Citations

Background Rheumatoid arthritis (RA) is an HLA-DR associated disease with a pivotal role of T cells in the pathogenesis. The mechanisms underlying the HLA association and the generation of a synovial T-cell response are unclear. We have hypothesized that the selection of the primary T-cell repertoire is a predisposing factor for rheumatoid synovitis. Methods The repertoire of T-cell receptors (TCR) expressed by circulating naive CD4 + CD45RO − T cells was compared in 10 patients with RA, 11 HLA-DR matched normal donors and 10 mismatched normal donors by determining the frequencies of TCR BV-BJ combinations in 3 different BV gene segment families. Clonally expanded synovium-specific CD4 T cells were identified in 8 patients by TCR BV-BJ-specific PCR of purified T-cell subsets followed by size fractionation and sequencing of the PCR product. The TCR BV-BJ repertoires of naive peripheral T cells and of synovial clones were compared. Results The repertoires of naive circulating CD4 + CD45RO − T cells were different in RA patients and in HLA-DR matched and unmatched controls, suggesting HLA-DR as well as disease-specific features of T-cell selection. To test the disease relevance of the shifts in the naive repertoire, CD4 T cells undergoing joint-specific clonal expansion were identified. The usage of BV-BJ gene combinations in these synovium-specific clones was biased and significantly different from the expected distribution with a preference for combinations favored in the naive TCR repertoire of RA patients. Conclusions These data suggest that primary T-cell selection in RA patients is of functional importance for the generation of synovium-specific T-cell responses. The synovial repertoire is influenced by aberrations in the naive T-cell repertoire that might indicate a defect in thymic education with the selection of high-affinity self-reactive T cells in RA.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Objective To establish a framework for validating candidate biomarkers of cerebral small vessel diseases (SVD) associated with cognitive impairment and characterize individuals enrolled by the MarkVCID2 consortium under this framework. Methods Participants age 60 to 90 years were enrolled across 17 MarkVCID2 sites. Recruitment was targeted to enrich in cognitive symptoms (mild dementia, mild cognitive impairment, subjective cognitive decline), defined risk factors (diabetes mellitus, advanced hypertension), and Black/African American, White, and Hispanic/Latino subgroups. Enrolled participants underwent baseline visits that included cognitive testing, multimodal magnetic resonance imaging (MRI), and biofluid collection. Provisional risk for SVD‐related cognitive decline was estimated primarily by baseline cognitive symptoms plus SVD risk factors. Adjudicated risk status was estimated by cognitive symptoms plus presence of moderate‐to‐severe white matter hyperintensities, microbleeds, or lacunes on baseline MRI. Results MarkVCID2 enrolled 1883 individuals age 73.4 ± 7.5 years, 65.0% female, 24.2% Hispanic, and 27.1% non‐Hispanic Black. Among enrollees, 44.8% were provisionally designated high‐risk. After baseline MRI, 48.5% were categorized as adjudicated high‐risk status, with substantial recategorization both from low‐ to high‐risk (primarily because of MRI lesions without SVD risk factors) and high‐ to low‐risk (primarily suspected cognitive impairment at screening not confirmed by baseline testing). Interpretation MarkVCID2 baseline data indicate successful enrollment of diverse individuals enriched in factors associated with SVD‐related cognitive decline. Changes over 3 years of longitudinal follow‐up will be analyzed to validate the candidate biomarkers for 2 projected contexts of use: subject selection (identifying likelihood of future SVD progression) and study outcome (efficiently measuring SVD progression). ANN NEUROL 2026;99:449–458

