Evaluating the association ofAPOEgenotype and cognitive resilience in SuperAgers

Evaluating the association of APOE genotype and cognitive resilience in SuperAgers | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Evaluating the association of APOE genotype and cognitive resilience in SuperAgers Alaina Durant , View ORCID Profile Shubhabrata Mukherjee , Michael L. Lee , Seo-Eun Choi , View ORCID Profile Phoebe Scollard , View ORCID Profile Brandon S. Klinedinst , Emily H. Trittschuh , View ORCID Profile Jesse Mez , View ORCID Profile Lindsay A. Farrer , View ORCID Profile Katherine A. Gifford , View ORCID Profile Carlos Cruchaga , View ORCID Profile Jason Hassenstab , View ORCID Profile Adam C. Naj , View ORCID Profile Li-San Wang , View ORCID Profile Sterling C. Johnson , View ORCID Profile Corinne D. Engelman , View ORCID Profile Walter A. Kukull , View ORCID Profile C. Dirk Keene , View ORCID Profile Andrew J. Saykin , View ORCID Profile Michael L. Cuccaro , Brian W. Kunkle , Margaret A. Pericak-Vance , Eden R. Martin , David A. Bennett , View ORCID Profile Lisa L. Barnes , Julie A. Schneider , View ORCID Profile William S. Bush , View ORCID Profile Jonathan L. Haines , View ORCID Profile Richard Mayeux , View ORCID Profile Badri N. Vardarajan , Marilyn S. Albert , View ORCID Profile Paul M. Thompson , Angela L. Jefferson , The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , Alzheimer’s Disease Genetics Consortium (ADGC) , The Alzheimer’s Disease Sequencing Project (ADSP) , View ORCID Profile Paul K. Crane , View ORCID Profile Logan Dumitrescu , View ORCID Profile Derek B. Archer , View ORCID Profile Timothy J. Hohman , View ORCID Profile Leslie S. Gaynor doi: https://doi.org/10.1101/2025.01.07.25320117 Alaina Durant a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shubhabrata Mukherjee b Department of Medicine, University of Washington , 1959 NE Pacific Street, Seattle, WA 98195-6420, USA MS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shubhabrata Mukherjee Michael L. Lee MPH, PhDb Find this author on Google Scholar Find this author on PubMed Search for this author on this site Seo-Eun Choi b Department of Medicine, University of Washington , 1959 NE Pacific Street, Seattle, WA 98195-6420, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phoebe Scollard b Department of Medicine, University of Washington , 1959 NE Pacific Street, Seattle, WA 98195-6420, USA c University Bordeaux, Inserm, Bordeaux Population Health Research Center , UMR1219, 146 Rue Léo Saignat 11, Bordeaux, FranceL33076 MA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Phoebe Scollard Brandon S. Klinedinst b Department of Medicine, University of Washington , 1959 NE Pacific Street, Seattle, WA 98195-6420, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brandon S. Klinedinst Emily H. Trittschuh d Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine , 1959 NE Pacific Street, Seattle, WA 98195-6560, USA e VA Puget Sound Health Care System , GRECC, 1660 S Columbian Way, Seattle, WA 98108, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jesse Mez f Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Center , 85 East Concord Street, 1st floor, Boston, MA 02118, USA MS, MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jesse Mez Lindsay A. Farrer f Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston University Medical Center , 85 East Concord Street, 1st floor, Boston, MA 02118, USA g Department of Biostatistics, Boston University School of Public Health , Crosstown Building, 801 Massach us etts Avenue 3rd Floor, Boston, MA 02118, USA h Department of Medicine, Boston University Chobanian & Avedisian School of Medicine , 72 East Concord St., Boston, MA 02118, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lindsay A. Farrer Katherine A. Gifford a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA h Department of Medicine, Boston University Chobanian & Avedisian School of Medicine , 72 East Concord St., Boston, MA 02118, USA PsyD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine A. Gifford Carlos Cruchaga i Department of Psychiatry, Washington University in St. Louis , 4444 Forest Park Avenue, St. Louis, MO 63108, USA j NeuroGenomics and Informatics Center, Washington University in St. Louis , 4444 Forest Park Ave Room 5501, St. Louis, MO 63018, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos Cruchaga Jason Hassenstab k Department of Neurology and Department Psychological & Brain Sciences, Washington University in St Louis , 1 Brookings Drive, St. Louis, MO 63130, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jason Hassenstab Adam C. Naj l Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine , 423 Guardian Drive, Philadelphia, PA 19104-6021, USA m Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine , 3400 Spruce St., Philadelphia, PA 19104-4238, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Adam C. Naj Li-San Wang m Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine , 3400 Spruce St., Philadelphia, PA 19104-4238, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Li-San Wang Sterling C. Johnson n Alzheimer’s Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health , 600 Highland Ave J5/1 Mezzanine, Madison, WI 53792, USA o Department of Population Health Sciences, University of Wisconsin-Madison, School of Medicine and Public Health , 500 Lincoln Dr., Madison, WI 53706, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sterling C. Johnson Corinne D. Engelman n Alzheimer’s Disease Research Center, University of Wisconsin-Madison, School of Medicine and Public Health , 600 Highland Ave J5/1 Mezzanine, Madison, WI 53792, USA o Department of Population Health Sciences, University of Wisconsin-Madison, School of Medicine and Public Health , 500 Lincoln Dr., Madison, WI 53706, USA MSPH, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Corinne D. Engelman Walter A. Kukull p Department of Epidemiology, School of Public Health, University of Washington , 3980 15thAve NE, Seattle, WA 98195, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Walter A. Kukull C. Dirk Keene q Department of Laboratory Medicine and Pathology, University of Washington , 1959 NE Pacific St, Seattle, WA 98195, USA MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for C. Dirk Keene Andrew J. Saykin r Department of Radiology and Imaging Services, Indiana University School of Medicine, 550 University Blvd , Indianapolis, IN 46202, USA s Department of Medical and Molecular Genetics, School of Medicine, Indiana University , 975 W Walnut St IB 130, Indianapolis, IN 46202, USA PyD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew J. Saykin Michael L. Cuccaro t John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , 1501 NW 10th Ave, Miami, FL 33101, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael L. Cuccaro Brian W. Kunkle t John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , 1501 NW 10th Ave, Miami, FL 33101, USA MPH, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Margaret A. Pericak-Vance t John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , 1501 NW 10th Ave, Miami, FL 33101, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eden R. Martin t John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine , 1501 NW 10th Ave, Miami, FL 33101, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site David A. Bennett u Rush Alzheimer’s Disease Center, Rush University Medical Center , 1750 W Harrison St, Chicago, IL 60612, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lisa L. Barnes u Rush Alzheimer’s Disease Center, Rush University Medical Center , 1750 W Harrison St, Chicago, IL 60612, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa L. Barnes Julie A. Schneider u Rush Alzheimer’s Disease Center, Rush University Medical Center , 1750 W Harrison St, Chicago, IL 60612, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site William S. Bush v Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University , 2109 Adelbert Rd, Cleveland, OH 44106, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William S. Bush Jonathan L. Haines v Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University , 2109 Adelbert Rd, Cleveland, OH 44106, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan L. Haines Richard Mayeux w Department of Neurology, The Taub Institute for Research on Alzheimer’s Disease and The Aging Brain, Columbia University Medical Center and The New York Presbyterian Hospital , 710 W 168th St #3, New York, NY 10032, USA MD, MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard Mayeux Badri N. Vardarajan w Department of Neurology, The Taub Institute for Research on Alzheimer’s Disease and The Aging Brain, Columbia University Medical Center and The New York Presbyterian Hospital , 710 W 168th St #3, New York, NY 10032, USA MS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Badri N. Vardarajan Marilyn S. Albert x Department of Neurology, The Johns Hopkins University School of Medicine , 550 N Broadway #415, Baltimore, MD 21205, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul M. Thompson y Keck School of Medicine, University of Southern California , 1975 Zonal Ave, Los Angeles, CA 90033, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul M. Thompson Angela L. Jefferson a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA z Department of Neurology, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: leslie.gaynor{at}vumc.org Paul K. Crane b Department of Medicine, University of Washington , 1959 NE Pacific Street, Seattle, WA 98195-6420, USA MD, MPH Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul K. Crane Logan Dumitrescu a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA z Department of Neurology, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA aa Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA MS, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Logan Dumitrescu Derek B. Archer a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA z Department of Neurology, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA aa Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Derek B. Archer Timothy J. Hohman a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA z Department of Neurology, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA aa Vanderbilt Genetics Institute, Vanderbilt University Medical Center, 1211 Medical Center Drive , Nashville, TN 37232, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy J. Hohman Leslie S. Gaynor a Vanderbilt Memory & Alzheimer’s Center, Vanderbilt University Medical Center , 3319 West End Ave, Rm 819, Nashville, TN 37203, USA bb Division of Geriatric Medicine, Department of Medicine, Vanderbilt University Medical Center , 1161 21st Ave S, Nashville, TN 37232, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leslie S. Gaynor For correspondence: leslie.gaynor{at}vumc.org Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract INTRODUCTION “SuperAgers” are oldest-old adults (ages 80+) whose memory performance more closely resembles middle-aged adults. The present study examined APOE allele frequency in non-Hispanic Black (NHB) and non-Hispanic White (NHW) SuperAgers compared to controls and Alzheimer’s disease dementia cases. METHODS In 18,080 participants from eight cohorts, harmonized clinical diagnostics and memory, executive function, and language domain scores were used to identify SuperAgers, cases, and controls across age-defined bins. RESULTS NHW SuperAgers had significantly lower frequency of APOE- ε4 alleles and higher frequency of APOE -ε2 alleles compared to all cases and controls, including oldest-old controls. Similar patterns were found in a small yet substantial sample of NHB SuperAgers; however, not all comparisons with controls reached significance. DISCUSSION We demonstrated strong evidence that APOE allele frequency relates to SuperAger status. Further research is needed with a larger sample of NHB SuperAgers to determine if mechanisms conferring resilience differ across race groups. 1 Background “SuperAgers” is a term used to describe oldest-old (ages 80+) adults with episodic memory performance most closely resembling adults in their 50s to mid-60s. 1 , 2 Studies suggest that resilience to Alzheimer’s disease (AD) pathological changes and neurodegeneration may explain SuperAgers’ high memory scores. 3 – 6 Further research is needed to elucidate factors conferring resilience to AD-related brain changes and subsequent cognitive decline in SuperAgers. Moreover, research is needed to explore resiliency factors in non-Hispanic Black (NHB) SuperAgers, as this group is largely understudied. 7 APOE- ε4 is the strongest genetic risk factor for late-onset AD. 8 The Northwestern SuperAging project reported lower APOE- ε4 allele frequency in SuperAgers (N = 10-12) compared to non-demented older adults. 2 , 9 In contrast, most studies report no differences in APOE -ε4 allele frequency between SuperAgers and oldest-old adults with typical memory performance, both groups having lower APOE -ε4 allele frequency compared to AD dementia cases. 4 – 6 , 10 – 12 Notably, these studies have small SuperAger samples (N = 25-64) 4 – 6 , 10 – 12 oftentimes drawn from the same cohort, thus limiting their generalizability and reliability. Further, these studies exclusively include NHW participants. 4 – 6 , 10 – 12 To our knowledge, only one study has been published with a NHB SuperAger sample (N = 61) and did not find a significant difference in APOE -ε4 allele frequency between SuperAgers and same-age controls. 