Citations

Abstract Cerebrospinal fluid (CSF) is the biofluid most proximal to the central nervous system (CNS), yet its proteomic organization across neurological disease remains unresolved. We assembled a meta-atlas of 35 CSF proteomic datasets (2016–2026) comprising 10,560 quality-controlled samples spanning neurodegenerative, neuroinflammatory, neuro-oncological, infectious and structural-injury contexts. Meta-analysis of cross-cohort detection defined a 747-protein core mass-spectrometry-accessible CSF proteome that generalizes to held-out cohorts. We derived thirteen consensus meta-programs that transfer across independent cohorts and span the major neural, barrier, immune and plasma compartments. Unexpectedly, these programs are dominated by a single axis of variation opposing complement- and plasma-associated inflammation against neuronal-synaptic homeostasis. This injury–homeostasis axis is diagnosis-spanning and tracks clinical severity and disease activity across diverse neurological conditions. We show that a two-protein C5–NRCAM model can approximate the injury–homeostasis axis, bridging deep proteomic profiling toward bedside assessment.

Abstract Background Cardiac papillary fibroelastoma (PFE) is the most common benign primary cardiac tumor. Despite their benign pathology, PFEs pose a significant embolic risk, which can lead to severe neurological sequelae. However, subclinical or silent emboli often go undetected, presenting a diagnostic challenge. This study aims to assess the prevalence of silent emboli in patients with pathology-proven PFEs using brain magnetic resonance imaging (MRI). Methods We conducted a retrospective review of 385 cases of pathology-confirmed PFEs. Inclusion criteria was those with brain MRI performed within 5 years of surgical resection of the PFE and no clinical neurological events. Patients with a history of symptomatic cerebral infarction (stroke or TIA) were excluded. Patient demographics and PFE imaging features were analyzed by two cardiologists experienced in cardiac tumors. Clinical neurological data, and MRI findings were analyzed by two neurologists with expertise in cerebrovascular disease. Results Out of 385 patients, 132 underwent brain MRI. In the entire cohort, 105/385 patients (27%) experienced clinically overt embolic events. After excluding patients with symptomatic cerebral infarctions and MRI scans older than 5 years, 27 patients (mean age: 71±9.6, gender: 66% female) met the inclusion criteria. Of these, 8 patients (30%) showed signs of silent infarctions on MRI, with 4 (15%) displaying findings consistent with cerebral infarction highly suggestive for embolic source. Conclusion This study underscores the substantial risk of silent cerebral embolic events in patients with PFE. The limited availability of MRI data, largely attributable to the absence of standardized neuroimaging protocols, indicates that the true prevalence of silent embolization in this population is likely underestimated. Given the propensity of PFE to generate clinically silent emboli, systematic cerebral MRI should be strongly considered as part of the diagnostic assessment of patients with cardiac masses, irrespective of neurological symptoms. Early identification of subclinical cerebrovascular lesions may facilitate timely therapeutic decision-making and secondary prevention strategies, thereby reducing the risk of future symptomatic and potentially disabling ischemic strokes.

Summary Iron deficiency (ID) is often overlooked in patients with haematological malignancies due to other potential aetiologies. Daratumumab, an anti‐cluster of differentiation 38 (anti‐CD38) monoclonal antibody, is commonly used for treating multiple myeloma (MM) in the upfront and relapsed/refractory settings. Although anaemia is a common haematological toxicity associated with daratumumab, the contribution of ID in MM patients has not been systematically investigated. We conducted a retrospective case–control study to evaluate the prevalence of ID in newly diagnosed MM (NDMM) patients. Cases were patients receiving daratumumab, ixazomib, lenalidomide and dexamethasone (Dara‐IRD, n = 55). We compared this group with an age‐ and sex‐matched cohort of patients with NDMM who received bortezomib, lenalidomide and dexamethasone (VRD, n = 20) as induction therapy. At 1‐year, nearly 50% of Dara‐IRD‐treated patients met the criteria for ID (ferritin <30 mcg/L) compared with none of the VRD‐treated controls. All Dara‐IRD‐treated patients had a decrease in ferritin at 1‐year from baseline compared with only 45% of VRD‐treated patients. These findings identify a marked clinical signal of ID among NDMM patients receiving daratumumab‐based treatment. Although the study design does not establish causality or exclude regimen‐related confounding, it highlights the need for increased awareness of the importance of monitoring iron status in MM patients treated with daratumumab‐containing regimens.

Abstract INTRODUCTION Although cerebrospinal fluid (CSF) biomarkers reflect neurodegeneration in Alzheimer's disease (AD), it remains unclear whether these biomarkers track neurodegeneration in early‐onset Alzheimer's disease (EOAD). METHODS In 80 EOAD patients, we examined correlations between eight CSF biomarker levels and cortical thickness decreases within the EOAD cortical atrophy signature. Significant correlations were then entered into a multiple regression analysis. We also examined contributions of EOAD atrophy and CSF biomarkers to cognitive impairment. RESULTS The levels of four CSF biomarkers correlated to EOAD signature atrophy. Stepwise regression analyses revealed that CSF neurofilament light chain (NfL) levels best predicted EOAD signature atrophy. Multiple regression showed EOAD signature atrophy combined with CSF NfL levels explained more variance in cognitive impairment than either factor alone. DISCUSSION Within EOAD patients, CSF NfL levels relate to the magnitude of cortical atrophy, and a combination of EOAD signature atrophy and CSF NfL levels most robustly predict cognitive impairment.

