Citations

Abstract Background In vivo biomarkers that can detect long‐term neuropathologies from repetitive head impact (RHI) exposure are needed, especially for the neurodegenerative tauopathy chronic traumatic encephalopathy (CTE). Here, we evaluated plasma p ‐tau217 as a potential biomarker for CTE p ‐tau pathology, and examined the concordance between plasma p ‐tau217 and Aβ pathology in an at‐risk for CTE sample. Method The sample included 180 male former football players (120 professional, 60 college), and 56 asymptomatic men without RHI (i.e., controls). Participants completed blood draws, 18F‐florbetapir (Aβ+=SUVR≥1.10), and 18F‐flortaucipir PET. Traumatic encephalopathy syndrome (TES) diagnoses were made. Single molecule array for plasma p ‐tau217 (ALZpath) was performed (≥0.6 cutoff used to maximize sensitivity). Nine participants had post‐mortem tissue. ANCOVA examined group differences in p ‐tau217 (football vs controls; TES‐CTE no, TES‐CTE suggestive, TES‐CTE possible/probable). Multivariable regression models tested associations between p ‐tau217 and florbetapir/flortaucipir PET. Covariates included age, race and APOE e4 . Result Sample characteristics are in Table 1. p ‐tau217 concentrations were higher in former football players compared to controls (est. marginal mean difference=‐0.217, p = 0.005). There were no group differences in Aβ‐PET SUVR. No differences were found across TES‐CTE certainty levels. In football players, higher p ‐tau217 was associated with higher Aβ‐PET SUVR (B=1.380, 95%CI[0.597‐2.155], p = 0.001) but not when Aβ+ ( n = 17) participants and those with kidney/liver disease ( n = 5) were excluded. Aβ+ participants had the highest p ‐tau217 (Figure 1). When compared against Aβ‐PET, several false Aβ‐positives (high p ‐tau217, Aβ‐) were identified, including one extreme outlier (assay related) and a cluster of Aβ‐ participants with p ‐tau217 between 0.60–1.0. There were no associations with flortaucipir SUVR (frontal, mesial temporal, left parietal). Two extreme p ‐tau217 outliers had autopsy‐confirmed CTE stage III (AD‐, Table 2). Of the remaining donors, all were AD‐ and four had CTE (stages II‐IV) with ptau217 between 0.125‐0.449. Conclusion Plasma p ‐tau217 has usefulness in quantifying Aβ pathology but restricted utility for detection of CTE. In this at‐risk for CTE sample, p ‐tau217 and Aβ‐PET were associated at the group level. At the individual level, false Aβ‐positives (and negatives) existed, including Aβ‐ participants with high p ‐tau217. We will explore whether this discrepancy is due to disease or peripheral interference with the N‐terminal binding in p ‐tau assays.

Abstract Background Plasma neurofilament light chains (NfL) and glial fibrillary acidic protein (GFAP) are blood‐based biomarkers of axonal injury and astrocytic activation, respectively, that are elevated in those with Alzheimer's disease (AD) and mild cognitive impairment (MCI) relative to cognitively unimpaired (CU) individuals. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method capable of evaluating tissue microstructure at the axonal and dendritic level. We evaluated associations between temporal cortex orientation dispersion index (ODI), neurite density index (NDI) and isotropic volume fraction (ISOVF) and plasma NfL and GFAP concentrations. Method We included Mayo Clinic Study of Aging (MCSA) participants (ages 60‐95 years) with diagnoses or CU ( n = 356) or MCI ( n = 60), and Alzheimer's Dementia Research Center (ADRC) participants with dementia ( n = 51). NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. NfL and GFAP were measured using the Simoa® Neurology 4‐Plex E Advantage kit. We fit separate linear regression models within each MCALT_ADIR122 cortical ROI with regional NODDI as the primary predictor and log‐transformed plasma concentration as the outcome. Models were adjusted for age, sex, BMI, cortical thickness (or hippocampal & amygdalar volume) and total intracranial volume. Result The Table displays demographic data. Figures 1 and 2 show the relationship of NDI and ISOVF measures to plasma NfL concentrations across participants in individual ROIs (similar findings were noted with GFAP). A significant inverse relationship between NfL concentrations and NDI was observed in hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus as well as all temporal neocortical regions except the superior temporal gyrus ( p <0.001) (Figure 1), whereas higher NfL concentrations were associated with higher ISOVF in all regions ( p <0.001) (Figure 2). Fewer significant associations were also seen with ODI. After adjusting for age, cortical thickness/volume and other confounds, significant associations with NDI remain, particularly in hippocampal and amygdalar regions. Conclusion NODDI‐derived measures (NDI and ISOVF) within the temporal lobe cortical regions demonstrate significant associations with plasma biomarkers of axonal and astrocytic dysfunction that are elevated during cognitive decline. This association provides preliminary validation of the utility of NODDI in assessing cortical microstructural changes associated with cognitive decline.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.

