Temporal sequence of amyloid and tau PET positivity: APOE-ε4 and sex effects, and implications for Alzheimer’s disease progression

Temporal sequence of amyloid and tau PET positivity: APOE-ε4 and sex effects, and implications for Alzheimer’s disease progression | 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 Temporal sequence of amyloid and tau PET positivity: APOE -ε4 and sex effects, and implications for Alzheimer’s disease progression View ORCID Profile Marta Milà-Alomà , Isabella Hausle , Kellen K. Petersen , Pamela Thropp , View ORCID Profile Suzanne E. Schindler , Duygu Tosun , Alzheimer’s Disease Neuroimaging Initiative doi: https://doi.org/10.1101/2025.07.27.25332202 Marta Milà-Alomà 1 Northern California Institute for Research and Education , San Francisco, CA, USA 94141 2 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA, USA 94143 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marta Milà-Alomà Isabella Hausle 1 Northern California Institute for Research and Education , San Francisco, CA, USA 94141 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kellen K. Petersen 3 Department of Neurology, Washington University in St. Louis , St. Louis, MO, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pamela Thropp 1 Northern California Institute for Research and Education , San Francisco, CA, USA 94141 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Suzanne E. Schindler 3 Department of Neurology, Washington University in St. Louis , St. Louis, MO, US Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Suzanne E. Schindler Duygu Tosun 1 Northern California Institute for Research and Education , San Francisco, CA, USA 94141 2 Department of Radiology and Biomedical Imaging, University of California San Francisco , San Francisco, CA, USA 94143 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: duygu.tosun{at}ucsf.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Importance Alzheimer’s disease (AD) progression varies widely among individuals. Identifying factors influencing timing of pathology and clinical progression is crucial for optimizing early intervention trials. Objective To investigate how estimated age at amyloid and tau PET positivity, and the time interval between amyloid and tau PET positivity (“amyloid-tau interval”), relate to symptom onset and clinical progression, and whether APOE- ε4 status and sex modify these associations. Design Longitudinal observational study. Setting Multicenter study using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants ADNI participants with at least one positive amyloid PET scan (n=792) or at least one positive tau PET scan (n=212) were included. Both cognitively unimpaired and impaired individuals were included and all had information on sex, APOE- ε4 status, and longitudinal cognitive assessments. Exposures 18 F-florbetapir or 18 F-florbetaben amyloid PET, 18 F-flortaucipir tau PET, CDR global, CDR-SB. Main outcomes and measures We examined the influence of APOE -ε4 status, sex, and their interaction on the age at biomarker positivity and the amyloid–tau interval. Accelerated Failure Time (AFT) models were used to predict time to symptom onset based on biomarker positivity age and the amyloid–tau interval. Linear mixed-effects (LME) models evaluated differences in the rate of cognitive decline over five years following symptom onset, by biomarker positivity age and amyloid–tau interval duration. Additional models included interaction terms with sex or APOE -ε4 status. Results APOE- ε,4 carriers and women had earlier amyloid and tau PET positivity ages. APOE- ε,4 carriers, women, and those with older age at amyloid PET positivity had a shorter amyloid-tau interval. An older age at amyloid or tau PET positivity and a shorter amyloid-tau interval predicted earlier symptom onset. An older age at amyloid or tau PET positivity was also associated with slower rate of cognitive decline after symptom onset. The amyloid-tau interval did not influence time until symptom onset after tau PET positivity or rate of cognitive decline. Conclusions and relevance APOE- ε4 and sex influenced the timing of amyloid and tau PET positivity and the amyloid-tau interval, which in turn affected symptom onset and clinical progression. These factors should guide the identification of amyloid-positive individuals at highest risk of rapid AD progression, enabling more efficient selection of participants for clinical trials. Introduction Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles, which begin years before development of clinical symptoms 1 , 2 . Amyloid deposition typically precedes widespread tau aggregation 1 , and both pathologies accumulate relatively consistently across individuals 1 , 3 , 4 ; however, interindividual variability exists in the onset and temporal sequence of amyloid and tau pathologies and their contribution to clinical progression 3 , 4 . Understanding the factors driving this variability is essential for improving disease staging, prognosis, and treatment timing. Positron emission tomography (PET) imaging enables the in vivo quantification of amyloid and tau pathologies. PET-based modeling can estimate an individual’s age at which a specific biomarker positivity threshold is crossed 3 , 4 . These estimated ages for PET positivity offer a valuable tool for studying the temporal dynamics of AD pathophysiology at an individual level and identifying factors influencing key pathological milestones. Anchoring analyses to an individual’s biological disease timeline may also enhance the prediction of clinical outcomes, such as symptom onset or rate of cognitive decline. Although advanced age is the primary risk factor for AD, genetic and demographic factors such as the apolipoprotein E ( APOE ) genotype