subsequent Flortaucipir Positron Emission Tomography Changes in Persons Without Cognitive Impairment

David S Knopman ^1,^✉, Emily S Lundt ², Terry M Therneau ², Sabrina M Albertson ², Jeffrey L Gunter ³, Matthew L Senjem ³, Christopher G Schwarz ³, Michelle M Mielke ², Mary M Machulda ⁴, Bradley F Boeve ¹, David T Jones ¹, Jon Graff-Radford ¹, Prashanthi Vemuri ³, Kejal Kantarci ³, Val J Lowe ³, Ronald C Petersen ^1,², Clifford R Jack Jr ³; and the Alzheimer’s Disease Neuroimaging Initiative

¹Department of Neurology, Mayo Clinic, Rochester, Minnesota

²Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota

³Department of Radiology, Mayo Clinic, Rochester, Minnesota

⁴Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota

Group Information: The Alzheimer’s Disease Neuroimaging Initiative is listed at the end of this article.

Accepted for Publication: August 7, 2020.

^✉

Corresponding Author: David S. Knopman, MD, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (knopman@mayo.edu).

Published Online: October 19, 2020. doi:10.1001/jamaneurol.2020.3921

Author Contributions: Dr Knopman had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Knopman, Mielke, Jack.

Acquisition, analysis, or interpretation of data: Knopman, Lundt, Albertson, Therneau, Gunter, Senjem, Schwarz, Machulda, Boeve, Jones, Graff-Radford, Vemuri, Kantarci, Lowe, Petersen, Jack.

Drafting of the manuscript: Knopman, Lundt.

Critical revision of the manuscript for important intellectual content: Knopman, Albertson, Therneau, Gunter, Senjem, Schwarz, Mielke, Machulda, Boeve, Jones, Graff-Radford, Vemuri, Kantarci, Lowe, Petersen, Jack.

Statistical analysis: Lundt, Albertson, Therneau.

Obtained funding: Knopman, Mielke, Vemuri, Kantarci, Lowe, Petersen, Jack.

Administrative, technical, or material support: Gunter, Senjem, Schwarz, Jones, Graff-Radford, Kantarci, Lowe, Jack.

Supervision: Lowe.

Conflict of Interest Disclosures: Dr Knopman reports having served on a data safety monitoring board for the Dominantly Inherited Alzheimer Network study; serving on a data safety monitoring board for a tau therapeutic agent for Biogen for no personal compensation; being an investigator in clinical trials sponsored by Biogen, Lilly Pharmaceuticals, and the University of Southern California; serving as a consultant for Samus Therapeutics, Third Rock, Hoffman–La Roche Inc, and Alzeca Biosciences for no personal compensation; and receiving research support from the National Institutes of Health (NIH) during the conduct of the study. Dr Jack serves on an independent data monitoring board for Roche and has served as a speaker for Eisai without compensation and receiving research support from the NIH and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic. Dr Vemuri receives research grants from the National Institute on Aging (NIA). Dr Mielke receives research support from the NIH (grants R01 AG49704, U54 AG44170, U01 AG06786, and RF1 AG55151), Department of Defense (grant W81XWH-15-1), and unrestricted research grants from Biogen, as well as other support from the Brain Protection Company outside the submitted work. Dr Machulda receives research support from the NIA and the National Institute on Deafness and Other Communication Disorders. Dr Lowe serves on scientific advisory boards for Bayer Schering Pharma, Philips Molecular lmaging, Life Molecular lmaging, AVID Radiopharmaceuticals, and GE Healthcare; receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, the NIA, and the National Cancer Institute; and receives personal fees from Eisai Inc, Avid Radiopharmaceuticals, and Piramal Imaging. Dr Kantarci receives research grants from the NIA and AVID Radiopharmaceuticals outside the submitted work. Dr Graff-Radford receives research grants from the NIA. Dr Boeve has served as an investigator for clinical trials sponsored by Biogen and Alector; receives publishing royalties from Behavioral Neurology of Dementia (Cambridge Medicine, 2009 and 2016); serves on the scientific advisory board of the Tau Consortium; receives research support from the NIH, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, and the Little Family Foundation; and reports grants from the NIH during the conduct of the study and Biogen, Alector, and EIP Pharma outside the submitted work. Dr Therneau receives research grants from the NIH. Dr Petersen is a consultant for Biogen Inc, Hoffman–La Roche Inc, Merck Inc, Genentech Inc, and Eisai Inc; has given educational lectures for GE Healthcare; receives publishing royalties from Mild Cognitive Impairment (Oxford University Press, 2003) and UpToDate; and receives research support from the NIA. Dr Schwarz reported grants from the NIH outside the submitted work. Dr Jones reported grants from the NIH and the Minnesota Partnership for Biotechnology and Medical Genomics outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by National Institutes of Health grants P50 AG016574, P30 AG062677, U01 AG006786, R01 AG034676, R01 AG41851, and R37 AG11378; the Elsie and Marvin Dekelboum Family Foundation; the GHR Foundation; the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic; the Liston Award; the Alzheimer’s Association; the Schuler Foundation; and the Mayo Foundation for Medical Education and Research. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health grant U01 AG024904) and the Department of Defense (award W81XWH-12-2-0012). The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica Inc, Biogen, Bristol-Myers Squibb Company, CereSpir Inc, Cogstate, Eisai Inc, Elan Pharmaceuticals Inc, Eli Lilly & Company, EuroImmun, F. Hoffmann–La Roche Ltd and its affiliated company Genentech Inc, Fujirebio, GE Healthcare, IXICO Ltd, Janssen Alzheimer Immunotherapy Research & Development LLC, Johnson & Johnson Pharmaceutical Research & Development LLC, Lumosity, Lundbeck, Merck & Co Inc, Meso Scale Diagnostics LLC, NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc, Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. In addition, AVID Radiopharmaceuticals Inc supplied the AV-1451 precursor, chemistry production advice and oversight, and the US Food and Drug Administration regulatory cross-filing permission and documentation needed for this work.

Role of the Funder/Sponsor: The funders of this research had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Information: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of the ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. The ADNI was launched in 2003 as a public-private partnership led by principal investigator Michael W. Weiner, MD. The primary goal of Alzheimer’s Disease Neuroimaging Initiative has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment and early Alzheimer disease. The ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Additional Contributions: We thank Carly T. Mester, BA, Mayo Clinic, for her assistance in statistical analyses. She was not compensated for this contribution. We gratefully acknowledge the contributions of the participants and our staff at the Mayo Alzheimer Center for their invaluable contributions to this work.

