Association of Klotho-VS Heterozygosity With Risk of Alzheimer Disease in Individuals Who Carry APOE4

Michael E Belloy; Valerio Napolioni; Summer S Han; Yann Le Guen; Michael D Greicius

doi:10.1001/jamaneurol.2020.0414

. 2020 Apr 13;77(7):849–862. doi: 10.1001/jamaneurol.2020.0414

Association of Klotho-VS Heterozygosity With Risk of Alzheimer Disease in Individuals Who Carry APOE4

Michael E Belloy ^1,^✉, Valerio Napolioni ¹, Summer S Han ^2,³, Yann Le Guen ¹, Michael D Greicius ¹, for the Alzheimer’s Disease Neuroimaging Initiative

¹Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, Stanford, California

²Department of Neurosurgery, Stanford University, Stanford, California

³Quantitative Sciences Unit, Stanford Medicine, Stanford, California

Group Information: The Alzheimer’s Disease Neuroimaging Initiative group member list is available at the end of this article.

Accepted for Publication: January 16, 2020.

^✉

Corresponding Author: Michael E. Belloy, PhD, Department of Neurology and Neurological Sciences, Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Stanford University, 780 Welch Rd, Stanford, CA (mbelloy@stanford.edu).

Published Online: April 13, 2020. doi:10.1001/jamaneurol.2020.0414

Author Contributions: Drs Belloy and Greicius had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Belloy and Napolioni contributed equally to this work.

Concept and design: Belloy, Napolioni, Greicius.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Belloy, Greicius.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Belloy, Greicius.

Obtained funding: Greicius.

Administrative, technical, or material support: Napolioni, Greicius.

Supervision: Napolioni, Han, Greicius.

Conflict of Interest Disclosures: None reported.

Funding/Support: Funding for this study was provided by the Iqbal Farrukh & Asad Jamal Center for Cognitive Health in Aging, The South Palm Beach County Foundation, and the National Institutes of Health (grants AG060747 and AG047366).

Role of the Funder/Sponsor: The funders 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.

Group Author Information: Alzheimer’s Disease Neuroimaging Initiative (ADNI) I, GO, II, and III. Part A: Leadership and Infrastructure: principal investigator (PI): Michael W. Weiner, MD (University of California, San Francisco, San Francisco); ATRI PI and director of coordinating center clinical core, Paul Aisen, MD (University of Southern California, Los Angeles); Executive committee: Michael Weiner, MD (University of California, San Francisco, San Francisco), Paul Aisen, MD (University of Southern California, Los Angeles), Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, Minnesota), Clifford R. Jack Jr, MD (Mayo Clinic, Rochester, Minnesota), William Jagust, MD (University of California Berkeley, Berkeley), John Q. Trojanowki, MD, PhD (University of Pennsylvania, Philadelphia), Arthur W. Toga, PhD (University of Southern California, Los Angeles), Laurel Beckett, PhD (University of California Davis, Davis), Robert C. Green, MD, MPH (Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts), Andrew J. Saykin, PsyD (Indiana University, Bloomington), John Morris, MD (Washington University in St Louis, St Louis, Missouri), and Leslie M. Shaw (University of Pennsylvania, Philadelphia). ADNI External Advisory Board: Zaven Khachaturian, PhD (Chair) (Prevent Alzheimer’s Disease 2020, Rockville, Maryland), Greg Sorensen, MD (Siemens), Maria Carrillo, PhD (Alzheimer’s Association, Chicago, Illinois), Lew Kuller, MD (University of Pittsburgh, Pittsburgh, Pennsylvania), Marc Raichle, MD (Washington University in St Louis, St Louis, Missouri), Steven Paul, MD (Cornell University, Ithaca, New York), Peter Davies, MD (Albert Einstein College of Medicine of Yeshiva University, Bronx, New York), Howard Fillit, MD (AD Drug Discovery Foundation, New York, New York), Franz Hefti, PhD (Acumen Pharmaceuticals), David Holtzman, MD (Washington University in St Louis, St Louis, Missouri), M. Marcel Mesulam, MD (Northwestern University, Chicago, Illinois), William Potter, MD (National Institute of Mental Health), Peter Snyder, PhD (Brown University, Providence, Rhode Island). ADNI 3 Private Partner Scientific Board: Veronika Logovinsky, MD, PhD (Chair) (Eli Lilly). Data and publications committee: Robert C. Green, MD, MPH (Chair) (Brigham & Women’s Hospital/Harvard Medical School, Boston, Massachusetts). Resource allocation review committee: Tom Montine, MD, PhD (Chair) (University of Washington in St Louis, St Louis, Missouri). Clinical core leaders: Ronald Petersen, MD, PhD (Core PI) (Mayo Clinic, Rochester, Minnesota) and Paul Aisen, MD (University of Southern California). Clinical informatics and operations: Gustavo Jimenez, MBS, Michael Donohue, PhD, Devon Gessert, BS, Kelly Harless, BA, Jennifer Salazar, MBS, Yuliana Cabrera, BS, Sarah Walter, MSc, Lindsey Hergesheimer, BS (University of Southern California, Los Angeles). Biostatistics core leaders and key personnel: Laurel Beckett, PhD (Core PI), Danielle Harvey, PhD, and Michael Donohue, PhD (University of California San Diego). Magnetic resonance imaging core leaders and key personnel: Clifford R. Jack Jr, MD (Core PI) and Matthew Bernstein, PhD (Mayo Clinic, Rochester, Minnesota), and Nick Fox, MD (University of London, London, England), Paul Thompson, PhD (University of California Los Angeles School of Medicine, Los Angeles), Norbert Schuff, PhD (University of California San Francisco Magnetic Resonance Imaging, San Francisco), and Charles DeCarli, MD (University of California Davis, Davis); and Bret Borowski, RT, Jeff Gunter, PhD, Matt Senjem, MS, Prashanthi Vemuri, PhD, David Jones, MD, Kejal Kantarci, and Chad Ward (Mayo Clinic). Positron emission tomography core leaders and key personnel: William Jagust, MD (Core PI) (University of California Berkeley, Berkeley), Robert A. Koeppe, PhD (University of Michigan, Ann Arbor), Norm Foster, MD (University of Utah, Salt Lake City), Eric M. Reiman, MD and Kewei Chen, PhD (Banner Alzheimer’s Institute, Phoenix, Arizona), Chet Mathis, MD (University of Pittsburgh, Pittsburgh, Pennsylvania), and Susan Landau, PhD (University of California Berkeley, Berkeley). Neuropathology core leaders: John C. Morris, MD, Nigel J. Cairns, PhD, Erin Franklin, MS, CCR, and Lisa Taylor-Reinwald, BA, HTL (ASCP) (past investigator) (Washington University in St Louis, St Louis, Missouri). Biomarkers core leaders and key personnel: Leslie M. Shaw, PhD, John Q. Trojanowki, MD, PhD, Virginia Lee, PhD, MBA, Magdalena Korecka, PhD, and Michal Figurski, PhD (University of Pennsylvania School of Medicine, Philadelphia). Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD, Karen Crawford, and Scott Neu, PhD (University of Southern California, Los Angeles). Genetics core leaders and key personnel: Andrew J. Saykin, PsyD, and Tatiana M. Foroud, PhD (Indiana University, Bloomington); Steven Potkin, MD (University of California Irvine, Irvine); and Li Shen, PhD, Kelley Faber, MS, CCRC, Sungeun Kim, PhD, and Kwangsik Nho, PhD (Indiana University, Bloomington). Initial concept planning & development: Michael W. Weiner, MD (University of California San Francisco, San Francisco); Lean Thal, MD (University of California San Diego, San Diego), and Zaven Khachaturian, PhD (Prevent Alzheimer’s Disease 2020, Rockville, Maryland). Early project proposal development: Leon Thal, MD (University of California San Diego, San Diego), Neil Buckholt (National Institute on Aging), Michael W. Weiner, MD (University of California San Francisco, San Francisco), Peter J. Snyder, PhD (Brown University, Providence, Rhode Island), William Potter, MD (National Institute of Mental Health), Steven Paul, MD (Cornell University, Ithaca, New York), Marilyn Albert, PhD (Johns Hopkins University, Baltimore, Maryland), Richard Frank, MD, PhD (Richard Frank Consulting), Zaven Khachaturian, PhD (Prevent Alzheimer’s Disease 2020, New York, New York), and John Hsiao, MD (National Institute on Aging).

