Lockhart, Samuel N.

Abstract INTRODUCTION To examine the generalizability of Alzheimer's disease (AD) biomarker models in real‐world older adults, we examined AD biomarker relationships with cognition in two multicenter cohorts that differ with respect to recruitment approach and health risk factors but were matched on a variety of characteristics. METHODS We compared harmonized health and demographic data, AD and cerebrovascular biomarkers, and cognitive performance in the community‐recruited U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) Imaging substudy and a matched sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI) which recruited primarily from academic specialty clinics. RESULTS Elevated β‐amyloid (Aβ) and tau were associated with cognitive performance in ADNI but not U.S. POINTER. Findings were consistent across different cohort matching schemes, and were not explained by discrepancies in vascular risk. DISCUSSION The role of Aβ and tau in cognitive performance may be reduced in real world samples compared to academic specialty clinics.

Importance Brain imaging biomarkers may (1) identify underlying mechanisms of intervention effects and (2) define brain characteristics of those most likely to benefit cognitively from the intervention. Objective To test whether (1) a lifestyle intervention is related to brain changes, (2) brain changes are correlated with intervention-specific cognition changes, and (3) brain imaging biomarkers provide information about who is likely to benefit from a lifestyle intervention. Design, Setting, and Participants In this randomized clinical trial, the POINTER Imaging study was an ancillary study of the 2-year, single-blind multicenter US POINTER clinical trial in which neuroimaging data were collected on eligible enrolled participants. The study was conducted from May 2019 to March 2023 (final follow-up, May 14, 2025). Participants were enrolled into the POINTER Imaging study following a standard procedure at 5 clinical sites in the US. Participant eligibility criteria included an age of 60 to 79 years, sedentary lifestyle, and suboptimal diet, plus at least 2 additional risk criteria for cognitive decline and no contraindications for neuroimaging. Interventions Participants were randomly assigned to receive the structured or self-guided intervention. Both interventions emphasized increased physical and cognitive activity, healthy nutrition, social engagement, and cardiovascular health monitoring, but they differed in intensity and accountability. Main Outcomes and Measures There were 4 primary imaging outcomes: global β-amyloid (Aβ) burden, tau burden in the entorhinal cortex (ERC), hippocampal (HC) volume, and white matter hyperintensity volume. The primary cognitive measure was a global cognitive composite. Results Among 1943 parent trial participants who were assessed for eligibility, 983 underwent at least 1 magnetic resonance imaging or positron emission tomography scanning session. The mean (SD) age was 68.4 (5.2) years; 605 participants (61.5%) were female and 378 male (38.5%). Five hundred sixteen participants received the structured intervention and 467 the self-guided intervention. There were no intervention group differences in longitudinal cognitive or imaging outcomes. Intervention group differences were not associated with or moderated by Aβ status or accumulation. There was a negative association between change in ERC tau and change in global cognition in the self-guided group that was attenuated in the structured group (difference in association: 0.289; 95% CI, 0.029 to 0.550; interaction P = .03). Lower baseline HC volume was associated with greater cognitive benefit for participants in the structured group vs the self-guided group (lower HC: 0.077 SD; 95% CI, 0.022 to 0.132; higher HC: 0.002 SD; 95% CI, −0.037 to 0.041; interaction P = .03). Conclusion and Relevance This study found that a high-intensity multidomain lifestyle intervention did not affect brain biomarker trajectories and was not associated with Aβ pathology, but older adults with specific at-risk brain characteristics, including lower baseline HC volume, showed a greater cognitive benefit of the structured intervention. Trial Registration ClinicalTrials.gov Identifier: NCT03688126

AbstractBackgroundThe residual approach has found wide application in researching cognitive resilience, a phenomenon conceptually understood as cognitive performance being better‐than‐typical for an individual, despite apparent AD pathology. The standard residual approach extracts information about an individual’s resilience from the residuals of a linear model predicting cognition. This approach is subject to several limiting assumptions. Here, we explore the implications of these assumptions and propose an alternative predictive modeling approach which circumvents them.MethodsOur alternative approach relies on training a machine learning model on a sample of participants who follow a “typical” clinical progression (Fig. 1A). Residuals are then extracted for a heterogeneous dataset that potentially includes resilient participants by comparing observed cognitive outcomes to those predicted by the “typical” model (Fig. 1B). Additionally, our estimate can be refined by effectively estimating and removing model error from the extracted residual by leveraging a second predictive model (Fig. 1C). Using simulated data, we explore the assumptions made by the standard approach and the consequences of their violation. We then contrast this against our approach.ResultsOur simulations demonstrated that the standard residual approach only extracted accurate resilience estimates if the predictors used in the model explained cognition sufficiently well, while being unrelated to resilience itself. That is, as the predictors explain more of the resilience, the residuals became increasingly determined by model error (Fig. 2). We found that residuals achieved with our approach are more precise and provide individualized estimates that are unaffected by the aforementioned trade‐off (Fig. 3).ConclusionsOur approach holds several advantages over the standard approach: The definition of resilience can be freely adjusted and enforced via different definitions of “typical”, it allows for incorporating complex, non‐linear models without removing relevant information from the residual, the error component of the residual can be estimated through cross‐validation, and all predictors that potentially explain cognition can be included into the model without losing resilience information from the residual. Our approach can be used to measure any phenomenon that can be operationalized as a deviation from a typical outcome and, thus, is also applicable for researching resistance to pathology.

