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Published in final edited form as: Neurology. 2025 Dec 2;105(12):e214405. doi: 10.1212/WNL.0000000000214405 Search in PMC Search in PubMed View in NLM Catalog Add to search Physical and Cognitive Activities and Trajectories of AD Neuroimaging Biomarkers Longitudinal Analysis in the Mayo Clinic Study of Aging Janina Krell-Roesch Janina Krell-Roesch 1 Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Germany; 2 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Find articles by Janina Krell-Roesch 1, 2 , Jeremy A Syrjanen Jeremy A Syrjanen 2 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Find articles by Jeremy A Syrjanen 2 , Allison L Hansen Allison L Hansen 3 Department of Radiology, Creighton University School of Medicine, Phoenix, AZ; Find articles by Allison L Hansen 3 , Prashanthi Vemuri Prashanthi Vemuri 4 Department of Radiology, Mayo Clinic, Rochester, MN; Find articles by Prashanthi Vemuri 4 , Eugene L Scharf Eugene L Scharf 5 Department of Neurology, Mayo Clinic, Rochester MN; Find articles by Eugene L Scharf 5 , Julie A Fields Julie A Fields 6 Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN; Find articles by Julie A Fields 6 , Walter K Kremers Walter K Kremers 2 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Find articles by Walter K Kremers 2 , Val J Lowe Val J Lowe 4 Department of Radiology, Mayo Clinic, Rochester, MN; Find articles by Val J Lowe 4 , Jonathan Graff-Radford Jonathan Graff-Radford 5 Department of Neurology, Mayo Clinic, Rochester MN; Find articles by Jonathan Graff-Radford 5 , Clifford R Jack Jr Clifford R Jack Jr 4 Department of Radiology, Mayo Clinic, Rochester, MN; Find articles by Clifford R Jack Jr 4 , Ronald C Petersen Ronald C Petersen 5 Department of Neurology, Mayo Clinic, Rochester MN; Find articles by Ronald C Petersen 5 , Susan B Racette Susan B Racette 7 College of Health Solutions, Arizona State University, Phoenix; Find articles by Susan B Racette 7 , Alexander Woll Alexander Woll 1 Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Germany; Find articles by Alexander Woll 1 , Maria Vassilaki Maria Vassilaki 2 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Find articles by Maria Vassilaki 2 , Yonas E Geda Yonas E Geda 8 Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ. Find articles by Yonas E Geda 8 Author information Article notes Copyright and License information 1 Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Germany; 2 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; 3 Department of Radiology, Creighton University School of Medicine, Phoenix, AZ; 4 Department of Radiology, Mayo Clinic, Rochester, MN; 5 Department of Neurology, Mayo Clinic, Rochester MN; 6 Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN; 7 College of Health Solutions, Arizona State University, Phoenix; 8 Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ. ✉ Correspondence: Dr. Krell-Roesch ( [email protected] ) Issue date 2025 Dec 23. PMC Copyright notice PMCID: PMC13083612 NIHMSID: NIHMS2161728 PMID: 41329905 The publisher's version of this article is available at Neurology Abstract Background and Objectives: Engagement in physical and cognitive activities is associated with a decreased risk of mild cognitive impairment (MCI) and dementia, but the association with Alzheimer’s disease (AD) neuroimaging biomarkers is less clear. We thus examined associations of physical and cognitive activities with longitudinal trajectories of AD neuroimaging biomarkers among older adults free of dementia. Methods: We conducted a longitudinal study within the population-based Mayo Clinic Study of Aging (mean follow-up times, 1.3 – 3.4 years). Participants were aged 50 years or older and were cognitively unimpaired or had MCI at baseline. Engagement in physical and cognitive activities during 12 months before baseline was assessed through questionnaires. Participants underwent AD neuroimaging biomarker assessments at 1 or more time points. We ran linear mixed-effect models to examine associations between physical and cognitive activities composite scores and trajectories for individual yearly change in amyloid deposition (PiB-PET centiloid), tau burden (Tau-PET SUVR), and regional glucose hypometabolism (FDG-PET SUVR), adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity. Results: We included 1176 participants (47% female; mean [SD] age, 68.7 [9.6] years) for PiB-PET trajectories, 399 participants (49% female; mean [SD] age, 71.9 [11.0] years) for Tau-PET trajectories, and 983 participants (46% female; mean [SD] age, 67.9 [9.2] years) for FDG-PET trajectories. PiB- and Tau-PET measures increased during follow-up (3.4 [SD 4.0] and 1.3 [SD 2.1] years, respectively), whereas FDG-PET values decreased over 2.9 (SD 3.5) years of follow-up. Participants with higher total physical activity (interaction [int] estimate 0.0017; 95% CI 0.0003, 0.0031; p=0.021) and higher moderate-to-vigorous physical activity (int est. 