Citations

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.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [ 18 F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [ 18 F]flortaucipir (ADNI), [ 18 F]MK‐6240 (CPAS), and [ 18 F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([ 18 F]flortaucipir) and AIBL ([ 18 F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [ 18 F]flortaucipir and [ 18 F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([ 18 F]PI‐2620) are underway.

Abstract TP53 and PPM1D are key regulators of DNA damage response and repair, and somatic mutations in these genes often co-occur in hematopoietic cells, expanding under genotoxic stress. Unlike TP53 mutations, where mechanisms of progression are defined, pathways underlying clonal fitness and transformation in PPM1D mutant cells remain unclear. In collaboration with 5 academic institutions, we analyzed the clinical and molecular landscape of 337 patients with clonal hematopoiesis (CH) and clonal cytopenia of undetermined significance (CCUS) across 4 genotypes: PPM1Dmt/TP53wt (n = 170 [50%]), PPM1Dmt/TP53mt (n = 25 [7%]), TP53mt/PPM1Dwt (n = 17 [5%]), and TP53wt/PPM1Dwt (n = 125 [38%]). All PPM1D variants were truncating, located in exon 6 of the gene, with a median variant allele frequency (VAF) of 6% (range, 0.3%-64%). The PPM1Dmt/TP53mt genotype was most frequently encountered in therapy-related CH/CCUS (t-CH/t-CCUS; 80%, 66.5%, 76.5%, and 19%; P ≤ .001) and had a shorter time interval to detection from last genotoxic exposure (6.2, 5.9, 11.25, and 24.5 months; P ≤ .001) compared with PPM1Dmt/TP53wt, TP53mt/PPM1Dwt, and TP53wt/PPM1Dwt genotypes, respectively. Acknowledging the short follow-up duration, rates of malignant transformation were lower in the PPM1Dmt/TP53wt (2%) and PPM1Dmt/TP53mt (4%) groups compared with PPM1Dwt/TP53wt (12%) and PPM1Dwt/TP53mt (17%) groups (P ≤ .001), respectively. In summary, PPM1D mutations are frequently observed in t-CH/t-CCUS, with low median VAFs, and are associated with low rates of progression, even when comutated with TP53.

Abstract Waldenström macroglobulinemia (WM) is characterized by recurrent MYD88 and CXCR4 mutations, whose prognostic value in patients treated with chemoimmunotherapy remains unclear. Moreover, the typically prolonged progression-free survival (PFS) correlates inconsistently with overall survival (OS), underscoring the importance of examining other surrogates. Progression of disease within 24 months (POD24), an established early end point, delineates functionally high-risk patients in other indolent lymphomas. This international study evaluated 253 patients receiving frontline fixed-duration bendamustine-rituximab (BR), a common chemoimmunotherapy for WM. At a median follow-up of 5.9 years, 5-year PFS and OS were 65% and 87%, respectively; 5-year PFS was similar between MYD88L265P (90%) and MYD88wild-type (WT) subcohorts (64% each; P = .4). Among 89 patients with known CXCR4 status, the subcohort with CXCR4mutation (28%) had shorter PFS (median 3.3 vs 8.8 years; hazard ratio [HR], 2.8; P = .0036) and OS (HR, 2.6; P = .036) compared with CXCR4WT. POD24 occurred in 11.5% of patients who demonstrated inferior subsequent OS (5-year OS, 71% vs 86%; HR, 3.1; P = .005) and higher mortality (standardized mortality ratio [SMR], 3.7), unlike the non-POD24 group, whose mortality was comparable to the matched general population (SMR, 1.1). In conclusion, BR is effective irrespective of the MYD88 status, but CXCR4 mutations and POD24 portend worse outcomes. Patients without POD24 represent a cohort with distinctly favorable outcome.

Abstract Older adults with dementia account over 15% of intensive care unit (ICU) admissions and face higher risks of functional decline and complications. We systematically evaluated the difference in ICU-related outcomes in patients with and without dementia, (e.g., mortality, resuscitation and code status decisions, complications, duration of life support and hospital stay). Searches were performed in MEDLINE, EMBASE, SCOPUS, and Web of Science between 2003 and 2024. A random-effects meta-analysis was done of odds ratios (OR) and mean differences (MD) and their corresponding 95% confidence intervals (CI) were calculated. Fifteen studies, including 1,231,037 patients (12% with dementia), met the inclusion criteria. Adjusted analysis showed that patients with dementia had a higher risk of ICU mortality (OR = 1.05, 95% CI 1.00–1.11), 30-day mortality (OR = 1.54, 95% CI 1.47–1.62) and 12-month mortality (OR = 1.95, 95% CI 1.88–2.02). They were less likely to receive mechanical ventilation (OR = 0.73, 95% CI 0.62–0.85, I2 0.0%) and more likely to develop delirium (OR = 4.80, 95% CI 1.81–12.72). In patients with dementia, ICU stays were slightly shorter (MD = –1 day, 95% CI –1.7 to –0.4), and discharge home was less common (OR = 0.40, 95% CI 0.39–0.42). Older adults with dementia experienced greater risk of mortality and delirium, alongside less aggressive intervention use and healthcare utilization (i.e., shorter ICU stays). These findings underscore the need for prospective studies to optimize ICU decision-making and post-discharge care for this vulnerable population.

Abstract Background Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an “AI label feedback loop” in a clinically implemented AI-augmented kidney stone composition test. Methods An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. Results V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. Conclusions The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.