7 Even fewer studies have explored the relationship of APOE -ε2, the protective APOE allele, and SuperAger status, 11 – 13 likely due to the low minor allele frequency of APOE -ε2. Studies of APOE -ε2 allele frequency and superior memory in the oldest-old have not found a significant relationship 11 – 13 ; however, questions of statistical power, generalizability, and reliability remain. The present study aims to explore APOE -ε4 and -ε2 allele frequency in SuperAgers compared to AD dementia cases and controls in a large, harmonized multicohort dataset from the Alzheimer’s Disease Sequencing Project Phenotype Harmonization Consortium (ADSP-PHC). Using harmonized clinical diagnoses and cognitive domain scores (e.g., memory, executive function, language), we classified NHW and NHB middle-aged, old, and oldest-old adults as cases, controls, or SuperAgers, and compared APOE -ε4 and -ε2 allele frequency of SuperAgers to cases and controls by age bin. Although prior literature suggests that there is not a relationship between optimal memory in oldest-old age and APOE genotype, this is likely due to a limitation of sample size. The ADSP-PHC has enabled us to complete, to our knowledge, the largest and most racially diverse study to date of APOE allele frequency and SuperAger status. We hypothesize that SuperAgers will possess a lower frequency of APOE -ε4 alleles and a higher frequency of APOE -ε2 alleles compared to both AD dementia cases and controls. 2 Methods 2.1 Study Population The ADSP-PHC was assembled in 2021 to provide large-scale harmonization of ADSP cohorts, spanning markers of cognition, neuroimaging, fluid biomarkers, and neuropathology. Cohorts that are part of ADSP-PHC and were included in the present study are: Adult Changes in Thought (ACT), 14 Alzheimer’s Disease Neuroimaging Initiative (ADNI), 15 Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD), 16 National Alzheimer’s Coordinating Centers (NACC), 17 National Institute on Aging Alzheimer’s Disease Family Based Study (NIA-AD FBS), 18 Religious Orders Study/Rush Memory and Aging Project/Minority Aging Research Study (ROS/MAP/MARS), 19 , 20 , 21 Knight Alzheimer’s Disease Research Center (Knight ADRC), 22 and Wisconsin Registry for Alzheimer’s Prevention (WRAP). 23 Written informed consent was obtained from all participants in each cohort, and research was carried out with protocols approved by each site’s institutional review board. These secondary analyses were approved by the Vanderbilt University Medical Center institutional review board. 2.2 Cognitive Domain Scores Each cohort used different neuropsychological assessment tools to measure cognition which may not be on the same scale. We derived co-calibrated and harmonized cognitive domain scores for four domains (i.e., memory, executive function, language, and visuospatial) using confirmatory factor analysis. 24 Consistent with previous definitions of SuperAgers, only memory, executive function, and language domains were used in present analyses. 1 , 2 To ensure inclusion of high-quality harmonization, cognitive domain scores with a standard error of measurement > 0.6 (estimated during the co-calibration and composite generation procedure) were excluded. Visit the data dictionary at https://vmacdata.org/adsp-phc/data/data-dictionary/cognition to explore the cognitive variables from each cohort that make up each cognitive domain. For the purpose of this study, regression-based cognitive domain scores were created for participant classification. Age, years of education, sex, and cohort were regressed on cognitive domain score at each available timepoint. Residuals were used to capture cognitive performance not explained by demographic variables or cohort differences. Separate models were used for NHW and NHB participants. 2.3 Participant Classification Participants were grouped by race (self or study reported), clinical status, and age ( Figure 1A ). Age bins were decided based on the age-related criteria of SuperAgers (ages 80+) and the middle-aged adults whose memory performance is compared to oldest-old adults to determine SuperAger status (ages 50-64). 25 Therefore, the age bins included middle-aged (ages 50-64), old (ages 65-79), and oldest-old (ages 80+) adults. Participant classification was determined separately for NHW and NHB adults. Given that longitudinal data were available for most participants, and some participants’ ages spanned multiple age bins, participant classification was decided using a predetermined schema ( Figure 1B ). Download figure Open in new tab Figure 1. Flow Diagram for Participant Classification of SuperAgers, Cases, and Controls. (A) Flowchart depicting inclusion and exclusion criteria for identifying SuperAgers, AD dementia cases, controls. (B) Flowchart depicting selection order of SuperAgers, cases, and controls. Age range of participants indicated by line segment with arrows on each end. Age of participant classification is indicated by position of shorter, labeled line segments. Closed circles at the end of line segments indicate inclusion of age, such that age range is less-than-or-equal-to or greater-than-or-equal-to the age with which the circle aligns, while open circles indicate exclusion of age, such that age range is less-than or greater-than the age with which the circle aligns. Sequence of selection is indicated by line height, higher lines indicating earlier selection. Abbreviations: ADSP-PHC, Alzheimer’s Disease Sequencing Project – Phenotype Harmonization Consortium; AD, Alzheimer’s Disease; CN, Cognitively Normal; MEM, Memory; EXF, Executive Functioning; LAN, Language. Firstly, participants with a clinical diagnosis of AD dementia at least once were considered cases. Across all cohorts, a diagnosis of AD dementia followed standard published criteria (National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association, or NINCDS-ADRDA). 26 – 28 Given that the present study focused on dementia cases likely due to AD, participants with comorbid neurological disorders (e.g., stroke, traumatic brain injury with significant loss of consciousness) or primary diagnoses of non-AD dementia (e.g., Dementia with Lewy Bodies, Vascular Dementia) at any timepoint were excluded. Age bin was determined based on age at first AD dementia diagnosis. Next, SuperAgers were identified using previously published criteria. 1 , 2 Across all cohorts, SuperAgers were defined as oldest-old adults with (1) memory scores at or above the mean of middle-aged adults (ages 50-64), (2) executive function and language residual scores no more than 1 standard deviation below their same-aged peers (ages 80+) at the same visit as their superior memory performance, and (3) whose diagnosis remained “cognitively normal” for the duration of study participation. Regarding classification as a control, given the importance of oldest-old (ages 80+) participants to our central analyses, we sequentially identified oldest-old, middle-aged, and then old controls. Criteria for controls included (1) memory, executive function, and language residual scores no lower than 1 standard deviation below their same-aged peers at a single visit, and (2) whose diagnosis remained “cognitively normal” for the duration of their study participation. 