Part B: Investigators by site. Oregon Health & Science University: Joseph Quinn, MD, Lisa C. Silbert, MD, Betty Lind, BS, Jeffrey A. Kaye, MD, (past investigator), Raina Carter, BA (past investigator), and Sara Dolen, BS (past investigator); University of Southern California, Los Angeles: Lon S. Schneider, MD, Sonia Pawluczyk, MD, Mauricio Becerra, BS, Liberty Teodoro, RN, and Bryan M. Spann, DO, PhD (past investigator); University of California San Diego, San Diego: James Brewer, MD, PhD, Helen Vanderswag, RN, and Adam Fleisher, MD (past investigator); University of Michigan, Ann Arbor: Jaimie Ziolkowski, MA, BS, TLLP, Judith L. Heidebrink, MD, MS, and 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, and 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, Rachelle S. Doody, MD, PhD (Past Investigator), Victoria Shibley, MS (past investigator), Munir Chowdhury, MBBS, MS (past investigator), Susan Rountree, MD (past investigator), and 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, and Randy Yeh, MD; Washington University in St Louis, 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), and Stacy Schneider, APRN, BC, GNP (past investigator); University of Alabama–Birmingham: Daniel Marson, JD, PhD, David Geldmacher, MD, Marissa Natelson Love, MD, Randall Griffith, PhD, ABPP (past investigator), David Clark, MD (Past Investigator), and John Brockington, MD (past investigator); Mount Sinai School of Medicine, New York, New York: Hillel Grossman, MD, and Effie Mitsis, PhD (past investigator); Rush University Medical Center, Chicago, Illinois: Raj C. Shah, MD, Melissa Lamar, PhD, and Patricia Samuels; Wien Center, Miami, Florida: Ranjan Duara, MD, Maria T. Greig-Custo, MD, and Rosemarie Rodriguez, PhD; Johns Hopkins University, Baltimore, Maryland: Marilyn Albert, PhD, Chiadi Onyike, MD, Daniel D’Agostino II, BS, and Stephanie Kielb, BS (past investigator); New York University, New York: Martin Sadowski, MD, PhD, Mohammed O. Sheikh, MD, Jamika Singleton-Garvin, CCRP, Anaztasia Ulysse, and Mrunalini Gaikwad; Duke University Medical Center, Durham, North Carolina: P. Murali Doraiswamy, MBBS, Jeffrey R. Petrella, MD, Olga James, MD, Salvador Borges-Neto, MD, Terence Z. Wong, MD (past investigator), and 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), and 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, and Gary Conrad, MD; University of Pittsburgh, Pittsburgh, Pennsylvania: Oscar L. Lopez, MD, MaryAnn Oakley, MA, and 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), and Connie Brand, RN (Past Investigator); University of California Irvine, IMIND, Irvine: Gaby Thai, MD, Aimee Pierce, MD, Beatriz Yanez, RN, Elizabeth Sosa, PhD, and Megan Witbracht, PhD; University of Texas Southwestern Medical School, Houston: Kyle Womack, MD, Dana Mathews, MD, PhD, and Mary Quiceno, MD; Emory University, Atlanta, Georgia: Allan I. Levey, MD, PhD, James J. Lah, MD, PhD, and Janet S. Cellar, DNP, PMHCNS-BC; University of Kansas Medical Center, Kansas City: Jeffrey M. Burns, MD, Russell H. Swerdlow, MD, and 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), and George Bartzokis, MD (past investigator); Mayo Clinic, Jacksonville, Florida: Neill R Graff-Radford, MBBCH, Francine Parfitt, MSH, CCRC, and 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, and Scott Herring, RN, CCRC; Yale University School of Medicine, New Haven, Connecticut: Christopher H. van Dyck, MD, Richard E. Carson, PhD, and Pradeep Varma, MD; McGill University and Montreal-Jewish General Hospital, Montreal, Canada: Howard Chertkow, MD, Howard Bergman, MD, and Chris Hosein, MEd; Sunnybrook Health Sciences, Ontario, Canada: Sandra Black, MD, Bojana Stefanovic, PhD, and Chris (Chinthaka) Heyn, BSC, PhD, MD; UBC Clinic for Alzheimer Disease & Related Disorders: Ging-Yuek Robin Hsiung, MD, MHSc, Benita Mudge, BS, Vesna Sossi, PhD, Howard Feldman, MD, (Past Investigator), and Michele Assaly, MA (Past Investigator); Cognitive Neurology, St. Joseph’s Health Care London, London, Ontario, Canada: Elizabeth Finger, MD, Stephen Pasternack, MD, PhD, William Pavlosky, MD, Irina Rachinsky, MD (past investigator), Dick Drost, PhD (Past Investigator), and Andrew Kertesz, MD (past investigator); Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, Ohio: Charles Bernick, MD, MPH, and 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), and Nancy Johnson, PhD (past investigator); Premiere Research Institute, Palm Beach Neurology, West Palm Beach, Florida: Carl Sadowsky, MD, and Teresa Villena, MD; Georgetown University Medical Center, Washington, DC: Raymond Scott Turner, MD, PhD, Kathleen Johnson, NP, and Brigid Reynolds, NP; Brigham and Women's Hospital, Boston, Massachusetts: Reisa A. Sperling, MD, Keith A. Johnson, MD, and Gad A. Marshall, MD; Stanford University, Stanford, California: Jerome Yesavage, MD, Joy L. Taylor, PhD, Steven Chao, MD, PhD, Barton Lane, MD (Past Investigator), Allyson Rosen, PhD (past investigator), and Jared Tinklenberg, MD (past investigator); Banner Sun Health Research Institute, Sun City, Arizona: Edward Zamrini, MD, Christine M. Belden, PsyD, and Sherye A. Sirrel, CCRC; Boston University, Boston, Massachusetts: Neil Kowall, MD, Ronald Killiany, PhD, Andrew E. Budson, MD, Alexander Norbash, MD (Past Investigator), and Patricia Lynn Johnson, BA (past investigator); Howard University, Washington, DC: Thomas O. Obisesan, MD, MPH, Ntekim E. Oyonumo, MD, PhD, Joanne Allard, PhD, and Olu Ogunlana, BPharm; Case Western Reserve University, Cleveland, Ohio: Alan Lerner, MD, Paula Ogrocki, PhD, Curtis Tatsuoka, PhD, and Parianne Fatica, BA, CCRC; University of California, Davis, Sacramento: Evan Fletcher, PhD, Pauline Maillard, PhD, John Olichney, MD, Charles DeCarli, MD, and Owen Carmichael, PhD (Past Investigator); Neurological Care of CNY, Syracuse, New York: Smita Kittur, MD (past investigator); Parkwood Institute, London, Ontario, Canada: Michael Borrie, MB ChB, T-Y Lee, PhD, and Dr Rob Bartha, PhD; University of Wisconsin: Sterling Johnson, PhD, Sanjay Asthana, MD, and 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), and Stephanie Reeder, BA (past investigator); Dent Neurologic Institute, Amherst, New York: Vernice Bates, MD, Horacio Capote, MD, and Michelle Rainka, PharmD, CCRP; Ohio State University, Columbus: Douglas W. Scharre, MD, Maria Kataki, MD, PhD, and Rawan Tarawneh, MD; Albany Medical College, Albany, New York: Earl A. Zimmerman, MD, Dzintra Celmins, MD, and David Hart, MD; Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, Connecticut: Godfrey D. Pearlson, MD, Karen Blank, MD, and Karen Anderson, RN; Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire: Laura A. Flashman, PhD, Marc Seltzer, MD, Mary L. Hynes, RN, MPH, and Robert B. Santulli, MD (past investigator); Wake Forest University Health Sciences, Winston-Salem, North Carolina: Kaycee M. Sink, MD, MAS, Mia Yang, MD, and Akiva Mintz, MD, PhD; Rhode Island Hospital, Providence: Brian R. Ott, MD, Geoffrey Tremont, PhD, and Lori A. Daiello, Pharm.D, ScM; Butler Hospital, Providence, Rhode Island: Courtney Bodge, PhD, Stephen Salloway, MD, MS, Paul Malloy, PhD, Stephen Correia, PhD, and Athena Lee, PhD; University of California San Francisco, San Francisco: Howard J. Rosen, MD, Bruce L. Miller, MD, David Perry, MD; Medical University 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), and Dick Drost, MD (past investigator); Nathan Kline Institute, Orangeburg, New York: Nunzio Pomara, MD, Raymundo Hernando, MD, and 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, and Susan K. Schultz, MD (Past Investigator); Cornell University, Ithaca, New York: Norman Relkin, MD, PhD, Gloria Chiang, MD, Michael Lin, MD, and Lisa Ravdin, PhD; University of South Florida, USF Health Byrd Alzheimer’s Institute, Tampa: Amanda Smith, MD, Christi Leach, MD, Balebail Ashok Raj, MD (past investigator) and Kristin Fargher, MD (past investigator).