and sex significantly contribute to disease risk and progression 5 . The APOE- ε4 allele, the strongest genetic risk factor for late-onset AD, is consistently associated with earlier amyloid and tau accumulation, and earlier development of clinical symptoms 6 . Sex differences have also been reported in AD prevalence and progression, with men showing less vulnerability to tau pathology and slower clinical decline than women 7 – 12 . Furthermore, interactions between APOE -ε,4 and sex have been observed to influence AD risk and progression 13 – 15 . Despite these established connections, it remains unclear to what extent APOE- ε4 status and sex, independently and interactively, influence the timing of amyloid and tau PET positivity, and how these temporal dynamics influence the disease course. While amyloid positivity is necessary, the emergence of tau and subsequent cognitive decline signal imminent clinical progression 16 – 20 . A better understanding of factors influencing the time interval from reaching amyloid positivity to subsequent tau positivity, which we term “the amyloid-tau interval,” could identify individuals at higher risk of rapid progression once amyloid positivity is established. This has significant implications for early-stage clinical trials, which often enroll participants based solely on amyloid burden. Refining participant selection by considering both amyloid burden and individual trajectories toward tau positivity and symptom onset could improve trial efficiency and therapeutic impact. Investigating how APOE genotype and sex impact these key events can offer valuable insight into the mechanisms driving variability in tau emergence, with direct implications for prognostic modeling and selection of individuals for early intervention in clinical trials. This study investigated how estimated ages at amyloid and tau PET positivity, and the amyloid-tau interval, relate to symptom onset and clinical progression. We also assessed the independent and interactive effects of APOE- ε4 status and sex on these associations. By characterizing how these factors modulate the temporal sequence of events in AD pathogenesis, we aim to better understand individual variability in AD progression and its potential clinical consequences. Methods 1. Study participants This study included cognitively unimpaired (CU) and cognitively impaired (CI) participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study (adni.loni.usc.edu). ADNI is a public-private partnership focused on identifying biomarkers for mild cognitive impairment (MCI) and early AD progression 21 , 22 . All participants provided written consent. For this study, we required at least one positive amyloid PET scan or tau PET scan to estimate biomarker positivity ages ( eMethods in Supplement 1 ). All included participants had longitudinal cognitive assessments, self-identified sex, and APOE genotype data, which was part of the standard ADNI protocol. 2. Amyloid and tau PET measures Amyloid PET imaging used 18 F-florbetapir (FBP) or 18 F-florbetaben (FBB) with standardized ADNI protocols 23 . Global cortical standardized uptake value ratio (SUVR) was calculated by averaging PET uptake across cortical regions (frontal, cingulate, parietal, and lateral temporal) and normalizing to a composite reference region (whole cerebellum, brainstem/pons, and subcortical eroded white matter) 24 . Amyloid positivity was defined as SUVR>0.78 for FBP or SUVR>0.74 for FBB, per ADNI guidelines (ADNI_UCBerkeley_AmyloidPET_Methods_v2_2023-06-29.pdf). Tau PET imaging used 18 F-flortaucipir (FTP) with standardized ADNI protocols. Mesial-temporal meta-region of interest (ROI) uptake (including entorhinal, parahippocampus, and amygdala) was normalized to the inferior cerebellar grey matter uptake 25 . We determined mesial-temporal tau PET positivity threshold of 1.41 SUVR using a two-component Gaussian mixture model (GMM) applied to cross-sectional mesial-temporal FTP-PET SUVR from the entire ADNI study ( n =907). 3. Clinical and neuropsychological assessments Participants underwent comprehensive clinical assessments, including collateral interviews, neurological examinations, and the Clinical Dementia Rating® (CDR®) and the CDR-Sum of Boxes (CDR-SB) 26 . Individuals were classified as CI if they had two consecutive visits with a CDR>0 and a CDR>0 in their last visit. Participants who reverted to a CDR=0 were excluded. Symptom onset was defined as the first visit with a CDR>0. 4. Statistical analyses We estimated ages at amyloid and tau PET positivity using a previously validated method for FBP PET and FTP PET in ADNI 3 , 27 . We extended this method to FBB PET (in 19.9% of participants with amyloid PET), showing similar performance ( eMethods and eFigure 1 in Supplement 1 ). We used linear regression to assess the independent effects of APOE- ε4 status ( APOE- ε4 carrier vs. APOE- ε4 non-carrier) and sex on estimated amyloid and tau PET positivity ages. We then examined the association between these biomarker positivity ages, adjusting for sex and APOE- ε4 status, and included the APOE- ε4 status by sex interaction term in additional models. The amyloid-tau interval was calculated by subtracting the estimated age at amyloid PET positivity from the estimated age at tau PET positivity. We used linear regression to examine the effects of APOE- ε4 status, sex, and their interaction on the amyloid-tau interval. Additional models included the estimated age at amyloid PET positivity and its interaction with APOE- ε4 status, sex, and the three-way interaction. We used Accelerated Failure Time (AFT) survival models with a Weibull distribution to assess how biomarker positivity ages and the amyloid–tau interval influenced time until symptom onset (from CDR=0 