Journal of Thrombosis and Haemostasis

Anti-{beta}2-glycoprotein I (anti-{beta}2GPI) antibodies are central to the pathogenesis of antiphospholipid syndrome (APS), an autoimmune disease characterized by a strong predisposition to venous thromboembolism (VTE). In this study, we conducted a multi-ancestry genome-wide association study (GWAS) of quantitative total anti-{beta}2GPI levels in 5,969 participants enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) and identified a genome-wide significant association at the APOH locus. Paradoxically, genetically determined increases in anti-{beta}2GPI levels at this locus were associated with lower VTE risk. Fine-mapping and functional genomics prioritized the missense variant rs1801690 (W335S) in {beta}2GPI (apolipoprotein H, [APOH]) as the most likely causal variant. This variant has an allele frequency of 5-6% in European and East Asian ancestries but only 1% in African ancestries. Integrating prior experimental studies, molecular dynamics simulations and structure-based epitope prediction, we propose a dual-effect mechanism whereby W335S reduces thrombotic risk by disrupting phospholipid binding in Domain V, yet increases autoantibody production through conformational changes that enhance epitope exposure in Domains I and II. These findings mechanistically uncouple autoantibody formation from thrombotic risk in carriers of the W335S variant, and suggest that APOH genotype may represent a clinically relevant genetic biomarker with potential utility for thrombotic risk stratification in anti-{beta}2GPI-positive individuals.

ObjectivePost-thrombotic syndrome (PTS), a common complication of deep vein thrombosis, lacks objective diagnostic biomarkers and its molecular mechanisms remain poorly understood. This study aimed to identify plasma biomarkers and clarify pathways using integrated multi-omics and machine learning. MethodsProteomic and metabolomic profiling of 75 PTS patients and 75 controls was performed. Differential expression analysis, pathway enrichment, and protein-metabolite network analysis were conducted. A multi-algorithm machine learning with 8 feature selection methods prioritized biomarkers. Validations and 14 models were assessed. Results1,104 proteins and 1,891 metabolites were differentially expressed. Citrate cycle and unsaturated fatty acid biosynthesis were enriched. Three proteins, namely DIP2B, KNG1, and SUCLG2, were consistently selected as core biomarkers. All of these proteins were significantly downregulated in PTS and externally validated. A random forest model utilizing these proteins achieved an accuracy of 97.7% in independent testing, with SUCLG2 being the most influential predictor. ConclusionThis study identifies a novel three - protein biomarker panel for the diagnosis of PTS and reveals an immunometabolic axis in the pathogenesis of PTS, which links inflammatory regulation with mitochondrial energy metabolism. These findings provide valuable insights into the development of diagnostic tools and targeted therapeutic approaches.

BackgroundAnthracyclines are central to childhood cancer therapy but predispose patients to cardiotoxicity leading to long-term cardiovascular risk. Endothelial injury and impaired repair contribute to this, yet pediatric data remain limited. ObjectiveTo longitudinally assess endothelial injury and repair in childhood cancer patients treated with anthracyclines by quantifying circulating endothelial cells (CECs) and endothelial progenitor cells (EPCs). MethodsIn a single-centre retrospective cohort, children (<18 years) diagnosed with leukemia (n=35) or lymphoma (n=13) were studied at four timepoints: pre-treatment ("Pre"), [~]1-month- ("End"), 3 months- (3M), and 1 year- (1Y) post-treatment. Peripheral blood mononuclear cells were analyzed by flow cytometry to quantify CECs and EPCs, and EPC fate was assessed by p16 (senescence) and Annexin V (apoptosis). Cardiac injury biomarkers and left ventricular function were assessed at each timepoint. ResultsLongitudinal trends of CEC and EPC counts were similar between leukemia and lymphoma participants. CECs were highest at pre-treatment and declined significantly thereafter, though they remained marginally elevated during remission compared with healthy controls, indicating that endothelial damage had largely subsided following treatment. EPCs were also highest at pre-treatment and decreased to levels below healthy controls during remission, suggesting impaired baseline endothelial maintenance and repair. Furthermore, EPCs were predominantly senescent up to 1-year post-treatment. ConclusionsEndothelial injury resolves by treatment completion, but repair remains impaired during remission with EPC pools dominated by senescent cells. This suggests defective endothelial regeneration, rather than persistent injury, drives long-term cardiovascular complications and underscores the need to restore EPC viability and function in childhood cancer survivors.