2.4 APOE Genotyping APOE haplotypes were determined from the single nucleotide variants rs7412 and rs429358 for ACT, BIOCARD, NACC, NIA-AD FBS, Knight ADRC, and WRAP and from pyrosequencing of APOE codons 112 and 158 for ADNI and ROS/MAP/MARS. 29 , 30 2.5 Statistical Analyses Statistical analyses were performed using R Statistical Software (v4.2.3.). 31 Logistic regression models examined differences in APOE -ε4 and -ε2 allele frequency of SuperAgers compared to cases and controls at all age bins . APOE allele positivity was determined by allele presence (0 = no allele present, 1 = one or more alleles present). Models covaried for sex and years of education due to their known modifying effects on the relationship of APOE and late-life cognition. 32 , 33 Sensitivity analyses included analyses removing individuals with APOE -ε2/ε4 genotype and adding APOE genotyping method as a covariate. Additionally, to determine whether differences in sample size explained differences in the significance of comparisons across NHW and NHB samples, we included sensitivity analyses randomly down-sampling the NHW sample by participant classification and cohort. Correction for multiple comparisons was applied using Benjamini-Hochberg false discovery rate (FDR) procedure. 34 Results tables include odds ratios (OR), confidence intervals (CI), and FDR-corrected p -values. 3 Results 3.1 Participant Characteristics In total, 18,080 participants were included in the present analyses with a total of 78,549 datapoints ( Table 1 ). Participants completed an average of 4 ± 4 visits over 5 ± 5 years. The number of follow-up visits and length of follow-up varied by cohort due to differences in study design. View this table: View inline View popup Table 1. Participant Characteristics by Cohort. Average baseline age varied by cohort (Age [all cohorts] = 72 ± 10); the youngest cohorts on average, BIOCARD and WRAP, primarily recruited cognitively normal participants. Generally, cohorts were highly educated (Years of Education [all cohorts] = 15 ± 3), mostly female (62.9%), and mostly NHW (85.4%). The frequency of each APOE genotype differed by cohort, with higher frequency of ε3/ε4 and ε4/ε4 genotypes in cohorts with a higher frequency of AD dementia cases. 3.2 Participant Classification SuperAgers made up 9% of all participants (N = 1,623). The two youngest cohorts, BIOCARD and WRAP, did not contribute any SuperAgers. Cognitively unimpaired controls comprised 42% of all participants (N = 7,628), and cases made up 49% of all participants (N = 8,829). Table 2 displays participant characteristics of NHW and NHB SuperAgers, controls, and cases in age-defined bins. On average, NHW SuperAgers (N = 1,412) were somewhat older, had more years of education, and included more males than NHB SuperAgers (N = 211). There was not a difference in the proportion of participants in each participant classification (SuperAger, control, case) across racialized groups ( 2 [2] = 3.79, p = 0.15). Comparing NHW and NHB participants across age-defined bins, all NHW bins had greater average years of education and a higher proportion of males than NHB bins. View this table: View inline View popup Table 2. Characteristics of SuperAgers, Cases, and Controls by Race and Age Bin. A 3.3 APOE Allele Frequency In NHW comparisons ( Table 3A ), SuperAgers had a significantly higher frequency of APOE -ε2 alleles ( Figure 2A ; middle-aged controls: OR = 1.38, 95%CI[1.13, 1.68], P FDR = 0.002; old controls: OR = 1.24, 95%CI[1.05, 1.47], P FDR = 0.015; oldest-old controls: OR = 1.28, 95%CI[1.03, 1.59], P FDR = 0.035; middle-aged cases: OR = 4.55, 95%CI[3.30, 6.27], P FDR < 0.001; old cases: OR = 4.02, 95%CI[3.26, 4.96], P FDR < 0.001; oldest-old cases: OR = 2.03, 95%CI[1.68, 2.44], P FDR < 0.001) and a significantly lower frequency of APOE -ε4 alleles ( Figure 2B ; middle-aged controls: OR = 0.37, 95%CI[0.31, 0.43], P FDR < 0.001; old controls: OR = 0.55, 95%CI[0.47, 0.64], P FDR < 0.001; oldest-old controls: OR = 0.81, 95%CI[0.67, 0.99], P FDR = 0.044; middle-aged cases: OR = 0.18, 95%CI[0.15, 0.21], P FDR < 0.001; old cases: OR = 0.09, 95%CI[0.08, 0.11], P FDR < 0.001; oldest-old cases: OR = 0.32, 95%CI[0.27,0.37], P FDR < 0.001) compared to all cases and controls. In contrast, NHB SuperAgers ( Table 3B ) had a significantly higher frequency of APOE -ε2 alleles only compared to cases ( Figure 2C ; middle-aged cases: OR = 3.59, 95%CI[1.60, 8.06], P FDR = 0.005; old cases: OR = 4.56, 95%CI[2.75, 7.55], P FDR < 0.001; oldest-old cases: OR = 2.73, 95%CI[1.75, 4.25], P FDR < 0.001), and a significantly lower frequency of APOE -ε4 alleles compared to all cases and controls except oldest-old controls ( Figure 2D ; middle-aged controls: OR = 0.48, 95%CI[0.33, 0.71], P FDR = 0.001; old controls: OR = 0.66, 95%CI[0.47, 0.94], P FDR = 0.035; middle-aged cases: OR = 0.12, 95%CI[0.07, 0.22], P FDR < 0.001; old cases: OR = 0.12, 95%CI[0.08, 0.18], P FDR < 0.001; oldest-old cases: OR = 0.39, 95%CI[0.27, 0.57], P FDR < 0.001). View this table: View inline View popup Table 3. Logistic Regression Model Results Comparing APOE- ε2 and -ε4 Allele Frequency among SuperAgers, Cases, and Controls. A 3.4 Sensitivity Analyses Analyses were repeated removing individuals with an APOE -ε2/ε4 genotype. Given their low frequency, very few participants were removed ( Supplementary Table 1; NHW SuperAgers: N = 1,393; NHB SuperAgers: N = 200) and findings were preserved ( Supplementary Table 2 ). Findings were also preserved when APOE genotyping method was included as a covariate ( Supplementary Table 3 ). To determine the impact of sample size on the significance of NHB comparisons, we randomly down-sampled the NHW sample by participant classification and cohort ( Supplementary Table 4 ). In the smaller sample, differences in APOE -ε2 and -ε4 allele frequency between NHW SuperAgers and controls were no longer significant, except for the comparison between NHW SuperAgers and middle-aged controls ( Supplementary Table 5 ). 