The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline

The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline | 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 The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline AJ Paulsen , I Driscoll , BM Breidenbach , MP Glittenberg , SR Lose , Y Ma , MA Sager , CM Carlsson , CL Gallagher , BP Hermann , K Blennow , H Zetterberg , S Asthana , SC Johnson , TJ Betthauser , BT Christian , DB Cook , OC Okonkwo doi: https://doi.org/10.1101/2025.03.03.25323245 AJ Paulsen a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site I Driscoll a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site BM Breidenbach a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site MP Glittenberg a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site SR Lose a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Y Ma a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site MA Sager a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site CM Carlsson a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA c Geriatric Research Education and Clinical Center, William S. Middleton VA Hospital , 2500 Overlook Terrace, Madison, Wisconsin, USA , 53705 Find this author on Google Scholar Find this author on PubMed Search for this author on this site CL Gallagher c Geriatric Research Education and Clinical Center, William S. Middleton VA Hospital , 2500 Overlook Terrace, Madison, Wisconsin, USA , 53705 d Department of Neurology, University of Wisconsin School of Medicine and Public Health , 1685 Highland Avenue, Madison, Wisconsin, 53705, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site BP Hermann b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA d Department of Neurology, University of Wisconsin School of Medicine and Public Health , 1685 Highland Avenue, Madison, Wisconsin, 53705, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site K Blennow e Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg , Box 100, 405 30 Gothenburg, Mölndal, Sweden f Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital , SE-43180, Göteborg, Sweden g Paris Brain Institute, ICM, Pitié-Salpêtrière Hospital, Sorbonne University , 47 Bd de l’Hôpital, 75013, Paris, France h Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, and Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC , No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P.R. China Find this author on Google Scholar Find this author on PubMed Search for this author on this site H Zetterberg a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA e Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg , Box 100, 405 30 Gothenburg, Mölndal, Sweden f Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital , SE-43180, Göteborg, Sweden i Department of Neurodegenerative Disease, UCL Institute of Neurology , Queen Square, London, UK j UK Dementia Research Institute at UCL , Gower Street, London, WC1E 6BT, London, UK k Hong Kong Center for Neurodegenerative Diseases , Clear Water Bay, Units 1501-1502, 1512-1518, 15/F Building 17W, 17 Science Park W Ave, Science Park, Hong Kong, PR China Find this author on Google Scholar Find this author on PubMed Search for this author on this site S Asthana a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA c Geriatric Research Education and Clinical Center, William S. Middleton VA Hospital , 2500 Overlook Terrace, Madison, Wisconsin, USA , 53705 Find this author on Google Scholar Find this author on PubMed Search for this author on this site SC Johnson a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site TJ Betthauser a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site BT Christian Find this author on Google Scholar Find this author on PubMed Search for this author on this site DB Cook l Department of Medical Physics, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 111 Highland Ave, Room 1005, Madison, Wisconsin, USA , 53705 m William S. Middleton Memorial Veterans Hospital , 2500 Overlook Terrace, Madison, Wisconsin, USA , 53705 n Department of Kinesiology, University of Wisconsin School of Education , 1300 University Avenue, 285 Medical Sciences Center , Madison, Wisconsin, USA , 53706 Find this author on Google Scholar Find this author on PubMed Search for this author on this site OC Okonkwo a Wisconsin Alzheimer’s Disease Research Center, Department of Medicine, University of Wisconsin School of Medicine and Public Health , 600 Highland Avenue, MC2420, Madison, Wisconsin, 53792, USA b Wisconsin Alzheimer’s Institute , 610 Walnut St, Suite 957, Madison, Wisconsin, 53726, USA c Geriatric Research Education and Clinical Center, William S. Middleton VA Hospital , 2500 Overlook Terrace, Madison, Wisconsin, USA , 53705 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: apaulsen{at}medicine.wisc.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT INTRODUCTION Relationships between core Alzheimer’s disease (AD) biomarker accumulation and cognitive decline are well-established and the literature generally suggests a favorable relationship of cardiorespiratory fitness (CRF) on AD biomarker accumulation and cognition. Differences in risk of biomarker status conversion or accumulation rates by CRF, or their potential interactive relationships with cognitive decline remain largely unknown. METHODS Participants (N=533; MeanAGE=65, 70% female) from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention underwent serial blood draws, and cognitive and imaging assessments (MeanFollow-up=6.0 years). PET imaging of amyloid-β (Aβ) and tau (T) and plasma phosphorylated tau-217 (pTau-217) were used to determine biomarker status (+/-). Sex-specific estimated CRF (eCRF) tertiles were created using a validated equation. Kaplan-Meier survival curves and Cox-proportional hazards models characterized the risk of becoming biomarker-positive. Linear mixed effects models estimated associations between baseline eCRF and core AD biomarker accumulation and whether eCRF modified relationships between biomarker accumulation and cognitive decline. Analyses were stratified by biomarker +/- status. RESULTS No significant relationships were observed between eCRF and biomarker trajectories. However, those in the high eCRF group who were also Aβ-(HR[95%CI]=0.42[0.20, 0.88]) and pTau-217-(HR[95%CI]=0.45[0.21, 0.97]) at baseline had a significantly lower risk of becoming biomarker-positive. There was a significant attenuation of the detrimental relationship between Aβ accumulation and cognitive decline for those with high eCRF and Aβ+/T+. DISCUSSION While CRF did not influence core AD biomarker accumulation trajectories, high CRF did seem to protect against becoming biomarker-positive and attenuate the known deleterious relationship between biomarker accumulation and cognitive decline in Aβ+/T+. 