Citations

Abstract In response to a Cochrane review (published in April 2026) on the clinical trials of monoclonal antibodies targeting beta amyloid, this perspective outlines a number of critiques. The scientists, methodologists, subject‐matter experts, and clinicians on this perspective strongly recommend that the Cochrane editorial board consider a call for editorial response, correction of press materials, or commissioned reply to this review for four overarching reasons: (1) decision to pool antibodies that do and do not clear beta amyloid, (2) decision to include studies from a time when biomarkers of Alzheimer's disease were not available, (3) analytical and methodological limitations of the approach used, (4) conflating the distinct roles and methodological processes of systematic review and guideline methodologies, (5) contextualizing the present critique within the existing evidence‐synthesis literature, and (6) mechanistic inference beyond the scope of the presented analyses. Conclusions that may not accurately reflect treatment‐specific effects could cause significant downstream clinical and psychosocial harm to those currently undergoing treatment and their family members as well as individuals considering participation in Alzheimer's clinical trials.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract Histopathological assessment has served as the gold standard for diagnosing Alzheimer’s disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10−11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10−10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10−201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10−8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10−8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10−7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.

Abstract In 2011, a workgroup of the Alzheimer's Association Research Roundtable introduced recommendations for detecting and monitoring amyloid‐related imaging abnormalities (ARIA) in Alzheimer's disease (AD) clinical trials. Since then, anti‐amyloid immunotherapies have received regulatory approval for AD treatment and are beginning to enter clinical practice, underscoring the importance of informing healthcare providers, researchers, and patients about ARIA's implications in real‐world settings. In response, the Alzheimer's Association convened a new workgroup to review current knowledge of ARIA, including underlying mechanisms, clinical presentations, associated risk factors, mitigation strategies, radiologic detection methods, patients’ perspectives in treatment decision‐making, and outstanding challenges. Here, we outline key insights from this workgroup, highlighting that effective ARIA detection and monitoring in clinical practice requires adherence to robust protocols to mitigate risks and enhance patient safety. Limited availability of clinical and pathologic data on predictors of symptomatic and severe ARIA underscores the importance of continued real‐world data collection.

Importance Alzheimer disease (AD) biomarker and genetic testing results are increasingly disclosed to cognitively unimpaired adults in research and could in the future inform clinical treatment decisions in this population. Objectives To assess psychological outcomes after returning 3 categories of amyloid biomarker results as well as apolipoprotein E ( APOE ) genotypes. Design, Setting, and Participants This cohort study was a secondary analysis of data collected as part of screening for the multisite AHEAD preclinical AD trial. Participants were individuals aged 55 to 80 years undergoing screening from July 14, 2020, to October 15, 2024. Exposure Participants were informed whether they had not-detected, intermediate, or elevated amyloid positron emission tomography levels, as well as their APOE genotype, which were categorized as noncarrier, ε4 heterozygote, or ε4 homozygote. Main outcomes and measures Impact of Events Scale (IES; 15 items to assess intrusive thoughts and avoidance; each item is scored as not at all [0], rarely [1], sometimes [3], or often [5]; total range, 0-75), collected 24 to 72 hours after disclosure, and change in a scale measuring concerns about AD dementia (adapted scale using 6 items in which participants indicated their level of agreement with statements related to their perceived probability of developing AD dementia; items scored as strongly disagree [1] through strongly agree [5]; total range, 6-30), calculated by subtracting the score collected before biomarker testing from 1 collected after biomarker and genetic test results disclosure. Results Among 3414 included individuals, the mean (SD) age was 68.8 (6.0) years and 2116 (62%) were female. Group mean IES scores were below clinically significant thresholds. Nevertheless, across genetic groups, learning an elevated amyloid result (1184 participants) was associated with higher IES (mean [SD], 10.5 [10.9]) than intermediate amyloid (482 participants; mean [SD] IES, 8.8 [9.8]), and intermediate amyloid was associated with higher scores than not-detected amyloid (1748 participants; mean [SD] IES, 6.5 [8.4]). Across amyloid groups, learning APOE ε4 homozygosity (337 participants) was associated with higher mean (SD) IES (12.7 [11.6]) than heterozygosity (1609 participants; 9.1 [10.2]), and heterozygosity was associated with higher IES than noncarrier status (1468 participants; mean [SD] IES, 6.2 [8.1]). Both types of information were significant in an analysis of covariance model; no interaction effect was observed. In contrast, only biomarker disclosure was associated with differential change in concerns about AD dementia. Those with elevated amyloid showed a mean (SD) increase in concern (0.8 [3.5]), those with intermediate amyloid showed a smaller increase (0.4 [3.7]), and those with not-detected amyloid showed decreased concerns (−1.1 [4.2]). Conclusions and Relevance In this cohort study of cognitively unimpaired adults, associations with intrusive thoughts were observed to differ among genetic and biomarker subgroups; such associations were limited to biomarker subgroups for measures of perceived dementia risk.