0.0015; 95% CI 0.0001, 0.0029; p=0.040) had less pronounced decrease in FDG-PET over time. Participants with higher cognitive activity experienced less pronounced increase in PiB-PET (int est. −0.2253; 95% CI −0.4437, −0.0070; p=0.043) and a smaller decrease in FDG-PET (int est. 0.0015; 95% CI 0.0001, 0.0028; p=0.038) over time. Discussion: Physical activity was associated with less synaptic dysfunction and cognitive activity with less synaptic dysfunction and lower amyloid burden over time, albeit effect sizes were small. Further research is needed to validate findings and clarify causal inference between physical and cognitive activities and AD neuroimaging biomarkers. Introduction Engagement in physical activity 1 – 3 as well as cognitive or mentally stimulating activities 4 – 7 is associated with a decreased risk of mild cognitive impairment (MCI) and dementia. Recently, studies have aimed to understand the underlying mechanism for the relationships between lifestyle factors, including physical or cognitive activities, and biomarkers of Alzheimer’s disease (AD) pathophysiology, such as amyloid plaque and tau neurofibrillary tangles. While animal model studies have shown relationships between increased physical activity and decreased levels of amyloid and tau within the brain, human studies have not shown the same consistent relationship. 8 A systematic review that included 40 observational studies reported that there were only limited associations between physical activity and cerebrospinal fluid (CSF)-derived AD biomarkers or neuroimaging AD biomarkers (e.g., amyloid deposition based on positron emission tomography [PET] and brain glucose metabolism using fluorodeoxyglucose [FDG]-PET). 9 A recent meta-analysis of observational studies found no evidence for an association between physical activity and amyloid deposition as measured by PET, but there was a positive association between physical activity and CSF-derived amyloid beta (Aβ). 10 Only a few longitudinal studies examined both physical and cognitive activities as independent variables when examining AD biomarker outcomes. While these studies showed no associations of physical activity or cognitive activities with AD neuroimaging 11 or CSF-derived biomarkers, 12 they were limited by sample sizes of 70 and 464 participants, respectively. A longitudinal analysis of 393 participants in the Mayo Clinic Study of Aging 13 aged ≥ 70 years and free of dementia examined associations between midlife cognitive activities and physical activity with AD neuroimaging biomarkers derived from three imaging modalities, i.e., Pittsburgh Compound-B (PiB)-PET, FDG-PET and magnetic resonance imaging (MRI). The study showed that in APOE ε4 carriers with high education, engagement in higher midlife cognitive activity was associated with lower amyloid deposition, whereas physical activity was not related to AD biomarkers. 13 The aim of this longitudinal study was to build and expand on previous research by examining associations between self-reported engagement in physical and cognitive activities (independent variables) performed in the 12 months before baseline assessment and trajectories of AD neuroimaging biomarkers, namely PiB-PET centiloid, Tau-PET standardized uptake value ratio (SUVR), and FDG-PET SUVR (dependent variables), in a large sample of more than 1000 community-dwelling adults aged 50 years or older and free of dementia enrolled in the Mayo Clinic Study of Aging. Methods Study sample and design This study was conducted within the population-based Mayo Clinic Study of Aging in Olmsted County, MN, USA. 14 In brief, the Mayo Clinic Study of Aging is a prospective cohort study established in 2004 which was designed to examine cognitive aging. Participants aged ≥ 30 years are randomly selected from the Olmsted County population using a stratified sampling approach based on age and sex, and by utilizing the Rochester Epidemiology Project medical records-linkage system. 15 Upon providing informed consent, participants undergo an initial baseline assessment, followed by follow-up evaluations at approximately 15-month intervals. During each study visit, participants undergo a comprehensive face-to-face evaluation including a neurological examination and medical history review by a physician, a study coordinator interview, and cognitive testing by a psychometrist covering four domains (i.e., memory, attention, language, visuospatial skills). Participants are classified as having MCI, dementia or being cognitively unimpaired by a consensus expert panel of a study coordinator, a nurse, a physician, and a neuropsychologist, after reviewing all information available for each participant and based on published criteria. For the current study, we included participants aged ≥ 50 years, with valid information on self-reported engagement in physical activities and/ or cognitive activities in the 12 months prior to baseline assessment, who had undergone at least one AD neuroimaging biomarker assessment at baseline (approximately half had at least one follow-up assessment), and were either cognitively unimpaired or had MCI at baseline. Mean follow-up times ranged between 1.3 years for the outcome of Tau-PET trajectories to 3.4 years for the outcome of PiB-PET trajectories. Assessment of independent variables Engagement in physical activities in the 12 months prior to baseline assessment was reported by participants using a questionnaire derived from validated instruments, i.e., 1985 National Health Interview Survey, 16 and Minnesota Heart Survey intensity codes. 