AbstractBackgroundNeuritic plaques with fibrillar beta‐amyloid (Aβ) peptides and tau‐protein neurofibrillary tangles, hallmark features of Alzheimer’s disease (AD) pathology, have been concomitantly associated with white matter (WM) integrity loss, while a unique effect of each pathology on WM integrity in a more demographically diverse population remains unknown.MethodTo examine the degree to which each pathology affects WM integrity in a more diverse non‐demented cohort, Aβ and tau PET, diffusion‐weighted imaging (DWI), and cognition (memory and executive function composites) were examined from the U.S. POINTER neuroimaging participants (N=608; age=68.34±5.2; 61.8% females; 29% Aβ+) and a subset of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (N=291; age=72.85±7.3; 50% females; 39% Aβ+) (Table). Aβ‐positivity was determined using tracer‐specific standardized cut‐offs. Tau SUVR was estimated for each Braak stage. For WM integrity, voxel‐based GLMs were applied to FSL‐processed fractional anisotropy (FA) maps using Braak stage tau SUVR and Aβ‐positivity status, along with covariates of age, sex, and b‐value profiles.ResultIn U.S. POINTER, independent from Aβ‐positivity status, greater tau burden in Braak stages I and II was related to lower FA across the tracts including the superior longitudinal fasciculus, fornix (cres), corpus callosum, and anterior thalamic radiation (Figure). Average FA from supra‐threshold clusters for Braak I and II significantly predicted executive function but not episodic memory. No association between FA and tau burden in Braak stages III/IV or V/VI was found. In the ADNI sample, greater tau burden in Braak I was related to lower FA mostly in left inferior longitudinal fasciculus and left uncinate fasciculus, while negative associations with FA became more diffuse with tau load in Braak III/IV and V/VI. Average FA from supra‐threshold clusters significantly predicted both episodic memory and executive function scores. Across both samples, after accounting for tau burden in each Braak stage, there were no association between Aβ‐positivity status and WM integrity.ConclusionDistinct associations between tau burden, WM integrity, and cognition were found across the studies, indicating greater impact of early tau burden on WM integrity in U.S. POINTER, accounting for Aβ‐positivity status. Factors contributing to the distinct associations across studies need further investigation.

AbstractBackgroundNeuritic plaques with fibrillar beta‐amyloid (Aß) peptides and tau‐protein neurofibrillary tangles, hallmark features of Alzheimer’s disease (AD) pathology, have been concomitantly associated with white matter (WM) integrity loss, while a unique effect of each pathology on WM integrity in a more demographically diverse population remains unknown.MethodTo examine the degree to which each pathology affects WM integrity in a more diverse non‐demented cohort, Aß and tau PET, diffusion‐weighted imaging (DWI), and cognition (memory and executive function composites) were examined from the U.S. POINTER neuroimaging participants (N = 608; age = 68.34±5.2; 61.8% females; 29% Aß+) and a subset of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants (N = 291; age = 72.85±7.3; 50% females; 39% Aß+) (Table). Aß‐positivity was determined using tracer‐specific standardized cut‐offs. Tau SUVR was estimated for each Braak stage. For WM integrity, voxel‐based GLMs were applied to FSL‐processed fractional anisotropy (FA) maps using Braak stage tau SUVR and Aß‐positivity status, along with covariates of age, sex, and b‐value profiles.ResultIn U.S. POINTER, independent from Aß‐positivity status, greater tau burden in Braak stages I and II was related to lower FA across the tracts including the superior longitudinal fasciculus, fornix (cres), corpus callosum, and anterior thalamic radiation (Figure). Average FA from supra‐threshold clusters for Braak I and II significantly predicted executive function but not episodic memory. No association between FA and tau burden in Braak stages III/IV or V/VI was found. In the ADNI sample, greater tau burden in Braak I was related to lower FA mostly in left inferior longitudinal fasciculus and left uncinate fasciculus, while negative associations with FA became more diffuse with tau load in Braak III/IV and V/VI. Average FA from supra‐threshold clusters significantly predicted both episodic memory and executive function scores. Across both samples, after accounting for tau burden in each Braak stage, there were no association between Aß‐positivity status and WM integrity.ConclusionDistinct associations between tau burden, WM integrity, and cognition were found across the studies, indicating greater impact of early tau burden on WM integrity in U.S. POINTER, accounting for Aß‐positivity status. Factors contributing to the distinct associations across studies need further investigation.