4 Discussion Mechanisms conferring resilience to memory decline in oldest-old age are yet unknown. This is the largest published sample of SuperAgers and the largest study to date of the relationship of APOE -ε4 and -ε2 allele frequency and SuperAger status. Across 8 national aging cohorts, we identified 1,623 NHW and NHB SuperAgers with APOE genotyping using longitudinal harmonized cognitive and clinical data. As expected, we found that SuperAgers had a higher frequency of APOE -ε2 alleles and a lower frequency of APOE -ε4 alleles compared to individuals with AD dementia. Unlike most previous studies of APOE genotype in NHW SuperAgers, we found significant differences in APOE genotype compared to controls of all ages, including oldest-old controls. Specifically, the odds of having 1 ε4 allele was about 20% lower in NHW SuperAgers compared to same-age controls, and the odds of having 1 ε2 allele was about 30% higher in NHW SuperAgers compared to same-age controls. This finding held when ε2/ε4 carriers were removed from analyses and when APOE genotyping method was added as a covariate. Although NHB SuperAgers also had a higher frequency of APOE -ε2 alleles and a lower frequency of APOE -ε4 alleles compared to oldest-old controls, comparisons did not rise to the level of significance. Sensitivity analyses down-sampling NHW SuperAgers suggest that sample size likely impacted the significance of comparisons with NHB SuperAgers. APOE -ε4 is the strongest genetic risk factor for late-onset AD, 8 and has been shown to be related to increased entorhinal and hippocampal atrophy 35 and amnestic cognitive impairment. 36 , 37 Our data are supportive of these findings, such that AD dementia cases had a higher frequency of APOE -ε4 alleles compared to controls and SuperAgers. 38 Unlike most studies of genetic resilience in SuperAgers, we found that NHW SuperAgers had a lower frequency of APOE -ε4 alleles compared to oldest-old adults with typical memory performance. This was found in two previous studies published from the same cohort (N = 10-12), 2 , 9 but was not replicated in subsequent studies with larger samples of SuperAgers. 4 – 6 , 10 – 12 This finding is relatively unexpected. Across racialized groups, the effect of APOE -ε4 is most impressive prior to age 70. 39 , 40 Additionally, APOE -ε4 carriership is related to increased mortality. 41 , 42 In line with these studies, we found lower APOE -ε4 allele frequency in oldest-old compared to middle-aged and old controls. Despite NHW oldest-old controls being older than NHW SuperAgers on average, we found that SuperAgers had a significantly lower frequency of APOE -ε4 alleles compared to oldest-old controls, indicating that APOE -ε4 allele carriership influences memory even in adults who live past age 80. Moreover, dementia incidence increases exponentially with age, 43 suggesting that the oldest-old control group also represents exceptional aging. We found that NHW SuperAgers had a significantly lower frequency of APOE -ε4 alleles compared to same-age peers, thus underscoring the utility of the SuperAgers phenotype for uncovering factors conferring resilience in cognitive aging. The protective APOE -ε2 allele is related to lower likelihood of late-onset AD dementia 13 , 44 and better cognitive performance in older adults even in the presence of AD neuropathology. 45 Unlike APOE -ε4, APOE -ε2 carriership was previously shown to affect cognition after age 80; more precisely, although oldest-old APOE -ε2 carriers were as likely as APOE -ε4 carriers to meet neuropathologic criteria for AD, they were less likely to be diagnosed with dementia. 44 Nonetheless, no previous studies have found a relationship of APOE -ε2 allele frequency and SuperAger status. 11 – 13 The present study is the first to find that NHW SuperAgers had a significantly higher frequency of APOE -ε2 alleles compared to controls. Our results suggest that APOE -ε2 not only reduces the likelihood of dementia in oldest age but increases the likelihood one will possess optimal memory in oldest age. Future studies are needed to determine whether SuperAgers have similar levels of AD neuropathology compared to AD dementia cases as was previously found in oldest-old APOE -ε2 carriers without dementia. 44 Genetic factors underlying the superior memory performance of NHB SuperAgers, a critically under-diagnosed and understudied group, are relatively unknown. 7 Previous research suggests that, while NHB individuals have a higher frequency of APOE -ε4 alleles compared to NHW individuals, APOE -ε4 carriership is associated with attenuated risk for late-onset AD, yet similar mortality risk, in NHB individuals compared to NHW individuals. 40 , 46 – 48 The effect is likely related to global population ancestry; more specifically, APOE -ε4 carriership is associated with a greater risk of AD in NHB older adults with decreased global African ancestry or increased global European ancestry. 40 Similar to APOE -ε4, NHB individuals have a higher frequency of APOE -ε2 alleles compared to NHW individuals. 46 Unlike APOE -ε4, researchers did not detect differences in the protective effect of APOE -ε2 alleles related to global population ancestry. 40 In fact, a recent study from the MARS cohort found that NHB older adults (ages 65+) with more APOE -ε2 alleles had slower cognitive decline over a 10-year study period. 49 Additionally, APOE -ε2 carriership was related to better survival in a sample of NHB and NHW individuals from NACC with and without AD neuropathology. 50 In the present study, NHB SuperAgers had a significantly lower frequency of APOE- ε4 alleles compared to cases and younger controls, and a significantly higher frequency of APOE- ε2 alleles compared only to cases. Importantly, the present study included a substantially smaller sample of NHB SuperAgers compared to NHW SuperAgers, although the sample was still far larger than has been previously reported. Sensitivity analyses suggest that sample size likely impacted the significance of comparisons with NHB SuperAgers. It is also possible that the lack of significant difference in APOE allele carriership between NHB SuperAgers and oldest-old controls is partially explained by survivorship bias; recent research indicates that NHB adults have a higher probability of survival from ages 70 and 80 to 100 compared to NHW adults. 51 Thus, NHB SuperAgers and cognitively normal oldest-old adults may share environmental and genetic factors, including APOE- ε2 allele carriership, that support survival and reduced mortality risk in older age, resulting in a lack of significant difference in APOE genotype between these two, very similar, exceptional aging groups. 7 Still, more research and targeted recruitment of high-performing NHB oldest-old adults is necessary to determine the role of APOE genotype in their sustained optimal memory performance. The present study has several strengths, including being the largest of its kind to explore the relationship of APOE genotype and optimal memory performance in both NHW and NHB SuperAgers. This is the largest study to date to identify differences in APOE- ε4 allele frequency based on SuperAger status, and the first study of SuperAgers to find a relationship between APOE- ε2 allele frequency and SuperAger status. Our study also has several limitations. Across cohorts, participants were highly educated, limiting generalizability to lower-educated or socioeconomically diverse populations. Additionally, studies have identified other genetic factors that may confer greater AD risk in NHB older adults compared to APOE , including ABCA7 . 