17 The questionnaire inquired about three different intensities of non-exercise physical activity (i.e., light intensity such as laundry or vacuuming, moderate intensity such as scrubbing floors or gardening, and vigorous intensity such as digging or carrying heavy objects), as well as exercise-related physical activity (i.e., light intensity such as leisurely walking or slow dancing, moderate intensity such as brisk walking or swimming, and vigorous intensity such as jogging or tennis singles). Participants were asked about the frequency at which they carried out each of these activities, i.e., ≤1 time/ month, 2–3 times/ month, 1–2 times/ week, 3–4 times/ week, 5–6 times/ week, or daily. For statistical analysis, we assigned the following metabolic equivalents of task (MET) to the intensity categories based on published MET values for different physical activities: 18 light physical activity (2.5 MET), moderate physical activity (4.0 MET), heavy physical activity (6.5 MET), light physical exercise (3.0 MET), moderate physical exercise (5.5 MET), and vigorous physical exercise (8.0 MET). We then calculated two scores: (1) a composite total physical activity score by multiplying the MET values for light, moderate, and heavy/vigorous physical activities and exercise by the frequency (days per week) at which the corresponding activities were carried out, and then summing the values to derive an overall score; and (2) a moderate-to-vigorous physical activity (MVPA) score by adding the MET values multiplied by frequency (days per week) for moderate physical exercise and vigorous physical exercise only. Both the composite total physical activity score and the MVPA score were converted to z-scores for use in the statistical models. A higher score reflects a higher level of physical activity, similar to our previous publications. 19 Engagement in cognitive activities in the 12 months prior to baseline assessment was reported by participants using a questionnaire derived from previously validated instruments. 20 – 22 The questionnaire assessed engagement in ten cognitive activities, i.e., artistic activities, playing games, reading books, reading magazines, reading newspapers, playing music, computer activities, craft activities, group activities, and social activities. Participants were asked about the frequency at which they carried out each of these activities, i.e., ≤1 time/ month, 2–3 times/ month, 1–2 times/ week, 3–4 times/ week, 5–6 times/ week, or daily. For statistical analysis, we calculated a composite score for cognitive activities (possible range: 0–70) by first expressing the frequency of each of the ten cognitive activities as days per week (0–7) and then summing the days per week of the ten cognitive activities. The composite score was then converted to z- score for use in the statistical models. A higher score reflects higher engagement in cognitive activities. Assessment of dependent variables Neuroimaging biomarkers of AD included amyloid deposits assessed using PiB-PET, tau burden assessed using flortaucipir Tau-PET, and brain glucose metabolism assessed using FDG-PET. Neuroimaging was conducted at baseline for all participants and at one or more follow-up visits for approximately half the sample. All PET scans were conducted using a 3-dimensional mode PET/ CT scanner (GE or Siemens). The PiB-PET and Tau-PET scans consisted of four 5-minute dynamic frames, and were acquired from 40 to 60 minutes (PiB-PET) and 80 to 100 minutes (Tau-PET) after intravenous injections. The FDG-PET scan consisted of four 2-minute dynamic frames and was performed approximately 30 minutes after intravenous injection. Analysis was carried out via an in-house automated image processing pipeline. 23 A global cortical PiB SUVR was created from prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/ precuneus regions of interest normalized to the cerebellar crus gray matter, without partial volume correction, and then calibrated to the centiloid scale using established conversion equations. 24 The Tau-PET temporal meta region of interest included the amygdala, entorhinal cortex, fusiform gyrus, parahippocampal gyrus and inferior temporal and middle temporal gyri, without partial volume correction. The FDG-PET meta region of interest consisted of bilateral angular gyri, posterior cingulate/ precuneus, and inferior temporal cortical regions of interest from both hemispheres normalized to the pons and the cerebellar vermis, without partial volume correction. Atlas and image recognition steps were based on a 3D T1-weighted volume MRI sequence. 