Citations

Abstract Background People living with HIV (PLHIV) are at increased risk of atherosclerotic cardiovascular disease (ASCVD), but the immunological mechanisms driving plaque formation remain unclear. This study investigated the association between peripheral immune cell subsets and carotid atherosclerotic plaques in PLHIV without prior clinical ASCVD. Methods In this multi-center cross-sectional study, virally suppressed PLHIV receiving antiretroviral therapy were enrolled from two Dutch cohorts: a discovery cohort (n = 994) and a validation cohort (n = 200). Participants underwent carotid ultrasound imaging to assess plaques using standardized criteria. Immune profiling was performed using high-dimensional flow cytometry of 355 immune cell populations. Associations with plaque were analyzed using linear regression adjusted for relevant confounders, and key findings were validated in the second cohort. Transcriptomic profiles of main cell populations were evaluated using deconvoluted bulk RNA sequencing to identify differential expression and pathway enrichment. Results Carotid plaques were present in 584 participants (49%)—502 of 994 (51%) in the discovery and 82 of 200 (41%) in the validation cohort. Plaques were associated with higher counts of several CD8+ T cell subsets, particularly PD-1-expressing cytotoxic T cells with an interferon secretion profile (CD8TC1PD1+). Transcriptomic analysis of CD8+ T cells revealed downregulated mitochondrial function and upregulated type 1 interferon and epidermal growth factor receptor signaling, indicating a senescent phenotype. Conclusions CD8+ T cell senescence may contribute to early atherosclerotic plaque formation in PLHIV. Targeting immune senescence could offer a novel strategy for cardiovascular risk reduction in this population. Clinical Trials Registration ClinicalTrials.gov Identifier: NCT03994835

Abstract Secondary central nervous system (CNS) large B-cell lymphoma (SCNSL) occurs in the de novo setting, as a CNS-isolated relapse, or synchronous (concomitant CNS and systemic) relapse. SCNSL is a devastating event without therapeutic consensus. Thus, we aimed to evaluate treatment outcomes in an international cohort. Progression-free survival (PFS), overall survival (OS), and cumulative incidence of relapse (CIR, estimated using competing-risk models) were reported. Prognostic factors were identified in a 6-month landmark multivariate analysis. Outcomes after thiotepa autologous stem cell transplant (ASCT) and chimeric antigen receptor (CAR) T-cell therapy (CAR-T) delivered at relapse were compared after propensity score matching (PSM). A total of 1139 patients were included in the analysis (de novo: 537; relapsed SCNSL: 602). Two-year PFS estimates were 40.4%, 43.9%, and 16.2% for de novo SCNSL, CNS-isolated relapse, and synchronous relapse, respectively. Patients with CNS-isolated relapse demonstrated low rates of systemic recurrence (24-month CIR, 6%). Thiotepa-ASCT correlated with longer survival in de novo SCNSL (PFS: hazard ratio [HR], 0.57; P = .005; and OS: HR, 0.62; P = .023) and CNS-isolated relapses (PFS: HR, 0.55; P = .002; and OS: HR, 0.39; P< .0001). ASCT (thiotepa or no thiotepa) also associated with improved survival in synchronous relapses (PFS: HR, 0.57; P = .023; and OS: HR, 0.48; P = .019). Higher survival with thiotepa-ASCT than CAR-T was observed after PSM (PFS: HR, 0.45; P = .005 and OS: HR, 0.41; P = .014). These data support thiotepa-ASCT in eligible patients, particularly de novo disease and CNS-isolated relapses. CNS-isolated relapse was infrequently associated with systemic recurrence, supporting treatment regimens adopted from primary CNS lymphoma.

Abstract Background Aortic valve calcification (AVC), as measured by gold-standard computed tomography (CT) Agatston score, provides an anatomic assessment of aortic stenosis (AS) severity and is a key predictor of AS progression and need for valve replacement. AVC detection and quantification from transthoracic echocardiography (TTE) could expand AS early diagnosis and risk stratification, currently limited by CT availability and radiation exposure. Methods A multi-view video-based deep learning framework was developed using 1166 TTE aortic valve videos from 187 TTE studies acquired in 110 patients with available AVC score by CT. EchoAVC architecture includes a feature extraction model followed by a quality-aware model that aggregates video-level information to obtain patient-level predictions for AVC detection and quantification (score). The framework was validated internally with 173 TTE studies from 86 patients across seven centres, and externally using 430 TTE studies from 280 patients across four different centres. The associations between EchoAVC estimations and AS severity, progression, and need for aortic valve replacement were examined. Results EchoAVC demonstrated excellent performance in AVC detection (AUROC 0.98, accuracy 94.4%), and quantification (R = 0.64) in the external multi-centre testing set. EchoAVC score was correlated with echocardiographic AS severity descriptors, including aortic valve mean pressure gradient (ρ = 0.749) and peak velocity (ρ = 0.757), and predicted future increase in mean pressure gradient (ρ = 0.382), peak velocity (ρ = 0.433) and calcium score by CT (ρ = 0.650). In 361 patients followed for a median of 3.8 years, 139 underwent aortic valve replacement. Baseline presence and extent of AVC as predicted by EchoAVC showed strong risk-stratification power for aortic valve replacement, remarkably in line with those obtained by CT, and incremental over TTE AS descriptors. EchoAVC was further tested in routine clinical practice images, confirming strong associations with AS severity and progression, including stratification for incident AS in previously unaffected individuals (p<0.001). Conclusions EchoAVC enables accurate and non-invasive detection and quantification of AVC, offering substantial diagnostic and prognostic value for aortic stenosis progression and need for valve replacement. This technique holds promise as a scalable tool for early detection and clinical management of aortic valve stenosis. Clinical Perspective What Is New? * Aortic valve calcification can be detected and quantified on standard, two-dimensional transthoracic echocardiography * EchoAVC score is associated with echocardiographic metrics of aortic stenosis severity * EchoAVC predicts progressive increase in aortic stenosis severity and need for aortic valve replacement What Are the Clinical Implications? * This openly available deep learning framework can be used to estimate aortic valve calcium in patients at risk of or presenting aortic stenosis to predict aortic stenosis progression * EchoAVC may help identify patients likely to have high aortic valve calcium and prioritize them for confirmatory CT assessment, thereby supporting earlier identification of calcific aortic valve disease in clinical pathways.