BackgroundCardiovascular (CV) disease risk is increased in rheumatoid arthritis (RA) and is the leading cause of mortality. Improved CV risk stratification tools in RA could enhance use of preventative care and improve outcomes. MethodsWe previously studied biomarkers of CV disease - adiponectin, hsCRP, Lp(a), osteoprotegerin (OPG), high-sensitivity cardiac troponin T (hsTnT), serum amyloid A (SAA), YKL-40, soluble TNF receptor1 (sTNFR1) -- that were associated with CV risk. In the current study, these biomarkers were tested in an unrelated external cohort of RA patients followed at a single academic medical center without a history of CV events. CV events were identified through Medicare and Medicaid administrative data or through medical record review of self-reported events. Biomarkers were assessed at cohort entry among a nested cohort of cases and controls, matched 1:1 on sex and age. Analyses were conducted using conditional logistic regression. We examined whether the candidate biomarkers added to clinical CV risk factors improved model prediction, using the area under the curve (AUC) as well as the net reclassification index (NRI). ResultsFrom a cohort of 1,345 eligible patients with RA, we identified 123 patients with confirmed CV events. Cases and matched controls were typical of RA: median age 63 years, 77% women, RA disease duration 11 years, 72% seropositive, 85% used a biologic or conventional disease modifying anti-rheumatic drug, 58% non-steroidal anti-inflammatory drugs, and 30% oral glucocorticoids. From the candidate biomarkers, LASSO regression selected hsTnT and sTNFR1 as associated with CV events. The AUC for models that included only clinical risk factors was 0.758 (95% CI 0.689-0.829); after adding hsTnT and sTNFR1, the AUC increased to 0.802 (95% CI 0.718-0.998). The NRI of the model with biomarkers was 16.3%, with improvement only observed in patients who did not have CV events during follow-up. ConclusionsAdding selected biomarkers to clinical risk factors enhances the discrimination of models predicting CV events among patients with RA. These risk models require prospective testing to see if they have value in clinical practice decision-making regarding preventative care.

BackgroundThe comparative roles of triglyceride-rich lipoproteins (TRLs) and low-density lipoproteins (LDLs) in abdominal aortic aneurysm (AAA) pathogenesis are unclear. ObjectivesTo evaluate the putative causal role of TRLs in AAA, quantify the relative effect on AAA risk ("aneurysmogenicity") of TRL vs LDL particles, and prioritize lipid-lowering drug targets for AAA prevention and treatment. MethodsWe performed summary-level and individual-level Mendelian randomization (MR) analyses. Genetic variants were selected from 383,983 UK Biobank participants and ranked into 10 sets of variants where set 1 predominantly affected LDL cholesterol (LDL-C) and set 10 predominantly affected TRL cholesterol (TRL-C; and with mixed effects for intermediate variant sets). AAA outcome data were obtained from AAAgen (37,214 cases), FinnGen (4,439 cases), and the VA Million Veteran Program (MVP; 23,848 cases). Multivariable MR was used to assess the independent roles of LDL-C and TRL-C in AAA. For each set of variants, MR or logistic regression was used to estimate AAA odds ratios (ORs) per 10 mg/dL higher apolipoprotein B (apoB). Interaction analyses were conducted between a statin-like LDL-C-lowering variant set (set 3) and a TRL-C-lowering variant set (set 10). Drug-target MR was performed to evaluate lipid-lowering targets relevant to LDL-C- and TRL-C-lowering. ResultsGenetically predicted LDL-C and TRL-C concentrations were each associated independently with genetic liability for AAA after mutual adjustment, with 3.0 to 5.5 times stronger associations for TRL-C compared to LDL-C on a per-cholesterol basis. In AAAgen, the AAA OR per 10 mg/dL increased apoB concentrations were 1.10 (95% CI, 1.05-1.14) for variant set 1 (LDL-C-predominant) and 1.89 (95% CI, 1.69-2.11) for variant set 10 (TRL-C-predominant). Using the ratio of log(OR) per 10 mg/dL apoB for set 10 versus set 1 as a conservative estimate of relative aneurysmogenicity, TRLs were approximately 3.2 to 6.9 times more aneurysmogenic than LDLs across the three studies. No evidence of interaction was observed between LDLs and TRLs, indicating additive contribution to AAA risk. Drug-target MR supported strong protective associations for genetically proxied inhibition of TRL-pathway targets, particularly APOC3 and LPL, with AAA risk. ConclusionsTRLs are at least threefold more aneurysmogenic than LDLs on a per-particle basis. Therapeutic strategies targeting TRL-C --especially via APOC3 and LPL--should be prioritized for AAA prevention and treatment.