DOD ADNI. Part A: Leadership and Infrastructure: PI: Michael W. Weiner, MD (University of California, San Francisco); ATRI PI and director of coordinating center clinical core: Paul Aisen, MD (University of Southern California, Los Angeles). Executive committee: Michael Weiner, MD (University of California San Francisco, San Francisco), Paul Aisen, MD (University of Southern California, Los Angeles), Ronald Petersen, MD, PhD (Mayo Clinic, Rochester, Minnesota), Robert C. Green, MD, MPH (Brigham and Women’s Hospital/Harvard Medical School, Boston, Massachusetts), Danielle Harvey, PhD (University of California Davis, Davis), Clifford R. Jack Jr, MD (Mayo Clinic, Rochester, Minnesota), William Jagust, MD (University of California Berkeley, Berkeley), John C. Morris, MD (Washington University in St Louis, St Louis, Missouri), Andrew J. Saykin, PsyD (Indiana University, Bloomington), Leslie M. Shaw, PhD (Perelman School of Medicine, University of Pennsylvania, Philadelphia), Arthur W. Toga, PhD (University of Southern California, Los Angeles), and John Q. Trojanowki, MD, PhD (Perelman School of Medicine, University of Pennsylvania, Philadelphia); Psychological Evaluation/Post-traumatic Stress Disorder Core: Thomas Neylan, MD (University of California San Francisco, San Francisco); Traumatic brain injury core: Jordan Grafman, PhD (Rehabilitation Institute of Chicago, Feinberg School of Medicine, Northwestern University, Chicago, Illinois); Data and Publication Committee: Robert C. Green, MD, MPH (Chair) (Brigham & Women’s Hospital/Harvard Medical School, Boston, Massachusetts); Resource Allocation Review Committee: Tom Montine, MD, PhD (Chair) (University of Washington, Seattle); Clinical Core Leaders: Michael Weiner, MD (Core PI), Ronald Petersen, MD, PhD (Core PI) (Mayo Clinic, Rochester, Minnesota), and Paul Aisen, MD (University of Southern California, Los Angeles); Clinical Informatics and Operations: Gustavo Jimenez, MBS, Michael Donohue, PhD, Devon Gessert, BS, Kelly Harless, BA, Jennifer Salazar, MBS, Yuliana Cabrera, BS, Sarah Walter, MSc, Lindsey Hergesheimen, BS (University of Southern California, Los Angeles); San Francisco Veterans Affairs Medical Center, San Francisco, California: Thomas Neylan, MD, Jacqueline Hayes, and Shannon Finley (University of California San Francisco, San Francisco); Biostatistics Core Leaders and Key Personnel: Danielle Harvey, PhD (Core PI) (University of California Davis, Davis) and Michael Donohue, PhD (University of California San Diego, San Diego); Magnetic resonance imaging core leaders and key personnel: Clifford R. Jack Jr, MD (Core PI) (Mayo Clinic, Rochester, Minnesota), Matthew Bernstein, PhD (Mayo Clinic, Rochester, Minnesota), Bret Borowski, RT (Mayo Clinic), Jeff Gunter, PhD (Mayo Clinic), Matt Senjem, MS (Mayo Clinic), Kejal Kantarci (Mayo Clinic), and Chad Ward (Mayo Clinic); Positron emission tomography core leaders and key personnel: William Jagust, MD (Core PI) (University of California Berkeley, Berkeley), Robert A. Koeppe, PhD (University of Michigan, Ann Arbor), Norm Foster, MD (University of Utah, Salt Lake City), Eric M. Reiman, MD (Banner Alzheimer’s Institute, Phoenix, Arizona), Kewei Chen, PhD (Banner Alzheimer’s Institute, Phoenix, Arizona), and Susan Landau, PhD (University of California Berkeley, Berkeley); Neuropathology Core Leaders: John C. Morris, MD, Nigel J. Cairns, PhD, and Erin Householder, MS (Washington University in St Louis, St Louis, Missouri); Biomarkers Core Leaders and Key Personnel: Leslie M. Shaw, PhD, John Q. Trojanowki, MD, PhD,Virginia Lee, PhD, MBA, Magdalena Korecka, PhD, and Michal Figurski, PhD (Perelman School of Medicine, University of Pennsylvania, Philadelphia); Informatics core leaders and key personnel: Arthur W. Toga, PhD (Core PI), Karen Crawford, and Scott Neu, PhD (University of Southern California, Los Angeles); Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Tatiana M. Foroud, PhD, Li Shen, PhD, Kelley Faber, MS, CCRC, Sungeun Kim, PhD, and Kwangsik Nho, PhD (Indiana University, Bloomington) and Steven Potkin, MD (University of California Irvine, Irvine); Initial concept planning & development: Michael W. Weiner, MD (University of California San Francisco, San Francisco) and Karl Friedl (Retired) (Department of Defense). Part B: Investigators by site: University of Southern California, Los Angeles: Lon S. Schneider, MD, MS, Sonia Pawluczyk, MD, and Mauricio Becerra; University of California, San Diego, San Diego: James Brewer, MD, PhD, and Helen Vanderswag, RN; Columbia University Medical Center, New York, New York: Yaakov Stern, PhD, Lawrence S. Honig, MD, PhD, and Karen L. Bell, MD; Rush University Medical Center, Chicago, Illinois: Debra Fleischman, PhD, Konstantinos Arfanakis, PhD, and Raj C. Shah, MD; Wien Center, Miami, Florida: Ranjan Duara, MD (PI), Daniel Varon, MD (Co-PI), and Maria T. Greig (HP Coordinator); Duke University Medical Center, Durham, North Carolina: P. Murali Doraiswamy, MBBS, Jeffrey R. Petrella, MD, and Olga James, MD; University of Rochester Medical Center, Rochester, New York: Anton P. Porsteinsson, MD, Bonnie Goldstein, MS, NP, and Kimberly S. Martin, RN; University of California Irvine, Irvine: Steven G. Potkin, MD, Adrian Preda, MD, and Dana Nguyen, PhD; Medical University South Carolina, Charleston: Jacobo Mintzer, MD, MBA, Dino Massoglia, MD, PhD, and Olga Brawman-Mintzer, MD; Premiere Research Institute, Palm Beach Neurology, Palm Beach, Florida: Carl Sadowsky, MD, Walter Martinez, MD, and Teresa Villena, MD; University of California, San Francisco, San Francisco: William Jagust, MD, Susan Landau PhD, Howard Rosen, MD, and David Perry; Georgetown University Medical Center, Washington, DC: Raymond Scott Turner, MD, PhD, Kelly Behan, and Brigid Reynolds, NP; Brigham and Women's Hospital, Boston, Massachusetts: Reisa A. Sperling, MD, Keith A. Johnson, MD, and Gad Marshall, MD; Banner Sun Health Research Institute, Sun City, Arizona: Marwan N. Sabbagh, MD Sandra A. Jacobson, MD, and Sherye A. Sirrel, MS, CCRC; Howard University, Washington, DC: Thomas O. Obisesan, MD, MPH, Saba Wolday, MSc, and Joanne Allard, PhD; University of Wisconsin: Sterling C. Johnson, PhD, J. Jay Fruehling, MA, and Sandra Harding, MS; University of Washington, Seattle: Elaine R. Peskind, MD, Eric C. Petrie, MD, MS, and Gail Li, MD, PhD; Stanford University, Stanford, California: Jerome A. Yesavage, MD, Joy L. Taylor, PhD, Ansgar J. Furst, PhD, and Steven Chao, MD; Cornell University, Ithaca, New York: Norman Relkin, MD, PhD, Gloria Chiang, MD, and Lisa Ravdin, PhD. ADNI Depression. Part A: Leadership and Infrastructure: PI: Scott Mackin, PhD (University of California, San Francisco, San Francisco); ATRI PI and Director of Coordinating Center Clinical Core: Paul Aisen, MD (University of Southern California, Los Angeles), Rema Raman, PhD (University of Southern California, Los Angeles); Executive Committee: Scott Mackin, PhD(University of California San Francisco, San Francisco), Michael Weiner, MD (University of California San Francisco, San Francisco) Paul Aisen, MD (University of Southern California, Los Angeles), Rema Raman, PhD (University of Southern California, Los Angeles), Clifford R. Jack Jr, MD (Mayo Clinic, Rochester, Minnesota), Susan Landau, PhD (University of California Berkeley, Berkeley), Andrew J. Saykin, PsyD (Indiana University, Bloomington), Arthur W. Toga, PhD (University of Southern California, Los Angeles), Charles DeCarli, MD (University of California Davis, Davis), Robert A. Koeppe, PhD (University of Michigan, Ann Arbor). Data and Publication Committee: Robert C. Green, MD, MPH (Chair), Erin Drake, MA (Director) (Brigham & Women’s Hospital/Harvard Medical School, Boston, Massachusetts); Clinical Core Leaders: Michael Weiner, MD (Core PI), Paul Aisen, MD, Rema Raman, PhD, Mike Donohue, PhD (University of Southern California, Los Angeles); Clinical Informatics, Operations and Regulatory Affairs: Gustavo Jimenez, MBS, Devon Gessert, BS, Kelly Harless, BA, Jennifer Salazar, MBS, Yuliana Cabrera, BS, Sarah Walter, MSc, Lindsey Hergesheimer, BS, Elizabeth Shaffer, BS (University of Southern California, Los Angeles); Psychiatry Site Leaders and Key Personnel: Scott Mackin, PhD, Craig Nelson, MD, David Bickford, BA (University of California San Francisco, San Francisco) and Meryl Butters, PhD and Michelle Zmuda, MA (University of Pittsburgh, Pittsburgh, Pennsylvania); Magnetic Resonance Imaging Core Leaders and Key Personnel: Clifford R. Jack Jr, MD (Core PI), Matthew Bernstein, PhD, Bret Borowski, RT, Jeff Gunter, PhD, Matt Senjem, MS, Kejal Kantarci, MD, Chad Ward, BA, Denise Reyes, BS (Mayo Clinic, Rochester, Minnesota); Positron Emission Tomography Core Leaders and Key Personnel: Robert A. Koeppe, PhD (University of Michigan, Ann Arbor), Susan Landau, PhD (University of California, Berkeley, Berkeley); Informatics Core Leaders and Key Personnel: Arthur W. Toga, PhD (Core PI), Karen Crawford, and Scott Neu, PhD (University of Southern California, Los Angeles); Genetics Core Leaders and Key Personnel: Andrew J. Saykin, PsyD, Tatiana M. Foroud, PhD, Kelley M. Faber, MS, CCRC, Kwangsik Nho, PhD, and Kelly N. Nudelman (Indiana University, Bloomington). 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, and Dariella Fernandez, BA; University of Pittsburgh, Pittsburgh, Pennsylvania: Meryl Butters, PhD, Michelle Zmuda, MA, Oscar L. Lopez, MD, MaryAnn Oakley, MA, and Donna M. Simpson, CRNP, MPH.