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.

Objective: The Society of Critical Care Medicine PICU Liberation ABCDEF (A–F) Campaign Collaborative aimed to implement best practices for analgesia (A), breathing (B), comfort/consciousness (C), delirium (D), early mobilization (E), and family engagement and empowerment (F). Our post hoc study of Mayo Clinic data sought to compare the pre-implementation rounding process, which used a nursing script organized by organ systems, with a novel, liberation-focused rounding process emphasizing A–F bundle elements. Design: Secondary analysis of pre- vs. post-implementation surveys collected by independent observers during rounds. Setting: Medical/surgical PICU in a quaternary-care, academic medical center during April to July 2017. Subjects: The observed participants included PICU physicians, nurses, respiratory therapists, pharmacists, and dietitians. Interventions: None. Measurements and Main Results: Survey responses from 145 team members pre-implementation and 67 post-implementation showed improved team satisfaction, communication, and understanding of daily plans (all p < 0.05). Respondents reported an increased frequency of discussions on bundle components A–E (all p < 0.01). Observations of 41 pre-implementation and 46 post-implementation rounds showed an increase in the frequency of discussion of all components of the bundle (all p < 0.01). However, this finding was not at the expense of increased duration of individual rounds since we failed to find a difference in pre- vs. post-implementation duration per patient (480 vs. 544 s per patient, p = 0.20). Conclusions: In 2017, around the time of introducing the nurse-led PICU Liberation rounding process to our practice, we found that after implementation, there was an associated increase in discussions about bundle elements and improved communications and satisfaction in team members.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Small Cell Lung Cancer provide recommendations for diagnostic workup, staging, and treatment. These NCCN Guidelines Insights highlight recent updates, with a particular focus on changes to systemic therapies and radiation treatment in the NCCN Guidelines for Small Cell Lung Cancer.

The treatment landscape for multiple myeloma (MM) is evolving rapidly and includes several new options. The management must be tailored to the individual. The NCCN Guidelines for MM provide a framework to base decisions regarding diagnosis, workup, treatment, and follow-up of both newly diagnosed and previously treated relapsed/refractory MM. This manuscript outlines the recommendations from NCCN Guidelines for MM specific to newly diagnosed MM (NDMM).

Abstract Background The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution. In typical (memory‐predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicenters. However, atypical (non‐amnestic‐predominant) AD variants with heterogeneous tau patterns provide a key opportunity to assess the universality of connectivity as a scaffold for tau progression. Method We included tau‐PET data from 320 subjects with atypical AD, characterized by highly heterogeneous tau patterns ( n = 139 posterior cortical atrophy/PCA‐AD; n = 103 logopenic variant primary progressive aphasia/lvPPA‐AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS‐AD) from 14 sites, with a subset of patients ( n = 78) having longitudinal tau‐PET data. As an independent sample, we further included regional post‐mortem tau stainings from 93 atypical AD patients from two sites ( n = 19 PCA‐AD, n = 32 lvPPA‐AD, n = 23 bvAD, n = 19 CBS‐AD). Gaussian mixture modeling was used to harmonize different tau‐PET tracers by transforming tau‐PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether 1) brain regions with stronger functional connectivity showed greater covariance in cross‐sectional and longitudinal tau‐PET and post‐mortem tau pathology, and 2) functional connectivity of tau‐PET epicenters and tau‐PET accumulation epicenters was associated with cross‐sectional and longitudinal tau patterns. Result Tau‐PET epicenters—defined as the 5% brain regions with the highest tau load—aligned with clinical variants, e.g. a posterior pattern in PCA‐AD (“visual AD”) and left‐hemispheric temporal predominance in lvPPA‐AD (“language AD”) (Figure 1). More strongly functionally connected regions showed correlated concurrent tau‐PET levels, which was confirmed with post‐mortem data (Figure 2). Moreover, the connectivity profile of tau‐PET epicenters and accumulation epicenters corresponded to tau‐PET progression patterns (Figure 3). Conclusion Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicenters, across all AD clinical variants. Since tau proteinopathy is a key driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant‐specific endpoints in clinical trials.

