Harrison, Theresa M.

Abstract Background Given low representation of diverse populations in Alzheimer's disease (AD) research, it remains unclear if AD biomarker relationships observed primarily in non‐Hispanic White (NHW) cohorts generalize to other groups. Here we evaluate associations between key AD biomarkers and cognitive performance among cognitively unimpaired non‐Hispanic Blck (NHB) and NHW participants across 2 cohorts with unique sample characteristics. Method Participants included cognitively unimpaired NHB and NHW participants with Aβ and tau PET in ADNI (69 NHB, 402 NHW) and POINTER Imaging (135 NHB, 637 NHW). We examined harmonized measures of global Aβ (in centiloids), entorhinal cortex (EC) tau (quantified with MK6240 in POINTER and FTP in ADNI) and Preclinical Alzheimer's Cognitive Composite (PACC) scores. Regression models within each ethnoracial group evaluated relationships between 1) Aβ and EC tau, and 2) EC tau and PACC performance. Interaction models within each cohort were also conducted to assess differences in these associations by ethnoracial group. All models were adjusted for age and gender. Models with PACC additionally adjusted for education. Result In US POINTER, NHBs had lower Aβ and EC tau burden compared to NHWs (Table 1). In ADNI, there was no difference in EC tau between groups, and no differenence in Aβ after adjusting for age and gender. In US POINTER, the positive Aβ‐EC tau relationship was observed in both ethnoracial groups whereas in ADNI, Aβ was associated with higher EC tau in the NHW but not the NHB group. In both cohorts, EC tau was associated with worse PACC performance in both NHB and NHW and there were no differences in this association by ethnoracial group in interaction models (Figure 1). Conclusion NHBs had lower Aβ and EC tau than NHWs in POINTER but not ADNI, and there were inconsistent associations between Aβ and EC tau across cohorts. Despite these differences, AD biomarkers appear similarly related to cognitive outcomes across ethnoracial groups in both cohorts. Future analyses will attempt to further determine the generalizability of AD biomarker relationships in diverse samples by considering matching on relevant health and demographic characteristics, and inclusion of additional cohorts with harmonized biomarker and cognitive measurements.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract INTRODUCTION New methods estimate amyloid positivity onset age (EAOA) from amyloid positron emission tomography (PET). We explore the genetics of EAOA to identify molecular factors underlying the earliest Alzheimer's disease (AD) changes. METHODS Harmonized amyloid PET data from 4216 participants were used in genome‐wide survival, tissue‐specific gene expression, and genetic covariance analyses of EAOA. RESULTS Variants in apolipoprotein E ( APOE ), ABCA7 , and RASGEF1C associated with earlier EAOA. APOE ε4/ε4 and ε3/ε4 converted 6.3 and 5 years earlier than ε3/ε3, respectively. ε2 was protective against earlier EAOA. rs4147929, an expression quantitative trait locus for ABCA7 , associated with a 4 year earlier EAOA. This variant was associated with lower brain expression of ABCA7 , which was associated with increased amyloid pathology at autopsy. Multiple immune‐related diseases shared genetic covariance with EAOA. DISCUSSION APOE , ABCA7 , and RASGEF1C associated with earlier EAOA, with supporting evidence from tissue‐specific expression analyses, offering insights into intervenable targets at early stages of AD. Highlights Novel methods estimate how long ago a patient converted to amyloid positivity. Estimating this amyloid clock allows us to determine the onset of the earliest Alzheimer's disease changes. We evaluated what genes influence when someone converts to amyloid positivity. Apolipoprotein E ( APOE ) , ABCA7 , and RASGEF1C associated with earlier age of amyloid positivity. Genetic results were supported by tissue‐specific expression analyses.

Abstract INTRODUCTION The Consortium for Clarity in Alzheimer's disease related dementias (ADRD) Research Through Imaging (CLARiTI) is a study that aims to collect standardized imaging and plasma biomarkers on 2000 Clinical Core participants enrolled across all Alzheimer's Disease Research Centers (ADRC) sites. We sought to summarize the known heterogeneity across centers regarding scientific focus and initial enrollment plans for CLARiTI. METHODS We developed and distributed a survey capturing information on the 36 CLARiTI site's theme/expertise, recruitment plans, and the intersection of CLARiTI with other ADRC imaging efforts. RESULTS Anticipated CLARiTI enrollees spanned 11 different categories of suspected etiologies underlying impairment. A wide range of risk factors were endorsed across sites regarding the enrollment of unimpaired individuals. Variability also existed regarding site‐level strategies in enrollment into CLARiTI versus other imaging efforts. DISCUSSION We anticipate that the 2000 individuals that will enroll into CLARiTI will reflect the clinical heterogeneity already in place across the ADRC network. Highlights The ADRC Consortium for Clarity in ADRD Research Through Imaging (CLARiTI) will leverage and contribute to the existing Alzheimer's Disease Research Centers (ADRC) program by supporting standardized imaging and plasma collection across all centers. We summarize the variation in scientific focus and enrollment plans across ADRC sites participating in CLARiTI. The anticipated CLARiTI cohort will reflect the clinical heterogeneity that already exists across the ADRC network. CLARiTI will contribute to scientific goals related to the detection of multi‐etiological signatures relevant for Alzheimer's disease and related disorders (ADRDs).