Group Author Information: I. ADNI I, GO, II and III: Part A: Leadership and Infrastructure Principal Investigator: University of California San Francisco, San Francisco: Michael W. Weiner, MD; ATRI Principal Investigator and Director of Coordinating Center Clinical Core: University of Southern California, Los Angeles, Paul Aisen, MD; Executive Committee: University of California San Francisco, San Francisco, Michael Weiner, MD; University of Southern California, Los Angeles, Paul Aisen, MD; Mayo Clinic, Rochester, Minnesota, Ronald Petersen, MD, PhD; Mayo Clinic, Rochester, Minnesota, Clifford R. Jack, Jr, MD; University of California Berkeley, Berkeley, William Jagust, MD; University of Pennsylvania, Philadelphia, John Q. Trojanowki, MD, PhD; University of Southern California, Los Angeles, Arthur W. Toga, PhD; University of California Davis, Sacramento, Laurel Beckett, PhD; Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, Robert C. Green, MD, MPH; Indiana University, Bloomington, Andrew J. Saykin, PsyD; Washington University, St. Louis, Missouri, John Morris, MD; University of Pennsylvania, Philadelphia, Leslie M. Shaw; ADNI External Advisory Board (ESAB): Prevent Alzheimer’s Disease 2020 (Chair), Zaven Khachaturian, PhD; Siemens, Greg Sorensen, MD; Alzheimer’s Association, Maria Carrillo, PhD; University of Pittsburgh, Pittsburgh, Pennsylvania, Lew Kuller, MD; Washington University, St. Louis, Missouri, Marc Raichle, MD; Cornell University, Ithaca, New York, Steven Paul, MD; Albert Einstein College of Medicine of Yeshiva University, New York, New York, Peter Davies, MD; AD Drug Discovery Foundation, Howard Fillit, MD; Acumen Pharmaceuticals, Franz Hefti, PhD; Washington University, St. Louis, Missouri, David Holtzman, MD; Northwestern University, Chicago, Illinois, M. Marcel Mesulam, MD; National Institute of Mental Health, Bethesda, Maryland, William Potter, MD; Brown University, Providence, Rhode Island, Peter Snyder, PhD; ADNI 3 Private Partner Scientific Board (PPSB): Eli Lilly (Chair), Veronika Logovinsky, MD, PhD; Data and Publications Committee: Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Chair), Robert C. Green, MD, MPH; Resource Allocation Review Committee: University of Washington (Chair), Seattle, Tom Montine, MD, PhD; Clinical Core Leaders: Mayo Clinic, Rochester, Minnesota (Core PI), Ronald Petersen, MD, PhD; University of Southern California, Los Angeles, Paul Aisen, MD; Clinical Informatics and Operations: University of Southern California, Los Angeles, Gustavo Jimenez, MBS; Michael Donohue, PhD; Devon Gessert, BS; Kelly Harless, BA; Jennifer Salazar, MBS; Yuliana Cabrera, BS; Sarah Walter, MSc; Lindsey Hergesheimer, BS; Biostatistics Core Leaders and Key Personnel: University of California Davis, Sacramento (Core PI), Laurel Beckett, PhD; University of California Davis, Sacramento, Danielle Harvey, PhD; University of California San Diego, La Jolla, Michael Donohue, PhD; MRI Core Leaders and Key Personnel: Mayo Clinic, Rochester, Minnesota (Core PI), Clifford R. Jack, Jr, MD; Mayo Clinic, Rochester, Minnesota, Matthew Bernstein, PhD; University of London, London, UK, Nick Fox, MD; UCLA School of Medicine, Los Angeles; Paul Thompson, PhD; University of California San Francisco Magnetic Resonance Imaging, San Francisco, Norbert Schuff, PhD; University of California Davis, Sacramento; Charles DeCArli, MD; Mayo Clinic, Rochester, Minnesota, Bret Borowski, RT; Jeff Gunter, PhD; Matt Senjem, MS; Prashanthi Vemuri, PhD; David Jones, MD; Kejal Kantarci; Chad Ward; PET Core Leaders and Key Personnel: University of California Berkeley, Berkeley (Core PI), William Jagust, MD; University of Michigan, Ann Arbor, Robert A. Koeppe, PhD; University of Utah, Salt Lake City, Norm Foster, MD; Banner Alzheimer’s Institute, Phoenix, Arizona, Eric M. Reiman, MD; Kewei Chen, PhD; University of Pittsburgh, Pittsburgh, Pennsylvania, Chet Mathis, MD; University of California Berkeley, Berkeley, Susan Landau, PhD; Neuropathology Core Leaders: Washington University, St. Louis, Missouri, John C. Morris, MD; Nigel J. Cairns, PhD, FRCPath; Erin Franklin, MS, CCRP; Washington University, St. Louis, Minnesota, Lisa Taylor-Reinwald, BA, HTL, (ASCP)–past investigator; Biomarkers Core Leaders and Key Personnel: University of Pennsylvania School of Medicine, Philadelphia, Leslie M. Shaw, PhD; John Q. Trojanowki, MD, PhD; Virginia Lee, PhD, MBA; Magdalena Korecka, PhD, Michal Figurski, PhD; Informatics Core Leaders and Key Personnel: University of Southern California, Los Angeles (Core PI), Arthur W. Toga, PhD; University of Southern California, Los Angeles, Karen Crawford; Scott Neu, PhD; Genetics Core Leaders and Key Personnel: Indiana University, Bloomington, Andrew J. Saykin, PsyD; Tatiana M. Foroud, PhD; University of California Irvine, Irvine, Steven Potkin, MD; Indiana University, Bloomington, Li Shen, PhD, Kelley Faber, MS, CCRC, Sungeun Kim, PhD, Kwangsik Nho, PhD; Initial Concept Planning & Development: University of California San Francisco, San Francisco, Michael W. Weiner, MD; University of California San Diego, San Diego: Lean Thal, MD; Prevent Alzheimer’s Disease 2020: Zaven Khachaturian, PhD; Early Project Proposal Development: University of California San Diego, San Diego; Leon Thal, MD; National Institute on Aging, Baltimore, Maryland, Neil Buckholtz; University of California San Francisco, San Francisco, Michael W. Weiner, MD; Brown University, Providence, Rhode Island, Peter J. Snyder, PhD; National Institute of Mental Health, Bethesda, Maryland: William Potter, MD; Cornell University, Ithaca, New York, Steven Paul, MD; Johns Hopkins University, Baltimore, Maryland, Marilyn Albert, PhD; Richard Frank Consulting, New York, New York, Richard Frank, MD, PhD; Prevent Alzheimer’s Disease 2020, Zaven Khachaturian, PhD; NIA: National Institute on Aging, Baltimore, Maryland, John Hsiao, MD. Part B: Investigators By Site: Oregon Health & Science University, Portland: Joseph Quinn, MD, Lisa C. Silbert, MD, Betty Lind, BS, Jeffrey A. Kaye, MD, A.—Past Investigator, Raina Carter, BA—Past Investigator, Sara Dolen, BS—Past Investigator; University of Southern California, Los Angeles: Lon S. Schneider, MD, Sonia Pawluczyk, MD, Mauricio Becerra, BS, Liberty Teodoro, RN, Bryan M. Spann, DO, PhD—Past Investigator; University of California San Diego, San Diego: James Brewer, MD, PhD, Helen Vanderswag, RN, Adam Fleisher, MD—Past Investigator; University of Michigan, Ann Arbor: Jaimie Ziolkowski, MA, BS, TLLP, Judith L. Heidebrink, MD, MS, Joanne L. Lord, LPN, BA, CCRC—Past Investigator; Mayo Clinic, Rochester, Minnesota: Ronald Petersen, MD, PhD, Sara S. Mason, RN, Colleen S. Albers, RN, David Knopman, MD, Kris Johnson, RN—Past Investigator; Baylor College of Medicine, Houston, Texas: Javier Villanueva-Meyer, MD, Valory Pavlik, PhD, Nathaniel Pacini, MA, Ashley Lamb, MA, Joseph S. Kass, MD, LD, FAAN, Rachelle S. Doody, MD, PhD—Past Investigator, Victoria Shibley, MS—Past Investigator, Munir Chowdhury, MBBS, MS—Past Investigator, Susan Rountree, MD—Past Investigator, Mimi Dang, MD—Past Investigator; Columbia University Medical Center, New York, New York: Yaakov Stern, PhD, Lawrence S. Honig, MD, PhD, Karen L. Bell, MD, Randy Yeh, MD; Washington University, St. Louis, Missouri: Beau Ances, MD, PhD, MSc, John C. Morris, MD, David Winkfield, BS, Maria Carroll, RN, MSN, GCNS-BC, Angela Oliver, RN, BSN, MSG, Mary L. Creech, RN, MSW—Past Investigator, Mark A. Mintun, MD—Past Investigator, Stacy Schneider, APRN, BC, GNP—Past Investigator; University of Alabama–Birmingham, Birmingham: Daniel Marson, JD, PhD, David Geldmacher, MD, Marissa Natelson Love, MD, Randall Griffith, PhD, ABPP—Past Investigator, David Clark, MD—Past Investigator, John Brockington, MD—Past Investigator; Mount Sinai School of Medicine, New York, New York: Hillel Grossman, MD, Effie Mitsis, PhD—Past Investigator; Rush University Medical Center, Chicago, Illinois: Raj C. Shah, MD, Melissa Lamar, PhD Patricia Samuels; Wien Center, Miami Beach, Florida: Ranjan Duara, MD, Maria T. Greig-Custo, MD, Rosemarie Rodriguez, PhD; Johns Hopkins University, Baltimore, Maryland: Marilyn Albert, PhD, Chiadi Onyike, MD, Daniel D’Agostino II, BS, Stephanie Kielb, BS—Past Investigator; New York University, New York: Martin Sadowski, MD, PhD, Mohammed O. Sheikh, MD, Jamika Singleton-Garvin, CCRP, Anaztasia