1. Introduction Accumulation of amyloid-β (Aβ) and tau (T), neuropathologic hallmark proteins of Alzheimer’s disease (AD), begins decades prior to clinically relevant symptoms, making them the earliest known markers of AD-related pathological changes.[ 1 – 5 ] Phosphorylated tau (pTau) has also recently been suggested as a key biomarker for the diagnosis of AD, even in preclinical stages.[ 1 , 2 , 6 , 7 ] The timing of these pathologic changes is helpful in identifying people at greatest risk while in the preclinical stages along the AD dementia continuum and are highly relevant for research aimed at identifying preventative strategies for AD.[ 2 , 3 , 8 ] Age, genetics, and family history of AD are the greatest known risk factors for AD.[ 9 ] Conversely, existing literature suggests that higher levels of physical activity (PA) and better cardiorespiratory fitness (CRF) may positively alter both AD neuropathological progression[ 10 – 13 ] and age-related cognitive decline[ 10 , 13 – 17 ], thereby reducing risk for AD and related dementias. AD biomarker profiles of highly fit individuals seem to be less adversely impacted by genetic risk compared to those less fit.[ 12 ] Moreover, the literature suggests that the negative effects of Aβ accumulation on cognitive function are mitigated by higher CRF, and conversely, that downstream effects of physical inactivity moderate the effect of Aβ on cognitive decline.[ 15 , 16 ] The majority of this literature is cross-sectional in nature and limited by modest sample sizes, leaving a knowledge gap in our understanding of the impact of CRF on AD biomarker accumulation. Moreover, trajectories of AD biomarker accumulation differ based on biomarker positivity status (i.e., participants who are already Aβ+ tend to accumulate Aβ at a faster rate than those who are Aβ-, who may not accumulate Aβ at all).[ 18 , 19 ] The present study examines the relationship between baseline CRF with trajectories of core AD biomarker accumulation, namely Aβ and tau measured using positron emission tomography (PET), and pTau-217 measured in plasma, and the risk of conversion to biomarker positivity for those who were negative at baseline. Additionally, we investigate the potential modification of the relationships between accumulation trajectories of each of these core AD biomarkers and cognitive decline by CRF. Importantly, the present study examines the aforementioned relationships based on AD biomarker status (+/-). 2. Methods 2.1 Participants The present sample included 533 participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and the Wisconsin Alzheimer’s Disease Research Center (WADRC), two prospective cohorts enriched for parental history of AD at enrollment, consisting of more than 2000 middle-aged and older adults. Detailed descriptions of enrollment criteria have been previously published.[ 20 , 21 ] The current sample was comprised of participants with available data of interest, predominantly white (92%; 5% Black/African American; 3% all other groups), on average college educated, and cognitively unimpaired at baseline. Participants ranged in age between 46-89 years, with a mean follow-up (SD) of 6.0 (3.9) years (number of visits ranging from 1 to 7). All participants provided written informed consent prior to participation and all procedures were approved by the University of Wisconsin Health Sciences Institutional Review Board. 2.2 Cardiorespiratory fitness An estimated CRF (eCRF) was calculated using a validated equation,[ 22 ] which accounts for age, sex, body mass index, resting heart rate, and self-reported PA: with a resulting estimate of VO 2peak in mL/kg/min. BMI was calculated as weight in kilograms divided by height in meters squared. Resting heart rate was measured using a GE Dinamap Pro 400 V2 Vital Signs Monitor (GE Medical Systems Information Technologies, Inc., Milwaukee, WI). Responses to questionnaires characterizing participants’ habitual activity levels (duration, intensity, and frequency per week) were used to calculate the PA score as previously described.[ 22 – 24 ] Briefly, the PA score incorporates the characteristics of habitual PA to assign five distinct PA levels with scores assigned to each level ranging from 0 to 3.03. In addition to the continuous measure of eCRF, a categorical eCRF variable was created by defining sex-specific tertile cut-points across all participants in WRAP/WADRC, which were then applied to the current sample to create low, middle, and high eCRF groups. 2.3 Biomarker Measurements Aβ burden was measured using 11 C-Pittsburgh Compound B (PiB) PET imaging. PiB acquisition and postprocessing protocols have been previously detailed.[ 25 ] Briefly, participants underwent PiB imaging with a 6-minute transmission scan after bolus injection and a 70-minute dynamic scan. Cortical Aβ burden was quantified based on derived distribution volume ratio (DVR) maps using the Logan method with cerebellar gray matter as reference.[ 25 ] Amyloid positivity (Aβ+) was defined by a global cortical DVR>1.19.[ 26 ] Tau was measured using 18 F-MK-6240 PET tracer. Acquisition and postprocessing has been previously described.[ 4 ] Briefly, standard uptake value ratios (SUVR) were calculated following a 20-minute dynamic acquisition scan. Tau positivity (T+) was defined by tracer uptake in the entorhinal cortex where neurofibrillary tangles (NFT) are expected to first occur (entorhinal SUVR>1.27) or regional tau positivity in NFT associated regions corresponding to Braak staging.[ 5 , 27 , 28 ] Participants provided blood samples at each study visit from which pTau-217, which is indicative of Aβ positivity and may also reflect the entire AD pathological process,[ 29 – 31 ] was assayed using ALZpath (ALZpathDX, Carlsbad, CA) at the Clinical Neurochemistry Laboratory in the University of Gothenburg, Sweden. Detailed information on the collection and processing of samples and quantification of pTau-217 has been previously published.[ 30 , 31 ] A binary pTau-217 +/- status was created using a previously determined center-wide reference cut-point of >0.63 pg/mL.[ 29 ] Analyses including pTau-217 (N=445) and tau-PET (N=457) had slightly smaller sample sizes due to missingness of those measures. 