Glioblastoma is the most common form of brain cancer in adults and remains one of the most lethal forms of cancer. Treating glioblastoma is uniquely challenging due to its aggressive nature and its critical location, the brain, which must remain largely undamaged. Due to these difficulties, there is a lack of effective treatment options. Computer models simulating tumors and their response to therapy, known as digital twins, provide a promising new strategy. These model systems are mathematical equations that capture the biological knowledge of how cells interact during tumor growth and in response to treatment. By solving these equations, we can create virtual patients that provide accurate predictions of real patients’ tumor progression. This approach has the potential to improve outcomes by designing therapies for the specific needs of each patient, moving beyond the limitations of the “one-size-fits-all” approach that is currently used.

Objectives To understand the trade-offs between different statistical modeling approaches, using real world data with small sub-populations, with rare exposures and increasingly rare outcomes. In particular, to compare adjusted regression, inverse probability weighting, and matching. Methods Data for these analyses came from the RADAR (N=1,134) and combined CNICS/JHHCC (N=14,434) cohorts. We estimated prevalence ratios (PRs) for self-reported use of specific substances comparing subpopulations (SP), SP-1 vs SP-3 and SP-2 vs SP-3, where SP-1 was the largest proportion (92% in RADAR, 81% in CNICS/JHHCC), SP2 was moderate proportion (18% in CNICS/JHHCC) and SP-3 was the smallest proportion (8% in RADAR, 1% in CNICS/JHHCC) of the population. We calculated PRs using 1) unadjusted relative risk regression (RR); and adjusted estimates controlling for age, race/ethnicity, study site, and year of interview using: 2) standard adjustment in RR; 3) stabilized inverse probability of treatment weighting (IPTW); and 4) matching with up to 3 matches from SP-1 or SP-2 per SP-3 participant. Results For most substances, all methods yielded consistent estimates. There were large weights in some of the IPTW analyses and in three cases these resulted in substantially divergent estimates. For the comparison between SP-1 and SP-3, the estimate for smoking was 1.3-fold greater in the matched analysis (PR=1.33, 95% CI: 1.02-1.75) than in IPTW (PR=1.03, 95% CI: 0.78-1.37 ATE and PR=1.05, 95% CI: 0.88-1.26 ATT). Even more extreme divergence in estimates was observed for differences between SP-2 and SP-3 with respect to methamphetamine/amphetamine, (IPTW-ATE: PR=1.51, 95% CI: 0.79-2.89; IPTW-ATT: PR=2.36, 95% CI: 1.36-4.12); vs Matching: PR=2.71, 95%CI: 1.47-4.99) and cocaine (IPTW-ATE: PR=1.15, 95% CI: 0.55-2.38; IPTW-ATT: PR=1.81, 95% CI: 1.03-3.18); vs Matching: PR=1.38, 95%CI: 0.76-2.53). Conclusion The combination of a rare exposure and a rare outcome can produce challenges for commonly used confounding adjustment strategies, and it is often best to compare different modeling approaches to gain greater insight.

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

Importance Myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) lacks proven relapse-preventive therapies. While clinical trials are ongoing, safety data may be limited and approved drugs are costly. Studies of interleukin 6 receptor blocker (IL-6RB) in MOGAD are limited by small numbers and no comparative studies, contributing to low use. Objective To evaluate the impact of IL-6RB therapy on relapse rates in MOGAD and compare relapse frequency with intravenous immunoglobulin (IVIG). Design, Setting, and Participants This international, multicenter, retrospective cohort study (January 1, 2015, through December 31, 2025) included a historical IVIG-treated cohort of varying doses. The study took place across sites in North and South America (US, Canada, Mexico, Argentina, Brazil, Chile, Colombia, and Peru). Patients with MOGAD (n = 116, no excluded patients) who received at least 1 dose of an IL-6RB were included. These data were analyzed in January 2026. Exposures Tocilizumab or satralizumab. Main Outcomes and Measures Annualized relapse rate (ARR) during IL-6RB therapy, time to next relapse after treatment initiation, and adverse events. Outcomes were compared with the IVIG cohort using inverse probability of treatment weighting (IPTW) adjusted for age, sex, prior ARR, and concomitant therapies. Results A total of 116 patients with MOGAD (89% relapsing) receiving IL-6RB (tocilizumab, 104 [90%] and satralizumab, 12 [10%]) were included; overall, 60.3% were female, 39.7% were male, and 18% were younger than 18 years. The median (IQR) IL-6RB treatment follow-up was 1.4 (0.7-2.5) years and 23 relapses occurred during 241.8 person-years of IL-6RB therapy. The ARR decreased from 0.64 (95% CI, 0.58-0.70) for relapsing MOGAD before IL-6RB to 0.09 (95% CI, 0.06-0.14) during IL-6RB treatment (incidence rate ratio, 0.08; 95% CI, 0.04-0.16). Adverse events occurred in 58 patients (50%), most commonly mild infections, although 10 (9%) had severe infections. In the IVIG cohort (n = 59), 30 relapses occurred over 133.8 person-years (ARR, 0.22; 95% CI, 0.15-0.32). After IPTW, IL-6RB was associated with a lower hazard ratio (HR) than the group who underwent IVIG therapy less than 1 g/kg every 4 weeks (HR, 4.5; 95% CI, 2.0-9.8), with no significant difference vs the group who underwent IVIG 1 g/kg or more every 4 weeks (HR, 2.0; 95% CI, 0.8-4.5). Conclusions and Relevance In this multicenter observational cohort, IL-6RB use in MOGAD was associated with low relapse rates and a favorable safety profile, though severe infections occurred occasionally. Relapse rates were lower than the group who underwent IVIG less than 1 g/kg every 4 weeks but not significantly different from the group who underwent IVIG 1 g/kg or more every 4 weeks. This supports IL-6RB as a potential relapse-prevention therapy in MOGAD; the wide availability and relative affordability of tocilizumab may enable broad global use.