Abstract Advances in biomarker technology, digital cognitive assessments, and amyloid‐targeting therapies have redefined the opportunities for accurate and early diagnosis and care of Alzheimer's disease (AD). These advances also create new possibilities for intervention before the onset of cognitive impairment. This paradigm shift has increased the focus on Stages 1 and 2 of AD, in which individuals are cognitively unimpaired but exhibit biological evidence of disease. While early identification of AD offers an opportunity to intervene early to delay progression and preserve quality of life, it also presents complex challenges related to communicating diagnostic results to patients and their families, contextualizing the cost effectiveness of early diagnosis and treatment, and implementation of and equitable access to treatments. Recent, successfully enrolled, preclinical AD trials highlight the complex strategies required to identify asymptomatic, biomarker‐positive individuals on a large scale, and demonstrate critical knowledge gaps in inclusion, follow‐up, and long‐term outcome measurement. The Spring 2025 Alzheimer's Association Research Roundtable (AARR) meeting brought together academics, clinicians, industry, and regulatory leaders to exchange perspectives on current challenges, key learnings, and potential strategies for identifying and treating individuals in very early stages of AD, effectively and safely. This paper presents key takeaways from the Spring 2025 AARR meeting. Highlights New criteria for Alzheimer's disease (AD) enable early identification of AD pathology. Blood tests and digital cognitive assessment tools may facilitate early and personalized care. Plasma biomarkers may be scalable in clinical settings but need confirmation or follow‐up assessments in those individuals with equivocal results. Early therapy shows best results in people with lower initial amyloid and tau burden.

Abstract INTRODUCTION The Centiloid scale is the standard for amyloid (Aβ) PET quantification in research and clinical settings. However, variability between tracers and scanners remains a challenge. METHODS This study introduces DeepSUVR, a deep learning method to correct Centiloid quantification, by penalizing implausible longitudinal trajectories during training. The model was trained using data from 2,129 participants (7,149 Aβ positron emission tomography [PET] scans) in the Australian Imaging, Biomarkers and Lifestyle Study of ageing (AIBL)/Alzheimer's Disease Neuroimaging Initiative (ADNI) and validated using 15,807 Aβ PET scans from 10,543 participants across 10 external datasets. RESULTS DeepSUVR increased correlation between tracers, and reduced variability in the Aß‐negatives. It showed significantly stronger association with cognition, visual reads, neuropathology, and increased longitudinal consistency between studies. DeepSUVR also increased the effect size for detecting small treatment related slowing of amyloid accumulation in the A4 study. DISCUSSION DeepSUVR substantially advances Aβ PET quantification, outperforming all standard approaches, which is particularly important for clinical decision making and to detect subtle or early changes in Aβ. Highlights Novel artificial intelligence (AI)‐method that penalizes biologically implausible longitudinal trajectories, enabling the model to learn standardized uptake value ratios (SUVR) correction factors without requiring longitudinal data at inference time. Improves Centiloid consistency across tracers and studies, significantly enhancing cross‐sectional and longitudinal amyloid positron emission tomography (PET) quantification. DeepSUVR‐derived Centiloids show stronger associations with cognition, visual reads, and neuropathology. Longitudinal variability is reduced three‐ to five‐fold, enabling more reliable tracking of amyloid accumulation and better detection of treatment effects. Novel reference and target masks derived from DeepSUVR replicate most of the model's performance, offering a practical alternative for integration into existing pipelines.