Importance Alzheimer disease (AD) predominates in females at almost twice the rate relative to males. Mounting evidence in adults without AD indicates that females exhibit higher tau deposition than age-matched males, particularly in the setting of elevated β-amyloid (Aβ), but the evidence for sex differences in tau accumulation rates is inconclusive. Objective To examine whether female sex is associated with faster tau accumulation in the setting of high Aβ (as measured with positron emission tomography [PET]) and the moderating influence of sex on the association between APOE ε4 carrier status and tau accumulation. Data Sources This meta-analysis used data from 6 longitudinal aging and AD studies, including the Alzheimer’s Disease Neuroimaging Initiative, Berkeley Aging Cohort Study, BioFINDER 1, Harvard Aging Brain Study, Mayo Clinic Study of Aging, and Wisconsin Registry for Alzheimer Prevention. Longitudinal data were collected between November 2004 and May 2022. Study Selection Included studies required available longitudinal [ 18 F]flortaucipir or [ 18 F]-MK-6240 tau-PET scans, as well as baseline [ 11 C] Pittsburgh Compound B, [ 18 F]flutemetamol or [ 18 F]florbetapir Aβ-PET scans. Recruitment criteria varied across studies. Analyses began on August 7, 2023, and were completed on February 5, 2024. Data Extraction and Synthesis In each study, primary analyses extracted estimates for the sex (female or male) and the sex by baseline Aβ-PET status (high or low) association with longitudinal tau-PET using a series of mixed-effects models. Secondary mixed-effects models extracted the interaction estimate for the association of sex by APOE ε4 carrier status with longitudinal tau-PET. Study-specific estimates for each mixed-effects model were then pooled in a meta-analysis, and the global fixed effect (β) and total heterogeneity ( I 2 ) across studies were estimated. This study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures Seven tau-PET outcomes that showed cross-sectional sex differences were examined across temporal, parietal, and occipital lobes. Results Among 6 studies assessed, there were 1376 participants (761 [55%] female; mean [range] age at first tau scan, 71.9 [46-93] years; 401 participants [29%] with high baseline Aβ; 412 APOE ε4 carriers [30%]). Among individuals with high baseline Aβ, female sex was associated with faster tau accumulation localized to inferior temporal (β = −0.14; 95% CI, −0.22 to −0.06; P = .009) temporal fusiform (β = −0.13; 95% CI, −0.23 to −0.04; P = .02), and lateral occipital regions (β = −0.15; 95% CI, −0.24 to −0.06; P = .009) compared with male sex. Among APOE ε4 carriers, female sex was associated with faster inferior-temporal tau accumulation (β = −0.10; 95% CI, −0.16 to −0.03; P = .01). Conclusions and Relevance These findings suggest that sex differences in the pathological progression of AD call for sex-specific timing considerations when administrating anti-Aβ and anti-tau treatments.

AbstractBackgroundThere is limited information on biomarker‐defined Alzheimer’s disease (AD) pathology in community‐recruited individuals of diverse racial and ethnic groups. Here, we assessed the association of race/ethnicity with baseline biomarkers and cognitive measures and hypothesized a lower impact of AD pathology in non‐Hispanic White (nHW) participants in the U.S. POINTER study clinical trial, which evaluates the benefits of a multidomain lifestyle intervention on cognition of community‐recruited at‐risk elderly individuals.MethodWe included 879 participants from the U.S. POINTER study enrolled in the imaging substudy (∼50% of participants) who self‐identified as nHW (n=644), African American/Black (AA/B, n=132), Asian (n=29), and Hispanic (n=74). Outcomes measures included global Aβ pathology (18F‐florbetaben), temporal meta‐ROI tau (18F‐MK‐6240), MRI‐measured white matter hyperintensity volume (WMH) volumes, global clinical dementia rating (gCDR), and global, executive, and memory standardized cognitive scores. The nHW participants were selected as reference. Power transformations normalized skewed distributions. Multivariable linear regressions include age, sex, education, Framingham risk score, and area deprivation index (ADI) as covariates.ResultAA/B harbored lower WMHs, and meta‐temporal tau ROI values. In the full model, 0.5 gCDR participants had higher centiloid values, were more likely to be AA/B and Hispanic, and had a greater burden of WMHs. In addition, higher centiloid values were associated with a 0.5 gCDR in all groups except for the AA/B participants. Hispanic, AA/B, and Asian participants had lower cognitive scores than the nHW group. A higher ADI score was associated with lower global and executive cognitive scores but not with WMH volume or Aβ centiloid score.ConclusionThe analyses of the community‐based U.S. POINTER indicate that AA/B individuals present differences in AD and related dementia biomarker values. nHW had higher fully adjusted cognitive scores. These results indicate a significant heterogeneity in the cognitive testing performance and biomarker values of individuals at risk for dementia in the community.

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

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community

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.