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E ( APOE ) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E ( APOE ) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

AbstractBackgroundThe U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a two‐year randomized controlled trial designed to evaluate the effects of lifestyle interventions on older adults aged 60‐79 years. The POINTER Imaging ancillary study employs arterial spin labeling (ASL) magnetic resonance imaging (MRI) with two inversion times, enabling the mapping of arterial transit time (ATT) and the investigation of its relationship with dementia risk factors. This study examines the effect of the Framingham Risk Score (FRS) on ATT, which provides hemodynamic information on the cerebral vasculature.MethodA subset of POINTER Imaging participants (n = 134; F/M = 81/53; age = 68.1±5.1; FRS = 11.3±10.4) from two imaging sites underwent two ASL MRI scans during the baseline session. Two 3D Pulsed ASL scans were obtained with TI1 of 790ms and TI2 values of 2000ms or 2750ms, providing delay times (between labeling and imaging) of 1210ms or 1960ms. ATT, the time required for blood to travel from the neck to the tissue, was estimated in the range of 1210ms and 1960ms using a kinetic model (Figure 1). ATT maps were normalized to a standard space, and a voxel‐wise linear regression (ATT∼age+sex+log(FRS)) was performed on ATT maps in gray matter to assess the effect of FRS on ATT. To control false positives, clusters smaller than 1.4ml were removed based on a simulation with α = 0.05.ResultFigure 2 depicts three clusters found in the occipital, precuneus and frontal gray matter regions, all of which demonstrate a positive relationship between ATT and log(FRS). Participants with higher FRS exhibit prolonged mean ATT values in the clusters as shown in Figure 3. After stratifying into sex groups, both groups show the same direction.ConclusionAlthough the detectable range of ATT with two ASL scans was limited, the analysis suggests that an increased risk of cardiovascular disease, as quantified by the Framingham Risk Score, can serve as an indicator of potential cerebrovascular disease leading to prolonged ATT, which is associated with arterial stiffness, and vascular resistance. The clusters demonstrating the association between FRS and ATT are the brain regions that may have heightened vulnerability to vascular risk factors, according to previous studies.

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aβ and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aβ‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aβ+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.

AbstractBackgroundThe U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a 2‐year randomized controlled trial to evaluate the effect of lifestyle interventions in cognitively unimpaired older adults (60‐79 years) at risk for decline/dementia. Here, using data from the POINTER Imaging ancillary substudy, we explore how Alzheimer’s disease pathology and cardiovascular risk influence brain structure, cognition and their relationship.MethodPOINTER Imaging participants underwent cross‐sectional MRI, [18F]Florbetaben (FBB) Aβ‐PET and [18F]MK6240 tau‐PET. FBB and MK6240 data were processed using previously validated methods. MRIs were processed with voxel‐based morphometry. To quantify cardiovascular risk, the Framingham risk score (FRS) was calculated for each participant. We used linear regression models in the whole sample as well as in Aβ status subgroups to evaluate voxel‐wise associations of brain structure, measured using voxel‐based morphometry, with age, FRS, AD pathology (cortical summary Aβ and entorhinal cortex (ERC) tau burden), and a global cognition composite score.ResultAcross all 624 participants (Table 1), cognition (age‐adjusted) was positively related to brain structure across temporal, frontal and medial parietal cortices (Fig. 1A). ERC tau‐PET was negatively associated with brain structure in bilateral medial temporal lobes (MTL) and, notably, in bilateral basal forebrain (Fig. 1B). FRS showed significant negative associations in superior temporal gyrus and occipital cortex (Fig. 1C), but tau‐PET associations remained similar when adjusting for FRS. In Aβ+ participants, cognition‐ and FRS‐structure relationships were weaker but tau‐PET‐structure associations were stronger and extended outside the MTL. In Aβ‐ participants, cognition‐ and FRS‐structure relationships were similar to those observed in the whole cohort and tau‐PET associations were no longer significant.ConclusionIn this heterogeneous cohort of older adults, tau was associated with brain structure in strikingly specific patterns that were related to Aβ but not to cardiovascular risk as measured by the FRS. Tau‐structure associations were restricted to temporal lobes and basal forebrain and were independent of more widespread cognition‐structure associations, indicating that factors other than tau may play a critical role. Our next steps include comparing the current cohort to others with different at‐risk characteristics (e.g., ADNI, Berkeley Aging Cohort Study).

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aß and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aß‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aß+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.