to CDR>0) following amyloid or tau PET positivity. The time-to-event variable was the difference between age at CDR>0 (or the last cognitive assessment for non-converters) and estimated amyloid or tau PET positivity age. Sex, APOE- ε4 status, and educational attainment were included as covariates. Additional models tested interactions between biomarker positivity ages or amyloid–tau interval with sex, APOE- ε4 status, and their three-way interaction. Lastly, we used linear mixed effect (LME) models with random slopes and intercepts to assess the effect of biomarker positivity age or amyloid-tau interval on the rates of cognitive decline, measured by CDR-SB, over five years following symptom onset (defined as the first visit with a CDR>0). We grouped participants by biomarker positivity age (75 years). We also grouped participants by the amyloid-tau interval: “concurrent amyloid-tau” (−4 years ≤ interval ≤ 4 years, reflecting cumulative mean absolute error for estimated biomarker positivity ages; see eMethods in Supplement 1 ), “Amyloid first <10 years” (4-10 years), and “Amyloid-first β10 years”. Six participants with tau PET positivity preceding amyloid PET positivity (interval ≤ −4 years) were excluded from modeling but included descriptively. Interaction terms between time since symptom onset and biomarker age group or interval group were evaluated. Next, pairwise contrasts were conducted to compare group-specific cognitive decline rates. We repeated these analyses adding interactions between time, biomarker positivity age or amyloid-tau interval group and sex or APOE -ε,4 status. All statistical analyses and figure generation were performed using R software (R version 4.2.2). Results 1. Study participants characteristics and estimated ages at amyloid and tau PET positivity Our study included 792 participants with estimated amyloid PET ages and 212 with estimated tau PET positivity age; 152 participants had both estimates. Demographic and clinical characteristics were similar between these subgroups ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. Participant characteristics at first PET acquisition The mean estimated amyloid and tau PET positivity ages were 63.3±10.2 years and 69.9±9.9 years, respectively. Both APOE -ε,4 carriership and female sex were significantly associated with earlier biomarker positivity ages (all P <0.05; Figure 1A and 1B ). A significant APOE -ε,4 status by sex interaction was found for amyloid PET positivity age ( P =0.040), with the association between APOE -ε,4 and earlier amyloid PET positivity age being stronger in women ( Figure 1A ). No significant interaction effect was observed for tau PET positivity age ( P =0.52; Figure 1B ). Download figure Open in new tab Figure 1. Estimated ages at amyloid and tau PET positivity by APOE -ε.4 status and sex A, B. Estimated ages at amyloid (A) or tau (B) PET positivity by APOE -χ4 status and sex. Box plots depict median (horizontal bar), IQR (hinges), and 1.5 × IQR (whiskers). C, D. Scatter plots show the association between estimated age at amyloid PET positivity and at tau PET positivity by APOE -χ4 status (C) and sex (D). Solid lines indicate the regression line and 95% CIs. The dashed diagonal line represents the identity line (y = x), indicating equal ages at amyloid and tau PET positivity. Beta coefficients and P -values from linear regression models are reported for the main effects of APOE -χ4 status, sex or estimated age at amyloid PET positivity, and the interaction terms with APOE -χ4 and sex. All models included APOE -χ4 status and sex as covariates. Interaction terms were added in additional models. Among 152 participants with both estimates, earlier amyloid PET positivity age was significantly associated with an earlier tau PET positivity age (β=0.67, P <0.0001). This association was significantly modified by APOE -ε,4 status ( P =0.002; Figure 1C ), showing a stronger effect in APOE -ε,4 carriers. Sex did not moderate this association ( P =0.54, Figure 1D ), nor did the three-way interaction between amyloid PET positivity age, APOE -ε,4 status, and sex ( P =0.92). eTable 1 in Supplement 1 shows participant characteristics by estimated biomarker positivity age groups (75 years old). 2. Amyloid-tau interval: Distribution and differences by APOE -ε,4 status and sex The mean amyloid-tau interval was 8.2±6.8 years. The amyloid-tau interval distribution ( Figure 2A ) showed 34 individuals (22.4%) had a “concurrent amyloid-tau” interval (−4 years ≤ interval ≤4 years). Most individuals (n=112; 73.7%) showed amyloid PET positivity preceding tau PET positivity (‘amyloid-first’ group), with 65 (58.0%) of them having an interval exceeding 10 years. Six individuals (3.9%) had tau PET positivity before amyloid PET positivity (‘tau-first’ group). Sex and APOE -ε,4 status distribution or cognitive status did not differ across amyloid-interval groups (eTable 2 in Supplement 1). Download figure Open in new tab Figure 2. Characteristics of the amyloid-tau interval and effect of APOE -ε,4 status and sex A. Distribution of the amyloid-tau interval. The “concurrent amyloid-tau” group was defined by an interval of ± 4 years, reflecting cumulative mean absolute error for estimated positivity ages (see Supplementary Methods). Participants that became amyloid-positive first and tau-positive 4 to 10 years later were classified as “Amyloid first <10 years”, and participants that became amyloid-positive first and tau-positive more than 10 years later were classified as “Amyloid-first ≥10 years”. B. Amyloid-tau interval by APOE -χ4 status and sex. Box plots depict median (horizontal bar), IQR (hinges), and 1.5 × IQR (whiskers). C, D-Scatter plots show the association between estimated age at amyloid PET positivity and