23 The reader is referred to the detailed descriptions of the methodology of AD neuroimaging biomarker ascertainment in prior Mayo Clinic Study of Aging publications on amyloid (PiB-PET) and Tau-PET imaging, 23 , 25 – 27 as well as FDG-PET. 25 , 27 – 31 Assessment of confounding variables In addition to traditional covariates (i.e., age, biological sex), we adjusted the analyses for APOE ε4 genotype status, which was determined using standard methods for DNA extracted from blood, and medical comorbidity through the weighted Charlson Index. Models on cognitive activities were also adjusted for years of education. Statistical Analysis Descriptive statistics were calculated and are presented as mean values (M) with standard deviation (SD) for continuous variables or frequencies (N) and percentages (%) for categorical variables. We ran linear mixed-effect models with random, correlated subject-specific intercepts and slopes for years since baseline, adjusted for age, sex, APOEɛ4 carrier status, and medical comorbidity to examine the associations between baseline physical activities scores and cognitive activities scores (all z-scored, continuous variables) with longitudinal changes in neuroimaging biomarkers. Models on cognitive activities were also further adjusted for education. We ran the models separately for total physical activity, MVPA, and cognitive activities, and for each of the AD neuroimaging biomarker outcomes (i.e., PiB-PET centiloid, Tau-PET SUVR, and FDG-PET SUVR). All models included physical activities or cognitive activities at baseline as independent variables, time in years from baseline, as well as interactions between time and physical activities or cognitive activities, and time and age. Age was scaled per decade and centered (i.e., subtract mean) so that the longitudinal results can be interpreted as being for those at the average age. Furthermore, we also examined if cognitive diagnosis at baseline (i.e., cognitively unimpaired vs. MCI) modified the effect of physical and cognitive activities on longitudinal AD biomarkers by additionally running models including three-way interactions (i.e., diagnosis*physical or cognitive activity*time). Beta coefficients, 95% confidence intervals (CIs), and p-values were computed for each model. All analyses were conducted using the conventional two-tailed alpha level of 0.05 and performed with SAS 9.4 (SAS Institute, Inc., Cary, NC) and R version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria). Standard Protocol Approvals, Registrations, and Patient Consents The Mayo Clinic Study of Aging protocols have been approved by the institutional review boards of the Mayo Clinic and Olmsted Medical Center in Rochester, MN, USA. All study participants provided written informed consent. In participants with cognitive impairment sufficient to interfere with capacity, a legally authorized representative provided assent. Data availability Data from this study are available upon reasonable request. Results A total of 1181 participants (46.7% female) out of 7437 unique individuals enrolled in the Mayo Clinic Study of Aging (15.9%) were included in the analyses. Participants in this study had a mean (SD) age of 68.68 (9.64) years, mean (SD) years of education was 14.85 (2.47), 28.5% were APOEɛ4 carriers, 89.9% were cognitively unimpaired and 10.1% had MCI. Baseline demographics for the sample as categorized by the neuroimaging outcomes of interest (i.e., PiB-PET trajectories, Tau-PET trajectories, and FDG-PET trajectories) are given in Table 1 . Table 1: Baseline characteristics of study sample (stratified by outcome of interest) PiB-PET N = 1176 Tau-PET N = 399 FDG-PET N = 983 Age in years, M (SD) 68.68 (9.63) 71.94 (10.98) 67.93 (9.15) Female sex, N (%) 550 (46.8) 195 (48.9) 450 (45.8) Cognitive status, N (%) Cognitively unimpaired 1057 (89.9) 349 (87.5) 889 (90.4) Mild cognitive impairment 119 (10.1) 50 (12.5) 94 (9.6) APOEɛ4 carriers, N (%) 333 (28.3) 115 (28.8) 280 (28.5) Education in years, M (SD) 14.85 (2.47) {2} 15.01 (2.43) {2} 14.82 (2.47) {1} Charlson index score, M (SD) 2.75 (2.87) 3.33 (3.12) 2.58 (2.78) Physical activities score, M (SD) Total PA 55.31 (32.50) {4} 53.53 (35.31) {2} 55.25 (31.45) {3} MVPA 17.52 (18.99) {2} 16.41 (19.63) {1} 17.58 (18.45) {2} Cognitive activities score, M (SD) 22.32 (9.27) {3} 22.41 (10.07) {2} 22.26 (9.03) {2} PiB-PET centiloid, M (SD) 22.53 (28.52) - - Tau-PET SUVR, M (SD) - 1.20 (0.13) - FDG-PET SUVR, M (SD) - - 1.58 (0.15) Follow-up in years, M (SD) 3.44 (4.03) 1.32 (2.10) 2.89 (3.52) %-proportion of all MCSA participants (N = 7437) 15.8 5.4 13.2 Open in a new tab Abbreviations: FDG, fluorodeoxyglucose; MCSA, Mayo Clinic Study of Aging; MVPA, moderate-vigorous physical activity; PA, physical activity; PiB, Pittsburgh Compound