Additional Contributions: Biological samples used in this study were stored at principal investigators’ institutions and at the National Cell Repository for Alzheimer’s Disease at Indiana University, Bloomington, which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA). Phenotypic data were provided by principal investigators, the NIA-funded Alzheimer’s Disease Centers, and the National Alzheimer’s Coordinating Center (NACC). Genetic data were contributed by principal investigators on projects that were individually funded by National Institute on Aging, other National Institutes of Health institutes, private US organizations, or foreign governmental or nongovernmental organizations. Data for this study were prepared, archived, and distributed by the NIA Alzheimer’s Disease Data Storage Site at the University of Pennsylvania (U24-AG041689-01); Alzheimer’s Disease Genetics Consortium (grants U01 AG032984 and RC2 AG036528) the NACC (grants U01 AG016976) National Institute on Aging Genetics Initiative for Late-Onset Alzheimer Disease (Columbia University) (grants U24 AG026395, U24 AG026390, and R01AG041797); Banner Sun Health Research Institute (grant P30 AG019610); Boston University (grants P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01 AG048927, R01AG33193, and R01 AG009029); Columbia University (grants P50 AG008702, R37 AG015473, R01 AG037212, and R01 AG028786); Duke University (grants P30 AG028377 and AG05128); Group Health Research Institute (grants UO1 AG006781, UO1 HG004610, UO1 HG006375, and U01 HG008657); Indiana University (grants P30 AG10133, R01 AG009956, and RC2 AG036650); Johns Hopkins University (grants P50 AG005146 and R01 AG020688); Massachusetts General Hospital (grant P50 AG005134); Mayo Clinic (grant P50 AG016574, R01 AG032990, and KL2 RR024151); Mount Sinai School of Medicine (grants P50 AG005138 and P01 AG002219); New York University (grants P30 AG08051, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, and 1R01AG035137); Northwestern University (grant P30 AG013854); Oregon Health & Science University (grants P30 AG008017 and R01 AG026916); Rush University (grants P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG030146, R01 AG01101, RC2 AG036650, R01 AG22018); Translational Genomics Research Institute (grant R01 NS059873); University of Alabama at Birmingham (grants P50 AG016582 and UL1RR02777); University of Arizona (grant R01 AG031581); University of California, Davis (grant P30 AG010129); University of California, Irvine (grants P50 AG016573, P50 AG016575, P50 AG016576, and P50 AG016577); University of California, Los Angeles (grant P50 AG016570); University of California, San Diego (grant P50 AG005131); University of California, San Francisco (grants P50 AG023501 and P01 AG019724); University of Kentucky (grants P30 AG028383 and AG05144); University of Michigan (grants P30 AG053760 and AG063760); University of Pennsylvania (grant P30 AG010124); University of Pittsburgh (grants P50 AG005133, AG030653, AG041718, AG07562, and AG02365); University of Southern California (grant P50 AG005142); University of Texas Southwestern (grant P30 AG012300); University of Miami (grants R01 AG027944, AG010491, AG027944, AG021547, and AG019757); University of Washington (grants P50 AG005136 and R01 AG042437); University of Wisconsin (grant P50 AG033514); Vanderbilt University (grant R01 AG019085); and Washington University (grants P50 AG005681, P01 AG03991, and P01 AG026276). The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS (grant NS39764), the National Institute of Mental Health (grant MH60451) and Glaxo Smith Kline. Genotyping of the Translational Genomics Research Institute series 2 (TGEN2) cohort was supported by Kronos Science. The Translational Genomics Research Institute series was also funded by the NIA (grant AG041232), the Banner Alzheimer’s Foundation, the Johnnie B. Byrd Sr. Alzheimer’s Institute, the Medical Research Council, and the state of Arizona and also includes samples from Newcastle Brain Tissue Resource (funding via the Medical Research Council, local National Health Services trusts, and Newcastle University), Medical Research Council London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council), South West Dementia Brain Bank (funding via numerous sources, including the Higher Education Funding Council for England, Alzheimer’s Research Trust, BRACE, the North Bristol National Health Services Trust Research, and Innovation 58 Department and DeNDRoN), the Netherlands Brain Bank (funding via numerous sources, including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, and Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, and Universitat de Barcelona. The NACC database is funded by the NIA (grant U01 AG016976), and NACC data are contributed by the NIA-funded Alzheimer’s Disease Centers (grants P30 AG019610, P30 AG013846, P30 AG062428-01, P50 AG008702, P50 AG025688, P50 AG047266, P30 AG010133, P50 AG005146, P30 AG062421-01, P30 AG062422-01, P50 AG005138, P30 AG008051, P30 AG013854, P30 AG008017, P30 AG010161, P50 AG047366, P30 AG010129, P50 AG016573, P30 AG062429-01, P50 AG023501, P30 AG035982, P30 AG028383, P30 AG053760, P30 AG010124, P50 AG005133, P50 AG005142, P30 AG012300, P30 AG049638, P50 AG005136, P30 AG062715-01, P50 AG005681, and P50 AG047270). The genotypic and associated phenotypic data used in the study Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimer’s Disease (GenADA) were provided by the GlaxoSmithKline and R&D Limited. The ROSMAP study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois. Data collection was supported by the NIA (grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, and U01AG46152), the Illinois Department of Public Health, and the Translational Genomics Research Institute. The AddNeuroMed data are from a public-private partnership supported by European Pharmaceutical Industries and Associationscompanies and small and medium-sized enterprises as part of InnoMed (Innovative Medicines in Europe), an Integrated Project funded by the European Union of the Sixth Framework program priority (FP6-2004-LIFESCIHEALTH-5). Alzheimer’s Disease Neuroimaging Initiative: data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health grant U01 AG024904) and the Department of Defense (award W81XWH-12-2-0012); ADNI is funded by the NIA, 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 and Company, EuroImmun, F. Hoffmann–La Roche Ltd, and its affiliated company Genentech, Fujirebio, GE Healthcare, IXICO Ltd, Janssen Alzheimer Immunotherapy Research & Development LLC, Johnson & Johnson, Lumosity, Lundbeck, Merck & Co, Meso Scale Diagnostics, NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals, 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. The ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The authors thank the Clinical and Genetics Cores of the Knight ADRC at Washington University for clinical and cognitive assessments of the participants and for APOE genotypes (Charles and Joanne Knight Alzheimer’s Research Initiative of the Washington University Alzheimer’s Disease Research Centre) and the Biomarker Core of the Adult Children Study at Washington University for the cerebrospinal fluid collection and assays; recruitment and cerebrospinal fluid studies at University of Washington were supported by the National Institutes of Health (grant PO1 AGO5131).

^✉

Corresponding author.

PMCID: PMC7154955 PMID: 32282020

Key Points

Question

Does Klotho-VS heterozygosity protect against Alzheimer disease (AD) in individuals who carry APOE4?

Findings

In this study, associations were evaluated across 22 AD cohorts (n = 20 928), 3 longitudinal cohorts (n = 3008), and 4 cohorts collecting β-amyloid measurements (cerebrospinal fluid, n = 556; brain, n = 251). In individuals who carry APOE4, Klotho-VS heterozygosity was associated with reduced AD risk and more favorable β-amyloid profiles in the brain and cerebrospinal fluid of older control participants. Klotho-VS heterozygosity was also associated with reduced AD conversion risk in individuals who carry APOE4.

Meaning

Pathways associated with KL merit exploration for novel AD drug targets, and the KL-VS genotype should be considered in conjunction with APOE genotype to refine prediction models used in clinical trial enrichment.

This study combined data from 35 independent case-control, family-based, and longitudinal cohorts that examined Alzheimer disease to determine if Klotho-VS heterozygosity is associated with reduced Alzheimer disease risk and β-amyloid (Aβ) pathology in individuals who carry APOE4.

Abstract

Importance

Identification of genetic factors that interact with the apolipoprotein e4 (APOE4) allele to reduce risk for Alzheimer disease (AD) would accelerate the search for new AD drug targets. Klotho-VS heterozygosity (KL-VS^HET+ status) protects against aging-associated phenotypes and cognitive decline, but whether it protects individuals who carry APOE4 from AD remains unclear.

Objectives

To determine if KL-VS^HET+ status is associated with reduced AD risk and β-amyloid (Aβ) pathology in individuals who carry APOE4.

Design, Setting, and Participants

This study combined 25 independent case-control, family-based, and longitudinal AD cohorts that recruited referred and volunteer participants and made data available through public repositories. Analyses were stratified by APOE4 status. Three cohorts were used to evaluate conversion risk, 1 provided longitudinal measures of Aβ CSF and PET, and 3 provided cross-sectional measures of Aβ CSF. Genetic data were available from high-density single-nucleotide variant microarrays. All data were collected between September 2015 and September 2019 and analyzed between April 2019 and December 2019.

Main Outcomes and Measures

The risk of AD was evaluated through logistic regression analyses under a case-control design. The risk of conversion to mild cognitive impairment (MCI) or AD was evaluated through competing risks regression. Associations with Aβ, measured from cerebrospinal fluid (CSF) or brain positron emission tomography (PET), were evaluated using linear regression and mixed-effects modeling.

Results

Of 36 530 eligible participants, 13 782 were excluded for analysis exclusion criteria or refusal to participate. Participants were men and women aged 60 years and older who were non-Hispanic and of Northwestern European ancestry and had been diagnosed as being cognitively normal or having MCI or AD. The sample included 20 928 participants in case-control studies, 3008 in conversion studies, 556 in Aβ CSF regression analyses, and 251 in PET regression analyses. The genotype KL-VS^HET+ was associated with reduced risk for AD in individuals carrying APOE4 who were 60 years or older (odds ratio, 0.75 [95% CI, 0.67-0.84]; P = 7.4 × 10⁻⁷), and this was more prominent at ages 60 to 80 years (odds ratio, 0.69 [95% CI, 0.61-0.79]; P = 3.6 × 10⁻⁸). Additionally, control participants carrying APOE4 with KL-VS heterozygosity were at reduced risk of converting to MCI or AD (hazard ratio, 0.64 [95% CI, 0.44-0.94]; P = .02). Finally, in control participants who carried APOE4 and were aged 60 to 80 years, KL-VS heterozygosity was associated with higher Aβ in CSF (β, 0.06 [95% CI, 0.01-0.10]; P = .03) and lower Aβ on PET scans (β, −0.04 [95% CI, −0.07 to −0.00]; P = .04).

Conclusions and Relevance

The genotype KL-VS^HET+ is associated with reduced AD risk and Aβ burden in individuals who are aged 60 to 80 years, cognitively normal, and carrying APOE4. Molecular pathways associated with KL merit exploration for novel AD drug targets. The KL-VS genotype should be considered in conjunction with the APOE genotype to refine AD prediction models used in clinical trial enrichment and personalized genetic counseling.

Introduction

Klotho (KL) is a transmembrane protein and longevity factor implicated in reducing aging-associated phenotypes and cognitive decline.^1,2 Two KL missense variants (F352V [rs9536314] and C370S [rs9527025]), in perfect linkage disequilibrium, form a functional haplotype known as KL-VS. Specifically, heterozygosity for KL-VS (KL-VS^HET+ status) has been shown to increase serum levels of KL and exert protective effects on healthy aging and longevity when compared with individuals who are homozygotes for the major or minor alleles (KL-VS^HET−).^2,3,4,5 It currently remains unclear if KL-VS^HET+ status also provides protection against aging-associated neurodegenerative disorders, such as Alzheimer disease (AD).

The apolipoprotein E4 (APOE4) allele is the strongest genetic risk factor for late-onset AD.⁶ The most established pathogenic effect of APOE4 is β-amyloid (Aβ) accumulation in the brain, which correlates with decreased Aβ in the cerebrospinal fluid (CSF).^7,8 Brain Aβ accumulation likely represents a central early step in AD pathogenesis⁹; Aβ accumulates before symptom onset in individuals during early old age (60-80 years) before it reaches plateau levels and individuals convert to experiencing mild cognitive impairment (MCI) and/or AD.^10,11,12 Over this age range, Aβ accumulation and correlated cognitive decline are most prominent in individuals who carry APOE4.^13,14,15,16 Similarly, during this time, APOE4 is most strongly associated with AD risk.^17,18,19 In the search for new AD drug targets, it is thus critical to identify genetic factors that interact with APOE4 to reduce risk for AD by lowering Aβ burden.²⁰

Two recent studies evaluated whether KL-VS^HET+ status confers protection against AD in individuals who were cognitively normal. One cohort study²¹ (N = 309; mean age, 61 years) showed that KL-VS^HET+ status reduced Aβ burden in individuals who carry APOE4. The second cohort study²² (N = 581; mean age, 71 years) showed that KL-VS^HET+ did not protect against cognitive decline, and this was not modulated by APOE4 status. Here, we test on a larger scale and across the age span older than 60 years whether KL-VS^HET+ status is associated with reduced risk for AD and conversion to MCI or AD. We also reevaluate in larger samples the putative protective association of KL-VS^HET+ status with Aβ burden assessed by CSF and positron emission tomography (PET) scanning measures. Similar to the prior studies, we stratified analyses by APOE4 status to determine if the associations of KL-VS with outcome measures are specific to individuals who carry APOE4. Because the role of APOE4 in AD is most pronounced between age 60 to 80 years and genetic risk varies importantly in relatively younger individuals (60-80 years) compared with older individuals (≥80 years),²³ we also tested the hypothesis that the associations of KL-VS^HET+ status with AD risk would differ between those aged 60 to 80 years and those older than 80 years.