Ulysse, Mrunalini Gaikwad; Duke University Medical Center, Durham, North Carolina: P. Murali Doraiswamy, MBBS, FRCP, Jeffrey R. Petrella, MD, Olga James, MD, Salvador Borges-Neto, MD, Terence Z. Wong, MD—Past Investigator, Edward Coleman—Past Investigator; University of Pennsylvania, Philadelphia: Jason H. Karlawish, MD David A. Wolk, MD, Sanjeev Vaishnavi, MD, Christopher M. Clark, MD – Past Investigator, Steven E. Arnold, MD—Past Investigator; University of Kentucky, Lexington: Charles D. Smith, MD, Greg Jicha, MD, Peter Hardy, PhD, Riham El Khouli, MD, Elizabeth Oates, MD, Gary Conrad, MD; University of Pittsburgh, Pittsburgh, Pennsylvania: Oscar L. Lopez, MD, MaryAnn Oakley, MA, Donna M. Simpson, CRNP, MPH; University of Rochester Medical Center, Rochester, New York: Anton P. Porsteinsson, MD, Kim Martin, RN, Nancy Kowalksi, MS, RNC, Melanie Keltz, RN, Bonnie S. Goldstein, MS, NP – Past Investigator, Kelly M. Makino, BS—Past Investigator, M. Saleem Ismail, MD–Past Investigator, Connie Brand, RN—Past Investigator; University of California Irvine IMIND, Irvine: Gaby Thai, MD, Aimee Pierce, MD, Beatriz Yanez, RN, Elizabeth Sosa, PhD, Megan Witbracht, PhD; University of Texas Southwestern Medical School, Dallas: Kyle Womack, MD, Dana Mathews, MD, PhD, Mary Quiceno, MD; Emory University, Atlanta, Georgia: Allan I. Levey, MD, PhD, James J. Lah, MD, PhD, Janet S. Cellar, DNP, PMHCNS-BC; University of Kansas Medical Center, Kansas City: Jeffrey M. Burns, MD, Russell H. Swerdlow, MD, William M. Brooks, PhD; University of California, Los Angeles, Los Angeles: Ellen Woo, PhD, Daniel H. S. Silverman, MD, PhD, Edmond Teng, MD, PhD, Sarah Kremen, MD, Liana Apostolova, MD—Past Investigator, Kathleen Tingus, PhD—Past Investigator, Po H. Lu, PsyD—Past Investigator, George Bartzokis, MD—Past Investigator; Mayo Clinic, Jacksonville, Florida: Neill R Graff-Radford, MBBCH, FRCP (London), Francine Parfitt, MSH, CCRC, Kim Poki-Walker, BA; Indiana University, Bloomington: Martin R. Farlow, MD, Ann Marie Hake, MD, Brandy R. Matthews, MD–Past Investigator, Jared R. Brosch, MD; Scott Herring, RN, CCRC; Yale University School of Medicine, New Haven, Connecticut: Christopher H. van Dyck, MD, Richard E. Carson, PhD, Pradeep Varma, MD; McGill University Montreal–Jewish General Hospital, Montreal, Quebec, Canada: Howard Chertkow, MD, Howard Bergman, MD, Chris Hosein, MEd; Sunnybrook Health Sciences, Toronto, Ontario, Canada: Sandra Black, MD, FRCPC, Bojana Stefanovic, PhD, Chris (Chinthaka) Heyn, BSC, PhD, MD, FRCPC; UBC Clinic for Alzheimer Disease and Related Disorders, Vancouver, British Columbia, Canada: Ging-Yuek Robin Hsiung, MD, MHSc, FRCPC, Benita Mudge, BS, Vesna Sossi, PhD, Howard Feldman, MD, FRCPC—Past Investigator, Michele Assaly, MA—Past Investigator; Cognitive Neurology–St. Joseph's, London, Ontario, Canada: Elizabeth Finger, MD, Stephen Pasternack, MD, PhD, William Pavlosky, MD, Irina Rachinsky, MD—Past Investigator, Dick Drost, PhD—Past Investigator, Andrew Kertesz, MD—Past Investigator; Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, Ohio: Charles Bernick, MD, MPH, Donna Munic, PhD; Northwestern University, Chicago, Illinois: Marek-Marsel Mesulam, MD, Emily Rogalski, PhD, Kristine Lipowski, MA, Sandra Weintraub, PhD, Borna Bonakdarpour, MD, Diana Kerwin, MD–Past Investigator, Chuang-Kuo Wu, MD, PhD—Past Investigator, Nancy Johnson, PhD—Past Investigator; Premiere Research Institute (Palm Beach Neurology), West Palm Beach, Florida: Carl Sadowsky, MD, Teresa Villena, MD; Georgetown University Medical Center, Washington, DC: Raymond Scott Turner, MD, PhD, Kathleen Johnson, NP, Brigid Reynolds, NP; Brigham and Women's Hospital, Boston, Massachusetts: Reisa A. Sperling, MD, Keith A. Johnson, MD, Gad A. Marshall, MD; Stanford University, Palo Alto, California: Jerome Yesavage, MD, Joy L. Taylor, PhD, Steven Chao, MD, PhD, Barton Lane, MD—Past Investigator, Allyson Rosen, PhD— Past Investigator, Jared Tinklenberg, MD— Past Investigator; Banner Sun Health Research Institute, Phoenix, Arizona: Edward Zamrini, MD, Christine M. Belden, PsyD Sherye A. Sirrel, CCRC; Boston University, Boston, Massachusetts: Neil Kowall, MD, Ronald Killiany, PhD, Andrew E. Budson, MD, Alexander Norbash, MD—Past Investigator, Patricia Lynn Johnson, BA—Past Investigator; Howard University, Washington, DC: Thomas O. Obisesan, MD, MPH, Ntekim E. Oyonumo, MD, PhD, Joanne Allard, PhD, Olu Ogunlana, BPharm; Case Western Reserve University, Cleveland, Ohio: Alan Lerner, MD, Paula Ogrocki, PhD, Curtis Tatsuoka, PhD, Parianne Fatica, BA, CCRC; University of California Davis, Sacramento: Evan Fletcher, PhD, Pauline Maillard, PhD, John Olichney, MD, Charles DeCarli, MD, Owen Carmichael, PhD—Past Investigator; Neurological Care of CNY, Syracuse: Smita Kittur, MD—Past Investigator; Parkwood Institute, London, Ontario, Canada: Michael Borrie, MB ChB, T-Y Lee, PhD, Dr Rob Bartha, PhD; University of Wisconsin, Madison: Sterling Johnson, PhD, Sanjay Asthana, MD, Cynthia M. Carlsson, MD, MS; Banner Alzheimer's Institute, Phoenix, Arizona: Pierre Tariot, MD Anna Burke, MD Joel Hetelle, BS, Kathryn DeMarco, BS, Nadira Trncic, MD, PhD, CCRC—Past Investigator, Adam Fleisher, MD—Past Investigator, Stephanie Reeder, BA—Past Investigator; Dent Neurologic Institute, Amherst, New York: Vernice Bates, MD Horacio Capote, MD, Michelle Rainka, PharmD, CCRP; Ohio State University, Columbus: Douglas W. Scharre, MD Maria Kataki, MD, PhD, Rawan Tarawneh, MD; Albany Medical College, Albany, New York: Earl A. Zimmerman, MD, Dzintra Celmins, MD, David Hart, MD; Hartford Hospital, Olin Neuropsychiatry Research Center, Hartford, Connecticut: Godfrey D. Pearlson, MD, Karen Blank, MD, Karen Anderson, RN; Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire: Laura A. Flashman, PhD, Marc Seltzer, MD, Mary L. Hynes, RN, MPH, Robert B. Santulli, MD—Past Investigator; Wake Forest University Health Sciences, Winston-Salem, North Carolina: Kaycee M. Sink, MD, MAS, Mia Yang, MD, Akiva Mintz, MD, PhD; Rhode Island Hospital, Providence: Brian R. Ott, MD, Geoffrey Tremont, PhD,Lori A. Daiello, Pharm.D, ScM; Butler Hospital, Providence, Rhode Island: Courtney Bodge, PhD, Stephen Salloway, MD, MS, Paul Malloy, PhD, Stephen Correia, PhD, Athena Lee, PhD; University of California San Francisco, San Francisco: Howard J. Rosen, MD, Bruce L. Miller, MD, David Perry, MD; Medical University of South Carolina, Charleston: Jacobo Mintzer, MD, MBA, Kenneth Spicer, MD, PhD, David Bachman, MD; St. Joseph’s Health Care, London, Ontario, Canada: Elizabeth Finger, MD, Stephen Pasternak, MD, Irina Rachinsky, MD, John Rogers, MD, Andrew Kertesz, MD—Past Investigator, Dick Drost, MD—Past Investigator; Nathan Kline Institute, Orangeburg, New York: Nunzio Pomara, MD, Raymundo Hernando, MD, Antero Sarrael, MD; University of Iowa College of Medicine, Iowa City: Delwyn D. Miller, PharmD, MD, Karen Ekstam Smith, RN, Hristina Koleva, MD, Ki Won Nam, MD, Hyungsub Shim, MD, Susan K. Schultz, MD—Past Investigator; Cornell University, Ithaca, New York: Norman Relkin, MD, PhD, Gloria Chiang, MD, Michael Lin, MD, Lisa Ravdin, PhD: University of South Florida USF Health Byrd Alzheimer’s Institute, Miami: Amanda Smith, MD, Christi Leach, MD, Balebail Ashok Raj, MD—Past Investigator, Kristin Fargher, MD—Past Investigator. Department of Defense ADNI: Part A: Leadership and Infrastructure Principal Investigator, University of California, San Francisco, San Francisco: Michael W. Weiner, MD. ATRI PI and Director of Coordinating Center Clinical Core: University of Southern California, Los Angeles: Paul Aisen, MD; Executive Committee: University of California San Francisco, San Francisco: Michael Weiner, MD; University of Southern California, Los Angeles: Paul Aisen, MD; Mayo Clinic, Rochester, Minnesota: Ronald Petersen, MD, PhD; Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, Robert C. Green, MD, MPH; University of California Davis, Sacramento, Danielle Harvey, PhD; Mayo Clinic, Rochester, Minnesota, Clifford R. Jack, Jr, MD; University of CaliforniaBerkeley, Berkeley, William Jagust, MD; Washington University, St. Louis, Missouri, John C. Morris, MD; Indiana University, Bloomington, Andrew J. Saykin, PsyD; Perelman School of Medicine, University Pennsylvania, Philadelphia, Leslie M. Shaw, PhD; University of Southern California, Los Angeles, Arthur W. Toga, PhD; Perelman