B; SUVR, standardized uptake value ratio. Data are presented as mean (SD) or n (%). {n} indicates number missing for the corresponding variable. Cross-sectionally, at baseline, participants with higher total physical activity and MVPA had higher FDG-PET SUVR, and those with higher total physical activity also had marginally significantly lower PiB-PET SUVR ( Table 2 ). Longitudinally, as expected, participants showed an increase in PiB-PET and Tau-PET, and a decrease in FDG-PET over time, on average, as indicated by positive β estimates for PiB-PET and Tau-PET, and negative β estimates for FDG-PET ( Table 2 ). Table 2: Association between baseline physical and cognitive activities and longitudinal change in AD neuroimaging biomarkers IV β (95% CI) p Time β (95% CI) p IV * Time Interaction β (95% CI) p PiB-PET Total PA −1.4143 (−2.8690, 0.0404) 0.057 2.5355 (2.3197, 2.7513) <0.001 −0.0048 (−0.2211, 0.2115) 0.965 MVPA −0.9640 (−2.4116, 0.4836) 0.192 2.5378 (2.3227, 2.7529) <0.001 0.1314 (−0.0842, 0.3470) 0.232 Cognitive activities −0.8232 (−2.3657, 0.7193) 0.295 2.5439 (2.3288, 2.7591) <0.001 −0.2253 (−0.4437, −0.0070) 0.043 Tau-PET Total PA 0.0110 (−0.0019, 0.0240) 0.093 0.0046 (0.0019, 0.0074) 0.001 −0.0007 (−0.0034, 0.0021) 0.625 MVPA 0.0086 (−0.0041, 0.0212) 0.180 0.0048 (0.0021, 0.0076) <0.001 0.0011 (−0.0020, 0.0041) 0.487 Cognitive activities 0.0010 (−0.0126, 0.0146) 0.880 0.0047 (0.0020, 0.0075) <0.001 0.0009 (−0.0020, 0.0038) 0.554 FDG-PET Total PA 0.0086 (0.0004, 0.0168) 0.040 −0.0116 (−0.0130, −0.0102) <0.001 0.0017 (0.0003, 0.0031) 0.021 MVPA 0.0133 (0.0052, 0.0213) 0.001 −0.0116 (−0.0130, −0.0102) <0.001 0.0015 (0.0001, 0.0029) 0.040 Cognitive activities 0.0006 (−0.0081, 0.0093) 0.889 −0.0117 (−0.0131, −0.0103) <0.001 0.0015 (0.0001, 0.0028) 0.038 Open in a new tab Abbreviations: β = beta coefficient; FDG = fluorodeoxyglucose; IV = independent variable; MVPA = moderate-to-vigorous physical activity; PA = physical activity; PiB = Pittsburgh compound B; SUVR = standardized uptake value ratio. Dependent variables (outcomes) are PiB-PET centiloid, tau-PET SUVR, and FDG-PET SUVR. IVs are total PA, MVPA, and cognitive activities. Time reflects the annual change in outcome for participants with average IV. Interaction = difference in slopes over time for each 1 SD increase in value for the IV. PiB-PET: each row represents a unique model adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity (and education for the models on cognitive activities), including age × time interaction (significant [p < 0.001] for all models; not shown). Tau-PET: each row represents a unique model adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity (and education for the models on cognitive activities), including age × time interaction (not significant [p > 0.05] for all models; not shown). FDG-PET: each row represents a unique model adjusted for age, sex, APOE ɛ4 carrier status, and medical comorbidity (and education for the models on cognitive activities), including age × time interaction (not significant [p > 0.05] for all models; not shown). Age was scaled per decade and centered (i.e., subtract mean). Participants with higher total physical activity and MVPA had less pronounced decreases in FDG-PET over time ( Table 2 ). In addition, participants with higher cognitive activities experienced less pronounced increase in PiB-PET and less pronounced decrease in FDG-PET over time. To put into context with an example, for those with average age and average total physical activity, FDG-PET SUVR decreased, on average, annually by 0.0116 (time effect; Table 2 ) whereas those with one standard deviation above the mean total physical activity level decreased annually, on average, by 0.0099 (time effect + interaction effect = −0.0115 + 0.0017). No statistically significant associations were observed related to Tau-PET ( Table 2 ). In the models including 3-way interactions between cognitive diagnosis, physical or cognitive activities and time, none of the interactions was statistically significant (data not shown), suggesting that cognitive diagnosis does not modify our observed associations between physical or cognitive activities and longitudinal AD biomarker trajectories. Discussion We observed that participants with higher composite score in physical or cognitive activities during the 12 months before the baseline assessment had less synaptic dysfunction, as indicated by a smaller decrease in brain glucose metabolism based on FDG-PET, and those with higher mentally stimulating activities also had less amyloid burden as indicated by a less pronounced increase in PiB-PET over time; however, the effect sizes were small and possibly not clinically meaningful. We did not observe any associations with tau protein accumulation based on Tau-PET. The literature regarding the associations between physical activity and cognitive or mentally stimulating activity and AD biomarkers is mixed. Several cross-sectional studies examined associations between physical activity 32 – 35 or a combination of lifestyle factors, i.e., physical and cognitive activities 36 – 39 with AD-related neuroimaging biomarkers, and have reported inconsistent results. For example, a study in a volunteer sample of 65 cognitively unimpaired older individuals, 10 persons with AD, and 11 younger controls revealed an association between engaging in cognitively stimulating activities, particularly in early and middle life, with reduced PiB-PET uptake, reflective of lower beta-amyloid deposition, but engaging in physical activity was not related to beta-amyloid deposition. 