2.4 Neuropsychological Assessment Participants undergo biannual neuropsychological assessment as part of their WRAP/WADRC participation. The present study employed a modified Preclinical Alzheimer’s Cognitive Composite (PACC-3) score, comprised of the Rey Auditory Verbal Learning Test Delayed Recall (RAVLT), the Wechsler Memory Scale Logical Memory II, and the Trail Making Test Part B (TMTB), as the utility of global composite scores is superior to performance on individual tests when assessing longitudinal cognitive decline.[ 32 , 33 ] The composite score was z-transformed across the entire WRAP and WADRC cohort for analyses purposes, so that scores among a cognitively unimpaired sample had an approximately normal distribution with mean=0 and SD=1. 2.5 Statistical Analyses All analyses were conducted using RStudio version 2023.12.1+402 (Posit Software, PBC). Analyses were stratified by Aβ-PET status (Aβ- or Aβ+) at baseline given that biomarker accumulation trajectories tend to differ based on baseline Aβ positivity status, and Aβ is considered the earliest indication of AD pathological change.[ 18 , 19 , 27 , 34 ] Differences in baseline characteristics by Aβ+/- status were determined using t-tests for continuous variables and chi-squared tests for categorical variable. Analyses of change in cognition were conducted stratified by Aβ and tau+/- status at any timepoint (i.e., Aβ-T-were never positive and Aβ+T+ were positive for both biomarkers at their most recent visit) as the combined pathology confers the greatest risk of cognitive change[ 27 , 35 , 36 ] and few participants were T+ at baseline in the current sample, limiting power for stratified analyses. The resultant groups were Aβ-T-, Aβ+T-, Aβ-T+, and Aβ+T+. Statistical significance was determined with a two-tailed p <0.05. Linear mixed effects (LME) models were constructed by including baseline eCRF, years of follow-up for biomarker measurement, and their interaction to investigate whether rates of biomarker accumulation over time differ based on eCRF. This relationship was tested using continuous eCRF (per SD increase) and by eCRF group with the low fitness group as the reference. All models were adjusted for baseline age, sex, years of education, APOE ε4 carrier status, parental history of AD, years of follow-up, and random intercept and slope per participant. To evaluate the relationship between eCRF and conversion to biomarker positivity during follow-up for those who were biomarker negative at baseline, Kaplan-Meier survival curves were constructed by eCRF group. Additional Cox-Proportional Hazards models were created to estimate the difference in risk of conversion to biomarker positivity by Aβ-PET, tau-PET, and plasma pTau-217 respectively for participants who were negative for each biomarker at baseline controlling for age, sex, years of education, APOE ε4 carrier status, and parental history of AD. Coefficients from these models were exponentiated to provide Hazard Ratios (HR) detailing the difference in risk of conversion by eCRF group. LME models were used to evaluate the modification of the association between time-varying biomarker levels (per SD) and cognitive trajectory (PACC-3 score) by eCRF. These models were stratified by Aβ and tau status at any time point (Aβ-T-, Aβ+T-, Aβ-T+, and Aβ+T+) due to the potential synergistic effects of Aβ and T on cognitive change.[ 27 , 34 – 36 ] Models included baseline eCRF, biomarker level, years of follow-up, interaction terms between each of these variables, and a three-way interaction term between baseline eCRF, biomarker level, and years of follow-up. This three-way interaction term provided the coefficient of interest indicating differing rates of cognitive change per SD change in biomarker per year of follow-up based on eCRF group. These models were adjusted for baseline age, sex, years of education, APOE ε4 carrier status, parental history of AD, and random intercept and slope per participant. 3. Results 3.1 Participants Baseline characteristics of the full sample and the Aβ+/- (by PiB-PET) sub-samples are provided in Table 1 . The entire sample (N=533) was 70% female and majority of participants (79%) were Aβ-at baseline. These participants were younger, less likely to be APOE ε4 carriers or have a parental history of AD compared to those Aβ+ at baseline (all p s≤0.01; Table 1 ). Significant differences in baseline AD biomarker levels were observed between Aβ+ and Aβ-groups (all p s<0.001). The Aβ+ group also had significantly lower baseline PACC3 scores ( p <0.001). View this table: View inline View popup Table 1: Baseline characteristics of the entire sample and by baseline Aβ status. 3.2 eCRF and biomarker trajectories Aβ accumulation rates based on global cortical PiB-PET DVR scores (all p s≥0.08; Table 2 ) or by plasma pTau-217 (all p s≥0.25; Table 2 ) did not significantly differ by eCRF group regardless of baseline Aβ status. This lack of association between eCRF and pTau-217 remained when analyses were re-stratified by baseline pTau-217 status (pTau-217+/-; data not shown). There were no significant differences in accumulation rates of tau-PET by eCRF. (all p s≥0.15; Table 2 ). This lack of association between eCRF and tau remained unchanged when re-stratifying analyses by baseline T+/- status based on tau-PET (data not shown). View this table: View inline View popup Download powerpoint Table 2: Difference in rates of biomarker accumulation (95% CI) per eCRF standard deviation and by eCRF group, stratified by baseline Aβ status. 3.3 eCRF and biomarker conversion For those Aβ-at baseline, the high eCRF group had a lower risk of conversion to Aβ+ status over the course of follow-up, with only 5.8% of those in the high compared to 10.8% in the low eCRF group converting. A Kaplan-Meier survival analysis confirmed a significantly lesser risk of conversion to Aβ+ status over the course of follow up for the high ( p <0.001), but not the middle ( p =0.18), eCRF group; Figure 1 . Cox-proportional hazards model, adjusted for age, sex, years of education, APOE e4 carrier status, and parental history of AD, yielded similar results, whereby the high eCRF group had lesser risk (HR [95% CI = 0.42 [0.20, 0.88]) of conversion to Aβ+ compared to low eCRF group, indicating a 58% decrease in risk of conversion. No significant differences in risk were observed for the middle compared to the low eCRF group (HR [95%CI] = 0.85 [0.46, 1.54]; Table 3 ). Among those who were pTau-217-at baseline, the high eCRF group had a significantly lower risk of conversion via both Kaplan-Meier survival analysis ( p =0.04) and Cox proportional hazards model (HR[95%CI]= 0.45 [0.20, 0.97]), which revealed a 55% risk reduction; Figure 1 , Table. 