School of Medicine, University of Pennsylvania, Philadelphia: John Q. Trojanowki, MD, PhD; Psychological Evaluation/Posttraumatic Stress Disorder Core: University of California San Francisco, San Francisco: Thomas Neylan, MD; Traumatic Brain Injury Core: Rehabilitation Institute of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois: Jordan Grafman, PhD; Data and Publication Committee: Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Chair): Robert C. Green, MD, MPH; Resource Allocation Review Committee: University of Washington, St Louis, Missouri (Chair), Tom Montine, MD, PhD; Clinical Core Leaders: Core PI:, Michael Weiner, MD; Mayo Clinic, Rochester, Minnesota (Core PI): Ronald Petersen, MD, PhD; University of Southern California, Los Angeles, Paul Aisen, MD; Clinical Informatics and Operations: University of Southern California, Los Angeles, Gustavo Jimenez, MBS, Michael Donohue, PhD, Devon Gessert, BS, Kelly Harless, BA, Jennifer Salazar, MBS, Yuliana Cabrera, BS, Sarah Walter, MSc, Lindsey Hergesheimen, BS; San Francisco Veterans Affairs Medical Center, San Francisco, California: University of California San Francisco, San Francisco, Thomas Neylan, MD, Jacqueline Hayes, Shannon Finley; Biostatistics Core Leaders and Key Personnel: University of California Davis, Sacramento (Core PI): Danielle Harvey, PhD; University of California San Diego, San Diego, Michael Donohue, PhD; Magnetic Resonance Imaging Core Leaders and Key Personnel: Mayo Clinic, Rochester, Minnesota (Core PI): Clifford R. Jack, Jr, MD; Mayo Clinic, Rochester, Minnesota, Matthew Bernstein, PhD; Mayo Clinic, Rochester, Minnesota, Bret Borowski, RT; Jeff Gunter, PhD, Matt Senjem, MS, Kejal Kantarci, Chad Ward; Positron Emission Tomography Core Leaders and Key Personnel: University of California Berkeley, Berkeley (Core PI): William Jagust, MD; University of Michigan, Ann Arbor, Robert A. Koeppe, PhD; University of Utah, Salt Lake City, Norm Foster, MD; Banner Alzheimer’s Institute, Phoenix, Arizona, Eric M. Reiman, MD, Kewei Chen, PhD; University of California Berkeley, Berkeley, Susan Landau, PhD; Neuropathology Core Leaders: Washington University, St Louis, Missouri, John C. Morris, MD; Nigel J. Cairns, PhD, FRCPath, Erin Householder, MS; Biomarkers Core Leaders and Key Personnel: Perelman School of Medicine, University of Pennsylvania, Philadelphia, Leslie M. Shaw, PhD, John Q. Trojanowki, MD, PhD, Lee, PhD, MBA; Magdalena Korecka, Michal Figurski, PhD; Informatics Core Leaders and Key Personnel: University of Southern California, Los Angeles (Core PI): Arthur W. Toga, PhD; University of Southern California, Los Angeles, Karen Crawford, Scott Neu, PhD; Genetics Core Leaders and Key Personnel: Indiana University, Bloomington, Andrew J. Saykin, PsyD, Tatiana M. Foroud, PhD; University of California Irvine, Irvine, Steven Potkin, MD, UC; Indiana University, Bloomington, Li Shen, PhD, Kelley Faber, MS, CCRC, Sungeun Kim, PhD, Kwangsik Nho, PhD; Initial Concept Planning and Development: University of California San Francisco, San Francisco, Michael W. Weiner, MD; Department of Defense (retired), Karl Friedl. Part B: Investigators By Site: University of Southern California, Los Angeles: Lon S. Schneider, MD, MS, Sonia Pawluczyk, MD, Mauricio Becerra; University of California San Diego, San Diego: James Brewer, MD, PhD, Helen Vanderswag, RN; Columbia University Medical Center, New York, New York: Yaakov Stern, PhD, Lawrence S. Honig, MD, PhD, Karen L. Bell, MD; Rush University Medical Center, Chicago, Illinois: Debra Fleischman, PhD, Konstantinos Arfanakis, PhD, Raj C. Shah, MD; Wien Center, Miami Beach, Florida: PI: Dr Ranjan Duara MD; Co-PI: Dr Daniel Varon MD; HP Coordinator: Maria T Greig; Duke University Medical Center, Durham, North Carolina: P. Murali Doraiswamy, MBBS, Jeffrey R. Petrella, MD, Olga James, MD; University of Rochester Medical Center, Rochester, New York: (director): Anton P. Porsteinsson, MD; (coordinator); Bonnie Goldstein, MS, NP; Kimberly S. Martin, RN; University of California Irvine, Irvine: Steven G. Potkin, MD, Adrian Preda, MD, Dana Nguyen, PhD; Medical University South Carolina, Charleston: Jacobo Mintzer, MD, MBA, Dino Massoglia, MD, PhD, Olga Brawman-Mintzer, MD; Premiere Research Institute (Palm Beach Neurology), West Palm Beach, Florida: Carl Sadowsky, MD, Walter Martinez, MD, Teresa Villena, MD; University of California San Francisco, San Francisco: William Jagust, MD, Susan Landau, PhD, Howard Rosen, MD, David Perry; Georgetown University Medical Center, Washington, DC: Raymond Scott Turner, MD, PhD, Kelly Behan, Brigid Reynolds, NP; Brigham and Women's Hospital, Boston, Massachusetts: Reisa A. Sperling, MD, Keith A. Johnson, MD, Gad Marshall, MD; Banner Sun Health Research Institute, Phoenix, Arizona: Marwan N. Sabbagh, MD, Sandra A. Jacobson, MD, Sherye A. Sirrel, MS, CCRC; Howard University, Washington, DC: Thomas O. Obisesan, MD, MPH, Saba Wolday, MSc, Joanne Allard, PhD; University of Wisconsin, Madison: Sterling C. Johnson, PhD, J. Jay Fruehling, MA, Sandra Harding, MS; University of Washington, Seattle: Elaine R. Peskind, MD, Eric C. Petrie, MD, MS, Gail Li, MD, PhD; Stanford University, Palo Alto, California: Jerome A. Yesavage, MD, Joy L. Taylor, PhD, Ansgar J. Furst, PhD, Steven Chao, MD; Cornell University, Ithaca, New York: Norman Relkin, MD, PhD; Gloria Chiang, MD; Lisa Ravdin, PhD. ADNI Depression: Part A: Leadership and Infrastructure Principal Investigator, University of California San Francisco, San Francisco. Scott Mackin, PhD; ATRI PI and Director of Coordinating Center Clinical Core: University of Southern California, Los Angeles: Paul Aisen, MD, Rema Raman, PhD; Executive Committee: University of California San Francisco, San Francisco, Scott Mackin, PhD, Michael Weiner, MD; University of Southern California, Los Angeles, Paul Aisen, MD, Rema Raman, PhD; Mayo Clinic, Rochester: Clifford R. Jack, Jr, MD; University of California Berkeley, Berkeley, Susan Landau, PhD; Indiana University, Bloomington, Andrew J. Saykin, PsyD; University of Southern California, Los Angeles, Arthur W. Toga, PhD; University of California Davis, Sacramento, Charles DeCarli, MD; University of Michigan, Ann Arbor, Robert A. Koeppe, PhD; Data and Publication Committee (DPC): Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Chair), Robert C. Green, MD, MPH; Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Director), Erin Drake, MA; Clinical Core Leaders: Core PI, Michael Weiner MD; University of Southern California, Los Angeles, Paul Aisen, MD, Rema Raman, PhD, Mike Donohue, PhD; Clinical Informatics, Operations and Regulatory Affairs: University of Southern California, Los Angeles, Gustavo Jimenez, MBS, Devon Gessert, BS, Kelly Harless, BA, Jennifer Salazar, MBS, Yuliana Cabrera, BS, Sarah Walter, MSc, Lindsey Hergesheimer, BS, Elizabeth Shaffer, BS; Psychiatry Site Leaders and Key Personnel: University of California San Francisco, San Francisco, Scott Mackin, PhD, Craig Nelson, MD, David Bickford, BA; University of Pittsburgh, Pittsburgh, Pennsylvania, Meryl Butters, PhD, Michelle Zmuda, MA; MRI Core Leaders and Key Personnel: Mayo Clinic, Rochester, Minnesota (Core PI), Clifford R. Jack, Jr, MD; Mayo Clinic, Rochester, Minnesota, Matthew Bernstein, PhD; Bret Borowski, RT, Jeff Gunter, PhD, Matt Senjem, MS, Kejal Kantarci, MD, Chad Ward, BA, Denise Reyes, BS; PET Core Leaders and Key Personnel: University of Michigan, Ann Arbor, Robert A. Koeppe, PhD; University of California Berkeley, Berkeley, Susan Landau, PhD; Informatics Core Leaders and Key Personnel: University of Southern California, Los Angeles, (Core PI), Arthur W. Toga, PhD; University of Southern California, Los Angeles, Karen Crawford, Scott Neu, PhD; Genetics Core Leaders and Key Personnel: Indiana University, Bloomington, Andrew J. Saykin, PsyD, Tatiana M. Foroud, PhD, Kelley M. Faber, MS, CCRC, Kwangsik Nho, PhD, Kelly N. Nudelman. Part B: Investigators By Site: University of California San Francisco, San Francisco: Scott Mackin, PhD, Howard Rosen, MD, Craig Nelson, MD, David Bickford, BA, Yiu Ho Au, BA, Kelly Scherer, BS, Daniel Catalinotto, BA, Samuel Stark, BA, Elise Ong, BA, Dariella Fernandez, BA; University of Pittsburgh, Pittsburgh, Pennsylvania: Meryl Butters, PhD, Michelle Zmuda, MA, Oscar L. Lopez, MD, MaryAnn Oakley, MA, Donna M. Simpson, CRNP, MPH.