Methods

Ascertainment of Genotype and Phenotype Data

Twenty-two late-onset AD cohorts with genotype data were used for case-control analyses (Table 1).^{24,25,26,27,28,29,30,31,32,33,34,35,36,37,38} Ascertainment and collection of genotype and phenotype data for each cohort are summarized in the eMethods in the Supplement and described in detail elsewhere.³⁸ The National Alzheimer Coordinating Center’s Alzheimer’s Disease Center data sets 1 through 7 (NACC [ADC1-7]) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Memory and Aging Project (ROSMAP) longitudinal cohorts provided data on the age at MCI or AD diagnosis and were used in conversion-risk analyses. Genotyping was performed using various high-density single-nucleotide variant (formerly single-nucleotide polymorphism) microarrays across cohorts (eTable 1 in the Supplement). Participants or their caregivers provided written informed consent in the original studies.

Table 1. Demographics of Cohorts Used in the Alzheimer Disease Case-Control Regression Analysis.

Cohort		Diagnosis		Female, No. (%)	Age, mean (SD) [%]				Age, No. (%), y
Name^a	Participants after quality control, No.	Type	No.		At death	At last visit	At examination^b	At onset^c	60-80 y		≥80 y
Name^a	Participants after quality control, No.	Type	No.		At death	At last visit	At examination^b	At onset^c	All	APOE4+	All	APOE4+
ACT	2132	CN	1604	881 (54.9)	83.6 (5.7) [56.9]	80.3 (5.9) [43.1]	NA	NA	630	144 (22.9)	974	199 (20.4)
ACT	2132	AD	528	342 (64.8)	86.0 (3.5) [0.6]	NA	83.1 (5.2) [20.6]	80.7 (6.6) [78.8]	232	138 (59.5)	296	99 (33.4)
NACC
ADC1	1790	CN	404	243 (60.1)	85.5 (8.6) [38.4]	78.0 (8.4) [61.6]	NA	NA	196	68 (34.7)	208	36 (17.3)
ADC1	1790	AD	1386	770 (55.6)	83.5 (6.3) [1.40]	NA	79.7 (8.7) [7.4]	72.4 (7.2) [91.3]	1155	830 (71.9)	231	122 (52.8)
ADC2	705	CN	105	72 (68.6)	86.1 (7.0) [19.0]	78.8 (9.4) [81.0]	NA	NA	54	19 (35.2)	51	8 (15.7)
ADC2	705	AD	600	317 (52.8)	NA	NA	77.2 (7.5) [1.5]	72.9 (7.0) [98.5]	518	370 (71.4)	82	30 (36.6)
ADC3	1036	CN	380	238 (62.6)	88.8 (8.1) [20.0]	77.6 (8.5) [80.0]	NA	NA	209	59 (28.2)	171	26 (15.2)
ADC3	1036	AD	656	368 (56.1)	99.0 (NA) [0.2]	NA	80.4 (8.8) [4.9]	74.3 (8.1) [95.0]	512	367 (71.7)	144	56 (38.9)
ADC4	629	CN	325	200 (61.5)	86.9 (8.2) [19.4]	77.8 (7.6) [80.6]	NA	NA	174	57 (32.8)	151	26 (17.2)
ADC4	629	AD	304	164 (53.9)	NA	NA	72.5 (0.7) [0.7]	73.4 (7.0) [99.3]	257	173 (67.3)	47	7 (14.9)
ADC5	807	CN	498	336 (67.5)	89.0 (6.4) [20.3]	80.2 (8.3) [79.7]	NA	NA	222	58 (26.1)	276	52 (18.8)
ADC5	807	AD	309	170 (55.0)	NA	NA	NA	73.4 (7.3) [100]	259	193 (74.5)	50	21 (42.0)
ADC6	535	CN	253	182 (71.9)	86.8 (8.6) [20.6]	77.6 (7.9) [79.4]	NA	NA	149	52 (34.9)	104	20 (19.2)
ADC6	535	AD	282	154 (54.6)	NA	NA	NA	73.7 (7.6) [100]	233	161 (69.1)	49	14 (28.6)
ADC7	1035	CN	601	395 (65.7)	84.1 (8.4) [9.0]	76.5 (7.4) [91.0]	NA	NA	404	132 (32.7)	197	52 (26.4)
ADC7	1035	AD	434	236 (54.4)	NA	NA	NA	72.3 (7.6) [100]	371	262 (70.6)	63	29 (46.0)
ADDNEURO	239	CN	115	64 (55.7)	NA	78.5 (7.2) [100]	NA	NA	77	23 (29.9)	38	9 (23.7)
ADDNEURO	239	AD	124	77 (62.1)	NA	NA	79.8 (6.6) [9.7]	73.3 (6.9) [90.3]	101	67 (66.3)	23	9 (39.1)
ADNI	724	CN	291	149 (51.2)	84.0 (0.3)	78.2 (6.8) [99.7]	NA	NA	188	62 (33.0)	103	22 (21.4)
ADNI	724	AD	433	183 (42.3)	NA	NA	75.2 (6.6) [100]	NA	340	251 (73.8)	93	49 (52.7)
ADOD	72	CN	72	0	NA	70.3 (5.3) [100]	NA	NA	69	17 (24.6)	3	1 (33.3)
ADOD	72	AD	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
GenADA	1371	CN	687	436 (63.5)	NA	74.3 (7.1) [100]	NA	NA	545	131 (24.0)	142	34 (23.9)
GenADA	1371	AD	684	398 (58.2)	NA	NA	85.2 (6.4) [2.2]	73.7 (6.7) [97.8]	576	390 (67.7)	108	51 (47.2)
NIA-LOAD	1693	CN	718	443 (61.7)	85.9 (5.9) [2.9]	74.8 (7.8) [97.1]	NA	NA	556	190 (34.2)	162	36 (22.2)
NIA-LOAD	1693	AD	975	631 (64.7)	NA	NA	80.3 (8.0) [0.9]	72.2 (6.7) [99.1]	881	705 (80.0)	94	38 (40.4)
MAYO	1738	CN	1079	557 (51.6)	NA	73.3 (4.3) [100]	NA	NA	1079	301 (27.9)	NA	NA
MAYO	1738	AD	659	387 (58.7)	NA	NA	73.8 (4.9) [100]	NA	659	442 (67.1)	NA	NA
MAYO2	122	CN	62	28 (45.2)	83.0 (7.7) [100]	NA	NA	NA	19	2 (10.5)	43	5 (11.6)
MAYO2	122	AD	60	39 (65.0)	83.9 (5.5) [100]	NA	NA	NA	60	33 (55.0)	NA	NA
MIRAGE	481	CN	211	116 (55.0)	NA	71.6 (7.4) [100]	NA	NA	184	74 (40.2)	27	9 (33.3)
MIRAGE	481	AD	270	168 (62.2)	NA	NA	73.4 (6.1) [1.90]	70.6 (6.6) [98.1]	252	163 (64.7)	18	8 (44.4)
OHSU	316	CN	226	120 (53.1)	85.6 (7.1) [100]	NA	NA	NA	42	16 (38.1)	184	34 (18.5)
OHSU	316	AD	90	58 (64.4)	NA	NA	88.5 (9.5) [4.4]	86.8 (7.2) [95.6]	16	9 (56.3)	74	26 (35.1)
ROSMAP	1379	CN	821	579 (70.5)	87.2 (6.8) [56.4]	84.7 (6.9) [43.6]	NA	NA	171	40 (23.4)	650	104 (16.0)
ROSMAP	1379	AD	558	411 (73.7)	88.2 (2.6) [1.1]	NA	83.8 (6.6) [98.9]	NA	163	88 (54.0)	395	131 (33.2)
TGEN2	946	CN	334	163 (48.8)	80.0 (8.7) [100]	NA	NA	NA	182	44 (24.2)	152	28 (18.4)
TGEN2	946	AD	612	410 (67.0)	83.2 (6.6) [85.1]	NA	NA	74.3 (7.1) [14.9]	526	359 (68.3)	86	48 (55.8)
UPITT	1664	CN	682	436 (63.9)	NA	75.6 (6.2) [100]	NA	NA	546	117 (21.4)	136	17 (12.5)
UPITT	1664	AD	982	628 (64.0)	NA	NA	76.7 (7.8) [11.7]	72.6 (6.4) [88.3]	844	523 (62.0)	138	56 (40.6)
UM/VU/MSSM	1198	CN	642	410 (63.9)	76.8 (10.7) [10.0]	73.2 (6.9) [90.0]	NA	NA	531	135 (25.4)	111	19 (17.1)
UM/VU/MSSM	1198	AD	556	358 (64.4)	83.8 (9.0) [6.1]	NA	81.3 (10.4) [2.9]	72.6 (7.3) [91.0]	453	310 (68.4)	103	49 (47.6)
WASHU	316	CN	127	81 (63.8)	NA	76.4 (8.5) [100]	NA	NA	92	31 (33.7)	35	4 (11.4)
WASHU	316	AD	189	108 (57.1)	NA	NA	NA	74.7 (7.5) [100]	146	91 (62.3)	43	17 (39.5)
Total	20 928	CN	10 237	6129 (59.9)	84.5 (7.6) [25.4]	76.4 (7.6) [74.6]	NA	NA	6319	1772 (28.0)	3918	741 (18.9)
Total	20 928	AD	10 691	6377 (59.6)	83.3 (6.6) [6.0]	NA	78.0 (7.6) [19.4]	73.5 (7.5) [74.6]	8554	5925 (69.3)	2137	860 (40.2)

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Abbreviations: ACT, Adult Changes in Thought; ADC1-7, Alzheimer’s Disease Center data sets 1 through 7; ADDNEURO, European Collaboration for the Discovery of Novel Biomarkers for Alzheimer’s Disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; ADOD, ADNI Department of Defense; GenADA, Multi-Site Collaborative Study for Genotype-Phenotype Associations in Alzheimers Disease; NA, not applicable; NACC, National Alzheimer Coordinating Center; NIAGADS, National Institute on Aging and Genetics of Alzheimer’s Disease Data Storage Site; NIA-LOAD, National Institute on Aging Genetics Initiative for Late-Onset Alzheimer’s Disease; MAYO, Mayo Clinic Alzheimer’s Disease Genetics Studies; MAYO2, Mayo RNaseq Study; MIRAGE, Multi-institutional Research on Alzheimer Genetics Epidemiology; OHSU, Oregon Health and Science University study; ROSMAP, Rush University Religious Orders Study/Memory and Aging Project; TGEN2, Translational Genomics Research Institute Series 2; UM/VU/MSSM, University of Miami/Vanderbilt University/Mt. Sinai School of Medicine Studies; UPITT, University of Pittsburgh Study; WASHU, Washington University Study.