the amyloid-tau interval by APOE -χ4 status (C) and sex (D). Solid lines indicate the regression line and 95% CIs. The dashed horizontal line at 0 years indicates concurrent amyloid and tau PET positivity. Positive values reflect individuals who became tau PET positive after amyloid PET positivity; negative values indicate tau PET positivity preceding amyloid PET positivity. Beta coefficients and P -values from linear regression models are reported for the main effects of APOE -χ4 status, sex or estimated age at amyloid PET positivity, and the interaction terms with APOE -χ4 and sex. All models included APOE -χ4 status and sex as covariates. Interaction terms were added in additional models. APOE -ε,4 status, sex, or their interaction did not significantly influence the amyloid-tau interval ( Figure 2B ). However, when amyloid PET positivity age was included in the model, an older age at amyloid PET positivity was significantly associated with a shorter amyloid-tau interval (β= −0.33, P <0.0001). In this extended model, APOE -ε,4 carriers had a significantly shorter amyloid-tau interval than non-carriers (β=-2.53, P =0.020; Figure 2C ), and women had a shorter interval than men (β=-2.80, P =0.005; Figure 2D ). A significant interaction between APOE- ε4 status and estimated age at amyloid PET positivity was observed ( P =0.002), indicating that the association between older amyloid PET positivity age and shorter amyloid-tau interval was more pronounced in APOE- ε4 non-carriers ( Figure 2C ). The interaction between amyloid PET positivity age and sex was not significant ( P =0.54; Figure 2D ). The three-way interaction was also not significant ( P =0.92). 3. Effects of biomarker positivity ages and amyloid–tau interval on symptom onset and clinical progression 3.1 Association with time until symptom onset AFT survival models assessed factors influencing the time until symptom onset (CDR>0) following amyloid or tau PET positivity. In a basic model not including biomarker positivity ages or the amyloid-tau interval (Model 1; Table 2 ), the time until symptom onset was longer for women after amyloid (23% longer; P =0.047) and tau (72% longer; P =0.003) PET positivity. This sex effect was significantly modified by APOE- ε4 status ( P =0.021; Model 2; Table 2 ), with a 66% longer time until symptom onset after amyloid PET positivity for women APOE-ε4 carriers compared to men carriers. No significant sex by APOE- ε4 interaction was observed after tau PET positivity ( P =0.50; Model 2; Table 2 ). Neither APOE- ε4 status nor educational attainment independently influenced the time until symptom onset. View this table: View inline View popup Table 2. Effects of estimated biomarker positivity age and amyloid-tau interval on progression rate Older age at biomarker positivity was associated with a shorter time until symptom onset; as the age at amyloid or tau PET positivity increased, each year of age corresponded to a 4% ( P <0.0001) or 3% ( P =0.002) shorter time until symptom onset, respectively (Model 3; Table 2 and eFigure 3A and 3B in Supplement 1 ). These effects were not significantly modified by sex, APOE -ε4 status, or their three-way interaction (Models 4-6; Table 2 ). Notably, once biomarker positivity age was included in the models (i.e., Model 1 vs Model 3), the independent effect of sex was no longer significant, suggesting that the observed longer time to symptom onset in women was largely accounted for by their earlier biomarker positivity age. Next, we examined the effect of the amyloid-tau interval on the time until symptom onset. A shorter amyloid-tau interval was associated with 6% shorter time until symptom onset for each year of age following amyloid PET positivity ( P <0.0001), independent of the amyloid PET positivity age (Models 1 and 4; Table 2 and eFigure 3C in Supplement 1 ). Conversely, the amyloid-tau interval had no significant effect on the time to symptom onset after tau PET positivity ( P =0.11; Models 1 and 4; Table 2 and eFigure 3D in Supplement 1 ). Older tau PET positivity age remained a significant predictor of shorter time to symptom onset ( P =0.013), irrespective of the amyloid-tau interval (Model 4; Table 2 ). While significant interactions with sex were initially observed, suggesting attenuation of the protective effect of a longer interval in women, these interactions became non-significant when amyloid or tau PET positivity age were included (Models 2 and 5; Table 2 ). APOE- ε4 status did not significantly modify these associations (Models 3 and 6; Table 2 ). Three-way interactions could not be evaluated due to sample size limitations. 3.2 Association with clinical progression Next, we assessed differences in the rates of cognitive decline after symptom onset, defined as the first visit with a CDR>0, across biomarker positivity ages or amyloid-tau interval groups of CI individuals (see eTable 3 in Supplement 1 for participant characteristics). There were significant differences across amyloid and tau PET positivity age groups in the rates of cognitive decline after symptom onset (time by amyloid age group interaction P =0.005 and time by tau age group interaction P =0.026; Figure 3A and 3B and eTable 4 in Supplement 1 ). Post-hoc pairwise comparisons showed that, after symptom onset, individuals who became amyloid PET positive before age 65 declined significantly faster than those >75 years (slope: 0.96 [0.84–1.09] vs. 0.41 [0.08–0.73]; P =0.005). A similar pattern was observed when grouping individuals by tau PET positivity age (slope: 1.07 [0.74–1.39] for 75; P =0.020; Figure 3A and 3B and eTable 4 in Supplement 1 ). In contrast, there was no significant effect of the amyloid-tau interval on the rates of cognitive decline after symptom onset (time by