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 Background Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self‐reported sex, educational attainment and race/ethnicity on amyloid‐positivity, and tau‐PET‐positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Method Logistic generalized estimating equations were used to estimate frequency of amyloid‐positivity (using cohort‐specific thresholds for amyloid‐PET [84%] or CSF) and tau‐PET‐positivity (cohort‐specific thresholds of 2SD above mean temporal uptake in amyloid‐negative controls). We assessed: i) sex and APOEε4 ( N = 10,098) associations, to complement earlier findings of a higher frequency of tau‐positivity in females, ii) effects of lower/higher education ( N = 10,970; cohort‐specific median‐split), and iii) effects of race/ethnicity (non‐Hispanic White [hereafter: White], N = 4880; Asian, N = 116; Black or African‐American [hereafter: Black], N = 353; Hispanic, N = 356, only from Northern‐American cohorts). Outcomes were frequency of amyloid‐positivity in CU individuals only, and tau‐PET‐positivity in both amyloid‐positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau‐positivity were assessed and only retained in the models when significant. Result Female sex was associated with an APOEε4 ‐independent increased frequency of amyloid‐positivity (β=0.51[0.22], p = 0.02) in CU and increase of tau‐positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p <0.01). Remarkably, tau‐positivity frequencies of female APOEε4 non‐carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid‐positivity frequency (β=‐0.12[0.05], p = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau‐positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non‐White groups compared to White (Hispanic:β interaction =‐0.05[0.01], p <0.01; Black:‐0.04[0.01], p <0.01; Asian:‐0.02[0.01], p = 0.04). This suggests that the impact of age on amyloid‐positivity was less pronounced in non‐White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau‐positivity frequency than White (β=0.51[0.22], p = 0.02; Figure 3). Conclusion In this multi‐center initiative comprised of clinical and community‐based cohorts, we observed that self‐reported sex, educational attainment and race/ethnicity were related to positivity‐frequencies of Alzheimer's disease pathology.

Abstract INTRODUCTION Tau species lacking truncation of the N‐terminal region, including plasma N‐terminal tau fragment 1 (NT1), have been previously associated with cognitive decline, neurodegeneration, and tau pathology in late‐onset sporadic Alzheimer's disease (AD). METHODS Here, we examined cross‐sectional and longitudinal plasma NT1 as a possible predictor of cognitive, clinical, and core AD biomarker trajectories in autosomal dominant AD (ADAD). RESULTS NT1 levels in ADAD mutation carriers (MC; n = 132) increased across the disease continuum, compared to non‐carriers (NC; n = 75), becoming elevated about a decade prior to estimated symptom onset. Cross‐sectional and longitudinal NT1 levels in MC were associated with clinical, cognitive, and biomarker changes. NT1 increases continued in symptomatic phases of disease, a distinct trajectory from that seen with CSF p‐tau217 and other phospho‐tau species. DISCUSSION Together, our results suggest that plasma NT1—alone or combined with other tau measures—may be useful in studying AD‐related clinical, cognitive, and biomarker outcomes. Highlights Leveraging a deeply phenotyped cohort of individuals carrying a pathogenic variant for autosomal dominant Alzheimer's disease (ADAD) and their non‐carrier family members, our results suggest that plasma N‐terminal tau fragment 1 (NT1) levels mirrored changes in clinical, cognitive, and neurodegenerative measures in ADAD, particularly in late asymptomatic and early symptomatic phases of disease. NT1 levels correlated with cerebrospinal fluid (CSF) measures of tau pathology but less so with CSF or imaging measures of β‐amyloid pathology. Together with previous supportive findings in preclinical and symptomatic sporadic AD, these results suggest that plasma NT1—alone or combined with other tau measures—may be useful in studying AD‐related tau pathology and neurodegeneration across a wide spectrum of disease.