^✉

Corresponding author.

PMCID: PMC7573795 PMID: 33074304

Key Points

Question

What is the association between β-amyloid on positron emission tomography and subsequent flortaucipir accumulation in persons without cognitive impairment?

Findings

This cohort study of 167 persons aged 65 to 85 years from a population-based study that used ¹¹C-Pittsburgh compound B Aβ and ¹⁸F-flortaucipir tau positron emission tomography serial imaging found that individuals with the highest levels of β-amyloid had much greater accumulation of flortaucipir on a subsequent positron emission tomography scan. Results were partially replicated in an Alzheimer’s Disease Neuroimaging Initiative sample.

Meaning

Substantial flortaucipir accumulation occurs when β-amyloid levels are 68 centiloid or more, while at lower β-amyloid levels, there is little flortaucipir accumulation; clinical trials intending to use a tau positron emission tomography tracer as an outcome measure should recruit persons with high β-amyloid levels.

This cohort study investigates the association between β-amyloid in positron emission tomography and subsequent tau accumulation in persons who were cognitively unimpaired, to gain insight into the temporal associations between β-amyloid and tau accumulation.

Abstract

Importance

Tau accumulation in Alzheimer disease (AD) is closely associated with cognitive impairment. Quantitating tau accumulation by positron emission tomography (PET) will be a useful outcome measure for future clinical trials in the AD spectrum.

Objective

To investigate the association of β-amyloid (Aβ) on PET with subsequent tau accumulation on PET in persons who were cognitively unimpaired (CU) to gain insight into temporal associations between Aβ and tau accumulation and inform clinical trial design.

Design, Setting, and Participants

This cohort study included individuals aged 65 to 85 years who were CU and had participated in the Mayo Clinic Study of Aging, with serial cognitive assessments, serial magnetic resonance imaging, ¹¹C-Pittsburgh compound B (Aβ) PET scans, and ¹⁸F-flortaucipir PET scans, collected from May 2015 to March 2020. Persons were excluded if they lacked follow-up PET scans. A similarly evaluated CU group from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were also studied. These data were collected from September 2015 to March 2020.

Exposures

Participants were stratified by index Aβ levels on PET into low Aβ (≤8 centiloid [CL]), subthreshold Aβ (9-21 CL), suprathreshold Aβ (22-67 CL), and high Aβ (≥68 CL).

Main Outcomes and Measures

Changes over a mean of 2.7 (range, 1.1-4.1) years in flortaucipir PET in entorhinal, inferior temporal, and lateral parietal regions of interest and an AD meta–region of interest (ROI).

Results

A total of 167 people were included (mean age, 74 [range, 65-85] years; 75 women [44.9%]); 101 individuals were excluded lacking follow-up, and 114 individuals from the ADNI were also studied (mean [SD] age, 74.14 [5.29] years; 64 women [56.1%]). In the Mayo Clinic Study of Aging, longitudinal flortaucipir accumulation rates in the high Aβ group were greater than the suprathreshold, subthreshold, and low Aβ groups in the entorhinal ROI (suprathreshold, 0.025 [95% CI, 0.013-0.037] standardized uptake value ratio [SUVR] units; subthreshold, 0.026 [95% CI, 0.014-0.037] SUVR units; low Aβ, 0.034 [95% CI, 0.02-0.049] SUVR units), inferior temporal ROI (suprathreshold, 0.025 [95% CI, 0.014-0.035] SUVR units; subthreshold, 0.027 [95% CI, 0.017-0.037] SUVR units; low Aβ, 0.035 [95% CI, 0.022-0.047] SUVR units), and the AD meta-ROI (suprathreshold, 0.023 [95% CI, 0.013-0.032] SUVR units; subthreshold, 0.025 [95% CI, 0.016-0.034] SUVR units; low Aβ, 0.032 [95% CI, 0.021-0.043] SUVR units) (all P < .001). Flortaucipir accumulation rates in the subthreshold and suprathreshold Aβ groups in temporal regions were nonsignificantly elevated compared with the low Aβ group. In the ADNI cohort, the variance was larger than in the Mayo Clinic Study of Aging but point estimates for annualized flortaucipir accumulation in the inferior temporal ROI were very similar. An estimated 216 participants who were CU per group with PET Aβ of 68 CL or more would be needed to detect a 25% annualized reduction in flortaucipir accumulation rate in the AD meta-ROI with 80% power.

Conclusions and Relevance

Substantial flortaucipir accumulation in temporal regions is greatest in persons aged 65 to 85 years who were CU and had high initial Aβ PET levels, compared with those with lower Aβ levels. Recruiting persons who were CU and exhibiting Aβ of 68 CL or more on an index Aβ PET is a feasible strategy to recruit for clinical trials in which a change in tau PET signal is an outcome measure.

Introduction

Tau accumulation as measured by positron emission tomography (PET) is a potential surrogate outcome measure for clinical trials in the Alzheimer disease (AD) spectrum, because it is closely linked to the appearance of cognitive decline.^1,2,3,4 Putatively elevated levels of β-amyloid (Aβ) on PET are associated with elevated tau and tau accumulation.^1,5,6,7 To our knowledge, it is not known whether the risk for tau accumulation is uniform in all individuals considered amyloid positive or whether risk rises at higher suprathreshold Aβ levels. In persons who are cognitively unimpaired (CU) and nominally Aβ negative, there is evidence for^7,8 and against^1,5,6 whether tau accumulates in association with Aβ levels. We aimed to determine what level of Aβ on PET would be most likely to identify individuals who are CU who subsequently accumulate tau on PET imaging. We explored this question in the cohort of individuals who are CU and aged 65 to 85 years in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who have had serial cognitive assessments, serial magnetic resonance imaging (MRI), and Aβ and tau PET scans. Our goals were to quantitate the association of Aβ with tau accumulation and use our findings to inform the design of secondary prevention trials for AD.

Methods

MCSA Participants

We included persons without cognitive impairment who were aged 65 to 85 years (Table 1). All participants in this analysis were adjudicated as CU at the time of the index visit via a consensus conference process. The evaluation process included a physician examination, a neuropsychological assessment (eMethods 1 in the Supplement), and an interview of the participant and a designated informant by a study coordinator, as previously described.^9,10 All raters were blinded to biomarker status. The principal aim of this analysis was to evaluate changes in flortaucipir (¹⁸F-AV1451) PET signal in association with an index Aβ (¹¹C-Pittsburgh compound B [PIB]) PET scan. Therefore, inclusion in the study cohort required that participants have an Aβ PET and 2 flortaucipir PET scans as well as MRI scans in the same time frame as the PET scans. Since the MCSA commenced PET scanning with PIB in 2008 and flortaucipir in 2015, most participants had completed several Aβ PET and MRI scans as well as several cognitive assessments prior to the first flortaucipir PET scan. We designated the first concurrent pair of Aβ and flortaucipir PET scans as the index scans.

Table 1. Characteristics of Participants in the Mayo Clinic Study on Aging, According to Index ¹¹C-Pittsburgh Compound B Positron Emission Tomography (PET) Global Standardized Uptake Value Ratio (SUVR) Units.