3 For those who were T-at baseline, Kaplan-Meier survival analysis indicated a trend toward better survival (non-conversion to T+); for both the middle and the high eCRF groups (all ps = 0.07); Figure 1 . This association was not confirmed, however, by multivariable adjusted Cox proportional hazards modeling (all p s≥0.11; Table 3 ). Download figure Open in new tab Figure 1: Kaplan-Meier curves depicting risk of becoming positive for core AD biomarkers over the course of follow-up among participants who were biomarker negative at baseline by eCRF group. Significantly lower risk of becoming positive for Aβ by PET ( p =<0.001) and pTau-217 ( p =0.04) was observed for high (but not the middle) compared to low eCRF group. A trend for lower risk of conversion to T+ was observed for the middle and high eCRF groups compared to the low eCRF group, though these relationships were not statistically significant (all ps >0.05). A: Aβ+ defined by a cortical PET-PiB DVR > 1.19. B: pTau-217+ defined by plasma pTau-217> 0.63 pg/mL. C: T+ defined by an entorhinal PET- 18 F-MK6240 SUVR> 1.27 or regional tau positivity in NFT associated regions corresponding to Braak staging. Abbreviations: Aβ = amyloid-β, eCRF = estimated cardiorespiratory fitness, NFT = neurofibrillary tangles, PiB DVR = 11 C Pittsburgh Compound B distribution volume ratio, PET= positron emission tomography, pTau = phosphorylated tau, SUVR = standard uptake value ratio. View this table: View inline View popup Download powerpoint Table 3: Risk of conversion to biomarker positive status during follow-up by eCRF category among participants who were biomarker negative at baseline.*, † 3.4 eCRF, biomarker trajectories and cognitive change In analyses stratified by Aβ/T status, we found a positive association between eCRF and cognitive trajectories among those who were Aβ+/T+, where high eCRF attenuated the negative association between higher levels of Aβ (per SD increase) and steeper cognitive decline; Table 4 , Figure 2 . Using estimates from the full model to calculate rates of cognitive change by eCRF holding covariates constant, the low eCRF group among Aβ+T+ had a decline in cognitive performance of -0.132/year per SD increase in Aβ, whereas those in the middle and high eCRF groups had significantly slower rates of cognitive decline (-0.023/year and -0.013/year per SD increase in Aβ, respectively, all p s <0.05 ). No significant relationships were observed between eCRF and cognitive decline for Aβ-T-, Aβ+T-, or Aβ-T+ groups (all p s≥0.24). Download figure Open in new tab Figure 2: Differences in rates of cognitive change (PACC3 score) in Aβ+/T+, by eCRF category and core AD biomarker levels. The figures display PACC3 trajectory for the low (faint lines) and high (bolded lines) eCRF groups with different levels of each biomarker (average and 1SD above average). A statistically significant interaction was observed for Aβ-PET (Panel A; p <0.05); those with both high eCRF and high Aβ (A; dark red line) experienced no decline in cognition, whereas those with low eCRF and high Aβ (panel A; pink line) experienced significant decline. Similar trends were observed for plasma pTau-217 (B; p =0.31) and tau-PET (C; p =0.27,), though these results were not statistically significant. Aβ+/- status defined by a cortical PET-PiB DVR > 1.19, T+/- status defined by entorhinal PET-18F-MK6240 SUVR>1.27 or regional tau positivity in NFT associated regions corresponding to Braak staging. Abbreviations: Aβ = amyloid-β, eCRF = estimated cardiorespiratory fitness, NFT = neurofibrillary tangles, PET = positron emission tomography, PACC3 = Preclinical Alzheimer’s Cognitive Composite modified 3 test score, PiB DVR = 11CPittsburgh Compound B distribution volume ratio, SD = standard deviation, SUVR =standardized uptake value ratio. Figures were created using the coefficients from multivariable linear mixed effects models, adjusted for baseline age, sex, years of education, APOE ε4 carrier status, parental history of AD, baseline eCRF, years of follow-up, random intercept, and random slope per participant. View this table: View inline View popup Download powerpoint Table 4: Differences in rates of cognitive decline (PACC-3) by eCRF category, stratified by Aβ/T status.*, † There were no significant associations between eCRF and cognitive decline with increasing plasma pTau-217 in Aβ-T- or Aβ+T-groups (all p s≥0.22). A significant negative association was observed in the Aβ-T+ group, whereby higher eCRF was associated with a steeper cognitive decline per SD increase in pTau-217 ( p <0.01); Table 4 . Among Aβ+T+, the middle and the high eCRF groups had slower cognitive decline compared to the low eCRF, though this association did not reach statistical significance (all p s≥0.17), Table 4 , Figure 2 . No statistically significant relationships were observed between eCRF and cognitive decline with increasing levels of tau-PET (all p s≥0.19), albeit there was a non-significant trend for less cognitive decline with increasing tau burden in the middle and high eCRF groups among Aβ+T+ (all p s≥0.27), Table 4 , Figure 2 . 4. Discussion In the current sample of middle-aged and older adults who were cognitively unimpaired at baseline from a cohort enriched for AD risk at enrollment, AD biomarker (Aβ and tau by PET, and plasma pTau-217) accumulation rates did not differ based on CRF. In those Aβ-at baseline, however, a 58% decrease in risk of conversion to Aβ+ status over the course of follow-up was observed for those with high compared to low CRF. Those with high CRF also had a 55% decrease in risk of conversion to pTau-217+. Importantly, the relationship between AD biomarker accumulation and cognitive decline seems to differ by CRF, such that for Aβ+T, high CRF was associated with slower rates of cognitive decline per SD increase in biomarkers. The extant literature supports a protective effect of PA or CRF on brain health.[ 10 , 11 , 13 , 17 , 37 ] Higher levels of PA and higher CRF are associated with lower levels of core AD biomarkers,[ 11 , 12 , 38 ] although, some recent findings suggest no associations with biomarker deposition.