interval group interaction P =0.41; Figure 3C and eTable 4 in Supplement 1 ). Download figure Open in new tab Figure 3. Clinical progression after symptom onset by estimated biomarker positivity age group or amyloid-tau interval group Linear mixed-effects models with random intercepts and slopes were used to model CDR-SB trajectories as a function of years since symptom onset (defined as the first visit with CDR > 0), grouping individuals by estimated age at amyloid PET positivity (A), tau PET positivity (B) or by the amyloid–tau interval (C). Interaction terms between time and biomarker age group or interval group were evaluated to assess differences in the rate of cognitive decline. Pairwise contrasts were conducted to compare group-specific cognitive decline rates. Individual trajectories are shown, where each line connects multiple observations from the same individual. In C, the tau-first group was excluded from the analyses due to the small sample size (n=6). *Statistically significant ( P <0.05); a Statistically significant ( P 75 years’ group No significant modifying effects of sex or APOE -ε,4 were found in these associations between biomarker positivity age groups or interval groups on the rate of cognitive decline ( eTable 4 in Supplement 1 ). Discussion This study investigated the timing and sequence of amyloid and tau PET positivity, and their associations with symptom onset and clinical progression, specifically considering APOE -ε4 status and sex differences. Our main findings were: 1) APOE- ε,4 carriers and women were younger when they became amyloid and tau PET positive; 2) APOE- ε,4 carriers, women, and individuals with older amyloid PET positivity ages had a shorter time from amyloid to tau PET positivity; 3) individuals who became amyloid and tau PET positive at older ages had a shorter time until cognitive symptoms but showed slower rates of cognitive decline after symptom onset; and 4) a shorter time between amyloid and tau PET positivity was associated with a shorter time until symptom onset, but this effect diminished once tau pathology emerged. These results contribute to our understanding of factors influencing heterogeneity in disease progression after amyloid PET positivity, with implications for identifying high-risk individuals for prevention and early therapeutic trials. Both APOE- ε4 carriership and female sex were independently associated with younger amyloid and tau PET positivity ages, consistent with their established AD vulnerability 6 – 9 . The sex effect was more pronounced among APOE- ε4 carriers for amyloid PET positivity, suggesting that female APOE- ε4 carriers may be at greatest risk for early development of AD pathology. While interactions between sex and APOE -ε4 have been reported for tau vulnerability 10 , 13 – 15 , we did not observe a statistically significant effect in our sample, potentially due to limited statistical power or the use of a mesial-temporal meta-ROI capturing very early tau accumulation, where sex differences may not yet be apparent. As expected, estimated amyloid and tau PET positivity ages were highly correlated, reflecting the sequential accumulation of AD pathology 1 , 2 . This association was significantly stronger in APOE -ε4 carriers than in non-carriers, suggesting a tighter coupling between amyloid and tau pathology in genetically at-risk individuals. A key aim of our study was to evaluate time between positivity by amyloid and tau PET (i.e. the amyloid-tau interval) and its implications for clinical progression. We found that most participants followed an amyloid-first trajectory, consistent with the canonical AD pathway 1 , 2 . Regarding factors affecting the amyloid-tau interval, APOE- ε4 carriers and women exhibited a significantly shorter interval, indicating faster progression to tau pathology after amyloid positivity. Combined with their earlier biomarker positivity ages, these findings reinforce their greater vulnerability to AD pathology. Importantly, older amyloid PET positivity age was associated with a shorter amyloid-tau interval, which may reflect higher prevalence of co-pathologies with increasing age 28 – 32 or reduced brain resistance at older ages 33 that accelerate tau spread after amyloid accumulation. The observation that older biomarker positivity age was associated with accelerated progression in early disease stages was consistent across both biological and clinical domains. Individuals who became amyloid and tau PET positive at an older age not only exhibited a shorter amyloid-tau interval but also experienced earlier symptom onset. However, following onset of symptoms, they showed slower rates of cognitive decline compared to those with younger biomarker positivity ages. This finding highlights a potential disconnect between vulnerability to symptom onset and subsequent rate of decline. One possible explanation is that younger individuals may show greater cognitive reserve or resilience mechanisms that delay the emergence of clinical symptoms despite pathology, but once a certain pathology threshold is crossed, decline accelerates more rapidly. Notably, our finding of a steeper cognitive decline in individuals with earlier biomarker positivity ages is consistent with previous reports of faster progression in early-onset AD (EOAD) compared to late-onset AD (LOAD) 34 – 36 . Beyond the effects of the age at biomarker positivity, a shorter time until tau PET positivity following amyloid PET positivity was associated with an earlier onset of symptoms. Conversely, the amyloid-tau interval duration did not influence symptom onset after tau PET positivity, or rates of cognitive decline. These findings support tau pathology as a primary driver of symptom onset in AD 17 – 20 and suggest that once