Characteristic	Participants with serial PET scans by β amyloid centiloid group				Participants with serial PET scans	Initial PET scan only^a
Characteristic	≤8	9-21	22-67	≥68	Participants with serial PET scans	Initial PET scan only^a
No. of participants	24	70	42	31	167	101
Age, y
Mean (SD)	71.6 (6.1)	72.7 (5.7)	75.6 (5.9)	77.2 (5.4)	74.1 (6.0)	73.8 (6.3)
Range	65.2-82.2	65.2-85.2	65.6-84.5	67.4-85.6	65.0-85.0	65.0-85.0
Female, No. (%)	6 (25)	35 (50)	20 (48)	14 (45)	75 (45)	48 (48)
Education, y
Mean (SD)	15 (2)	15 (2)	15 (2)	15 (3)	15 (2)	15 (2)
Range	12-20	12-20	12-19	10-19	10-20	6-20
Carrying APOE ε4, No. (%)	5 (21)	13 (19)	14 (33)	15 (48)	47 (28)	24 (24)
PIB SUVR
Mean (SD)	1.28 (0.05)	1.39 (0.04)	1.63 (0.14)	2.38 (0.31)	1.62 (0.41)	1.59 (0.40)
Range	1.19-1.33	1.33-1.46	1.48-2.00	2.01-3.22	1.19-3.22	1.25-3.25
Global z, adjusted^b
Mean (SD)	0.29 (0.77)	0.43 (0.73)	0.12 (0.76)	–0.17 (1.16)	0.22 (0.85)	0.19 (0.84)
Range	–1.94 to 1.42	–1.23 to 1.99	–1.78 to 1.75	–3.25 to 1.35	–3.25 to 1.99	–1.56 to 1.95
Memory z, adjusted
Mean (SD)	–0.03 (1.05)	0.28 (0.90)	0.02 (0.81)	–0.54 (1.50)	0.02 (1.07)	0.11 (0.95)
Range	–2.15 to 1.37	–1.66 to 2.26	–1.80 to 1.51	–3.20 to 2.09	–3.20 to 2.26	–2.45 to 2.19
Hippocampal volume (unadjusted), cm³^c
Mean (SD)	7.53 (0.65)	7.42 (0.65)	7.36 (0.87)	7.09 (0.69)	7.36 (0.73)	7.30 (0.71)
Range	6.68-8.68	5.51-9.00	5.39-9.08	5.99-8.92	5.39-9.08	5.60-8.98
Index flortaucipir values
AD–meta region of interest
Mean (SD)	1.16 (0.10)	1.19 (0.07)	1.21 (0.08)	1.29 (0.14)	1.21 (0.10)	1.20 (0.10)
Range	0.94-1.32	1.01-1.39	1.07-1.43	1.10-1.75	0.94-1.75	0.99-1.57
Entorhinal region
Mean (SD)	1.07 (0.11)	1.09 (0.10)	1.11 (0.09)	1.35 (0.26)	1.14 (0.17)	1.11 (0.15)
Range	0.88-1.27	0.89-1.43	0.94-1.35	1.03-2.11	0.88-2.11	0.87-1.82
Inferior temporal region
Mean (SD)	1.19 (0.09)	1.23 (0.08)	1.25 (0.08)	1.34 (0.17)	1.25 (0.11)	1.24 (0.10)
Range	1.00-1.35	1.06-1.44	1.10-1.46	1.11-1.99	1.00-1.99	1.01-1.62
Lateral parietal region
Mean (SD)	1.11 (0.10)	1.16 (0.07)	1.17 (0.08)	1.21 (0.10)	1.16 (0.09)	1.16 (0.09)
Range	0.89-1.27	0.97-1.36	0.97-1.37	1.08-1.53	0.89-1.53	0.90-1.42

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Abbreviations: AD, Alzheimer disease; APOE, apolipoprotein E; PIB, ¹¹C-Pittsburgh compound B.

^{^a}

There were no differences between those with serial PET scans and those eligible for but who did not have serial β amyloid and tau PET scans.

^{^b}

Differences among groups from a linear model analysis of variance with learning-corrected z scores adjusting for age, sex, and education; the global z score at index visit was an F₃ of 2.68 (P = .05); the memory z score at index visit, an F₃ of 3.05 (P = .03).

^{^c}

Differences among groups in index hippocampal volume, adjusted for age, sex, education, and total intracranial volume (F₃ = 1.87; P = .14), or annual change in hippocampal volume (F₃ = 5.37; P = .002). eMethods 3 in the Supplement presents hippocampal volume measurement methods.

Standard Protocol Approvals and Patient Consent

The MCSA was approved by the Mayo Clinic and Olmsted Medical Center institutional review boards. All participants provided written consent in accordance with the Mayo Clinic Foundation and Olmsted Medical Center institutional review boards. The ADNI study was approved by the institutional review boards of all of the participating institutions. Informed written consent was obtained from all participants at each site. All potential conflicts of interest and sources of funding are disclosed.

MCSA Imaging Evaluations

A detailed description of the MRI and PET imaging procedures used for this analysis is presented in eMethods 2 and eFigure 1 in the Supplement. In virtually all persons, the MRI and PET scans were performed within 1 week of each other. Data were collected from May 2015 to March 2020.

PET Acquisition

The PET scans were performed as previously described.^11,12,13 Tau PET scans were performed using ¹⁸F-flortaucipir, and β-amyloid PET scans were performed using ¹¹C-PIB on PET or CT scanners (GE or Siemens).^5,14,15,16 An automated image processing pipeline for PET image analysis included rigid registration of the PET volumes to each participant's own high-resolution, T1-weighted MRI for the segmentation of gray and white matter. Regions of interest (ROIs) were defined using the Mayo Clinic Adult Lifespan Template (https://www.nitrc.org/projects/mcalt/), masked to include only the voxels labeled primarily as gray or white matter.

Amyloid PET

An injection of approximately 628 MBq (range, 385-723 MBq) of ¹¹C-PIB was administered, followed by a 40-minute uptake period and a 20-minute PIB scan of four 5-minute dynamic frames. Regional PIB uptake was defined as the median uptake across all voxels in an ROI. We also defined a global PIB standardized uptake value ratio (SUVR) in the bilateral parietal (including posterior cingulate and precuneus), orbitofrontal, prefrontal, temporal, and anterior cingulate regions, referenced to the right and left cerebellar crus gray matter. No partial-volume correction was used. This meta-ROI was based on our prior work.¹⁷ We will refer to ¹¹C-PIB binding as Aβ PET.

Flortaucipir PET

As previously described,^11,12,13 an intravenous bolus injection of ¹⁸F-flortaucipir of approximately 370 MBq (range, 333-407 MBq) was administered and followed by an 80-minute uptake period and a 20-minute scan consisting of four 5-minute frames. No partial-volume correction was used. The reference region was the gray matter of the cerebellar crus. Comparison analyses using an eroded white matter reference region and partial-volume correction found no substantive differences in outcomes (eFigure 2 in the Supplement).

Tau PET ROIs

Initial and serial flortaucipir SUVRs were sampled in several individual regions in which early accumulation of tau^5,14 and associations with cognition^12,18 are observed, the entorhinal cortex and the inferior temporal cortex, and a region in which tau accumulation tends to occur later, the lateral parietal cortex. We also examined an AD meta-ROI constructed by using a voxel number–weighted mean of median uptakes in the Mayo Clinic Adult Lifespan Template atlas–based regions of the inferior temporal, fusiform, amygdala, entorhinal cortex, parahippocampal, and midtemporal gyrus.

Participant Classification by Aβ Status

Our principal aim was to use stratified PIB PET Aβ levels to prognosticate subsequent flortaucipir accumulation. While a single cut point defining Aβ positivity or elevation is universally used, a subdivision of the nonelevated and elevated ranges has not been performed previously, to our knowledge. Based on the association between an index PIB PET Aβ level and subsequent annualized Aβ accumulation rates (Figure 1), we developed 3 cut points that divided the initial PIB PET Aβ range into 4 levels. The inflection point where Aβ begins to accumulate is at 8 centiloid (CL), corresponding to the boundary between a Thal amyloid stage of 0 to 1.¹⁹ The cut point of 22 CL on PIB PET scan is the traditional Aβ cut point for so-called amyloid positivity. It represents the level at which Aβ reliably begins to accumulate.¹⁵ In clinicopathological analyses,^20,21 22 CL corresponds to an Aβ burden between Thal stages 1 and 2¹⁹ and is widely used to indicate elevated Aβ.^22,23,24,25 A PIB of 68 CL or more is the point at which Aβ acceleration equals 0 and corresponds to a Thal amyloid stage between 2 and 3. Participants were thus grouped into a low Aβ group (SUVR, 1.0-1.32; ≤8 CL) representing no Aβ accumulation; a subthreshold Aβ group (PIB SUVR, 1.33-1.47; 9-21 CL), which includes some individuals beginning to accumulate Aβ; a suprathreshold Aβ group (SUVR, 1.48-2.0; 22-67 CL), which includes individuals with elevated Aβ levels and positive accumulation rates; and a high Aβ group (PIB SUVR, >2.0; ≥68 CL), representing the individuals with high levels of Aβ and decelerating rates of accumulation.

Figure 1. — On the x-axis is global ¹¹C-Pittsburgh compound B (PIB) positron emission tomography (PET) results expressed in centiloids (CL) and also shown in standardized uptake value ratio (SUVR) units, and on the y-axis is the annualized change in global ¹¹C-Pittsburgh compound B (PIB) PET SUVR. A nonparametric smoother used to depict the association between the Aβ value from index scan and annualized change is shown in black. A value of 1.33 (8 CL) corresponds to the point at which accumulation of Aβ begins (black vertical line). A value of 1.48 (22 CL) on the x-axis is the point where the upper bound of the 50% prediction interval at 1.33 intersects with the curve when projected rightward, and this is interpreted as a more conservative or reliable estimate of where Aβ accumulation begins (blue vertical line). A value of 2.0 (68 CL) corresponds to the point at which the rate of accumulation of Aβ becomes 0 (orange vertical line). This Figure represents an update using more data and an updated processing pipeline from Jack et al¹⁵ and includes data from 849 participants in the Mayo Clinic Study of Aging (756 who were cognitively unimpaired, 90 with mild cognitive impairment, and 3 with dementia) with serial PIB PET examinations, including individuals in this analysis.

ADNI Participants

Persons who were CU were selected from the ADNI-2 and ADNI-3 cohorts based on the availability of serial flortaucipir PET scanning. Data used in the preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu/). Further details are in eMethods 4 in the Supplement.