[ 37 , 39 , 40 ] Our current results are in line with the more recent literature[ 37 , 39 , 40 ]– we too report no relationship between CRF and the rates of Aβ (by PET or plasma pTau-217) or tau (MK-6240 PET) accumulation. Of note, we stratified our sample based on baseline Aβ status, which no study reporting a significant association between PA or CRF and accumulation of core AD neuropathology previously accounted for. However, in those with high CRF who were Aβ-at baseline, we observed a ∼60% reduction in the risk of conversion to Aβ+ whether by PET or plasma pTau-217, both of which are indicative of clinically relevant Aβ accumulation. This is an important finding, as prevention or delay of accumulation to a point of clinical relevance could significantly reduce future negative sequalae of AD. While CRF did not confer resilience against AD biomarker accumulation in our sample, we did see potential CRF contributions to resilience against cognitive decline. The relationship between AD biomarker accumulation and cognitive decline is well-established in the literature, whereby higher levels of AD biomarkers are associated with poorer cognitive performance[ 27 , 41 ] and precipitate cognitive decline.[ 30 , 34 , 35 , 42 ] Our current findings suggest that CRF may offset the deleterious effects of core AD biomarker accumulation on cognitive decline. Those who were Aβ+T+ but concomitantly had higher eCRF declined cognitively at a slower rate even when experiencing increasing levels of AD biomarkers. Although this finding was only statistically significant with regards to Aβ, similar associations were observed with pTau-217 and tau-PET accumulation and warrant further investigation. Conversely, our finding of an unexpected deleterious association between high eCRF and cognitive decline in the Aβ-T+ group with increasing pTau-217, could indicate that once a threshold of tau accumulation is crossed (T+), CRF may no longer confer resilience against cognitive decline, though due to the relatively small sample size in this group as well as the other subgroups (i.e., Aβ-T+ high eCRF), caution should be used in interpretation of this finding. Furthermore, while pTau-217 is considered a marker of brain Aβ, it is also indicative of other non-Aβ-related changes across the AD continuum, which might account for the disparity in the results between PiB-PET and plasma pTau-217.[ 29 ] Additionally, research has suggested that the cognitive trajectory of people with the Aβ-T+ biomarker profile, frequently termed suspected non-Alzheimer’s disease pathophysiology (SNAP),[ 43 , 44 ] follows more closely with those who are Aβ-T-than with those on the AD continuum,[ 45 , 46 ] though this relationship may be dependent on additional measures of neurodegeneration.[ 47 ] Our findings may reflect differences in the pathological processes that contribute to non-AD accumulation of tau, which may be robust to potential protective effects of CRF, or could be due to the unknown levels of other neurodegenerative markers (i.e., brain atrophy, neurofilament light chain),[ 43 , 47 ] that may be present in A-T+ but are outside the scope of the present study. This warrants further investigation with a larger sample of A-T+ individuals and with differentiation by additional neurodegenerative biomarkers. While others have reported PA to be associated with better cognitive performance, they did not observe an attenuation of the adverse relationship between core AD pathology and cognition or did not evaluate the potential effect modification that CRF may have on this relationship.[ 14 , 24 , 48 – 50 ] Our current findings suggest that the deleterious relationship between accumulation of core AD neuropathology and cognitive decline could potentially be mitigated through exercise interventions and maintenance of CRF with age. In our sample, higher CRF seemed to be most beneficial for those participants who were Aβ+T+. Literature suggests that the “chronicity” of Aβ and tau positivity confers greater risk for and may have synergistic effects on cognitive decline.[ 5 ] Hence, the potential resilience conferred by CRF to those Aβ+T+ would be of utmost interest as a potential clinical intervention or preventative strategy. One potential limitation of the current study is the use of an estimated measure of CRF, which relies on questionnaire data that could be prone to recall or reporting biases. Even so, this measure correlates well with VO 2peak , the gold standard measure of CRF. [ 23 , 24 ] The WRAP and WADRC cohorts are predominantly highly educated and non-Hispanic white, making generalizability of the current findings unknown. Nonetheless, our findings are of value given the current lack of longitudinal studies on this and similar topics. The foregoing limitations notwithstanding, the WRAP and WADRC are prospective studies of well-characterized cohorts with high participation and retention rates and standardized procedures. This longitudinal design allows for characterization of biomarker trajectories and cognitive change, which is paramount to understanding the temporal relationships between potentially modifiable factors and AD-relevant outcomes. 4.1 Conclusions Overall, our findings suggest that while CRF may not have an impact on the rate of accumulation of core AD biomarkers, high CRF does seem to reduce the risk of conversion from Aβ-to Aβ+. Perhaps more importantly, higher CRF seems to attenuate the otherwise deleterious relationship between core AD biomarker accumulation and cognitive decline, specifically for those at highest risk for AD (Aβ+T+). Together, our findings suggest an important role for CRF in relation to AD hallmark pathologic changes and offer a potential avenue for intervention. Data Availability All data produced in the present study are available upon reasonable request to the authors, with center and institutional approval. 5. Declarations of Interest Declarations of Interest Dr. Ozioma Okonkwo receives grant support from the NIH. He also serves as the treasurer of the International Neuropsychological Society. He is also a guest editor of this journal but was not involved in the peer-review process of this article nor had access to any information regarding its peer-review. Drs. Ira Driscoll, Yue Ma, and Tobey Betthauser receive grant support from the NIH. Dr. Cynthia Carlsson receives grant support from the NIH, Department of Veterans Affairs, NIH/Lilly, NIH/Eisai, NIH/Cognition Therapeutics, and research medication support from Amarin Corporation. Dr. Kaj Blennow has served as a consultant, on advisory boards, or on data-monitoring committees for Acumen, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served on data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials, and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai, and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, which is a part of the GU Ventures Incubator Program (outside submitted work). Dr. Henrik Zetterberg is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union’s Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). Dr. Zetterberg has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, Roche, and WebMD, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Dr. Sanjay Asthana receives grant support from the NIH. He also receives royalty as an editor of a textbook entitled, Hazzard’s Geriatrics and Gerontology; McGraw Hill, Publisher. Dr. Sterling Johnson receives grant support from the NIH. He also serves as a consultant and on advisory boards for ALZPath and Enigma Biosciences. All other authors have no relevant disclosures to report. 