^{^a}

Cohort data were available through NIAGADS, the NACC, AMP-AD Knowledge Portal, the Database of Genotypes and Phenotypes, Rush Alzheimer’s Disease Center at Rush University, and the Image & Data Archive powered by Laboratory of Neuro Imaging. Cohorts included the ACT, ADC1-7 (for which phenotype data are managed by the NACC), ADDNEURO, ADNI, ADOD, GenADA, NIA-LOAD, MAYO, MAYO2, MIRAGE, OHSU, ROSMAP, TGEN2, UM/VU/MSSM, UPITT, and WASHU.

^{^b}

Age at examination represents a mixture of age types; when multiple data were available for a participant, the youngest age was taken to approximate age at onset.

^{^c}

Age at onset refers to the first onset of cognitive symptoms as reported by the participant or informant and generally precedes clinical diagnosis.

The current study protocol was granted an exemption by the Stanford University institutional review board because the analyses were carried out on deidentified, off-the-shelf data. Further informed consent was therefore not required.

The ADNI cohort provided longitudinal measures of Aβ42 in CSF and Aβ aggregates in the brain from florbetapir PET²⁴ (with sample and image processing described elsewhere^39,40). For Aβ levels on PET scans, we investigated standardized uptake value ratios (referenced to the cerebellum) in a set of brain regions (composite regions of interest: parietal, temporal, frontal, and cingulate cortices) commonly affected by amyloid pathology.^41,42 Associations with CSF Aβ42 were also evaluated in 3 cross-sectional cohorts that are available through National Institute on Aging Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS). The cohorts’ genetic data and CSF measures were made publicly available on NIAGADS as part of the data sharing associated with an article by Cruchaga et al.⁴³ Both the genetic data and CSF measures were processed in the Cruchaga et al article⁴³ and made available under their processed format. All data were collected between September 2015 and September 2019.

The conversion and Aβ analyses used cohorts that are largely overlapping with the main case-control analysis. Thus, these should be considered supportive rather than fully independent analyses.

Genetic Data Quality Control and Processing

Genetic data underwent standard quality control (Plink version 1.9 [the Laboratory of Biological Modeling and the Purcell Lab]), imputation, and ancestry determination (SNPweights version 2.1 [T. H. Chan School of Public Health at Harvard University]; eFigure 1 in the Supplement).^44,45,46 To obtain the largest and most homogeneous sample, only non-Hispanic individuals of Northwestern European ancestry were selected. Principal component analysis of genotyped single-nucleotide variants was performed to obtain principal components that capture population substructure (eFigure 2 in the Supplement). Participants’ relatedness was estimated from identity-by-descent analysis. If samples were from related individuals (identity-by-descent value ≥0.25; ie, second-degree relatives), only a single participant per relatedness cluster was used. Detailed descriptions of processing procedures and inclusion criteria are in the eMethods and eTable 2 in the Supplement.

Statistical Analyses

We evaluated the association of KL-VS^HET+ status with (1) relative risk for AD, (2) absolute risk of converting from being cognitively normal to having MCI or AD, and (3) Aβ levels. All analyses were stratified by groups who carried APOE4 (APOE-24/34/44) and did not carry APOE4 (APOE-22/23/33). Associations with AD risk and Aβ were evaluated across 3 age ranges: 60 years and older, 60 to 80 years, and 80 years and older. The full sample of those 60 years and older represents the primary analyses. The groups aged 60 to 80 years and 80 years or older were used to test the secondary hypothesis that outcomes of KL-VS status differ across age. Associations with conversion risk were evaluated in the full sample of individuals 60 years and older, whereas age stratification was not needed in these time-to-event analyses. We also evaluated the formal interaction of APOE4 with KL-VS^HET+ status in analyses that additionally included APOE4 and APOE4 × KL-VS^HET interactions as model covariates. Outcomes were evaluated per cohort and combined using inverse-variance–weighted meta-analysis. In all models, we adjusted the outcome measure for sex and the first 3 genetic principal components. For associations with AD risk and Aβ, we also adjusted for age, even within age-stratified groups, to account for remaining age-associated outcomes. Associations were considered significant at a threshold P value of less than .05 (2-tailed).

A schematic overview of all analyses is provided in eFigure 3 in the Supplement. The association between KL-VS^HET+ status and AD risk was evaluated using logistic regression analysis under a case-control design. When multiple age data were available, we prioritized age at onset (AAO) above age at examination, which was itself prioritized above age at death in affected individuals, and we prioritized age at death above age at last examination in control participants (Table 1). This priority ranking is consistent with prior AD studies^34,38 and reflects the reasoning that AAO best marks the advent of pathological changes, while age at death in control participants marks the total time spent without cognitive impairment. Association between KL-VS^HET+ status and absolute risk of conversion to MCI or AD, accounting for death as a competing risk, was evaluated using competing risk regression.^47,48 In competing risk regression, we also adjusted for years of education, which was available for most participants in cohorts with conversion data. Participants were required to be cognitively normal at baseline and have at least 3 years of follow-up.^49,50,51 Conversions were defined as the first clinical diagnosis of MCI or AD, while participants who were cognitively normal and did not convert or die were censored. Association testing with Aβ levels was restricted to control participants, as in prior studies.^21,22 Associations between KL-VS^HET+ status and Aβ measures in the ADNI study were evaluated by linear mixed-effects analysis to take into account the correlation between multiple measurements within each participant, additionally adjusting for diagnosis and participant as a random effect. The diagnosis term dealt with reversions from having MCI to being cognitively normal. Associations with Aβ CSF in the Cruchaga et al⁴³ sample were evaluated by means of multiple linear regression, additionally adjusting for cohort (eMethods in the Supplement).

To evaluate and quantify potential cohort bias, case-control and conversion risk analyses were repeated using mega-analyses that included the cohort as a covariate. To evaluate potential bias attributable to the heterogeneity in age information across different cohorts (Table 1), case-control analyses were repeated using only cases that had AAO data available (n = 7994). To increase the reliability of age at diagnosis, conversion risk analyses were repeated requiring 4 and 5 years of minimal follow-up.^49,50,51 In addition, we performed regression analyses to validate whether the association of APOE4 with risk for AD differs across age groups (60-80 years vs ≥80 years) and if APOE4 status affects AAO. All analyses were performed in R version 3.6.0 (nlme, metaphor, and cmprsk packages; R Foundation for Statistical Computing) between April 2019 and December 2019. Additional details for model/inclusion criteria are in the eMethods in the Supplement.

Results

KL-VS Heterozygosity and AD Risk per APOE4 Status

We evaluated the association of KL-VS^HET+ status with AD risk by meta-analyzing across 22 AD cohorts (Table 1). We investigated 3 different age ranges, stratified by APOE4 status (Table 2). While KL-VS^HET+ status is associated with decreased risk for AD in participants who carry APOE4 across the entire age range of those 60 years and older (odds ratio [OR], 0.75 [95% CI, 0.67-0.84]; P = 7.4 × 10⁻⁷), the outcome was driven mainly by the group aged 60 to 80 years (OR, 0.69 [95% CI, 0.61-0.79]; P = 3.6 × 10⁻⁸), with no significant association observed in the group 80 years and older (OR, 0.99 [95% CI, 0.77-1.27]; P = .94). There was no association found in any APOE4-negative group. The interaction between KL-VS^HET+ status and APOE4 status for AD risk in the group aged 60 to 80 years was significant and protective (OR, 0.76 [95% CI, 0.66-0.89]; P = 3.9 × 10⁻⁴). Forest plots in eFigure 4 in the Supplement show high cohort homogeneity of KL-VS^HET+ status association patterns in individuals who carry APOE4.

Table 2. Association of Klotho-VS Heterozygosity (KL-VS^HET+) Status With Alzheimer Disease Status in Age and Apolipoprotein E4 (APOE4) Strata^a.

Group	Association between KL-VS^HET+ and AD risk by APOE4 status				Interaction between KL-VS^HET+ and AD risk by APOE4 status
Group	Control participants with KL-VS^HET+ status, No./total No. (%)	Participants with AD with KL-VS^HET+ status, No./total No. (%)	Odds ratio (95% CI)	P value	Control participants with KL-VS^HET+ status, No./total No. (%)	Participants with AD with KL-VS^HET+ status, No./total No. (%)	Odds ratio (95% CI)	P value
60-80 y
APOE4+	528/1737 (30.4)	1475/5883 (25.1)	0.69 (0.61-0.79)	3.6 × 10⁻⁸	1694/6189 (27.3)	2137/8478 (25.2)	0.73 (0.61-0.87)	5.1 × 10⁻⁴
APOE4−	1166/4452 (26.2)	662/2595 (25.5)	0.98 (0.87-1.11)	.79	NA	NA	NA	NA
≥80 y
APOE4+	187/713 (26.2)	218/826 (26.4)	0.99 (0.77-1.27)	.94	972/3772 (25.9)	552/2053 (26.9)	0.92 (0.69-1.24)	.61
APOE4−	796/3090 (25.8)	339/1253 (27.1)	1.09 (0.93-1.28)	.28	NA	NA	NA	NA
Full sample
APOE4+	724/2488 (29.1)	1707/6752 (25.3)	0.75 (0.67-0.84)	7.4 × 10⁻⁷	2704/10 103 (26.8)	2718/10 631 (25.5)	0.76 (0.66-0.89)	3.9 × 10⁻⁴
APOE4−	1997/7670 (26.0)	1015/3906 (26.0)	1.01 (0.91-1.11)	.91	NA	NA	NA	NA

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Abbreviations: AD, Alzheimer disease; HET+, heterozygous; NA, not applicable.

^{^a}

This Table shows the results of meta-analyses including cohorts with a minimal sample size of 50 that had both affected individuals and control participants.

In sensitivity analyses, results were highly consistent when cohorts were combined through mega-analysis (eTable 3 in the Supplement). Additionally, given that 25.4% of cases did not have AAO data provided (Table 1), we repeated analyses using only affected individuals with AAO data and all control participants (eTables 4 and 5 in the Supplement). Despite smaller sample sizes, the protective association of KL-VS^HET+ status with AD in individuals carrying APOE4 was even more pronounced and remained strongest in the group of individuals who carried APOE4 and were between 60 and 80 years (meta-analysis; OR, 0.64 [95% CI, 0.55-0.74]; P = 4.0 × 10⁻⁹). In addition, we confirmed that, as expected, the association between APOE4 positivity and AD risk was stronger in those aged 60 to 80 years (OR, 5.79 [95% CI, 5.38-6.23]) compared with those 80 years or older (OR, 2.97 [95% CI, 2.63-3.35]; P < 2.2 × 10⁻¹⁶). Participants who carried APOE4 also had reduced AAO (mean [SD] age, 72.0 [6.7] years) compared with participants who did not carry APOE4 (mean [SD] age, 76.1 [8.1] years; P < 2.2 × 10⁻¹⁶).