ADNI Imaging Evaluations

Among the participants in the ADNI who were CU and between ages 65 and 85 years, most had florbetapir PET scans and a minority had florbetaben amyloid PET scans. We combined the 2 amyloid PET tracer groups using CL scaling. The distribution of amyloid levels in the ADNI cohort broken down into the 4 Aβ CL strata used in the MCSA resulted in cell sizes that approximated the MCSA (eTable 2 in the Supplement). Because of the differences in the anatomic atlas between the MCSA and the ADNI, only entorhinal cortex and inferior temporal ROIs were reasonably comparable between the 2 cohorts. These data were collected from September 2015 to March 2020.

Statistics

We used the same statistical approach for MCSA and ADNI analyses of serial tau PET. To compare initial levels of flortaucipir PET between groups with low, subthreshold, suprathreshold, and high Aβ, we fit linear regression models adjusted for age, sex, education years, and index Aβ group. Because of skewness in the flortaucipir PET measures, a log transformation of the SUVR was applied, which allowed estimated group differences to be interpreted as approximate mean percentage differences. Linear regression models were also fit for memory z scores, global z scores, and hippocampal volume.

We assessed the association between Aβ groups and annualized change on flortaucipir PET in each of the 4 tau ROIs on Aβ PET using a 2-stage approach. First, per-participant linear regressions were fit between the response of interest and the years since the index visit. We extracted the per-participant slopes, which can be interpreted as annualized changes. Next, linear regression models were fit on annualized changes of each response and adjusted for age, sex, education, and index Aβ group. Pairwise mean group differences and 95% CIs were estimated.

We computed sample sizes from the MCSA data needed to detect a 25% annualized reduction in flortaucipir accumulation. Our estimates did not account for attrition. We performed a bootstrap procedure using 2000 iterations to estimate the 95% CI of the sample sizes.

The nominal level of significance was sets at P < .05, 2 tailed. All statistical analyses were performed on R statistical software, version 3.6 (R Foundation for Statistical Computing).

Results

Table 1 presents demographic and imaging data from the index visit for the 167 participants in the MCSA (mean age, 74 [range, 65-85] years; 75 women [44.9%]) with serial flortaucipir PET scans, broken down by index Aβ PET range. Most participants (140 [83.8%]) had 2 flortaucipir PET scans; the remainder had 3 scans. The mean interval between first and last flortaucipir scans in the MCSA group was 2.7 (SD, 0.86; range, 1.1-4.1) years. Participants who were eligible for serial scanning but had not yet undergone a second flortaucipir PET scan did not differ from those in the longitudinal cohort. Cognitive test scores (eTable 1 in the Supplement), longitudinal changes in global Aβ (eFigure 3 in the Supplement), and the index visit regional flortaucipir SUVR values (eFigure 4 and eTables 2 and 3 in the Supplement) are described in the Supplement.

eTable 4 in the Supplement presents demographic data and baseline imaging data for the 114 participants in the ADNI who were CU (mean [SD] age, 74.14 [5.29] years; 64 women [56.1%]). Age and sex distributions were generally similar between the MCSA and the ADNI. Among the 114 participants in the ADNI, 84 had florbetapir PET scans. Thirty had florbetaben amyloid PET scans. The interval between first and last flortaucipir scans in the ADNI was 1.7 (range, 0.7-3.7) years.

Longitudinal Changes in Flortaucipir PET Signal in the MCSA

eFigure 5 in the Supplement shows a scatterplot of the association between global Aβ on the index PIB PET scan and annualized changes in flortaucipir in all 4 regions. The association was flat until about 50 CL was present, rose linearly in the range of 50 to 100 CL, and flattened out at a level of more than approximately 100 CL, except for in the entorhinal cortex, which had a curve that continued to rise after 100 CL.

Figure 2A depicts flortaucipir accumulation by Aβ group and demonstrates the contrasts between groups in Figure 2B. eTables 5 and 6 in the Supplement present the data depicted in Figure 2A and Figure 2B. In adjusted analyses using multivariate linear regression, the high Aβ group showed a statistically significantly increased mean accumulation of flortaucipir at the P < .001 level compared with Aβ groups in all 3 temporal ROIs (high Aβ vs suprathreshold Aβ groups: AD meta–ROI, mean, 0.0226 [95% CI, 0.0132-0.0320]; entorhinal ROI, mean, 0.025 [95% CI, 0.0127-0.0372]; inferior temporal ROI, mean, 0.0247 [95% CI, 0.0141-0.0354]). There were no between-group differences in flortaucipir accumulation among any of the lower Aβ groups in any of the temporal ROIs. The between–Aβ group differences in lateral parietal ROIs were slightly different, in that the low Aβ group differed from the subthreshold group (mean, 0.0129 [95% CI, 0.0030-0.0227]; P = .01), and the high Aβ group differed from the suprathreshold group (mean, 0.0138 [95% CI, 0.0049-0.0227]; P = .003) (eTable 6 in the Supplement).

In the inferior temporal ROI, the high Aβ group showed an annualized mean (SD) SUVR increase of 0.03 (0.033) in flortaucipir, starting from a mean SUVR of 1.34. In contrast, in the same ROI, flortaucipir in the suprathreshold Aβ group, starting from a lower initial value (a mean SUVR of 1.25) showed a mean (SD) increase of only 0.0047 (0.022) SUVR units, about 20% of the magnitude seen in the high Aβ group.

Longitudinal Changes in Flortaucipir PET Signal in the ADNI Cohort

The ADNI cohort had a similar pattern to the point estimates of annual increases in flortaucipir across Aβ groups in inferior temporal compared with the MCSA cohort, but there were no differences in flortaucipir accumulation between suprathreshold and high Aβ groups in the ADNI compared with the MCSA. In the ADNI, there were no differences in flortaucipir accumulation in the entorhinal cortex by Aβ group, in contrast with the MCSA (Table 2; eFigure 6 and eTable 7 in the Supplement). Baseline flortaucipir differences across Aβ groups in the ADNI cohort showed a similar pattern compared with the MCSA cohort in both regions (eFigure 7 and eTable 8 in the Supplement).

Table 2. Comparison of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Mayo Clinic Study of Aging (MCSA) Baseline and Annual Change Values in Entorhinal Cortex (ERC) and Inferior Temporal (IT) Regions of Interest.

Outcome	Mean (SD)
Outcome	<8 CL	9-21 CL	22-67 CL	>68 CL
Participants, No. (%)
ADNI	32 (28)	18 (16)	36(32)	28 (25)
MCSA	24 (14)	70 (42)	42 (25)	31 (19)
Baseline flortaucipir SUVR
ERC
ADNI	1.101 (0.088)	1.167 (0.108)	1.167 (0.112)	1.261 (0.180)
MCSA	1.068 (0.115)	1.088 (0.096)	1.112 (0.092)	1.346 (0.263)
IT
ADNI	1.184 (0.078)	1.222 (0.096)	1.251 (0.155)	1.323 (0.181)
MCSA	1.193 (0.094)	1.228 (0.078)	1.246 (0.078)	1.335 (0.172)
Annual change in flortaucipir SUVR
ERC
ADNI	0.015 (0.057)	–0.011 (0.076)	0.008 (0.037)	0.014 (0.045)
MCSA	–0.001 (0.029)	0.007 (0.019)	0.007 (0.021)	0.032 (0.039)
IT
ADNI	0.019 (0.057)	–0.021 (0.069)	0.013 (0.029)	0.030 (0.051)
MCSA	–0.004 (0.022)	0.004 (0.016)	0.005 (0.022)	0.030 (0.033)

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Abbreviation: CL, centiloid; SUVR, standardized uptake value ratio.

Sample Size Estimations

Using MCSA point estimates and variances, the number of participants who were 65 to 85 years old and CU per arm in a 2-arm placebo-controlled trial needed to detect, with 80% power, a 25% annualized reduction in flortaucipir accumulation was lowest for the group with PET Aβ of 68 CL or more. If the AD meta-ROI were the designated ROI, we estimated that 219 persons who were CU per arm with Aβ on PET scans of 68 CL or more would be required (95% CI, 101-619 CL). Numbers needed for a trial in persons with Aβ levels in the range of 22 to 67 CL were nearly 10 times larger (Table 3).

Table 3. Sample Size Estimates per Group in a 2-Arm Trial for 25% Reduction in Annual Change in Flortaucipir With 80% Power, Assuming No Attrition, in Persons Who Are Cognitively Unimpaired and 65 to 85 Years Old, Based on the Mayo Clinic Study on Aging.