6. Consent Statement Written informed consent was provided by all participants prior to study participation and approval was obtained from the institutional review board. 7. Funding This research was supported by grants from the National Institute on Aging, R01AG062167 and R01AG085592 (Dr. Okonkwo), P30AG062715 (Dr. Asthana), RF1AG027161 (Dr. Johnson), R01AG054059 (Dr. Carey Gleason), and an NIH National Center for Advancing Translational Sciences grant UL1TR002373 awarded to the UW-Madison ICTR CTSA program. This study was supported in part by a core grant to the Waisman Center from the National Institute of Child Health and Human Development (P50 HD105353) and a NIH High-End Instrumentation grant (S10 OD030415). The funding sources for this work did not have a role in the study design, data analysis or interpretation, writing, or decision to submit this manuscript. 8. Author Contributions AJP, BMB, and OCO designed the experiments. Data for the current project were provided by the laboratories of OCO, SCJ, SA, TJB, CLG, CMC, KB, and HZ. SRL and BMB collected and processed cardiorespiratory fitness data. ID, MPG, BTC and DBC were consulted on data acquisition, analyses or interpretation based on their respective expertise. AJP analyzed the data, wrote the manuscript, and prepared figures. All authors edited, revised, and approved the final version of the manuscript. 9. Acknowledgments We would like to acknowledge and thank the staff and study participants of the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center. Footnotes Declarations of Interest: Dr. Ozioma Okonkwo receives grant support from the NIH. He also serves as the treasurer of the International Neuropsychological Society. He is also a guest editor of this journal but was not involved in the peer-review process of this article nor had access to any information regarding its peer-review. Drs. Ira Driscoll, Yue Ma, and Tobey Betthauser receive grant support from the NIH. Dr. Cynthia Carlsson receives grant support from the NIH, Department of Veterans Affairs, NIH/Lilly, NIH/Eisai, NIH/Cognition Therapeutics, and research medication support from Amarin Corporation. Dr. Kaj Blennow has served as a consultant, on advisory boards, or on data-monitoring committees for Acumen, ALZPath, AriBio, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served on data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials, and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai, and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB, which is a part of the GU Ventures Incubator Program (outside submitted work). Dr. Henrik Zetterberg is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023-00356, #2022-01018 and #2019-02397), the European Union’s Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320). Dr. Zetterberg has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZpath, Amylyx, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Enigma, LabCorp, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Quanterix, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures sponsored by Alzecure, BioArctic, Biogen, Cellectricon, Fujirebio, Lilly, Novo Nordisk, Roche, and WebMD, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). Dr. Sanjay Asthana receives grant support from the NIH. He also receives royalty as an editor of a textbook entitled, Hazzard’s Geriatrics and Gerontology; McGraw Hill, Publisher. Dr. Sterling Johnson receives grant support from the NIH. He also serves as a consultant and on advisory boards for ALZPath and Enigma Biosciences. All other authors have no relevant disclosures to report. Abbreviations Aβ amyloid-β; AD Alzheimer’s disease; APOE ε4+ APOE ε4 allele carriage; BMI body mass index; CRF cardiorespiratory fitness DVR distribution volume ratio; eCRF estimated cardiorespiratory fitness, HR hazard ratio; LME linear mixed effects; NFT neurofibrillary tangle; PA physical activity; PACC Preclinical Alzheimer’s Cognitive Composite; PET positron emission tomography; PiB 11 C-Pittsburgh Compound B; pTau phosphorylated tau; RAVLT Rey Auditory Verbal Learning Test Delayed Recall, SD standard deviation; SNAP suspected non-Alzheimer’s disease pathophysiology; SUVR standard uptake value ratios; T tau; TMTB Trails Making Test B; VO 2peak peak oxygen consumption calculated for the present study using a validated eCRF equation; WADRC Wisconsin Alzheimer’s Disease Research Center; WRAP Wisconsin Registry for Alzheimer’s Prevention 10. References: 1. ↵ Gordon BA , Blazey TM , Su Y , et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study . Lancet Neurol . 2018 ; 17 ( 3 ): 241 – 250 . doi: 10.1016/S1474-4422(18)30028-0 OpenUrl CrossRef PubMed 2. ↵ Jack CR , Andrews SJ , Beach TG , et al. Revised criteria for the diagnosis and staging of Alzheimer’s disease . Nat Med . 2024 ; 30 ( 8 ): 2121 – 2124 . doi: 10.1038/s41591-024-02988-7 OpenUrl CrossRef PubMed 3. ↵ Sperling RA , Aisen PS , Beckett LA , et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease . Alzheimers Dement J Alzheimers Assoc . 2011 ; 7 ( 3 ): 280 – 292 . doi: 10.1016/j.jalz.2011.03.003 OpenUrl CrossRef PubMed Web of Science 4. ↵ Betthauser TJ , Cody KA , Zammit MD , et al. In Vivo Characterization and Quantification of Neurofibrillary Tau PET Radioligand 18 F-MK-6240 in Humans from Alzheimer Disease Dementia to Young Controls . J Nucl Med . 2019 ; 60 ( 1 ): 93 – 99 . doi: 10.2967/jnumed.118.209650 OpenUrl Abstract / FREE Full Text 5. ↵ 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 6. ↵ Barthélemy NR , Li Y , Joseph-Mathurin N , et al. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease . Nat Med . 