PurposeSepsis-associated immunothrombosis significantly contributes to high mortality, yet the role of N-glycosylation in this process remains poorly understood. This study aimed to comprehensively profile the plasma N-glycosylation landscape in sepsis and elucidate how its specific reprogramming in the complement and coagulation cascades influences immunothrombotic balance and patient outcomes. MethodsWe performed in-depth 4D-DIA proteomic and N-glycomic analyses on plasma from 43 sepsis patients and 9 healthy controls. Differential expression, weighted gene co-expression network analysis (WGCNA), and protein-glycosylation correlation analyses were used to characterize molecular features. Clinical relevance was assessed via correlation and survival analyses. ResultsExtensive N-glycosylation reprogramming was observed in sepsis plasma,with marked enrichment in complement and coagulation pathways(KEGG p=7.76x10- {superscript 2}{superscript 1}).Pro-coagulant proteins(eg,vWF,fibrinogen)showed increased abundance together with enhanced site-specific glycosylation,potentially amplifying their activity.In contrast,key anticoagulant proteins(eg,SERPINC1)displayed unchanged glycosylation at critical sites despite abundance changes,which may impair function.Survival analysis revealed distinct prognostic values of glycoproteins and specific glycosylation sites.For instance,high vWF protein levels predicted mortality(HR=2.83),whereas elevated glycosylation at vWF N211 was associated with improved survival(HR=0.135),suggesting a negative regulatory role.These glycosylation markers correlated closely with disease severity and prognosis,representing potential early-warning biomarkers independent of current clinical coagulation indicators. ConclusionOur study demonstrates widespread reprogramming of the plasma proteome and N-glycome in sepsis.We propose that decoupling of protein function from abundance through N-glycosylation in the complement-coagulation network contributes to immunothrombotic imbalance.Specific N-glycosylation sites may serve as novel prognostic biomarkers,offering new perspectives for early risk stratification and glycosylation-targeted therapies in sepsis. Key PointsO_LISepsis plasma exhibits specific N-glycosylation reprogramming overwhelmingly focused on the complement and coagulation cascade. C_LIO_LIA dominant "glycosylation-dominated co-upregulation" mode in procoagulant factors, coupled with a "silent" glycosylation state in key anticoagulants, drives prothrombotic imbalance. C_LIO_LISite-specific N-glycosylation levels provide prognostic information distinct from, and often superior to, their carrier protein abundance, offering novel early-risk biomarkers. C_LI

BackgroundInhaled combusted cannabis and co-use of combusted cannabis and nicotine electronic cigarettes (nECIGs) are on the rise, yet their long-term cardiovascular risk is unclear due to the high prevalence of confounders in observational human studies. Using primary plasma and monocytes and a novel ex vivo mechanistic model of two early steps in atherogenesis, this study examined whether chronic combusted cannabis use is associated with atherogenic changes, as estimated by 1) monocyte transendothelial migration (MTEM), and 2) monocyte-derived foam cell formation (MDFCF), and whether nECIG co-use further amplifies this risk. MethodsA cross-sectional parallel group comparison study was conducted in healthy adults (21-30 years) who chronically 1) used combusted cannabis, 2) co-used both combusted cannabis and nECIGs, and 3) were non-using controls. Using our ex vivo atherogenesis assay, primary outcomes of MTEM, MDFCF, and median fluorescence intensity (MFI) of the lipid-staining fluorochrome BODIPY were determined using primary plasma and autologous primary monocytes from participants. Using flow cytometry and the fluorochrome CELLROX, cellular oxidative stress (COS) in monocytes was determined. ResultsOf the 134 participants, 59 used cannabis, 26 co-used cannabis/nECIG, and 49 were non-using controls. The groups had similar age, sex, and race. Median MTEM was 1.13 fold greater in people who used cannabis compared to non-users 27.8% (IQR 26.1:29.2%) vs 24.5%, (IQR 22.9:27.4%), p<0.0001, and tended to be greater in people who co-used cannabis/nECIG by 1.22-fold 34.1%, (IQR 29.9:38.3%, p=0.17). Median MDFCF and MFI were also increased in people who used cannabis compared to non-users (MDFCF 36.3%, IQR 31.8:35.8%, vs 26.6%, IQR 23.8:25.8%, 1.36-fold and MFI 1163.8, IQR 1042.8:1155.0, vs 940.2 IQR 849.9:1101.4, 1.24-fold) and were further increased in people who co-used cannabis/nECIG (MDFCF 48.7%, IQR 37.3:52.4%, 1.34-fold, MFI 1433.7, IQR 1263.8:1686.4, 1.23-fold; all comparisons p<0.008). Foam cell formation, but not transendothelial migration, was strongly positively correlated with COS. All primary outcomes increased with greater frequency of cannabis and/or nECIG use. ConclusionsIn healthy young adults, exclusive cannabis use is associated with increased atherogenic properties of monocytes and plasma, and this atherogenic effect is further amplified by co-use of nECIGs.