β-Amyloid group centiloid range	Mean (SD) annualized change		Sample size per treatment arm with bootstrapped 95% CIs^b
β-Amyloid group centiloid range	In SUVR from initial flortaucipir PET scan^a	As a percentage change from index scan^a	Sample size per treatment arm with bootstrapped 95% CIs^b
AD meta–ROI
9-21	0.004 (0.014)	0.31 (1.17)	5227 (940->10 000)
22-67	0.005 (0.020)	0.39 (1.63)	2481 (577->10 000)
≥68	0.028 (0.028)	1.95 (1.94)	216 (101-619)
Entorhinal cortex
9-21	0.007 (0.019)	0.66 (1.78)	6153 (841->10 000)
22-67	0.007 (0.021)	0.58 (1.83)	4382 (672->10 000)
≥68	0.032 (0.039)	2.33 (2.66)	319 (127-1517)
Inferior temporal
9-21	0.004 (0.017)	0.32 (1.30)	5771 (1121->10 000)
22-67	0.005 (0.022)	0.37 (1.75	3006 (703->10 000)
≥68	0.030 (0.033)	2.00 (2.17)	239 (113-624)
Lateral parietal
9-21	0.003 (0.017)	0.20 (1.39)	2775 (640->10 000)
22-67	0.004 (0.019)	0.34 (1.67)	1252 (375->10 000)
≥68	0.017 (0.021)	1.34 (1.67)	282 (133-821)

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Abbreviations: AD, Alzheimer disease; PET, positron emission tomography; ROI, region of interest; SUVR, standardized uptake value ratio.

^{^a}

Unadjusted means and SDs. For tau PET signals, these are presented in SUVR units.

^{^b}

Sample size based on annualized change in SUVR and accounting for age, sex, and education.

Discussion

We explored the quantitative association between Aβ PET and subsequent increases in flortaucipir PET in a community sample from the MCSA of persons who were CU and aged 65 to 85 years. Using the index Aβ PET scan as a variable associated with subsequent flortaucipir accumulation over a mean of 2.7 (range, 1.1-4.1) years, we found that flortaucipir accumulation was low except in the high index-Aβ subgroup (≥68 CL), with that subgroup constituting of less than 20% of the CU group in this age range. Groups with lower index Aβ levels showed insufficient flortaucipir accumulation to support plausible sample sizes for a prevention study. Because the association between index Aβ and flortaucipir accumulation was roughly linear in the range 50 to 100 CL of Aβ, a slightly lower Aβ cut point would include more participants but lead to a lower rate of flortaucipir accumulation. Alternatively, an Aβ cut point that was slightly higher than 68 CL would be more restrictive for participants who were CU but have the expectation of higher flortaucipir accumulation.

We found a similar association in the inferior temporal ROI between baseline Aβ ranges and point estimates for initial levels and annualized flortaucipir accumulation, with the highest Aβ group showing the highest values in the ADNI cohort, supporting our contention that higher baseline Aβ is associated with greater flortaucipir accumulation. In the ADNI cohort, the lack of flortaucipir accumulation at any level of Aβ in entorhinal cortex ROI and the lack of difference between suprathreshold and high Aβ groups in the inferior temporal ROI was perhaps a consequence of the shorter duration of observation and smaller group sizes in the ADNI cohort compared with the MCSA. Other methodological differences between the 2 cohorts, including different Aβ tracers and different anatomic reference atlases, might also have contributed to increased heterogeneity in the ADNI cohort. Caution is advised in accepting the precision of the cut points we derived in the MCSA to divide the higher range of Aβ. However, the conceptual point appears to hold: persons with higher Aβ levels are the ones to experience measurable increases in flortaucipir.

Elevated Aβ is a necessary antecedent of tau accumulation.^6,22,23,24 It is also a factor more powerfully associated with flortaucipir accumulation than initial flortaucipir levels (Clifford R. Jack Jr, MD; June 26, 2020; personal communication). But how long does it take for Aβ to reach the critical level, and how much Aβ elevation is needed? The Aβ accumulates on a time scale of a decade or longer,^26,27 and the lag between initial Aβ and tau accumulations appears to be longer than a decade as well.²⁸ The Aβ value of 68 CL or more, associated with flortaucipir accumulation, was in the range in between mild cognitive impairment and dementia in a recent imaging-pathological study.²⁵ While there were elevated temporal and parietal levels of flortaucipir cross-sectionally in this group of elderly persons who were CU with global Aβ levels in the range more than 22 to 67 CL, the annual rate of flortaucipir accumulation in the groups with those Aβ levels was very low. The current results are consistent with modeling simultaneously of PIB SUVR, flortaucipir SUVR, and cortical thickness in the MCSA, which showed that declines in memory performance in individuals who were CU occurred largely when Aβ levels exceeded 68 CL.¹³ Furthermore, it is elevation in flortaucipir rather than Aβ elevation that is temporally linked to overt cognitive impairment.^{2,3,13,29,30,31,32,33,34} Consistent with the association between elevation in flortaucipir and declining cognition, we observed the greatest declines in memory z scores in the high Aβ group, the group with the largest increases in flortaucipir.

The estimates of flortaucipir SUVR change in the participants who were CU were comparable with some^1,7 but lower than others.⁶ However, once individuals with elevated Aβ and elevated flortaucipir are cognitively impaired, the acceleration of tau accumulation is 2-fold higher^1,5,7 compared with the present individuals who were CU with high Aβ. No other reports, to our knowledge, have stratified participants with so-called elevated Aβ who are CU, so significantly greater flortaucipir accumulation in the group with Aβ of 68 CL or more, compared with the group with 22 to 67 CL, appears to be a novel observation that requires replication in a cohort with a comparable period of observation.

Regional variations in flortaucipir accumulation as functions of initial Aβ levels are consistent with the much more direct association of tau with cognitive dysfunction.^34,35,36 The temporal lobe ROIs showed greater rates of accumulation of flortaucipir in the high Aβ group, consistent with the role of those regions in an evolving deficit in new learning. Moreover, in this cohort who were CU, only the AD meta-ROI and the inferior temporal ROI showed associations between initial level of flortaucipir and subsequent accumulation. Associations were weaker for the entorhinal cortex ROI and absent for the lateral parietal ROI. Lower rates of flortaucipir accumulation in the lateral parietal region would be expected in individuals who were initially asymptomatic.^{2,3,29,30,31,32,33,34}

Our sample size calculations are based on an assumption that a 25% effect size would be meaningful. Because there appears to be no empirical support for the meaningfulness of that magnitude of reduction in tau accumulation or knowledge of whether that magnitude is achievable, the current analysis is only a first approximation for quantifying tau accumulation as an outcome measure.

A strength of our study was the large number of participants who were drawn from the population-based MCSA. A population-based cohort may not be representative of volunteers for a clinical trial, and we acknowledge that the MCSA was almost entirely of European-American background. Further studies in persons of other races/ethnicities are critical to characterize tau PET as an outcome measure in diverse populations.

Limitations

There are technical limitations in our analysis that are relevant to the design of trials. Both the ¹⁸F- flortaucipir and ¹¹C-PIB SUVR methods are susceptible to artifacts, especially bleed-in from off-target binding and choice of reference regions. Newer tau PET tracers may have less off-target signals, which could improve the signal-to-noise ratio of serial tau PET measurements.

Conclusions

Substantial flortaucipir accumulation in temporal regions was greatest in persons aged 65 to 85 years who were CU and had high initial Aβ PET levels, compared with those with lower Aβ levels. Recruiting persons who were CU and exhibiting Aβ levels of 68 CL or more on an index Aβ PET is a feasible strategy to recruit for clinical trials in which a change in tau PET signal is an outcome measure.

Supplement.

eMethods 1. Methods for Cognitive Assessment in MCSA.

eMethods 2. Methods for Imaging in MCSA.

eMethods 3. Methods for MRI and PET imaging.

eFigure 1. Flowchart of the PET processing pipeline.

eFigure 2. Comparison of different reference regions and use-or-not of partial volume correction for annualized change in flortaucipir in AD-meta ROI.

eReferences. Imaging Methods References.

eMethods 4. ADNI Methods.

eTable 1. MCSA: Annualized change on memory and global test z-scores.

eFigure 3. MCSA: Annualized Changes in PIB PET.

eFigure 4. MCSA: Flortaucipir at Index Scan.

eTable 2. MCSA: Unadjusted Flortaucipir SUVR on index scan by index Aβ PET.

eTable 3. MCSA: Percent difference in Flortaucipir SUVr between Aβ groups by Region of Interest at index flortaucipir scan.

eTable 4. Demographic Features of ADNI cohort with serial flortaucipir PET.

eFigure 5. MCSA: Index Aβ PET on continuous scale (x-axis) versus flortaucipir accumulation (y-axis).

eTable 5. MCSA: Unadjusted Annual flortaucipr SUVR change per year (SUVR/yr) (supports main Figure 2).

eTable 6. MCSA: Pairwise comparisons of mean differences on annual change in flortaucipir in SUVR/yr between Aβ groups by Region of Interest. Comparisons refer to Figure 2.

eFigure 6. ADNI: Annual change in flortaucipir in SUVR/yr between Aβ groups by ROI and between group comparison.

eTable 7. ADNI: Pairwise comparisons of mean differences on annual change in flortaucipir in SUVR/yr between Aβ groups by Region of Interest.

eFigure 7. ADNI: Baseline Flortaucipir within Aβ group by ROI and between-group comparisons.

eTable 8. Baseline Flortaucipir within Aβ group by ROI between-group comparisons.

Click here for additional data file.^{(1.8MB, pdf)}

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