Abstract There is debate on the predictive value of multiday seizure cycles versus simple statistical baselines. Multidien seizure cyclicity is a prevalent, patient‐specific phenomenon with promise for epilepsy management. We challenge the assertion that cycle tracking is no better than a moving average, which is an inherently retrospective model that lags changes in seizure likelihood. This commentary compared a causal cyclic forecast to a prospectively applied moving average across a large seizure diary cohort ( n = 768) and two gold‐standard chronic EEG cohorts ( n = 24). For the EEG and diary cohorts, cycle tracking demonstrated significantly superior accuracy to the moving average for both hourly and daily forecasts ( p < 0.0001), using multiple performance metrics. These results confirm that event‐based cyclical models offer more accurate, simulated real‐world forecasts. Robust forecasting tools must prioritize the detection and modeling of seizure cycles to move beyond simple baseline performance and provide actionable clinical utility.

ABSTRACT Despite advances in therapy, extramedullary disease (EMD) remains an aggressive form of multiple myeloma associated with poor outcomes. Patients with true EMD, in which plasmacytomas have become completely independent of bone, have a particularly poor prognosis. The pathogenesis of EMD is driven by complex mechanisms involving loss of adhesion molecules, heterogeneous genetic and epigenetic changes, and a solid tumor‐like architecture within the microenvironment. Although the introduction of advanced imaging techniques and immunotherapy has led to improved detection and more promising outcomes and regimens, respectively, more prospective studies dedicated to true EMD are needed.

Abstract Background Our group recently performed the largest‐to‐date X‐chromosome wide association study (XWAS) of Alzheimer's disease (AD). Here, we expand on this with additional samples, novel sex‐ and APOE *4‐stratified analyses, and evaluate escape from random X chromosome inactivation (eXCI) to better capture female‐biased risk genes. We then integrate these results with XWASs of various AD‐relevant traits to elucidate shared genetic signals and ultimately uncover novel AD risk genes and mechanisms. Method The AD XWAS design is shown in Figure 1A and includes random X chromosome inactivation (rXCI) and eXCI models. We further assessed suggestive AD signals ( p <1x10 ‐3 ) for pleiotropy using genetic colocalization with 2852 AD‐relevant traits (Figure 2A). Loci exhibiting X chromosome‐wide significance ( p <1x10 ‐5 ) or apparent trait colocalization underwent quantitative trait locus (QTL) colocalization analyses across 62 tissues and cell types to functionally prioritize AD risk genes. Result Full sample analyses reproduced Belloy et al. 2024 findings and identified 3 additional loci, including a male‐specific signal near GLUD2 that impacts glutamate/glutamine metabolism (Figure 1B). APOE *4‐stratified analyses revealed 9 additional loci, including 3 eXCI hits in APOE *4 carriers (Figure 1C‐D). Pleiotropy colocalization identified 38 loci, most being novel and many driven by eXCI and/or female‐specific AD XWAS, including MOSPD2, PNMA3 and GABRE loci that showed extensive colocalization with brain imaging traits (Figure 2B). The most significant XWAS locus, SLC9A7 , colocalized with total testosterone, HDL‐C, and various brain imaging traits. Only the NLGN4X locus colocalized with amyloid positivity, linking its proposed role in synaptic dysfunction to amyloid pathology. The locus at KDM6A , previously implicated as an AD resilience gene, colocalized with a functional MRI (fMRI) phenotype. QTL colocalization analyses confirmed gene prioritizations in Belloy et al. 2024, and additionally provided support for PNMA3 , an antigen highly expressed in the brain, and 3 APOE ‐stratified loci, including ACLS4 , which impacts lipid metabolism. Conclusion This study provides novel insights into biological mechanisms that contribute to X‐linked genetic risk for AD in an APOE *4 and sex‐specific manner, identifying various novel risk loci. Notably, 29 female‐biased risk loci were uncovered, which may shed light on sex disparities in AD. Overall, our findings will aid in elucidating AD pathways and drug targets relevant to personalized medicine.