53 , 54 Future studies will need to consider other genetic factors that may also be relevant to exceptional aging in NHB older adults. Moreover, differences in the effect of genetic factors on AD risk have been associated with genetic ancestry, which was only available for a subset of participants in our sample. 40 Subsequent research leveraging advanced genetic analyses to consider admixture may further our understanding of genetic profiles underlying AD risk. The present study reveals important information about genetic factors associated with SuperAger status. While significant findings were restricted to NHW comparisons, study results importantly direct our attention to the need for targeted recruitment of high-performing NHB oldest-old adults to more definitively determine the role of genetics in sustained optimal memory performance in oldest-old age across racialized groups. There is an ongoing initiative that intends to answer complex questions about brain aging, resilience, and resistance in a well-characterized SuperAging cohort through longitudinal, multimodal data collection. 55 , 56 SuperAging research will contribute tremendously to our understanding of factors conferring resilience in oldest-old age, such as preserved regional brain volume, differences in lifestyle factors, and resistance to neuropathology. Data Availability Data availability, deidentified participant data, and data dictionary can be accessed at the following url: https://www.vmacdata.org/adsp-phc . Corresponding data are available for request via the NIAGADS DSS. https://vmacdata.org/adsp-phc/data/data-dictionary/cognition Consent Statement All participants provided informed consent in their respective cohort studies. Acknowledgements/Conflicts/Funding Sources The authors have nothing to disclose. The ADSP Phenotype Harmonization Consortium (ADSP-PHC) is funded by NIA (U24 AG074855, U01 AG068057 and R01 AG059716). This study was also funded by several additional funding sources, including K01 AG073584 (DBA) and K24 AG046373 (ALJ). The data contributed from the Adult Changes in Thought study was supported by NIA U19 AG066567. The data contributed from the Wisconsin Registry for Alzheimer’s Prevention was supported by NIA R01 AG021155, R01 AG0271761, R01 AG037639, and R01 AG054047. Data collection and sharing for this project was funded (in part) by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (NIA U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: 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, 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 ( www.fnih.org ). 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data contributed from MAP/ROS/MARS was supported by NIA R01 AG017917, P30 AG10161, P30 AG072975, R01 AG022018, R01 AG056405, UH2 NS100599, UH3 NS100599, R01 AG064233, R01 AG15819 and R01 AG067482, and the Illinois Department of Public Health (Alzheimer’s Disease Research Fund). Data can be accessed at www.radc.rush.edu . The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI David Holtzman, MD), P30 AG066518 (PI Lisa Silbert, MD, MCR), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI Julie A. Schneider, MD, MS), P30 AG072978 (PI Ann McKee, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Jessica Langbaum, PhD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Glenn Smith, PhD, ABPP), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P30 AG086401 (PI Erik Roberson, MD, PhD), P30 AG086404 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). NACC data can be accessed at naccdata.org . The recruitment and clinical characterization of research participants at Washington University were supported by NIH P30 AG066444 (JCM), P01 AG03991 (JCM), and P01 AG026276 (JCM). The National Institute on Aging-AD Family Based Study (NIA-AD FBS; https://www.neurology.columbia.edu/research/research-centers-and-programs/national-institute-aging-alzheimers-disease-family-based-study-nia-ad-fbs ) collected the samples used in this study and is supported by National Institute on Aging (NIA) grant U24 AG056270. Additional families were contributed to the NIA-AD FBS through NIH grants: R01 AG028786, R01 AG027944, R01 AG027944, RF1 AG054074, U01 AG052410. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/ ) and the Departments of Neurology and Psychiatry at Washington University School of Medicine. Data contributed from the Wisconsin Registry for Alzheimer’s Prevention were supported by R01 AG027161. Footnotes Abstract and main text of the manuscript has minor revisions. References ↵ Harrison , T. M. , Mesulam , M. M. , Rogalski , E. & Weintraub , S . Superior Memory and Higher Cortical Volumes in Unusually Successful Cognitive Aging . Journal of the International Neuropsychological Society 18 , 1081 – 1085 ( 2012 ). doi: 10.1017/S1355617712000847 OpenUrl CrossRef PubMed ↵ Rogalski , E. J. et al. Youthful Memory Capacity in Old Brains: Anatomic and Genetic Clues from the Northwestern SuperAging Project . Journal of Cognitive Neuroscience 25 , 29 – 36 ( 2013 ). doi: 10.1162/jocn_a_00300 OpenUrl CrossRef PubMed Web of Science ↵ de Godoy , L. L. et al. Understanding brain resilience in superagers: a systematic review . Neuroradiology 63 , 663 – 683 ( 2021 ). doi: 10.1007/s00234-020-02562-1 OpenUrl CrossRef PubMed ↵ Dekhtyar , M. et al. Neuroimaging markers associated with maintenance of optimal memory performance in late-life . Neuropsychologia 100 , 164 – 170 ( 2017 ). doi: 10.1016/j.neuropsychologia.2017.04.037 OpenUrl CrossRef PubMed Gefen , T. et al. Morphometric and histologic substrates of cingulate integrity in elders with exceptional memory capacity . Journal of Neuroscience 35 , 1781 – 1791 ( 2015 ). doi: 10.1523/JNEUROSCI.2998-14.2015 OpenUrl Abstract / FREE Full Text ↵ Harrison , T. M. , Maass , A. , Baker , S. L. & Jagust , W. J . Brain morphology, cognition, and β-amyloid in older adults with superior memory performance . Neurobiology of aging 67 , 162 – 170 ( 2018 ). doi: 10.1016/j.neurobiolaging.2018.03.024 OpenUrl CrossRef PubMed ↵ Trammell , A. R. , Goldstein , F. C. , Parker , M. W. & Hajjar , I. M . Characterization of African-American Super-Agers in the National Alzheimer’s Coordinating Center cohort . J Am Geriatr Soc 72 , 1995 – 2005 ( 2024 ). doi: 10.1111/jgs.18882 OpenUrl CrossRef PubMed ↵ Corder , E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families . Science 261 , 921 – 923 ( 1993 ). doi: 