tau pathology emerges, clinical decline proceeds similarly, regardless of when amyloid PET positivity occurred. Of interest, a longer time until symptom onset following biomarker positivity was observed in women compared to men. However, adjusting for women’s earlier biomarker positivity age largely explained this apparent female advantage, suggesting they experience earlier pathological changes but maintain cognitive resilience longer 37 , 38 . These findings highlight a sex-dependent vulnerability and the importance of considering both sex and biomarker positivity age when assessing disease progression. In contrast, no differences by sex or APOE -ε,4 status were found in the effect of biomarker positivity age or the amyloid-tau interval on rates of cognitive decline after symptom onset, although these analyses were limited by smaller sample sizes and should be further addressed in future studies. Overall, our findings contribute to a better understanding of the heterogeneity in the amyloid–tau relationship and how it shapes disease progression 4 , 39 – 41 . The key contribution of this study is to demonstrate that the age at amyloid and tau PET positivity varies by sex and APOE -ε4, and that time between amyloid and tau PET positivity varies considerably and is strongly influenced by age. Further, once tau pathology emerges, it becomes the primary driver of clinical decline, irrespective of when amyloid started to accumulate. Importantly, our findings are consistent and complement those of recent work 42 showing that at symptomatic stages, age was negatively associated with prevalence of tau positivity, possibly reflecting age-related heterogeneity in AD pathophysiology, where younger individuals follow a more tau-driven and aggressive trajectory, while older individuals may exhibit symptoms with less tau involvement or alternative non-AD processes. In line with our results, APOE -ε4 carriership and female sex were associated to younger ages at amyloid and tau positivity. Another recent study 43 evaluated factors influencing estimated age at tau positivity. Consistent with our results, they reported that higher amyloid burden and APOE-ε4 were associated with younger estimated age at tau positivity. They also found that older age at tau PET positivity associated with shorter time from tau PET positivity to dementia. However, unlike our results, they observed an association between low education and literacy and earlier tau positivity and did not report sex differences. Several methodological differences may account for these discrepancies, including variations in the biological clock modeling approach, our study’s use of an earlier tau PET meta-ROI to define the tau timeline, and differing clinical outcome definitions. Specifically, our study focused on the symptom onset rather than dementia stage, as it is a more pertinent milestone for preventive and early-intervention AD trials. This study has several limitations. First, estimated biomarker positivity ages are dependent on threshold definitions and PET tracers. Second, smaller sample sizes for analyses relative to years from tau PET positivity and the amyloid-tau interval in cognitively impaired individuals limited statistical power, particularly for interaction terms. Third, the low frequency of individuals reaching advanced disease stages limits generalizability to later AD stages, though it enhances translational relevance for prevention and early intervention trials. Finally, heterogeneity in timing of biomarker positivity and subsequent disease progression, influenced by factors such as comorbidities, co-pathologies, lifestyle factors, or social determinants of health, warrant further investigation. Conclusions In summary, this study evaluated how estimated ages at amyloid and tau PET positivity and their temporal sequence contribute to symptom onset and clinical progression, and differ by APOE- ε4 status and sex. We found that older age at biomarker positivity and a shorter interval between amyloid and tau PET positivity were associated with earlier symptom onset. Older age at biomarker positivity was also associated with slower subsequent cognitive decline. These findings are crucial for predicting “when” an amyloid-positive individual is likely to develop tau pathology and cognitive impairment, optimizing participant selection for AD clinical trials targeting early disease stages. The influence of APOE- ε4 and sex on these temporal dynamics underscores their importance in modeling disease trajectories and designing preventive and therapeutic interventions. Data Availability Data from this study and the study methodology report may be accessed from the ADNI Laboratory of NeuroImaging (LONI) database: adni.loni.usc.edu. The annotated code used for study analyses is provided in full. https://adni.loni.usc.edu/ Author Contributions Concept and design: Milà-Alomà, Schindler, Tosun Acquisition, analysis, or interpretation of data: All authors Drafting of the manuscript: Milà-Alomà, Tosun Critical review of the manuscript for important intellectual content: All authors Statistical analysis: Milà-Alomà, Hausle, Tosun Obtained funding: Milà-Alomà, Tosun Data sharing Data from this study and the study methodology report may be accessed from the ADNI Laboratory of NeuroImaging (LONI) database: adni.loni.usc.edu. The annotated code used for study analyses is provided in full. Conflict of Interest Disclosures Dr. Schindler has served on scientific advisory boards on biomarker testing and education for Eisai and Novo Nordisk and has received speaking fees for presentations on biomarker testing from Eisai, Eli Lilly, and Novo Nordisk. Dr. Tosun reported grants from National Institutes of Health during the conduct of the study. Funding/Support