Abstract Background The National Institute on Aging Alzheimer's Disease Family Based Study (NIA‐ AD FBS) is a multi‐site, longitudinal study aimed to be a data and bio sample resource for investigators worldwide. Since its inception in 2002 the FBS goal has been to promote greater cooperation and sharing of clinical and biological resources among researchers. Methods The focus of the Family Based Study (FBS) has been the recruitment of families with at least two affected individuals and a third first degree relative with or without dementia and willing to participate. Participants are from different ethnic backgrounds including Caucasian, African American and Hispanics. The FBS study is actively recruiting both late onset (LOAD) and early onset Alzheimer's Disease (EOAD) families as well as conducting follow up evaluations every two years, approximately. Uniform assessments are completed across all sites and include DNA, Plasma, PaXgene and PBMC samples collection and brain autopsies whenever possible. We conduct both in person and remote evaluations and use the services of a mobile phlebotomy company to collect bio samples. In addition, we’ve created a protocol for genetic testing on the proband for EOAD families where an additional sample is sent to a CLIA approved lab and after genetic counseling, the presence or not of AD related mutations is disclosed to the families. Results To date, this cohort has recruited 1,756 families and acquired data from 9,682 family members. Families are from Caucasian, African Americans and Hispanics ethnic groups. The cohort has longitudinal clinical data, cognitive assessment, family history and bio samples available for sharing. Genotype data includes APOE, GWAS, WES, WGS, brain methylation, RNA sequencing and biomarkers data. Conclusion The NIA‐LOAD FBS study is the largest collection of familial Alzheimer's Disease worldwide and many genetic studies of Alzheimer's disease (AD) have included cases and controls from this dataset. Over 140 publications have used data and/or samples from FBS to address the genetics of Alzheimer's Disease. The enriched resources provided by this cohort are invaluable to the scientific community.

Abstract Background The Aging and Cognitive Health Evaluation in Elders (ACHIEVE) randomized trial ( n = 977;ClinicalTrials.gov:NCT03243422) demonstrated that hearing intervention slowed 3‐year cognitive decline by 48% among a subgroup of participants. To further investigate differences by treatment with respect to cognitive benefit, we tested the hypothesis that hearing intervention is associated with improved neurodegeneration blood biomarkers 3‐years post‐randomization and assessed whether combining biomarkers and cognitive results improved power in the trial design. Method The ACHIEVE study is a multicenter, parallel‐arm, randomized trial (hearing intervention vs health education control) on 3‐year cognitive decline among adults 70–84years with untreated hearing loss and without substantial cognitive impairment. Participants were recruited from two populations: (1) a long‐standing cardiovascular health observational study (Atherosclerosis Risk in Communities [ARIC]), and (2) de novo community volunteers. Plasma was collected in year 3 in a subsample ( n = 540) while ARIC participants ( n = 164) also had baseline plasma. Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) neurodegenerative biomarkers were derived using the Alamar CNS protein panel. Regression models estimated the association of treatment with 3‐year biomarkers and global cognitive differences in the combined sample and by recruitment source and annualized change from baseline in the ARIC sample. We included a simple composite score of the standardized outcomes (Table 2). Result Among the subsample, baseline characteristics were balanced by treatment. There were no significant differences on 3‐year neurodegeneration biomarker levels by treatment in the combined (Table 1) or de novo groups (Table 2). Among ARIC participants, intervention resulted in lower 3‐year GFAP (mean:‐0.415;95%CI:‐0.712,‐0.118) and NfL (mean:‐0.349;95%CI:‐0.652,‐0.046) (Table 2). Annualized change from baseline analyses among ARIC participants (Table 3) revealed intervention was associated with a slower rate of cognitive decline (difference‐in‐means:0.059;95%CI:0.018,0.100) and a slower rate of increase in GFAP (difference‐in‐means: ‐0.060;95%CI:‐0.112,‐0.009). Differences in estimates suggest combined score, relative to cognition alone, may increase power in clinical trials that show a positive effect (ARIC subgroup: p = 0.02vs. 0.08[Table 2] and p = 0.0009vs.0.048[Table 3]). Conclusion Hearing intervention was associated with positive 3‐year effects on neurodegeneration blood biomarkers in ARIC participants which parallels cognitive decline results. Results provides objective evidence of brain changes following hearing intervention and potential signal of a more powerful combined outcome approach for future brain health clinical trials.

Regenerative medicine is transforming how we restore tissue function, leveraging advances in cell and molecular biology, biomaterials, and engineered microenvironments. While there have been notable advances and rapid progress over the past few decades, ongoing challenges persist in the technical development and effective translation of these advancements to clinical care. This perspective highlights clinically promising examples and critically assesses present challenges in translating tissue regenerative medicine therapies from the bench to the clinic. We further examine the evolving landscape of regenerative medicine by describing strategies to optimize the cellular microenvironment, the impact of patient demographics, and the use of artificial intelligence to shape the future of this field.