2020 ; 26 ( 3 ): 398 – 407 . doi: 10.1038/s41591-020-0781-z OpenUrl CrossRef PubMed 7. ↵ Barthélemy NR , Salvadó G , Schindler SE , et al. Highly accurate blood test for Alzheimer’s disease is similar or superior to clinical cerebrospinal fluid tests . Nat Med . 2024 ; 30 ( 4 ): 1085 – 1095 . doi: 10.1038/s41591-024-02869-z OpenUrl CrossRef 8. ↵ Jack CR , Bennett DA , Blennow K , et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease . Alzheimers Dement J Alzheimers Assoc . 2018 ; 14 ( 4 ): 535 – 562 . doi: 10.1016/j.jalz.2018.02.018 OpenUrl CrossRef PubMed 9. ↵ 2024 Alzheimer’s disease facts and figures . Alzheimers Dement . 2024 ; 20 ( 5 ): 3708 – 3821 . doi: 10.1002/alz.13809 OpenUrl CrossRef PubMed 10. ↵ Dougherty RJ , Jonaitis EM , Gaitán JM , et al. Cardiorespiratory fitness mitigates brain atrophy and cognitive decline in adults at risk for Alzheimer’s disease . Alzheimers Dement Diagn Assess Dis Monit . 2021 ; 13 ( 1 ): e12212 . doi: 10.1002/dad2.12212 OpenUrl CrossRef 11. ↵ Okonkwo OC , Schultz SA , Oh JM , et al. Physical activity attenuates age-related biomarker alterations in preclinical AD . Neurology . 2014 ; 83 ( 19 ): 1753 – 1760 . doi: 10.1212/WNL.0000000000000964 OpenUrl CrossRef PubMed 12. ↵ Schultz SA , Boots EA , Darst BF , et al. Cardiorespiratory fitness alters the influence of a polygenic risk score on biomarkers of AD . Neurology . 2017 ; 88 ( 17 ): 1650 – 1658 . doi: 10.1212/WNL.0000000000003862 OpenUrl Abstract / FREE Full Text 13. ↵ Tan ZS , Spartano NL , Beiser AS , et al. Physical Activity, Brain Volume, and Dementia Risk: The Framingham Study . J Gerontol A Biol Sci Med S.ci 2017 ; 72 ( 6 ): 789 – 795 . doi: 10.1093/gerona/glw130 OpenUrl CrossRef PubMed 14. ↵ Buchman AS , Yu L , Wilson RS , et al. Physical activity, common brain pathologies, and cognition in community-dwelling older adults . Neurology . 2019 ; 92 ( 8 ): e811 – e822 . doi: 10.1212/WNL.0000000000006954 OpenUrl Abstract / FREE Full Text 15. ↵ Clark LR , Koscik RL , Allison SL , et al. Hypertension and obesity moderate the relationship between β-amyloid and cognitive decline in midlife . Alzheimers Dement J Alzheimers Assoc . 2019 ; 15 ( 3 ): 418 – 428 . doi: 10.1016/j.jalz.2018.09.008 OpenUrl CrossRef PubMed 16. ↵ Schultz SA , Boots EA , Almeida RP , et al. Cardiorespiratory Fitness Attenuates the Influence of Amyloid on Cognition . J Int Neuropsychol Soc . 2015 ; 21 ( 10 ): 841 – 850 . doi: 10.1017/S1355617715000843 OpenUrl CrossRef PubMed 17. ↵ Vesperman CJ , Wang R , Schultz SA , et al. Cardiorespiratory fitness and cognition in persons at risk for Alzheimer’s disease . Alzheimers Dement Diagn Assess Dis Monit . 2022 ; 14 ( 1 ): e12330 . doi: 10.1002/dad2.12330 OpenUrl CrossRef 18. ↵ Landau SM , Horng A , Jagust WJ , For the Alzheimer’s Disease Neuroimaging Initiative. Memory decline accompanies subthreshold amyloid accumulation . Neurology . 2018 ; 90 ( 17 ): e1452 – e1460 . doi: 10.1212/WNL.0000000000005354 OpenUrl Abstract / FREE Full Text 19. ↵ Hatashita S , Wakebe D . Longitudinal assessment of amyloid-beta deposition in initially amyloid-negative non-demented individuals with [11C]-PIB PET imaging . Medicine (Baltimore ) . 2021 ; 100 ( 35 ): e27055 . doi: 10.1097/MD.0000000000027055 OpenUrl CrossRef PubMed 20. ↵ Asthana S , Golden RN . Wisconsin Alzheimer’s Disease Research Center-Notable Discoveries and Accomplishments in Dementia Research . WMJ Off Publ State Med Soc Wi.s 2024 ; 123 ( 1 ): 62 – 63 . OpenUrl 21. ↵ Johnson SC , Koscik RL , Jonaitis EM , et al. The Wisconsin Registry for Alzheimer’s Prevention: A review of findings and current directions . Alzheimers Dement Diagn Assess Dis Monit . 2018 ; 10 ( 1 ): 130 – 142 . doi: 10.1016/j.dadm.2017.11.007 OpenUrl CrossRef PubMed 22. ↵ Jurca R , Jackson AS , LaMonte MJ , et al. Assessing Cardiorespiratory Fitness Without Performing Exercise Testing . Am J Prev Med . 2005 ; 29 ( 3 ): 185 – 193 . doi: 10.1016/j.amepre.2005.06.004 OpenUrl CrossRef PubMed Web of Science 23. ↵ Boots EA , Schultz SA , Oh JM , et al. Cardiorespiratory fitness is associated with brain structure, cognition, and mood in a middle-aged cohort at risk for Alzheimer’s disease . Brain Imaging Behav . 2015 ; 9 ( 3 ): 639 – 649 . doi: 10.1007/s11682-014-9325-9 OpenUrl CrossRef PubMed 24. ↵ McIntyre CC , Gaitán JM , Edmunds KJ , et al. Insulin Homeostasis Mediates the Relationship Between Cardiorespiratory Fitness and Cognitive Speed in Aging Adults. Craft S, ed . J Alzheimers Dis . 2023 ; 93 ( 2 ): 577 – 584 . doi: 10.3233/JAD-221249 OpenUrl CrossRef PubMed 25. ↵ Johnson SC , Christian BT , Okonkwo OC , et al. Amyloid burden and neural function in people at risk for Alzheimer’s Disease . Neurobiol Aging . 2014 ; 35 ( 3 ): 576 – 584 . doi: 10.1016/j.neurobiolaging.2013.09.028 OpenUrl CrossRef PubMed Web of Science 26. ↵ Racine AM , Clark LR , Berman SE , et al. Associations between Performance on an Abbreviated CogState Battery, Other Measures of Cognitive Function, and Biomarkers in People at Risk for Alzheimer’s Disease . J Alzheimers Dis JAD . 2016 ; 54 ( 4 ): 1395 – 1408 . doi: 10.3233/JAD-160528 OpenUrl CrossRef 27. ↵ Betthauser TJ , Koscik RL , Jonaitis EM , et al. Amyloid and tau imaging biomarkers explain cognitive decline from late middle-age . Brain . 2020 ; 143 ( 1 ): 320 – 335 . doi: 10.1093/brain/awz378 OpenUrl CrossRef PubMed 28. ↵ Braak H , Alafuzoff I , Arzberger T , Kretzschmar H , Del Tredici K . Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry . Acta Neuropathol (Berl ) . 2006 ; 112 ( 4 ): 389 – 404 . doi: 10.1007/s00401-006-0127-z OpenUrl CrossRef PubMed Web of Science 29. ↵ Ashton NJ , Brum WS , Di Molfetta G , et al. Diagnostic Accuracy of a Plasma Phosphorylated Tau 217 Immunoassay for Alzheimer Disease Pathology . JAMA Neurol . 2024 ; 81 ( 3 ): 255 . doi: 10.1001/jamaneurol.2023.5319 OpenUrl CrossRef 30. ↵ Du L , Langhough RE , Wilson RE , et al. Longitudinal plasma phosphorylated-tau217 and other related biomarkers in a non-demented Alzheimer’s risk-enhanced sample . Alzheimers Dement . 2024 ; 20 ( 9 ): 6183 – 6204 . doi: 10.1002/alz.14100 OpenUrl CrossRef 31. ↵ Jonaitis EM , Janelidze S , Cody KA , et al. Plasma phosphorylated tau 217 in preclinical Alzheimer’s disease . Brain Commun . 2023 ; 5 ( 2 ): fcad057 . doi: 10.1093/braincomms/fcad057 OpenUrl CrossRef 32. ↵ Jonaitis EM , Koscik RL , Clark LR , et al. Measuring longitudinal cognition: Individual tests versus composites . Alzheimers Dement Diagn Assess Dis Monit . 2019 ; 11 ( 1 ): 74 – 84 . doi: 10.1016/j.dadm.2018.11.006 OpenUrl CrossRef 33. ↵ Sherman EMS , Tan JE , Hrabok M. A Compendium of Neuropsychological Tests: Administration, Tests, Norms, and Commentary . Fourth edition . Oxford University Press ; 2020 . 