Abstract Background The number of cancer survivors aged older than 65 years is rising rapidly. Current evidence‐based exercise guidelines lack specific guidance for older cancer survivors as a result of insufficient evidence. An expert panel was convened to develop consensus‐based recommendations for exercise in older cancer survivors. Methods The development of recommendations was guided by the Grading of Recommendations Assessment, Development, and Evaluation Evidence‐to‐Decision framework for good practice statements. The panel drew from the available literature, a Delphi survey of exercise and health professionals, other exercise guidelines, clinical and research expertise, and interest‐holder input provided by a community advisory board of older cancer survivors and caregivers ( n = 11). Recommendations had to be deemed accessible (i.e., no added barriers) to older cancer survivors and feasible to implement. The panel voted on the strength of the recommendation for or against each statement, with consensus set at 85% agreement. Results Consensus was reached on 11 recommendations covering the following areas: medical evaluation/clearance for exercise, pre‐exercise assessment, exercise prescription, exercise tolerance and safety, exercise delivery, and behavioral support. The recommendations aimed to promote engagement in and uptake of appropriately prescribed exercise programming by older cancer survivors, while keeping barriers and risks as low as possible. Conclusions Older cancer survivors can benefit from appropriately prescribed exercise, which should be an essential component of their cancer care. Exercise and health professionals need to consider the unique needs of older cancer survivors to ensure that exercise is safe and effective for this population, while also reducing barriers to reach as many people as possible.

Abstract Background The incidence of early onset breast cancers (BCs) has increased, paralleling rising trends in delayed childbearing. We hypothesize that a distinct postpartum BC (PPBC) subtype, identifiable by time since last birth (TSLB) and biomarker expression, contributes to this trend. Methods We applied GeoMx Digital Spatial Profiling (DSP) to measure associations between TSLB and 71 proteins in 640 BCs from women aged ≤40 years included in the Young Women’s Breast Cancer Study. We analyzed data using univariable linear regression and multivariable sliced inverse regression to account for higher order interactions among biomarkers. Results In keratin-rich segments, progesterone receptor (PR) (P = 1.00 × 10−4) and PTEN (P = 1.00 × 10−3) were associated with longer TSLB; multivariable analyses revealed positive associations for GZMB (P = 4.00 × 10−4), SMA (P = 1.00 × 10−4), and NF-1 (P = 1.00 × 10−3). In keratin-poor segments, univariable significant positive associations were found for PR (P = 2.00 × 10−4) and PTEN (P = 1.00 × 10−3), whereas CD20 (P = 3.00 × 10−4) and CTLA4 (P = 4.00 × 10−5) were negatively associated; multivariable significant associations were found for fibronectin (P = 3.00 × 10−5) and pan-Akt (P = 1.00 × 10−3). Associations persisted after adjustment for multiple comparisons and BC molecular subtypes. Associations including for PR, PTEN, and CD20 were strongest among women with the shortest TSLB. OPAL multiplex immunofluorescence assays for PR, PTEN, CD20, SMA, and CTLA4 replicated several DSP findings, particularly when stratified by subtype and with compartment matching. In TCGA, RNA species linked to proteins associated with TSLB correlated strongly with a T-cell exhaustion signature previously linked to poor prognosis among premenopausal women. Conclusion These data support PPBC as a biologically coherent phenotype defined by TSLB and biomarker profile, with potential implications for prevention and therapy.