10.1126/science.8346443 OpenUrl Abstract / FREE Full Text ↵ Rogalski , E. et al. Cognitive trajectories and spectrum of neuropathology in SuperAgers: The first 10 cases . Hippocampus 29 , 458 – 467 ( 2019 ). doi: 10.1002/hipo.22828 OpenUrl CrossRef PubMed ↵ Mapstone , M. et al. What success can teach us about failure: the plasma metabolome of older adults with superior memory and lessons for Alzheimer’s disease . Neurobiology of aging 51 , 148 – 155 ( 2017 ). doi: 10.1016/j.neurobiolaging.2016.11.007 OpenUrl CrossRef PubMed ↵ Spencer , B. E. et al. Alzheimer’s polygenic hazard score in SuperAgers: SuperGenes or SuperResilience? Alzheimer’s & Dementia: Translational Research & Clinical Interventions 8 , e12321 ( 2022 ). doi: 10.1002/trc2.1232 OpenUrl CrossRef ↵ Garo-Pascual , M. et al. Brain structure and phenotypic profile of superagers compared with age-matched older adults: a longitudinal analysis from the Vallecas Project . Lancet Healthy Longev 4 , e374 – e385 ( 2023 ). doi: 10.1016/S2666-7568(23)00079-X OpenUrl CrossRef ↵ Corder , E. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease . Nature genetics 7 , 180 – 184 ( 1994 ). doi: 10.1038/ng0694-180 OpenUrl CrossRef PubMed Web of Science ↵ Kukull , W. A. et al. Dementia and Alzheimer disease incidence: a prospective cohort study . Archives of neurology 59 , 1737 – 1746 ( 2002 ). doi: 10.1001/archneur.59.11.1737 OpenUrl CrossRef PubMed Web of Science ↵ Jack Jr , C. R. , et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods . Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine 27 , 685 – 691 ( 2008 ). doi: 10.1002/jmri.21049 OpenUrl CrossRef PubMed Web of Science ↵ Sacktor , N. et al. The BIOCARD Index: A Summary Measure to Predict Onset of Mild Cognitive Impairment . Alzheimer Disease & Associated Disorders 31 , 114 – 119 ( 2017 ). doi: 10.1097/WAD.0000000000000194 OpenUrl CrossRef PubMed ↵ Beekly , D. L. et al. The National Alzheimer’s coordinating center (NACC) database: an Alzheimer disease database . Alzheimer Disease & Associated Disorders 18 , 270 – 277 ( 2004 ). lww/alz/88441/03-070R OpenUrl PubMed ↵ Lee , J. H. et al. Analyses of the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study: Implication of Additional Loci . Archives of neurology 65 , 1518 – 1526 ( 2008 ). doi: 10.1001/archneur.65.11.1518 OpenUrl CrossRef PubMed Web of Science ↵ Bennett , D. A. et al. Religious orders study and rush memory and aging project . Journal of Alzheimer’s disease 64 , S161 – S189 ( 2018 ). doi: 10.3233/JAD-179939 OpenUrl Abstract / FREE Full Text ↵ Bennett , D. A. et al. Overview and findings from the Rush Memory and Aging Project . Current Alzheimer research 9 , 646 – 663 ( 2012 ). doi: 10.2174/156720512801322663 OpenUrl CrossRef PubMed Web of Science ↵ Barnes , L. L. , Shah , R. C. , Aggarwal , N. T. , Bennett , D. A. & Schneider , J. A . The Minority Aging Research Study: ongoing efforts to obtain brain donation in African Americans without dementia . Current Alzheimer research 9 , 734 – 745 ( 2012 ). doi: 10.2174/156720512801322627 OpenUrl CrossRef PubMed ↵ Fernandez , M. V. et al. Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center . Sci Data 11 , 768 ( 2024 ). doi: 10.1038/s41597-024-03485-9 OpenUrl CrossRef PubMed ↵ Johnson , S. C. et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions . Alzheimer’s & dementia (Amsterdam, Netherlands) 10 , 130 – 142 ( 2018 ). doi: 10.1016/j.dadm.2017.11.007 OpenUrl CrossRef PubMed ↵ Mukherjee , S. et al. Cognitive domain harmonization and cocalibration in studies of older adults . Neuropsychology 37 , 409 ( 2023 ). doi: 10.1037/neu0000835 OpenUrl CrossRef ↵ Harrison , T. M. , Weintraub , S. , Mesulam , M. M. & Rogalski , E . Superior Memory and Higher Cortical Volumes in Unusually Successful Cognitive Aging . Journal of the International Neuropsychological Society 18 , 1081 – 1085 ( 2012 ). doi: 10.1017/S1355617712000847 OpenUrl CrossRef PubMed ↵ McKhann , G. et al. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease . Neurology 34 , 939 – 944 ( 1984 ). doi: 10.1016/j.jalz.2011.03.005 OpenUrl CrossRef PubMed McKhann , G. M. et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease . Alzheimers & Dementia: The Journal of the Alzheimer’s Association 7 , 263 – 269 ( 2011 ). doi: 10.1016/j.jalz.2011.03.005 OpenUrl CrossRef PubMed Web of Science ↵ Dubois , B. et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria . The Lancet Neurology 6 , 734 – 746 ( 2007 ). doi: 10.1016/S1474-4422(07)70178-3 OpenUrl CrossRef PubMed Web of Science ↵ Yu , L. et al. Neuropathologic features of TOMM40 ’523 variant on late-life cognitive decline . Alzheimer’s & Dementia 13 , 1380 – 1388 ( 2017 ). doi: 10.1016/j.jalz.2017.05.002 OpenUrl CrossRef PubMed ↵ Naj , A. C. et al. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease . Nature Genetics 43 , 436 – 441 ( 2011 ). doi: 10.1038/ng.801 OpenUrl CrossRef PubMed ↵ R: A language and environment for statistical computing. ( R Foundation for Statistical Computing , Vienna, Austria , 2021 ). ↵ Lee , M. et al. Education and Cardiovascular Health as Effect Modifiers of APOE ε4 on Dementia: The Atherosclerosis Risk in Communities Study . Journals of Gerontology Series A, Biological Sciences & Medical Sciences 77 , 1199 – 1207 ( 2022 ). doi: 10.1093/gerona/glab299 OpenUrl CrossRef ↵ Hohman , T. J. et al. Sex-Specific Association of Apolipoprotein E With Cerebrospinal Fluid Levels of Tau . JAMA neurology 75 , 989 – 998 ( 2018 ). doi: 10.1001/jamaneurol.2018.0821 OpenUrl CrossRef PubMed ↵ Benjamini , Y. & Hochberg , Y . Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . Journal of the Royal Statistical Society: Series B (Methodological ) 57 , 289 – 300 ( 1995 ). doi: 10.1111/j.2517-6161.1995.tb02031.x OpenUrl CrossRef PubMed ↵ Juottonen , K. , Lehtovirta , M. , Helisalmi , S. , Riekkinen , P. J. , Sr. & Soininen , H . Major decrease in the volume of the entorhinal cortex in patients with Alzheimer’s disease carrying the apolipoprotein E ε4 allele. J Neurology , Neurosurgy, and Psychiatry 65 , 322 – 327 ( 1998 ). doi: 10.1136/jnnp.65.3.322 OpenUrl Abstract / FREE Full Text ↵ Snowden , J. S. et al. Cognitive phenotypes in Alzheimer’s disease and genetic risk . Cortex; a journal devoted to the study of the nervous system and behavior 43 , 835 – 845 ( 2007 ). doi: 10.1016/s0010-9452(08)70683-x OpenUrl CrossRef PubMed Web of Science ↵ Mukherjee , S. et al. Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups . Molecular Psychiatry 25 , 2942 – 2951 ( 2018 ). doi: 10.1038/s41380-018-0298-8 OpenUrl CrossRef ↵ Poirier , J. et al. Apolipoprotein E polymorphism and Alzheimer’s disease . The Lancet 342 , 697 – 699 ( 1993 ). doi: 10.1016/0140-6736(93)91705-q OpenUrl CrossRef ↵ Farrer , L. A. et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium . Jama 278 , 1349 – 1356 ( 1997 ). doi: 10.1001/jama.1997.03550160069041 OpenUrl CrossRef PubMed Web of Science ↵ Belloy , M. E. et al. APOE Genotype and Alzheimer Disease Risk Across Age, Sex, and Population Ancestry . JAMA neurology 80 , 1284 – 1294 ( 2023 ). doi: 10.1001/jamaneurol.2023.3599 OpenUrl CrossRef PubMed ↵ Rosvall , L. et al. APOE-related mortality: effect of dementia, cardiovascular disease and gender . Neurobiology of aging 30 , 1545 – 1551 ( 2009 ). doi: 10.1016/j.neurobiolaging.2007.12.003 OpenUrl CrossRef PubMed Web of Science ↵ McKay , G. J. et al. Variations in apolipoprotein E frequency with age in a pooled analysis of a large group of older people . American journal of epidemiology 173 , 1357 – 1364 ( 2011 ). doi: 10.1093/aje/kwr015 OpenUrl CrossRef PubMed Web of Science ↵ Jorm , A. F. & Jolley , D . The incidence of dementia: a meta-analysis . Neurology 51 , 728 – 733 ( 1998 ). doi: 10.1212/wnl.51.3.728 OpenUrl CrossRef PubMed ↵ Berlau , D. J. , Corrada , M. M. , Head , E. & Kawas , C. H . APOE ε2 is associated with intact cognition but increased Alzheimer pathology in the oldest old . Neurology 72 , 829 – 834 ( 2009 ). doi: 10.1212/01.wnl.0000343853.00346.a4 OpenUrl CrossRef PubMed ↵ Shinohara , M. et al. APOE2 eases cognitive decline during Aging: Clinical and preclinical evaluations . Annals of neurology 79 , 758 – 774 ( 2016 ). doi: 10.1002/ana.24628 OpenUrl CrossRef PubMed ↵ Rajan , K. B. et al. Racial Differences in the Association Between Apolipoprotein E Risk Alleles and Overall and Total Cardiovascular Mortality Over 18 Years . Journal American Geriatrics Society 65 , 2425 – 2430 ( 2017 ). doi: 10.1111/jgs.15059 OpenUrl CrossRef PubMed Beydoun , M. A. et al. Race, APOE genotypes, and cognitive decline among middle-aged urban adults . Alzheimers Research & Therapy 13 , 120 ( 2021 ). doi: 10.1186/s13195-021-00855-y OpenUrl CrossRef PubMed ↵ Tang , M. X. et al. The APOE-epsilon4 allele and the risk of Alzheimer disease among African Americans, whites, and Hispanics . Jama 279 , 751 – 755 ( 1998 ). doi: 10.1001/jama.279.10.751 OpenUrl CrossRef PubMed Web of Science ↵ Nsor , N. A. , Bourassa , K. J. , Barnes , L. L. & Brown , C. L . The Effects of APOE Alleles, Cognitive Activities, and Social Activities on Cognitive Decline in African Americans. The journals of gerontology. Series B, Psychological sciences and social sciences , gba e172 ( 2024 ). doi: 10.1093/geronb/gbae172 OpenUrl CrossRef ↵ Shinohara , M. et al. APOE2 is associated with longevity independent of Alzheimer’s disease . eLife 19 , e62199 ( 2020 ). doi: 10.7554/eLife.62199 OpenUrl CrossRef ↵ Ouellette , N. & Perls , T . Race and ethnicity dynamics in survival to 100 years in the United States . J Intern Med 297 , 2 – 21 ( 2025 ). doi: 10.1111/joim.20031 OpenUrl CrossRef PubMed Nemes , S. , Jonasson , J. M. , Genell , A. & Steineck , G . Bias in odds ratios by logistic regression modelling and sample size . BMC Medical Research Methodology 9 , 56 ( 2009 ). doi: 10.1186/1471-2288-9-56 OpenUrl CrossRef PubMed ↵ Logue , M. W. , Dasgupta , S. & Farrer , L. A . Genetics of Alzheimer’s Disease in the African American Population . J Clin Med 12 , 5189 ( 2023 ). doi: 10.3390/jcm12165189 OpenUrl CrossRef PubMed ↵ Reitz , C. et al. Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E L4,and the risk of late-onset Alzheimer disease in African Americans . Jama 309 , 1483 – 1492 ( 2013 ). doi: 10.1001/jama.2013.2973 OpenUrl CrossRef PubMed Web of Science ↵ Rogalski , E. J. et al. Enrollment and Scientific Progress of the Multisite SuperAging Research Initiative . Alzheimer’s & Dementia 19 , e078625 ( 2023 ). doi: 10.1002/alz.078625 OpenUrl CrossRef ↵ Rogalski , E. J. et al. The SuperAging Research Initiative: A multisite consortium focused on identifying factors promoting extraordinary cognitive aging . Alzheimer’s & Dementia 18 , e066407 ( 2022 ). doi: 10.1002/alz.066407 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted June 25, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Evaluating the association of APOE genotype and cognitive resilience in SuperAgers Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Evaluating the association of APOE genotype and cognitive resilience in SuperAgers Alaina Durant , Shubhabrata Mukherjee , Michael L. Lee , Seo-Eun Choi , Phoebe Scollard , Brandon S. Klinedinst , Emily H. Trittschuh , Jesse Mez , Lindsay A. Farrer , Katherine A. Gifford , Carlos Cruchaga , Jason Hassenstab , Adam C. Naj , Li-San Wang , Sterling C. Johnson , Corinne D. Engelman , Walter A. Kukull , C. Dirk Keene , Andrew J. Saykin , Michael L. Cuccaro , Brian W. Kunkle , Margaret A. Pericak-Vance , Eden R. Martin , David A. Bennett , Lisa L. Barnes , Julie A. Schneider , William S. Bush , Jonathan L. Haines , Richard Mayeux , Badri N. Vardarajan , Marilyn S. Albert , Paul M. Thompson , Angela L. Jefferson , The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , Alzheimer’s Disease Genetics Consortium (ADGC) , The Alzheimer’s Disease Sequencing Project (ADSP) , Paul K. Crane , Logan Dumitrescu , Derek B. Archer , Timothy J. Hohman , Leslie S. Gaynor medRxiv 2025.01.07.25320117; doi: https://doi.org/10.1101/2025.01.07.25320117 Share This Article: Copy Citation Tools Evaluating the association of APOE genotype and cognitive resilience in SuperAgers Alaina Durant , Shubhabrata Mukherjee , Michael L. Lee , Seo-Eun Choi , Phoebe Scollard , Brandon S. Klinedinst , Emily H. Trittschuh , Jesse Mez , Lindsay A. Farrer , Katherine A. Gifford , Carlos Cruchaga , Jason Hassenstab , Adam C. Naj , Li-San Wang , Sterling C. Johnson , Corinne D. Engelman , Walter A. Kukull , C. Dirk Keene , Andrew J. Saykin , Michael L. Cuccaro , Brian W. Kunkle , Margaret A. Pericak-Vance , Eden R. Martin , David A. Bennett , Lisa L. Barnes , Julie A. Schneider , William S. Bush , Jonathan L. Haines , Richard Mayeux , Badri N. Vardarajan , Marilyn S. Albert , Paul M. Thompson , Angela L. Jefferson , The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , Alzheimer’s Disease Genetics Consortium (ADGC) , The Alzheimer’s Disease Sequencing Project (ADSP) , Paul K. Crane , Logan Dumitrescu , Derek B. Archer , Timothy J. Hohman , Leslie S. Gaynor medRxiv 2025.01.07.25320117; doi: https://doi.org/10.1101/2025.01.07.25320117 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4426) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15222) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6589) Geriatric Medicine (667) Health Economics (997) Health Informatics (4525) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (971) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9221) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (711) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffea22d8fc81640',t:'MTc3OTQ4MjQwMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();