Dr. Milà-Alomà receives funding from Alzheimer’s Association Research Fellowship grant program (AARF-23-1141384). This work was supported by National Institutes of Health (NIH) grants (U19 AG024904, R01 AG091657, U01 AG068057, and U24 AG074855) to Dr. Tosun. Acknowledgements We would like to acknowledge ADNI for ADNI participant data analyzed in this study. Data collection and sharing for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is funded by the National Institute on Aging (National Institutes of Health Grant U19 AG024904). The grantee organization is the Northern California Institute for Research and Education. In the past, ADNI has also received funding from the National Institute of Biomedical Imaging and Bioengineering, the Canadian Institutes of Health Research, and private sector contributions through the Foundation for the National Institutes of Health (FNIH) including 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 Limited; and Transition Therapeutics. Footnotes ↵ * Alzheimer’s Disease Neuroimaging Initiative (ADNI): Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf References 1. ↵ Jack CR , Andrews JS , Beach TG , et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup . Alzheimers Dement . Published online June 27, 2024 . doi: 10.1002/alz.13859 OpenUrl CrossRef PubMed 2. ↵ McDade EM . Alzheimer Disease . Continuum (Minneap Minn) . 2022 ; 28 ( 3 ): 648 – 675 . doi: 10.1212/CON.0000000000001131 OpenUrl CrossRef PubMed 3. ↵ Schindler SE , Li Y , Buckles VD , et al. Predicting Symptom Onset in Sporadic Alzheimer Disease With Amyloid PET . Neurology . 2021 ; 97 ( 18 ): e1823 – e1834 . doi: 10.1212/WNL.0000000000012775 OpenUrl CrossRef PubMed 4. ↵ Betthauser TJ , Bilgel M , Koscik RL , et al. Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts . Brain . 2022 ; 145 ( 11 ): 4065 – 4079 . doi: 10.1093/brain/awac213 OpenUrl CrossRef PubMed 5. ↵ 2025 Alzheimer’s disease facts and figures . Alzheimer’s & Dementia . 2025 ; 21 ( 4 ): e70235 . doi: 10.1002/alz.70235 OpenUrl CrossRef 6. ↵ Corder E , Saunders A , Strittmatter W , et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families . Science . 1993 ; 261 ( 5123 ): 921 – 923 . doi: 10.1126/science.8346443 OpenUrl Abstract / FREE Full Text 7. ↵ Rajan KB , Weuve J , Barnes LL , McAninch EA , Wilson RS , Evans DA . Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020–2060) . Alzheimer’s & Dementia . Published online May 27, 2021 : alz.12362 . doi: 10.1002/alz.12362 OpenUrl CrossRef PubMed 8. Mielke MM . Sex and Gender Differences in Alzheimer’s Disease Dementia . The Psychiatric times . 2018 ; 35 ( 11 ): 14 – 17 . OpenUrl PubMed 9. ↵ Mielke MM , Ferretti MT , Iulita MF , Hayden K , Khachaturian AS . Sex and gender in Alzheimer’s disease - Does it matter? Alzheimer’s & Dementia . 2018 ; 14 ( 9 ): 1101 – 1103 . doi: 10.1016/j.jalz.2018.08.003 OpenUrl CrossRef PubMed 10. ↵ Buckley RF , Mormino EC , Chhatwal J , et al. Associations between baseline amyloid, sex, and APOE on subsequent tau accumulation in cerebrospinal fluid . Neurobiology of Aging . 2019 ; 78 : 178 – 185 . doi: 10.1016/j.neurobiolaging.2019.02.019 OpenUrl CrossRef PubMed 11. Buckley RF , O’Donnell A , McGrath ER , et al. Menopause Status Moderates Sex Differences in Tau Burden: A Framingham PET Study . Annals of Neurology . 2022 ; 92 ( 1 ): 11 – 22 . doi: 10.1002/ana.26382 OpenUrl CrossRef PubMed 12. ↵ Buckley RF , Scott MR , Jacobs HIL , et al. Sex Mediates Relationships Between Regional Tau Pathology and Cognitive Decline . Ann Neurol . 2020 ; 88 ( 5 ): 921 – 932 . doi: 10.1002/ana.25878 OpenUrl CrossRef 13. ↵ Altmann A , Tian L , Henderson VW , Greicius MD . Sex modifies the APOE-related risk of developing Alzheimer disease . Annals of Neurology . 2014 ; 75 ( 4 ): 563 – 573 . doi: 10.1002/ana.24135 OpenUrl CrossRef PubMed 14. Buckley RF , Mormino EC , Amariglio RE , et al. Sex, amyloid, and APOE ε4 and risk of cognitive decline in preclinical Alzheimer’s disease: Findings from three well-characterized cohorts . Alzheimer’s and Dementia . 2018 ; 14 ( 9 ): 1193 – 1203 . doi: 10.1016/j.jalz.2018.04.010 OpenUrl CrossRef PubMed 15. ↵ Hohman TJ , Dumitrescu L , Barnes LL , et al. Sex-specific association of apolipoprotein e with cerebrospinal fluid levels of tau . JAMA Neurology . 2018 ; 75 ( 8 ): 989 – 998 . doi: 10.1001/jamaneurol.2018.0821 OpenUrl CrossRef PubMed 16. ↵ Moscoso A , Heeman F , Raghavan S , et al. Frequency and Clinical Outcomes Associated With Tau Positron Emission Tomography Positivity . JAMA . Published online June 16, 2025 . doi: 10.1001/jama.2025.7817 OpenUrl CrossRef 17. ↵ Pontecorvo MJ , Devous MD , Kennedy I , et al. A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia . Brain . 2019 ; 142 ( 6 ): 1723 – 1735 . doi: 10.1093/brain/awz090 OpenUrl CrossRef PubMed 18. Biel D , Brendel M , Rubinski A , et al. Tau-PET and in vivo Braak-staging as prognostic markers of future cognitive decline in cognitively normal to demented individuals . Alzheimers Res Ther . 2021 ; 13 ( 1 ): 137 . doi: 10.1186/s13195-021-00880-x OpenUrl CrossRef PubMed 19. Strikwerda-Brown C , Hobbs DA , Gonneaud J , et al. Association of Elevated Amyloid and Tau Positron Emission Tomography Signal With Near-Term Development of Alzheimer Disease Symptoms in Older Adults Without Cognitive Impairment . JAMA Neurol . 2022 ; 79 ( 10 ): 975 – 985 . doi: 10.1001/jamaneurol.2022.2379 OpenUrl CrossRef PubMed 20. ↵ Ossenkoppele R , Pichet Binette A , Groot C , et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline . Nature Medicine . 2022 ; 28 ( 11 ): 2381 – 2387 . doi: 10.1038/s41591-022-02049-x OpenUrl CrossRef PubMed 21. ↵ Weiner MW , Veitch DP , Aisen PS , et al. The Alzheimer’s Disease Neuroimaging Initiative 3: continued innovation for clinical trial improvement . Alzheimers Dement . 