38 Other studies reported favorable associations between higher physical activity and less neuroimaging biomarker alterations, 32 , 33 , 40 , 41 suggesting less neurodegeneration. In contrast, two studies showed no association between engagement in physical or cognitive stimulating activities with AD biomarkers based on PiB-PET, FDG-PET or MRI. 36 , 37 Compared with cross-sectional research, there are only few longitudinal studies. A longitudinal analysis derived from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Aging did not find associations between physical activity and beta-amyloid as measured by PET imaging in 731 cognitively unimpaired adults aged ≥ 60 years. 42 Another study from Washington University in St. Louis found that higher levels of questionnaire-assessed physical activity over a 10-year period were associated with less decline in processing speed but were not related to any AD biomarkers including PiB-PET. 43 Similarly, a longitudinal study from the New York City area among 70 cognitively unimpaired persons aged 30 to 60 years reported that engagement in neither physical nor cognitive activities was related to AD neuroimaging biomarker trajectory. 11 Furthermore, a previous study utilizing the Mayo Clinic Study of Aging data looked at the associations between intellectual enrichment and trajectories of PiB-PET, FDG-PET, and MRI biomarkers and found that higher education levels combined with higher midlife cognitive activity were associated with lower longitudinal amyloid deposition in participants who were APOE ɛ4 carriers. 13 Finally, a longitudinal study of 464 individuals in Europe also found no association between cognitively stimulating activity and AD biomarkers, including hippocampal volume measured on MRI. 12 By contrast, in our study, we demonstrated an association between higher physical activity and cognitive activities in the 12 months before baseline assessment and less prominent decrease in glucose metabolism identified on FDG-PET over time, as well as an association between higher cognitive activities but not physical activity and less amyloid burden identified on PiB-PET over time. However, we did not observe any associations between physical or cognitive activities with tau protein biomarkers based on Tau-PET. While there are a fair number of studies investigating associations between physical or cognitive activities and AD biomarkers using PiB-PET or FDG-PET, as outlined above, fewer studies utilized Tau-PET. One cross-sectional study utilizing data from the AIBL study included 43 cognitively unimpaired individuals and categorized them as engaging in low-moderate or high physical activity based on International Physical Activity Questionnaire scores. 40 The researchers found a higher tau burden among individuals in the low-moderate physical activity group when compared to the high physical activity group. Another cross-sectional study of 354 middle-aged participants enrolled in the Framingham Heart Study revealed that a higher total physical activity score was associated with lower Tau-PET binding levels in the entorhinal cortex. 41 These results, albeit derived from cross-sectional studies, differ from ours, since we did not observe any longitudinal associations between either physical or cognitive activities and Tau-PET SUVR in our sample, although we need to acknowledge the shorter follow-up period between the independent variables and the Tau-PET SUVR assessed. We are not aware of another longitudinal study that examined associations between lifestyle activities and tau burden as indicated by PET imaging; more research is thus needed to examine whether lifestyle factors such as the ones included in our study are longitudinally related to tau burden in community-dwelling older adults. We did not examine potential mechanisms explaining associations between lifestyle factors and AD biomarker trajectories. One possibility for the limited level of evidence or lack of associations between lifestyle factors and AD biomarkers could be that physical activity recorded in many studies may not have been rigorous enough or span a long enough time frame. Additionally, preclinical stages of AD can begin decades before clinical symptoms manifest. 