Abstract Background Neuro-oncology education in American neurology departments is highly variable, with no clear guidelines on appropriate practices. To better understand its current state, we focus on the perspective of departmental Grand Rounds (GR), views of adult neurology residency program directors (ANRPD), neuro-oncologists, and residents. Methods First, we obtained GR series from academic years 2017–2018 and 2018–2019 via an online search and direct emails to neurology residency programs. Second, two online surveys were dispensed to ANRPD and neuro-oncologists. Third, a cohort of neurology residents completed surveys with pre/post-didactic tests. Results Neuro-oncology consisted of 7% (28/ 411) GR in 2017–2018 and 6% (29/463) in 2018–2019; approximately 20% of institutions had no neuro-oncology GR. Twenty neuro-oncologists and 25 of the 175 ANRPDs completed the surveys. Respondents thought 1-to-4 GR annually was adequate. Thirty-five residents completed the survey. Residents might consider neuro-oncology with more exposure (77%, 27/35) and a stronger knowledge base (57%, 20/35). After 8-hours of didactics, residents demonstrated significant (P = .019) pre-/post-test improvement. Conclusions Here, we begin to define the status of several aspects of neuro-oncology education: 1 in 5 academic institutions do not have any neuro-oncology GR lectures annually, and both ANRPDs and neuro-oncologists agree that 1-to-4 lectures annually are adequate. Residents overwhelmingly stated they would be more interested in neuro-oncology with more exposure, and a pilot study of 8 hours of didactic did show knowledge improvement. While these initial datasets require confirmation in larger studies, it suggests that small changes to the current state at some institutions could have a meaningful impact.

AbstractBackgroundAutosomal dominant Alzheimer disease (ADAD) is characterized by genetic mutations affecting the beta‐amyloid (Aβ) pathway. However, vascular and immune factors play important roles which are not completely understood. Understanding the function of the neurovascular unit (NVU) comprised of neurons, glial cells, and vasculature, at different disease stages appears ideal to developing and evaluating therapeutics. Omics approaches can inform NVU changes and their disease associations.MethodsCSF proteomic data using the Somalogic® platform was generated from 485 participants in the DIAN study, who also had PiB‐PET and MRI assessing Aβ burden, white matter hyperintensity (WMH), and cerebral microhemorrhage (CMH). From previously published AD studies, we identified 33 NVU‐associated proteins with corresponding Somalogic aptamers (Figure 1). We used Principal Component Analysis (PCA) on participants with complete data (n=262, 166 mutation‐carriers and 96 non‐carriers) to find groups of proteins associated with mutation status and disease stage as measured with estimated year to symptom onset (EYO), PiB‐PET, and clinical status. Linear‐mixed effect models and two‐part zero‐inflated negative binomial mixed models further evaluated the link between significant principal components (PCs) and WMH and CMH, respectively. Models accounted for age, APOE‐e4 status, sex, and education.ResultsThe first ten PCs explained >60% of the variance in NVU‐related protein levels (Figure 2A) with only PC5 (4.8%) associated with mutation*EYO, suggesting relationship with ADAD‐specific disease progression (***p<0.0001, Figure 2B). While asymptomatic PiB‐negative individuals were not significantly different from non‐carriers, asymptomatic PiB‐positive individuals had higher PC5 scores than non‐carriers and symptomatic individuals scored still higher (**p<0.005, ***p<0.0001, Figure 2C). The main proteins composing PC5 were: neurofilament light‐chain, angiopoietin‐2, SMOC1, LRP1, E‐selectin, and sTREM2, suggesting strong association with proinflammatory glial response from neuronal injury (Figure 2D). PC5 was also associated with WMH volumes (*p<0.05, Figure 3A) and with CMH count in participants with CMH (**p<0.005, Figure 3B).ConclusionsThis PCA approach indicates that some groups of NVU‐associated proteins are related with mutation status and disease stage, and that they are differentially associated with features of ADAD such as white matter integrity as measured by WMH volume. Weighted Correlation Network Analysis will be of interest to further understand these associations.Funding: K01AG080123, RF1‐AG044546, UF1AG032438