Abstract Background and Aims Celiac disease (CeD) is a chronic digestive autoimmune disorder affecting approximately 1% of the worldwide population. It is driven by T cells activated by specific HLA-DQ2 or HLA-DQ8 molecules leading to the destruction of intestinal villi. We aimed to characterize shared CeD-specific T cells in patients on and off a gluten-free diet (GFD) from a large cohort of cases and controls. Methods We performed bulk TCRβ immune sequencing of the peripheral blood of 1,604 biopsy-confirmed CeD patients (1,339 on GFD, 265 on normal diet) and 1,100 controls. We identified over 300 TCRβs enriched in CeD cases versus controls in an HLA-aware manner, controlling for CeD risk alleles. Results CeD-associated TCRβs were found to be more predictive of disease than previously characterized gliadin and glutenin-binding TCRs in a validation cohort. Furthermore, the clonal breadth of these TCRβs was associated with increased intestinal damage. Immune sequencing of the peripheral blood also uncovered repertoire-level differences between CeD patients and controls. CeD patients displayed significantly higher productive clonality compared to age-matched controls as well as expansion of TCRβs specific to cytomegalovirus (CMV) and Epstein-Barr Virus (EBV). Conclusions These findings underscore the value of unbiased immune repertoire sequencing to identify novel biomarkers for autoimmune disease and to discover new disease mechanisms which can improve both diagnosis and treatment of disease.

Abstract Background Alzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (A time ) based on longitudinal PET data. Method We evaluated A time in a longitudinal ADAD sample ( N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both A time and EYO. Result We observed a mean average error of 1.15 years between actual age at A+ ( N = 26) and the SILA‐predicted A time . Across all participants, SILA‐estimated age at A+ explained 39% of the variance in estimated age at symptom onset ( β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both A time and EYO. A time explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance. Conclusion SILA produces a valid estimate of time‐from‐amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.

Abstract Background Apolipoprotein E (APOE), the major genetic risk factor for Alzheimer's disease (AD), has three common alleles (2, 3 and 4), giving rise to genotypes that are associated with six levels of AD risk: (2/2<2/3<3/3<2/4<3/4<4/4). In our new Arizona APOE Cohort Study, we have begun to establish a longitudinal cohort of more than 300 cognitively unimpaired persons (50‐90 year‐old) stratified by APOE genotype and age decile, matched by age and sex, and have an extensive battery of cognitive, clinical, amyloid (NAV4694) and tau (MK6240) PET, MRI, cerebrospinal fluid (CSF), plasma, and DNA. The study is intended to clarify the impact of APOE4 and APOE2 allelic dose on the predisposition to and protection from AD and inform the design of secondary and primary prevention therapies. Here, we describe the study and the resource of data and samples that are now available to researchers. Methods Participants were recruited from across the United States using GeneMatch and other sources. Comprehensive clinical assessments, brain images, and lumbar punctures are performed every 2 years, 50mL blood samples are acquired annually, and whole genome sequencing is in progress. Results As of January 2025, baseline assessments were completed in 303 participants, 68±9 years of age, including 205 females and 98 males. Sixteen percent are from underrepresented groups. Baseline CSF samples were obtained in 89% of the participants. The complete cohort consists of 17, 63, 41, 65, 66, and 51 persons with the APOE 2/2, 2/3, 3/3, 2/4, 3/4, and 4/4 genotype, respectively. In the first 287 participants with available amyloid PET scans 6% of 2/2, 5% of 2/3, 14% of 3/3, 26% of 2/4, 29% of 3/4, and 52% of 4/4 participants had a positive amyloid PET (≥20 centiloids). Conclusions Building up on our decades of experience in APOE research, we launched the new Arizona APOE Cohort which promises to provide a valuable resource of data and samples to advance the study of APOE, its variants and interactions with other risk and protective factors, and the development, treatment, and prevention of AD. Baseline data are now available to interested and eligible research collaborators.

Abstract Background Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impacts (RHI). Autopsy studies show that RHI influences risk for other later‐life neuropathologies, particularly white matter and vascular injury. We have observed a unique pattern of white matter hyperintensities (WMH) on FLAIR MRI in people exposed to RHI (RHI‐WMH) characterized by small, discrete lesions near the depths of sulci (Figure 1a). The objective of this study was to characterize this pattern empirically in former American football players and to investigate neuropathology co‐localizing with the WMH. Method The sample included 164 male former football players ( n = 111 professional, n = 53 college) and 55 asymptomatic males without RHI from the DIAGNOSE CTE Project (Table 1). Visual ratings of WMH blind to the participant's RHI and clinical status were conducted to count lesions that were a.) within 1.0cm of the gray/white matter boundary, b.) as bright as the cortex, c.) spherical and d.) <1.0cm in size. ANCOVA compared groups on total number of WMH adjusted for age and vascular risk factors. We selected one individual (former professional player) with the typical RHI‐WMH pattern and who donated their brain for neuropathological examination. Guided by in vivo MRI, ex vivo MRI was done to locate the WMH near the depths of the sulci in the tissue. Tissue sections were tested for phosphorylated tau immunoreactivity and reactive astrocytes and microglia in areas surrounding the lesion seen on MRI. Result Number of RHI‐WMH was greater in football players compared to controls ( p = 0.007) (Figure 1b). Number of RHI‐WMH was greater in former professional players compared to controls ( p = 0.001) but not compared to the college players ( p = 0.983). In the neuropathology case study, we located the punctate RHI‐WMH on ex vivo MRI. Immunostaining revealed that the RHI‐WMH was adjacent to a sulcus with the pathognomonic CTE p ‐tau lesion and was characterized by myelin pallor, dense GFAP‐positive glial scarring, and increased total and activated microglia (Figure 2). Conclusion We provide empirical support for exposure to RHI as potentially leading to a unique pattern of WMH. Importantly, histopathological evidence showed that the RHI‐WMH co‐localized with RHI and CTE‐related neuropathologies at the depths of the sulci.