8 It is possible that many human studies do not encompass the preclinical ‘window of opportunity’ during which physical activity may have a greater impact on signs and symptoms of the disease. In our study of persons who were cognitively unimpaired or with MCI, we observed associations between physical and cognitive activities carried out within the last 12 months and neuroimaging biomarker trajectories. More research is needed to explore the potential differential effects of midlife versus late-life lifestyle factors on AD pathophysiological changes. Meaningful insights on the associations between lifestyle factors and AD neuroimaging biomarkers, in addition to observational research as outlined above, can also be derived from interventional research. To this end, systematic reviews of intervention studies 44 , 45 showed only a few effects of physical activity on neurotrophic and inflammatory biomarkers but not AD pathophysiology, while also acknowledging that only a small number of original studies could be identified. Similarly, a non-systematic review of interventional studies found overall no convincing evidence of a relationship between physical activity and AD biomarkers but identified studies which showed significant associations in specific populations, e.g., between physical activity and blood-based beta-amyloid levels in women with obesity, pre-diabetes, or depression, and between physical activity and CSF-derived beta-amyloid in APOE ɛ4 carriers with AD. 46 Furthermore, promising clinical trials have recently been conducted or are still ongoing, e.g., the EXERT trial examined the effects of an 18-month physical exercise intervention on cognitive function and other brain function measures in persons with MCI, 47 and the US POINTER trial examined the effects of multidomain lifestyle interventions including physical and cognitive activity on cognitive function in older adults at risk of cognitive decline and dementia. 48 Results from such trials will also be valuable in further understanding the mechanisms linking physical activity and brain health in older adults. Regarding the clinical significance of our observed associations, it is informative to consider findings from a publication from the Mayo Clinic Study of Aging team. 49 The study showed that a one interquartile range increase in plasma %p-tau217 was associated with a 0.5 unit increase in PiB centiloid slope over time. Our models suggest a protective effect of approximately half that magnitude (−0.23) for each SD increase in cognitive activity. Furthermore, in our sample, the average PiB centiloid value is 22.53, and slope over time for individuals with average age and activity was about 2.5. Given that the Mayo Clinic Study of Aging team uses a PiB centiloid cutoff of 25 to determine amyloid positivity, we can assume that the average participant in our data is about 1 year away from amyloid positivity. The protective effect of a SD increase in cognitive activity translates to a delay of approximately one month in reaching amyloid positivity [(0.23/2.5) × 12 = 1.1 month] which is a modest effect. However, considering the large SD of PiB centiloid in our sample (28.52), an individual one SD below the mean would need to increase by about 31 centiloid to reach the positivity threshold, equating to roughly 12.4 years for someone at average age and cognitive activity. For a person one SD above the mean in cognitive activity, the protective effect accumulates to about 1 additional year below the cutoff of 25. Individuals with higher levels of cognitive activity could extend their amyloid-negative status even further. The strengths of our study include the large sample size, consideration of engagement in physical or cognitive activities, and repeated AD neuroimaging measurements on a large sample of community-dwelling older adults. Limitations of the study are that both physical and cognitive activities were assessed through self-reported questionnaire; thus, as in any questionnaire-based assessment, recall bias may be present and may limit the validity of study findings. Future research is warranted that utilizes objective measurements such as accelerometry for physical activities, or ecological momentary assessment or digital monitoring for cognitive activities, or that obtains confirmation of a participant’s responses from study partners such as spouses or caregivers. In addition, due to the observational study design, we cannot conclude about the cause and effect in the association between physical and cognitive activities and AD neuroimaging biomarker changes, and reverse causality is possible. Thus, it is biologically plausible that persons with healthier brains (i.e., less amyloid deposition and higher FDG-PET uptake) were more cognitively and physically active at baseline, and they were the ones who accumulated amyloid at a slower rate (as indicated by less pronounced increase in PiB-PET) and had better FDG-PET outcomes (as indicated by less pronounced decrease in FDG-PET uptake over time); thus, lower levels of physical and cognitive activities at baseline are clinical markers of incipient disease. Furthermore, while we adjusted our analyses for age, sex, APOEɛ4 carrier status and medical comorbidity (and education for models on cognitive activities), adjustments for additional confounders may be needed in future analyses, and residual confounding is possible. Our study sample is relatively highly educated and approximately 98% of participants are White older adults; thus, the results may have limited generalizability to other populations. 