AbstractBackgroundAutosomal dominant Alzheimer disease (ADAD) is characterized by genetic mutations affecting the beta‐amyloid (Aß) pathway. However, vascular and immune factors play important roles which are not completely understood. Understanding the function of the neurovascular unit (NVU) comprised of neurons, glial cells, and vasculature, at different disease stages appears ideal to developing and evaluating therapeutics. Omics approaches can inform NVU changes and their disease associations.MethodsCSF proteomic data using the Somalogic® platform was generated from 485 participants in the DIAN study, who also had PiB‐PET and MRI assessing Aß burden, white matter hyperintensity (WMH), and cerebral microhemorrhage (CMH). From previously published AD studies, we identified 33 NVU‐associated proteins with corresponding Somalogic aptamers (Figure 1). We used Principal Component Analysis (PCA) on participants with complete data (n = 262, 166 mutation‐carriers and 96 non‐carriers) to find groups of proteins associated with mutation status and disease stage as measured with estimated year to symptom onset (EYO), PiB‐PET, and clinical status. Linear‐mixed effect models and two‐part zero‐inflated negative binomial mixed models further evaluated the link between significant principal components (PCs) and WMH and CMH, respectively. Models accounted for age, APOE‐e4 status, sex, and education.ResultsThe first ten PCs explained >60% of the variance in NVU‐related protein levels (Figure 2A) with only PC5 (4.8%) associated with mutation*EYO, suggesting relationship with ADAD‐specific disease progression (***p<0.0001, Figure 2B). While asymptomatic PiB‐negative individuals were not significantly different from non‐carriers, asymptomatic PiB‐positive individuals had higher PC5 scores than non‐carriers and symptomatic individuals scored still higher (**p<0.005, ***p<0.0001, Figure 2C). The main proteins composing PC5 were: neurofilament light‐chain, angiopoietin‐2, SMOC1, LRP1, E‐selectin, and sTREM2, suggesting strong association with proinflammatory glial response from neuronal injury (Figure 2D). PC5 was also associated with WMH volumes (*p<0.05, Figure 3A) and with CMH count in participants with CMH (**p<0.005, Figure 3B).ConclusionsThis PCA approach indicates that some groups of NVU‐associated proteins are related with mutation status and disease stage, and that they are differentially associated with features of ADAD such as white matter integrity as measured by WMH volume. Weighted Correlation Network Analysis will be of interest to further understand these associations. Funding: K01AG080123, RF1‐AG044546, UF1AG032438

Abstract Motivated by studies showing an association between beta blocker (BB) use and positive bone outcomes, a pilot randomized control trial was performed at the Mayo Clinic which randomized postmenopausal women to placebo, propranolol (40 or 80 mg twice daily), atenolol (50 mg/d), or nebivolol (5 mg/d) to determine changes in bone turnover markers (BTMs) and in BMD over 20 wk. Pharmacogenetic effects and microRNA-mediated mechanisms involving beta adrenergic receptor and related genes have previously been found. We sought to validate these effects and discover new candidates in an ancillary study to the pilot clinical trial. We genotyped all participants and performed microRNA (miRNA) sequencing at baseline and at 20 wk for 24 participants from the atenolol or placebo groups. We discovered several variants in ADRB1, ADRB2, and HDAC4 which showed significant pharmacogenetic effects with BMD at multiple sites and with BTMs. Our miRNA results showed a significant treatment effect for miR-19a-3p over time with atenolol use in the low-responder group compared to placebo. Overall, the longitudinal miRNA analysis showed a large number of miRNAs which were up-regulated over the trial in the low responders but not the high responders compared to placebo, of which miR-19a-3p was one example. Finally, we compared the response to atenolol treatment for cardiovascular traits (pulse and blood pressure) with the response for the bone resorption marker, C-terminal telopeptide, and found a largely independent effect. Our results have implications for personalized therapy and for understanding mechanisms of BB treatment effect on bone.

Aging is a prominent risk factor for numerous chronic diseases. Understanding the shared mechanisms of aging can aid in pinpointing therapeutic targets for age-related disorders. Chronic inflammation has emerged as a pivotal mediator of aging and a determinant in various age-related chronic conditions. Recent findings indicate that C-C motif chemokine ligand 2 and receptor 2 (CCL2-CCR2) signaling, an important physiological modulator in innate immune response and inflammatory defense, plays a crucial role in aging-related disorders and is increasingly recognized as a promising therapeutic target, highlighting its significance. This review summarizes recent advances in the investigation of CCL2-CCR2 signaling in cardiovascular and neural aging, as well as in various aging-related disorders. It also explores the underlying mechanisms and therapeutic potentials in these contexts. These insights aim to deepen our understanding of aging pathophysiology and the development of aging-related diseases.