KL-VS^HET+ Status and Risk of Conversion to MCI or AD in Individuals Stratified by APOE4 Status

We then assessed the association of KL-VS^HET+ status with risk for conversion to MCI or AD. Meta-analysis across the 3 investigated cohorts (eTable 6 in the Supplement) showed a significant protective association of KL-VS^HET+ status with conversion risk in those who carry APOE4 (hazard ratio [HR], 0.64 [95% CI, 0.44-0.94]; P = .02) but not in participants who did not carry APOE4 (HR, 1.06 [95% CI, 0.81-1.37]; P = .69; eTable 7 in the Supplement). The interaction between KL-VS^HET+ status and APOE4 status was significant and protective (HR, 0.62 [95% CI, 0.39-1.00]; P = .048). Figure 1 shows the cumulative conversion risk across the age span, where the protective association of KL-VS^HET+ status in the group with APOE4 begins around 77 years of age. Forest plots in eFigure 5 in the Supplement and cumulative risk plots in eFigure 6 in the Supplement show that these association and interaction patterns are consistent across all 3 cohorts. In sensitivity analyses, these findings remained consistent when evaluated through mega-analysis and after requiring minimum follow-up times of 4 or 5 years (eTable 8 in the Supplement).

Figure 1. — A, Individuals with apolipoprotein E4 (*APOE4*). The outcome of KL-VS^HET+ status, as determined from competing risk regression analysis meta-analyzed across 3 independent cohorts, is significant in individuals who carry *APOE4* (hazard ratio, 0.64 [95% CI, 0.4-0.94]; P = .02). B, Individuals without *APOE4* (hazard ratio, 1.06 [95% CI, 0.81-1.37]; P = .69). AD indicates Alzheimer disease; HET+, heterozygosity; HET-, nonheterozygosity; MCI, mild cognitive impairment.

We additionally evaluated the association of KL-VS^HET+ status with conversion from being cognitively normal or having MCI to having AD (eTables 9 and 10 and eFigure 7 in the Supplement). The KL-VS^HET+ status reduced conversion risk in the group carrying APOE4 (HR, 0.81 [95% CI, 0.66-1.00]; P = .047) but not in the group without APOE4 (HR, 1.12 [95% CI, 0.78-1.61]; P = .99). These outcomes were consistent for a minimum of 4 years and 5 years of follow-up. The interaction of KL-VS^HET+ status with APOE4 status was protective but significant only for patients with a minimum of 5 years of follow-up (HR, 0.68 [95% CI, 0.49-0.95]; P = .02; eTable 9 in the Supplement).

KL-VS^HET+ Status and Aβ in Control Participants Aged 60 to 80 Years Stratified by APOE4 Status

Similar to AD risk analyses, we evaluated whether there was an age-dependent association of KL-VS^HET+ status with Aβ CSF levels. In the age range of 60 to 80 years, KL-VS^HET+ status was significantly associated with increased Aβ CSF levels in control participants carrying APOE4 (β, 0.06 [95% CI, 0.01-0.10], P = .03) but not in control participants without APOE4 (β, 0.04 [95% CI, −0.02 to 0.09]; P = .22; Figure 2A). In the full age range (≥60 years), this association was not significant in control participants carrying APOE4 (β, 0.02 [95% CI, −0.03 to 0.06]; P = .50) or control participants without APOE4 (β, 0.02 [95% CI, −0.03 to 0.07]; P = .44; eFigure 8 in the Supplement). Forest plots in eFigure 9 in the Supplement show consistent associations for both cohorts in those aged 60 to 80 years who carried APOE4. Finally, we evaluated the association of KL-VS^HET+ status with Aβ findings on PET in an AD-relevant brain composite region of interest. Findings were highly consistent with those for CSF levels; that is, KL-VS^HET+ status significantly decreased Aβ on PET in the group who were positive for APOE4 and aged 60 to 80 years (β, −0.04 [95% CI, −0.07 to 0.00]; P = .04; Figure 2B) but not in those aged 60 to 80 years who did not carry APOE4 (β, 0.00 [95% CI, −0.02 to 0.01]; P = .69) or either of the other groups aged 60 years or older (eFigure 8 in the Supplement).

Figure 2. — A, Measured by cerebrospinal fluid samples. B, Measured by positron emission tomography imaging. Box plot error bars show the 95th-percentile range. Gray circles indicate values outside of the 95th percentile range. Meta-analyses between Alzheimer’s Disease Neuroimaging Initiative and Cruchaga et al⁴³ samples were significant in participants who carry *APOE4* (leftmost pairs in each graph; cerebrospinal fluid, β, 0.06 [95% CI, 0.01-0.10]; P = .03; positron emission tomography, β, −0.04 [95% CI, −0.07 to −0.00]; P = .04). HET+ indicates heterozygosity; HET-, nonheterozygosity.

Additional Analyses

In addition to comparing participants with KL-VS^HET+ status vs KL-VS^HET− status, we contrasted individuals with KL-VS^HET+ status vs those who did not carry KL-VS (eTables 11-15 in the Supplement). Results were highly consistent with the main analyses but had slightly reduced effect sizes. Because KL-VS homozygosity (KL-VS^HOM) has been associated with negative outcomes on life span,² brain-aging resilience,⁵² and cognition,⁴ we also evaluated individuals with KL-VS^HOM status compared with those who did not carry KL-VS (eTables 16-19 and eFigure 10 in the Supplement). In individuals who carry APOE4, results were consistent, with KL-VS^HOM status increasing risk, but only conversion risk from being cognitively normal or having MCI to having AD reached nominal significance. There were no significant results in participants who did not carry APOE4. Finally, given the biological ambiguity of individuals who carry APOE24 (both risk-increasing and decreasing alleles), we repeated analyses excluding these participants (eTables 20-24 in the Supplement). Again, results were highly consistent with the main analyses.

Discussion

Our results demonstrate that KL-VS^HET+ status was associated with reduced AD risk in individuals who carried APOE4, and this was so mostly between 60 and 80 years. In this age range, KL-VS^HET+ status was also associated with lower Aβ burden in individuals who are cognitively normal and carry APOE4. Additionally, starting close to 80 years of age, control participants who carried APOE4 and had KL-VS^HET+ status were at reduced risk of converting to MCI or AD.

To our knowledge, the current study is the largest to date to evaluate a heterozygous genetic association with AD risk. Specifically, we hypothesized that KL-VS^HET+ status would reduce risk of AD in those who carried APOE4. Furthermore, given that the genetic risk for AD attributable to APOE4 is higher between 60 and 80 years of age,^17,18,19 which was confirmed in our case-control analysis in which the OR for APOE4 was almost 2-fold higher in the group 60 to 80 years old (OR, 5.8) compared with those 80 years or older (OR, 3.0), we hypothesized that the protective association of KL-VS^HET+ status in those with APOE4 would be strongest in the 60-year to 80-year age range. We showed that protective outcomes of KL-VS^HET+ status on AD risk in those who carry APOE4 was highly significant across the entire age range older than 60 years but was considerably stronger between the ages of 60 and 80 years and was not detectable in the ages 80 years and older. This age-specific interaction of KL-VS^HET+ status with APOE4 is also consistent with recent work that showed how genome-wide risk for AD differs between 60 and 80 years and those older than 80 years.⁴³ The largest (to our knowledge) prior APOE4-stratified genome-wide association study of AD did not stratify by age and only evaluated additive genetic effects and so would not have picked up the KL-VS^HET+ status outcome identified here.⁵³

We then evaluated the association of KL-VS^HET+ status with conversion risk. In individuals who carry APOE4, KL-VS^HET+ status reduced risk of conversion from cognitively normal status to MCI or AD with a hazard ratio of approximately 0.65 and from cognitively normal status or MCI to AD with a hazard ratio of about 0.80. This suggests that the protective nature of KL-VS^HET+ status is stronger in control participants and diminishes in affected individuals who have already developed MCI. Ascertainment differences across cohorts represent a source of bias, but findings were consistent for both mega-analyses and meta-analyses. Additionally, by restricting our analyses to participants with a minimal follow-up time of 3, 4, or 5 years, we could increase confidence in the age at diagnosis.^49,50,51 For each model that required 5 or more years of minimal follow-up, we obtained significant results for KL-VS^HET+ status in the APOE4-positive groups and interactions of KL-VS^HET+ status with APOE4. Lastly, we could add years of education as a covariate in the conversion models, allowing us to account for MCI or AD risk mitigation attributable to possible differences in cognitive reserve.⁵⁴

Notably, the difference in conversion risk between participants who had KL-VS^HET+ status vs those with KL-VS^HET− status who carried APOE4 became apparent around 80 years of age. There are no prior reports on MCI or AD conversion risk attributable to having KL-VS^HET+ status to compare our findings with. However, Porter et al²² examined individuals who were cognitively normal with a mean age of 71 years and reported there was neither an association of KL-VS^HET+ status with longitudinal measures of global cognition nor a modifying association with APOE4 status. Other studies that evaluated the association of KL-VS^HET+ status with measures of cognition in control participants did not directly investigate interactions with APOE4 but did observe protective associations that were more pronounced closer to 80 years of age.^3,5,55 Overall, our findings appear consistent with prior literature, but further studies need to evaluate the interaction of age, APOE4, and KL-VS^HET+ status on cognition in control populations.