2017 ; 13 ( 5 ): 561 – 571 . doi: 10.1016/j.jalz.2016.10.006 OpenUrl CrossRef PubMed 22. ↵ Weiner MW , Aisen PS , Jack CR , et al. The Alzheimer’s disease neuroimaging initiative: progress report and future plans . Alzheimers Dement . 2010 ; 6 ( 3 ): 202 – 211 .e7. doi: 10.1016/j.jalz.2010.03.007 OpenUrl CrossRef PubMed Web of Science 23. ↵ Jagust WJ , Landau SM , Koeppe RA , et al. The Alzheimer’s Disease Neuroimaging Initiative 2 PET Core: 2015 . Alzheimers Dement . 2015 ; 11 ( 7 ): 757 – 771 . doi: 10.1016/j.jalz.2015.05.001 OpenUrl CrossRef PubMed 24. ↵ Koeppe , R.A . Data analysis for amyloid PET imaging: Longitudinal studies . In:; 2013 . 25. ↵ Diedrichsen J . A spatially unbiased atlas template of the human cerebellum . Neuroimage . 2006 ; 33 ( 1 ): 127 – 138 . doi: 10.1016/j.neuroimage.2006.05.056 OpenUrl CrossRef PubMed Web of Science 26. ↵ Morris JC , Blennow K , Ivanoiu A , et al. The Clinical Dementia Rating (CDR): current version and scoring rules . Neurology . 1993 ; 43 ( 11 ): 2412 – 2414 . doi: 10.1212/wnl.0b013e318221ad47 OpenUrl FREE Full Text 27. ↵ Milà-Alomà M , Tosun D , Schindler SE , et al. Timing of changes in Alzheimer’s disease plasma biomarkers as assessed by amyloid and tau PET clocks . Published online November 14, 2024 : 2024.10.25.24316144 . doi: 10.1101/2024.10.25.24316144 OpenUrl Abstract / FREE Full Text 28. ↵ Kawas CH , Kim RC , Sonnen JA , Bullain SS , Trieu T , Corrada MM . Multiple pathologies are common and related to dementia in the oldest-old: The 90+ Study . Neurology . 2015 ; 85 ( 6 ): 535 – 542 . doi: 10.1212/WNL.0000000000001831 OpenUrl Abstract / FREE Full Text 29. Arvanitakis Z , Leurgans SE , Barnes LL , Bennett DA , Schneider JA . Microinfarct pathology, dementia, and cognitive systems . Stroke . 2011 ; 42 ( 3 ): 722 – 727 . doi: 10.1161/STROKEAHA.110.595082 OpenUrl Abstract / FREE Full Text 30. Schneider JA , Arvanitakis Z , Bang W , Bennett DA . Mixed brain pathologies account for most dementia cases in community-dwelling older persons . Neurology . 2007 ; 69 ( 24 ): 2197 – 2204 . doi: 10.1212/01.wnl.0000271090.28148.24 OpenUrl Abstract / FREE Full Text 31. Robinson JL , Richardson H , Xie SX , et al. The development and convergence of co-pathologies in Alzheimer’s disease . Brain . 2021 ; 144 ( 3 ): 953 – 962 . doi: 10.1093/brain/awaa438 OpenUrl CrossRef PubMed 32. ↵ Power MC , Mormino E , Soldan A , et al. Combined neuropathological pathways account for age-related risk of dementia . Ann Neurol . 2018 ; 84 ( 1 ): 10 – 22 . doi: 10.1002/ana.25246 OpenUrl CrossRef PubMed 33. ↵ Arenaza-Urquijo EM , Vemuri P . Resistance vs resilience to Alzheimer disease . Neurology . 2018 ; 90 ( 15 ): 695 – 703 . doi: 10.1212/WNL.0000000000005303 OpenUrl Abstract / FREE Full Text 34. ↵ Jacobs D , Sano M , Marder K , et al. Age at onset of Alzheimer’s disease: relation to pattern of cognitive dysfunction and rate of decline . Neurology . 1994 ; 44 ( 7 ): 1215 – 1220 . doi: 10.1212/wnl.44.7.1215 OpenUrl Abstract / FREE Full Text 35. van der Vlies AE , Koedam ELGE , Pijnenburg Y a. L , Twisk JWR , Scheltens P , van der Flier WM . Most rapid cognitive decline in APOE epsilon4 negative Alzheimer’s disease with early onset . Psychol Med . 2009 ; 39 ( 11 ): 1907 – 1911 . doi: 10.1017/S0033291709005492 OpenUrl CrossRef PubMed 36. ↵ Panegyres PK , Chen HY . Differences between early and late onset Alzheimer’s disease . Am J Neurodegener Dis . 2013 ; 2 ( 4 ): 300 – 306 . OpenUrl PubMed 37. ↵ Calvo N , Einstein G . Steroid hormones: risk and resilience in women’s Alzheimer disease . Front Aging Neurosci . 2023 ; 15 : 1159435 . doi: 10.3389/fnagi.2023.1159435 OpenUrl CrossRef PubMed 38. ↵ Eissman JM , Dumitrescu L , Mahoney ER , et al. Sex differences in the genetic architecture of cognitive resilience to Alzheimer’s disease . Brain . 2022 ; 145 ( 7 ): 2541 – 2554 . doi: 10.1093/brain/awac177 OpenUrl CrossRef PubMed 39. ↵ Koscik RL , Betthauser TJ , Jonaitis EM , et al. Amyloid duration is associated with preclinical cognitive decline and tau PET . Alzheimers Dement (Amst) . 2020 ; 12 ( 1 ): e12007 . doi: 10.1002/dad2.12007 OpenUrl CrossRef PubMed 40. Birdsill AC , Koscik RL , Cody KA , et al. Trajectory of clinical symptoms in relation to amyloid chronicity . Alzheimers Dement (Amst) . 2022 ; 14 ( 1 ): e12360 . doi: 10.1002/dad2.12360 OpenUrl CrossRef 41. ↵ Cody KA , Langhough RE , Zammit MD , et al. Characterizing brain tau and cognitive decline along the amyloid timeline in Alzheimer’s disease . Brain . 2024 ; 147 ( 6 ): 2144 – 2157 . doi: 10.1093/brain/awae116 OpenUrl CrossRef PubMed 42. ↵ Ossenkoppele R , Coomans EM , Apostolova LG , et al. Tau PET positivity in individuals with and without cognitive impairment varies with age, amyloid-β status, APOE genotype and sex . Nat Neurosci . Published online July 16, 2025 : 1 – 12 . doi: 10.1038/s41593-025-02000-6 OpenUrl CrossRef 43. ↵ Heston MB , Teague JP , Cody KA , et al. Factors associated with age at tau pathology onset and time from tau onset to dementia . 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Share Temporal sequence of amyloid and tau PET positivity: APOE -ε4 and sex effects, and implications for Alzheimer’s disease progression Marta Milà-Alomà , Isabella Hausle , Kellen K. Petersen , Pamela Thropp , Suzanne E. Schindler , Duygu Tosun , Alzheimer’s Disease Neuroimaging Initiative medRxiv 2025.07.27.25332202; doi: https://doi.org/10.1101/2025.07.27.25332202 Share This Article: Copy Citation Tools Temporal sequence of amyloid and tau PET positivity: APOE -ε4 and sex effects, and implications for Alzheimer’s disease progression Marta Milà-Alomà , Isabella Hausle , Kellen K. Petersen , Pamela Thropp , Suzanne E. 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