BackgroundCirculating lipoprotein(a) [Lp(a)] levels are highly heritable and linked to atherosclerotic cardiovascular disease, yet clinical measurement rates remain low (<1%) in the United States. The high heritability of Lp(a) across populations makes genetic prediction an attractive approach for closing this testing gap, but existing polygenic scores transfer poorly across populations. Haplotype-based prediction models, which use standard genome-wide genotype data to capture common-, rare-, and structural-variation at the LPA locus, could bridge this gap, enabling opportunistic identification of individuals with elevated Lp(a) levels across diverse populations within existing large, genotyped cohorts. ObjectivesThis study sought to develop and validate a haplotype-based prediction model using genome-wide genotype data to identify individuals with elevated Lp(a) levels across diverse populations. MethodsWe developed an LPA-haplotype model using data from the All of Us Research Program and validated it in the Penn Medicine BioBank (PMBB), Mass General Brigham Biobank (MGBB), and Mount Sinai BioMe cohorts. Primary outcomes included model performance for predicting continuous Lp(a) concentrations (r{superscript 2}) and identifying elevated Lp(a) levels (>125 nmol/L) through positive predictive value (PPV) and number needed to test (NNT). ResultsAmong PMBB (n = 1856), MGBB (n = 1401), and BioMe (n = 1686) participants with available genotype and Lp(a) measurements, average age was 60 years, and 51% were female. Overall r{superscript 2} of the haplotype model was 0.46 (95% Credible Interval [CrI] 0.32 to 0.6), with similar performance across genetically inferred ancestries and cohorts. For identifying elevated Lp(a) levels >125 nmol/L the overall PPV was 0.81 (95% CrI 0.6 to 0.89), corresponding to a NNT of 1.2 (95% CrI 1.1 to 1.7) individuals predicted to have elevated levels needing to undergo clinical testing to identify one true elevation. In the full PMBB cohort (n = 49310), the haplotype model identified elevated Lp(a) at a rate of 128 per 1000 (95% CrI 125 to 130), corresponding to an estimated 14.4-fold improvement (95% CrI 13.1 to 15.9; P(improvement) = 1) in identification rate compared with the existing rate of clinical assessment. ConclusionsA haplotype-based genetic model effectively identified individuals with elevated Lp(a) levels across diverse populations, with potential utility for opportunistic screening among cohorts where genotype data is available, but Lp(a) testing rates are low.

Current clinical risk stratification for thoracic aortic aneurysms (TAA) relies primarily on maximum diameter, which is a poor predictor of rupture. Recent fluid-structure interaction studies have identified a dimensionless "flutter instability parameter" (N{omega} ) that accurately classifies abnormal aortic growth. However, this parameter currently serves as a static diagnostic snapshot. In this work, we propose a proof-of-concept computational framework that links flutter instability to microstructural tissue damage via a coupled system of ordinary differential equations (ODEs). We model a feedback loop where flutter-induced energy dissipation drives elastin degradation and collagen remodeling, which in turn reduces wall stiffness and amplifies the instability. To address the challenge of unobservable tissue properties, we implement a Bayesian inference engine to infer model parameters. We demonstrate feasibility on a synthetic patient cohort calibrated to published clinical growth rates and diameters. Our results show that this approach can infer hidden damage parameters and capture the qualitative bifurcation between stabilizing remodeling and runaway aneurysm expansion. While validation on real patient data remains essential, this work establishes the mathematical foundation for transforming a static physiomarker into a personalized prognostic trajectory.