34. ↵ Koscik RL , Betthauser TJ , Jonaitis EM , et al. Amyloid duration is associated with preclinical cognitive decline and tau PET . Alzheimers Dement Diagn Assess Dis Monit . 2020 ; 12 ( 1 ). doi: 10.1002/dad2.12007 OpenUrl CrossRef 35. ↵ Sperling RA , Mormino EC , Schultz AP , et al. The impact of amyloid-beta and tau on prospective cognitive decline in older individuals . Ann Neurol . 2019 ; 85 ( 2 ): 181 – 193 . doi: 10.1002/ana.25395 OpenUrl CrossRef PubMed 36. ↵ 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 . Nat Med . 2022 ; 28 ( 11 ): 2381 – 2387 . doi: 10.1038/s41591-022-02049-x OpenUrl CrossRef PubMed 37. ↵ Felisatti F , Gonneaud J , Palix C , et al. Role of Cardiovascular Risk Factors on the Association Between Physical Activity and Brain Integrity Markers in Older Adults . Neurology . 2022 ; 98 ( 20 ): e2023 – e2035 . doi: 10.1212/WNL.0000000000200270 OpenUrl Abstract / FREE Full Text 38. ↵ Yu F , Han SY , Salisbury D , et al. Feasibility and preliminary effects of exercise interventions on plasma biomarkers of Alzheimer’s disease in the FIT-AD trial: a randomized pilot study in older adults with Alzheimer’s dementia . Pilot Feasibility Stud . 2022 ; 8 ( 1 ): 243 . doi: 10.1186/s40814-022-01200-2 OpenUrl CrossRef PubMed 39. ↵ Vidoni ED , Morris JK , Watts A , et al. Effect of aerobic exercise on amyloid accumulation in preclinical Alzheimer’s: A 1-year randomized controlled trial. Bush AI, ed . PLOS ONE . 2021 ; 16 ( 1 ): e0244893 . doi: 10.1371/journal.pone.0244893 OpenUrl CrossRef PubMed 40. ↵ Sewell KR , Rainey-Smith SR , Pedrini S , et al. The impact of exercise on blood-based biomarkers of Alzheimer’s disease in cognitively unimpaired older adults . GeroScience . 2024 ; 46 ( 6 ): 5911 – 5923 . doi: 10.1007/s11357-024-01130-2 OpenUrl CrossRef PubMed 41. ↵ Aschenbrenner AJ , Gordon BA , Benzinger TLS , Morris JC , Hassenstab JJ . Influence of tau PET, amyloid PET, and hippocampal volume on cognition in Alzheimer disease . Neurology . 2018 ; 91 ( 9 ): e859 – e866 . doi: 10.1212/WNL.0000000000006075 OpenUrl Abstract / FREE Full Text 42. ↵ Levin F , Jelistratova I , Betthauser TJ , et al. In vivo staging of regional amyloid progression in healthy middle-aged to older people at risk of Alzheimer’s disease . Alzheimers Res Ther . 2021 ; 13 ( 1 ): 178 . doi: 10.1186/s13195-021-00918-0 OpenUrl CrossRef PubMed 43. ↵ Jack CR , Knopman DS , Chételat G , et al. Suspected non-Alzheimer disease pathophysiology — concept and controversy . Nat Rev Neurol . 2016 ; 12 ( 2 ): 117 – 124 . doi: 10.1038/nrneurol.2015.251 OpenUrl CrossRef PubMed 44. ↵ Vos SJB , Delvenne A , Jack CR , Thal DR , Visser PJ . The clinical importance of suspected non-Alzheimer disease pathophysiology . Nat Rev Neurol . 2024 ; 20 ( 6 ): 337 – 346 . doi: 10.1038/s41582-024-00962-y OpenUrl CrossRef PubMed 45. ↵ Vos SJ , Xiong C , Visser PJ , et al. Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study . Lancet Neurol . 2013 ; 12 ( 10 ): 957 – 965 . doi: 10.1016/S1474-4422(13)70194-7 OpenUrl CrossRef PubMed Web of Science 46. ↵ Erickson P , Simrén J , Brum WS , et al. Prevalence and Clinical Implications of a β-Amyloid–Negative, Tau-Positive Cerebrospinal Fluid Biomarker Profile in Alzheimer Disease . JAMA Neurol . 2023 ; 80 ( 9 ): 969 . doi: 10.1001/jamaneurol.2023.2338 OpenUrl CrossRef PubMed 47. ↵ Ebenau JL , Timmers T , Wesselman LMP , et al. ATN classification and clinical progression in subjective cognitive decline: The SCIENCe project . Neurology . 2020 ; 95 ( 1 ). doi: 10.1212/WNL.0000000000009724 OpenUrl Abstract / FREE Full Text 48. ↵ Meernik C , Eilat-Adar S , Leonard D , et al. Dietary patterns and cardiorespiratory fitness in midlife and subsequent all-cause dementia: findings from the Cooper Center Longitudinal Study . Int J Behav Nutr Phys Act . 2024 ; 21 ( 1 ): 109 . doi: 10.1186/s12966-024-01663-x OpenUrl CrossRef PubMed 49. Lipsitz LA Lerfald M , Allore H , Nilsen TIL , et al. Longitudinal Patterns of Systolic Blood Pressure, Diastolic Blood Pressure, Cardiorespiratory Fitness, and Their Association With Dementia Risk: The HUNT Study . Lipsitz LA , ed. J Gerontol A Biol Sci Med S.ci 2024 ; 79 ( 8 ): glae161 . doi: 10.1093/gerona/glae161 OpenUrl CrossRef PubMed 50. ↵ Nianogo RA , Rosenwohl-Mack A , Yaffe K , Carrasco A , Hoffmann CM , Barnes DE . Risk Factors Associated With Alzheimer Disease and Related Dementias by Sex and Race and Ethnicity in the US . JAMA Neurol . 2022 ; 79 ( 6 ): 584 – 591 . doi: 10.1001/jamaneurol.2022.0976 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 06, 2025. Download PDF 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 The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline 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 The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline AJ Paulsen , I Driscoll , BM Breidenbach , MP Glittenberg , SR Lose , Y Ma , MA Sager , CM Carlsson , CL Gallagher , BP Hermann , K Blennow , H Zetterberg , S Asthana , SC Johnson , TJ Betthauser , BT Christian , DB Cook , OC Okonkwo medRxiv 2025.03.03.25323245; doi: https://doi.org/10.1101/2025.03.03.25323245 Share This Article: Copy Citation Tools The impact of cardiorespiratory fitness on Alzheimer’s disease biomarkers and their relationships with cognitive decline AJ Paulsen , I Driscoll , BM Breidenbach , MP Glittenberg , SR Lose , Y Ma , MA Sager , CM Carlsson , CL Gallagher , BP Hermann , K Blennow , H Zetterberg , S Asthana , SC Johnson , TJ Betthauser , BT Christian , DB Cook , OC Okonkwo medRxiv 2025.03.03.25323245; doi: https://doi.org/10.1101/2025.03.03.25323245 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 Epidemiology Subject Areas All Articles Addiction Medicine (570) Allergy and Immunology (863) Anesthesia (301) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15232) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (669) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15924) Intensive Care and Critical Care Medicine (1104) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6609) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3338) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5450) Public and Global Health (9240) Radiology and Imaging (2203) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1197) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) 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:'a020d6bc3e4f1b23',t:'MTc3OTg0MTA2OQ=='};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())}}}})();