We observed significant protective interactions between APOE4 status and KL-VS^HET+ status for both risk of AD and risk of conversion, whereas KL-VS^HET+ status had no association with outcome in individuals who did not carry APOE4. This suggests that KL-VS interacts with aspects of AD pathology that are more pronounced in those who carry APOE4, such as Aβ accumulation during the presymptomatic phases of the disease. Our analyses of Aβ CSF and PET in control participants with APOE4 between ages 60 and 80 years indeed confirmed reduced Aβ burden attributable to KL-VS^HET+ status. Erickson et al²¹ reported similar results, in that those with KL-VS^HET+ status did not display the commonly expected difference in Aβ burden (in CSF levels and on PET scanning) between control participants with APOE4 vs without APOE4, but participants who were KL-VS^HET− did. All brain areas that we investigated in the composite region of interest also displayed consistent results in the study by Erickson et al. While Porter et al²² reported there was no association of KL-VS^HET+ status with cognition, they did not directly evaluate associations with Aβ. In that study,²² participants were classified as having low or high amounts of Aβ based on brain Aβ levels on PET scans. When we considered ratios of participants with low and high Aβ amounts, as reported in Table 2 of their article,²² we could derive risk estimates associated with high levels of Aβ for those with KL-VS^HET+ status and APOE4 (OR, 0.59) and without APOE4 (OR, 0.82). These are similar to our finding that KL-VS^HET+ status reduced Aβ on PET in those who carry APOE4. Overall, our findings associating KL-VS^HET+ status with Aβ appear consistent with results in 2 prior, independent studies.

Reduced Aβ burden attributable to KL-VS^HET+ status in control participants with APOE4 between ages 60 and 80 years may provide an explanation for the age shift between our case-control and conversion findings. The AD risk attributable to KL-VS^HET+ status in those who carry APOE4 was lower between ages 60 and 80 years, where the age for cases mainly represented AAO (mean age, 72 years). Protective associations of KL-VS^HET+ status with conversion risk became apparent around 77 years of age, roughly indicating a 5-year shift between the onset of symptoms and a formal diagnosis or conversion. Abnormal Aβ levels in control participants can precede conversion by 5 to 10 years,¹⁰ suggesting that KL-VS^HET+ status may delay conversion by reducing Aβ levels. Currently, there is an increasing need to identify risk factors that improve prognostication of AD conversion risk.⁵⁶ These risk factors can be used to stratify patients into high-risk groups who can be recruited into clinical prevention trials to increase their statistical power and efficiency. The APOE4 allele is a major genetic risk factor used for AD trial enrichment.⁵⁷ Our results suggest that for prevention trials, it will help to further select control participants who have KL-VS^HET− status and APOE4 (70% of the sample), who appear more likely to convert to AD. On an interesting, related matter, KL-VS^HET+ status has been associated with increased serum levels of KL,^3,52 while KL-VS^HOM has conversely been associated with decreased serum levels of KL.⁵² Both studies further found direct correlations between systemic KL levels and cognitive performance in mice³ and brain aging resilience in humans.⁵² Additionally, CSF levels of KL were shown to be lower in individuals with AD vs age-matched participants who were cognitively normal.⁵⁸ Combined with our findings that KL-VS^HET+ status is consistently associated with reductions (and KL-VS^HOM with increases) in AD conversion risk, this suggests that systemic KL levels may serve as a promising biomarker to help identify those who are positive for APOE4 and at higher risk for developing AD.

Currently, there is no known mechanism by which KL-VS interacts with APOE4 to modulate Aβ levels. Interestingly, KL expression is regulated by amyloid precursor protein (APP).⁵⁹ Furthermore, 3 enzymes linked to APP cleavage (a disintegrin and metalloproteinase domain-containing proteins 10 and 17 [ADAM10 and ADAM17] and β-secretase 1 [BACE1]) also cleave KL in the cell membrane leading to shedding of KL’s extracellular domain.^60,61,62 In AD mouse models, therapies aimed at increasing KL expression or KL cleavage were shown to reduce Aβ burden through autophagy-mediated clearance and confer neuroprotection through increased expression of ADAM10.^63,64 Overall, this raises the intriguing possibility of an interaction between APOE4, KL-VS, and the molecular APP processing machinery that produces Aβ. Other studies, in animal models and humans, indicate that KL-VS^HET+ status confers resilience to brain-aging and cognitive aging,^4,52,65,66 which may also contribute to protective associations against AD. Although lacking direct validation, our findings may also suggest that individuals with KL-VS^HET+ status are biologically younger than those who have KL-VS^HET− status. Indeed, previous studies reported both a slowed epigenetic age for individuals with KL-VS heterozygosity⁶⁷ and a direct correlation between telomerase activity and KL expression.⁶⁸ Notably, KL-VS^HET+ status showed an age-specific association with AD here, which is in line with prior findings on life span trajectories.^2,69 Future studies will need to explore these promising research avenues.

Limitations

One limitation for our analyses is the variability in age and diagnosis ascertainment across cohorts. However, we repeated all tests using both meta-analyses and mega-analyses. We also performed sensitivity analyses, including only individuals with AD who had AAO data available. Our findings were highly consistent across all models and displayed little to no heterogeneity, making it unlikely that the results were affected by cohort bias. The null findings in the groups 80 years and older may, however, also be attributable to limited sample sizes in this age stratum.

Conclusions

Overall, our findings suggest that KL-VS^HET+, possibly by increasing systemic KL levels, is associated with a protective outcome against AD that manifests in participants who carry APOE4 and are cognitively normal between the ages of 60 and 80 years. Our work paves the way for biological validation studies to elucidate the molecular pathways by which KL-VS and APOE interact. Information on KL-VS status should also prove useful in further refinement of genetic risk profiles for both clinical trial enrichment and personalized genetic counseling.

Supplement.

eMethods. Phenotype Ascertainment, Genetic Data Quality Control and Processing, and Statistical Analyses – Additional Model Criteria.

eFigure 1. Admixture plots for A) the five major super populations and B) three major sub-European populations across all 22 case-control cohorts.

eFigure 2. First three principal components of the genetic population structure in Northwestern European subjects across all 22 case-control cohorts.

eFigure 3. Schematic overview of data sets and performed analyses.

eFigure 4. Forest plots for the association of KL-VSHET+ with Alzheimer disease case-control status in 60-80 year old subjects.

eFigure 5. Forest plots for the association of KL-VSHET+ with risk of conversion to mild cognitive impairment or Alzheimer disease in subjects with a minimum of three years follow-up time.

eFigure 6. Cohort-specific risk of conversion to mild cognitive impairment or Alzheimer disease by KL-VS heterozygosity status in A) APOE4+ and B) APOE4- subjects.

eFigure 7. Risk of conversion to Alzheimer disease by KL-VS heterozygosity status in A) APOE4+ and B) APOE4- subjects.

eFigure 8. Association of KL-VS heterozygosity status with amyloid beta levels in 60+ years old controls stratified by APOE4 status, as measured from A) CSF samples and B) PET imaging.

eFigure 9. Forest plots for the association of KL-VSHET+ with amyloid beta CSF in APOE4+ controls of ages A) 60-80 and B) 60+ years.

eFigure 10. Association of KL-VSHOM, in contrast to KL-VSNC, with amyloid beta levels as measured from CSF samples, in A) 60-80 and B) 60+ years old controls, stratified by APOE4 status.

eTable 1. SNP microarray platforms per cohort.

eTable 2. Case-control sample sizes per cohort after sequential quality control and filtering steps (detailed in the titles above each column).

eTable 3. Association of KL-VSHET+ with Alzheimer disease case-control status in age- and APOE4-strata, determined by MEGA-analysis.

eTable 4. Association of KL-VSHET+ with Alzheimer disease case-control status in age- and APOE4-strata, determined by META-analysis and using only age-at-onset data for cases.

eTable 5. Association of KL-VSHET+ with Alzheimer disease case-control status in age- and APOE4-strata, determined by MEGA-analysis and using only age-at-onset data for cases.

eTable 6. Sample sizes per conversion risk cohort after sequential quality control and filtering steps.

eTable 7. Association of KL-VSHET+ with risk of conversion to mild cognitive impairment or Alzheimer disease, stratified by APOE4 status, determined by META-analysis.

eTable 8. Association of KL-VSHET+ with risk of conversion to mild cognitive impairment or Alzheimer disease, stratified by APOE4 status, determined by MEGA-analysis.

eTable 9. Association of KL-VSHET+ with risk of conversion to Alzheimer disease, stratified by APOE4 status, determined by META-analysis.

eTable 10. Association of KL-VSHET+ with risk of conversion to Alzheimer disease, stratified by APOE4 status, determined by MEGA-analysis.

eTable 11. Association of KL-VSHET+, in contrast to KL-VSNC, with Alzheimer disease case-control status in age- and APOE4-strata, determined by META-analysis.

eTable 12. Association of KL-VSHET+, in contrast to KL-VSNC, with Alzheimer disease case-control status in age- and APOE4-strata, determined by META-analysis and using only age-at-onset data for cases.

eTable 13. Association of KL-VSHET+, in contrast to KL-VSNC, with risk of conversion to mild cognitive impairment or Alzheimer disease, stratified by APOE4 status, determined by META-analysis.

eTable 14. Association of KL-VSHET+, in contrast to KL-VSNC, with risk of conversion to Alzheimer disease, stratified by APOE4 status, determined by META-analysis.

eTable 15. Association of KL-VSHET+, in contrast to KL-VSHET- or KL-VSNC, with Aβ levels in cognitively normal subjects, stratified by APOE4 status, determined by META-analysis.

eTable 16. Association of KL-VSHOM, in contrast to KL-VSNC, with Alzheimer disease case-control status in age- and APOE4-strata, determined by MEGA-analysis.

eTable 17. Association of KL-VSHOM, in contrast to KL-VSNC, with Alzheimer disease case-control status in age- and APOE4-strata, determined by MEGA-analysis and using only age-at-onset data for cases.

eTable 18. Association of KL-VSHOM, in contrast to KL-VSNC, with risk of conversion to mild cognitive impairment or Alzheimer disease, stratified by APOE4 status, determined by MEGA-analysis.

eTable 19. Association of KL-VSHOM, in contrast to KL-VSNC, with risk of conversion to Alzheimer disease, stratified by APOE4 status, determined by MEGA-analysis.

eTable 20. Association of KL-VSHET+ with Alzheimer disease case-control status in age- and APOE4-strata, but excluding APOE24 carriers, determined by META-analysis.

eTable 21. Association of KL-VSHET+ with Alzheimer disease case-control status in age- and APOE4-strata, but excluding APOE24 carriers, determined by META-analysis and using only age-at-onset data for cases.

eTable 22. Association of KL-VSHET+ with risk of conversion to mild cognitive impairment or Alzheimer disease, stratified by APOE4 status but excluding APOE24 carriers, determined by META-analysis.

eTable 23. Association of KL-VSHET+ with risk of conversion to Alzheimer disease, stratified by APOE4 status but excluding APOE24 carriers, determined by META-analysis.

eTable 24. Association of KL-VSHET+ with Aβ levels in cognitively normal subjects, stratified by APOE4 status but excluding APOE24 carriers, determined by META-analysis.

eReferences.

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References

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