Micro- and nanoplastics (MNPs) are increasingly detected in human tissues, yet their causal contribution to cardiovascular disease remains poorly understood. Here we show that oral exposure to polyethylene (PE) and polyvinyl chloride (PVC) -- the most abundant polymers found in human atheromas -- accelerates atherosclerosis in ApoE-/-mice through distinct, polymer-specific molecular mechanisms. While both polymers increased plaque burden and reduced contractile smooth muscle cell (SMC) markers, single-cell transcriptomic profiling revealed divergent phenotypic trajectories. PE exposure drives SMCs toward a chondromyocyte-like cell (CMC) state, characterized by upregulated osteogenic signaling and markedly increased vascular calcification. Conversely, PVC exposure promotes a fibromyocyte-like program associated with altered collagen metabolism and accelerated cell migration without enhancing calcification. These distinct SMC programs are reflected in the transcriptional signatures of symptomatic human carotid plaques, suggesting clinical relevance for polymer-specific vascular remodeling. Our findings establish a causal link between common environmental plastics and accelerated atherosclerosis, demonstrating that MNP-induced vascular risk is mediated by divergent SMC fate decisions. These results provide a mechanistic framework for assessing the cardiovascular impact of global plastic pollution and identifying potential therapeutic targets to mitigate MNP-associated vascular toxicity.

BackgroundIn patients with atrial fibrillation (AF) and stable coronary artery disease beyond 1 year after percutaneous coronary intervention (PCI), oral anticoagulant monotherapy is guideline-recommended; however, its efficacy and safety in patients with complex PCI remain uncertain. MethodsWe conducted a post-hoc analysis of the randomized ADAPT AF-DES trial comparing NOAC monotherapy versus NOAC plus clopidogrel in AF patients [&ge;]12 months after second- or third-generation drug-eluting stent implantation. Complex PCI was defined by one of the following characteristics: [&ge;]3 stents, [&ge;]3 lesions, bifurcation with 2 stents, total stent length [&ge;]60 mm, left main PCI, or chronic total occlusion PCI. Net adverse clinical events (NACE), ischemic composite outcomes, and bleeding composite outcomes were evaluated according to PCI complexity. ResultsAmong 960 patients, 247 (25.7%) underwent complex PCI and 713 (74.3%) underwent noncomplex PCI. NOAC monotherapy was associated with a lower risk of NACE compared with combination therapy in both the complex PCI group (9.5% vs 21.5%; hazard ratio 0.42, 95% confidence interval 0.21-0.83; P=0.01) and the noncomplex PCI group (9.6% vs 15.7%; hazard ratio 0.59, 95% confidence interval 0.39-0.90; P=0.02), with no significant interaction. Ischemic outcomes did not differ significantly between treatment strategies regardless of PCI complexity, whereas bleeding outcomes were consistently lower with NOAC monotherapy in both complex and noncomplex PCI groups. ConclusionsIn this post hoc analysis of the randomized ADAPT AF-DES trial, the clinical benefits of NOAC monotherapy beyond 12 months after PCI--characterized by reduced bleeding without a significant increase in ischemic events--were consistent regardless of PCI complexity. While hypothesis-generating, these findings support a long-term antithrombotic strategy prioritizing bleeding reduction in patients with AF, irrespective of prior PCI complexity. Trial registrationURL: http://www.clinicaltrials.gov; Unique identifier: NCT04250116. Clinical perspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIIn a randomized population of patients with AF and prior drug-eluting stent implantation, the efficacy and safety of NOAC monotherapy versus NOAC plus clopidogrel were evaluated according to anatomic PCI complexity. C_LIO_LIAmong patients with prior complex PCI, NOAC monotherapy was not associated with an increased risk of ischemic events and was associated with a substantial reduction in bleeding. C_LI What are the clinical implications?O_LINOAC monotherapy beyond 1 year after PCI was supported in patients with AF, including those with prior complex PCI. C_LIO_LILong-term antithrombotic decisions may place greater emphasis on bleeding risk than PCI complexity. C_LIO_LIThe optimal duration of combination antithrombotic therapy after complex PCI in patients with AF remains to be determined. C_LI