50 In addition, the effect sizes we observed are small, and pathological significance of the amplitude of change in neuroimaging biomarker trajectories is unclear. It is possible that our study was overpowered given its large sample size. Also, the follow-up period of 1 to 3 years (depending on the outcome of interest) may have been too short to reveal changes in neuroimaging biomarker trajectories among cognitively unimpaired participants, albeit our analyses showed that cognitive diagnosis does not modify the observed associations between physical or cognitive activities and longitudinal AD biomarker trajectories. Furthermore, when considering a PiB centiloid cutoff of 25 for determining amyloid positivity (A+), 17% of cognitively unimpaired participants in our sample were A+ at first visit and 36% were A+ at last visit. In the MCI sample, 37% were A+ at first visit and 53% were A+ at last visit. Similarly, using a tau-PET SUVR cutoff of 1.29 for determining tau positivity (T+), 12% of cognitively unimpaired participants in our sample were T+ at first visit and 17% were T+ at last visit. In the MCI sample, 26% were T+ at first visit and 32% were T+ at last visit. Therefore, both cognitively unimpaired participants and those with MCI progressed on neuroimaging. Of note, these numbers are based on participants with multiple visits, and participants that only had a single visit do not contribute to these numbers. In addition, censoring and attrition could be an additional limitation since we only get an incomplete trajectory over time for those participants who drop out. However, if we assume that participants lost to follow-up are more likely those experiencing more physical or cognitive decline, then our study estimates may even be conservative (underestimated); however, the pattern of attrition is not known. Another limitation is a possible non-linearity between physical and cognitive activities and neuroimaging biomarkers associations, but this was somewhat addressed by including interactions between baseline age and time which allowed trajectory over time to vary by age in our models. In conclusion, our study provides limited evidence of associations between higher physical activity and less synaptic dysfunction as indicated by FDG-PET, as well as higher cognitive activities with less synaptic dysfunction and lower amyloid burden as indicated by PiB-PET over time in community-dwelling older adults free of dementia. We also examined associations with Tau-PET, but no significant associations were observed. Overall, the number of associations was small, as were the observed effect sizes. Thus, more research is needed to explore the associations between physical and cognitive activities on AD pathology. Study Funding The authors thank the participants and staff at the Mayo Clinic Study of Aging. Support for this research was provided by NIH grants: National Institute on Aging (R01 AG057708, U01 AG006786, P30 AG062677, R37 AG011378, R01 AG041851, R01 NS097495); the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer’s Disease Research Program; the GHR Foundation; the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic; the Liston Award; the Schuler Foundation; the Mayo Foundation for Medical Education and Research; the Arizona Alzheimer’s Consortium; the Barrow Neurological Foundation; and used the resources of the Rochester Epidemiology Project (REP) medical records linkage system, which is supported by the National Institute on Aging (AG 058738), by the Mayo Clinic Research Committee, and by fees paid annually by REP users. Disclosure P. Vemuri receives research support from the NIH. J. Fields reports grants from NIH and grants from the Mangurian Foundation outside the submitted work. V.J. Lowe serves as a consultant for Bayer Schering Pharma, Piramal Life Sciences, Life Molecular Imaging, Eisai Inc., AVID Radiopharmaceuticals, Eli Lilly and Company, PeerView Institute for Medical Education, and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals, and the NIH (NIA, NCI). J. Graff-Radford serves on the DSMB for NINDS, is an associate editor for JAMA Neurology, and acts as the site PI for trials sponsored by Eisai and Cognition Therapeutics. C.R. Jack Jr. receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic. R.C. Petersen is a consultant for Roche, Genentech, Eli Lilly and Co., Eisai, Novartis and Novo Nordisk. He receives research funding from the NIH and royalties from Oxford University Press and UpToDate. S.B. 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