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

ObjectiveTo compare early cerebrospinal fluid (CSF) cytokine profiles in intracerebral hemorrhage (ICH) versus subarachnoid hemorrhage (SAH), with a focus on angiography-negative SAH (anSAH). MethodsWe conducted a retrospective observational cohort study of adults with spontaneous hemorrhagic stroke (ICH or SAH). For cytokine analyses, we included patients with external ventricular drains (EVDs) and analyzed the first CSF sample obtained within 72 hours of symptom onset. Cytokines were measured using a multiplex bead-based assay and included interleukin-6 (IL-6), interleukin-8 (IL-8), vascular endothelial growth factor A (VEGF-A), C-C motif chemokine ligand-2 (CCL2), and granulocyte colony-stimulating factor (G-CSF). Cytokine concentrations were log-transformed due to non-normal distribution. Functional outcomes were assessed using the modified Rankin Scale (mRS) at discharge and 3 months. ResultsCSF cytokine analyses included 120 patients with available CSF samples (43 ICH and 77 SAH), while functional outcome analyses included a broader cohort of 490 patients with ICH or SAH to characterize discharge and 3-month outcomes across hemorrhage subtypes. Compared with SAH, ICH demonstrated higher early CSF log[IL-8] and log[VEGF-A] and had worse functional outcomes at discharge and 3 months. Within SAH, anSAH had higher log[IL-8] and log[VEGF-A] than aSAH, and its cytokine profile more closely aligned with that of primary ICH in hemorrhages without vascular malformations. DiscussionEarly CSF cytokine patterns suggest anSAH shares a more ICH-like inflammatory signature than aneurysmal SAH, supporting anSAH as a potentially biologically distinct SAH phenotype.

BackgroundEvolocumab promotes coronary plaque regression in patients with coronary artery disease, but little is known regarding carotid plaques (CP). This study aimed to evaluate the impact of evolocumab on top of lipid-lowering therapy (ELLT) on carotid morphological stabilization (MS) and plaque regression (PR) compared to lipid-lowering therapy (LLT) alone. MethodsAsymptomatic patients with internal carotid stenosis[&ge;]50% and LDL-C[&ge;]100 mg/dL were randomized to ELLT or LLT and monitored by serial duplex ultrasound. The primary endpoint was a composite of 6-month-MS (i.e., switch from morphologic types I-II to III-IV) and/or 12-month-PR (i.e., reduction of carotid stenosis by at least 5% compared to baseline). The secondary endpoint was LDL-C change at 12 months. Major adverse vascular events (MAVE, i.e., cardiac death, stroke, myocardial infarction, carotid or coronary or peripheral revascularization) were recorded. ResultsA total of 170 patients were randomized. Mean carotid stenosis was 57%. At 6 months, MS occurred in the ELLT group (10.3%) only (p=0.29). At 12 months, PR was numerically more frequent in the ELLT group, without reaching statistical significance (43% versus 35.1%, p=0.42). The primary endpoint was met in 44.3% versus 35.1% (p=0.26). As compared to baseline, 6 and 12-month shifts from low to high-risk types were significantly higher in the LLT group (p=0.03). The 12-month LDL-C percentage reduction was -73.5% with ELLT, and -48.3% with LLT (p=0.0001). At 1 year, MAVE were significantly more frequent with LLT (14.6% versus 2.4%, p=0.005), and the absence of evolocumab was the only predictor (OR 7, p=0.014). ConclusionsIn patients with CP[&ge;]50% and LDL-C[&ge;]100 mg/dL, ELLT compared to LLT was associated with numerically but not statistically higher 6-month MS and/or 12-month PR. In the LLT group, 6- and 12-month changes from low to high-risk types, LDL-C, and MAVE were significantly higher. According to these results, evolocumab should be considered standard treatment for patients with CP[&ge;]50%. The study was registered at www.clinicaltrials.gov (NCT04730973) and Eudract (2020-005663-31). SHORT ABSTRACTPatients with carotid stenosis[&ge;]50% and LDL-C[&ge;]100 mg/dL were randomized to evolocumab on top of optimal lipid-lowering therapy (ELLT) or optimal lipid-lowering therapy (LLT) alone to assess the impact of ELLT on carotid plaque morphological stabilization (MS) and plaque regression (PR). At 6 and 12 months, MS and PR occurred in both groups, but were numerically higher in the ELLT group, without reaching statistical significance. In the LLT group, 6- and 12-month changes from low to high-risk types were significantly higher, and the rate of adverse vascular events was sevenfold higher. Evolocumab might become the standard treatment for patients with carotid artery stenosis [&ge;]50%. CLINICAL PERSPECTIVEO_ST_ABSWhat is new?C_ST_ABSO_LIThe CARUSO is the largest randomized trial evaluating the impact of evolocumab on top of lipid-lowering therapy (ELLT) on carotid morphological stabilization (MS) and plaque regression (PR) monitored by serial duplex ultrasound. C_LIO_LIThe primary endpoint was a composite of 6-month-MS (i.e., switch from morphologic types I-II to III-IV) and/or 12-month-PR (i.e., reduction of carotid stenosis by at least 5% compared to baseline) and was numerically higher in the ELLT group compared to lipid-lowering therapy (LLT) alone, without reaching statistical significance. C_LIO_LIThe 1-year rate of major adverse vascular events (MAVE) was sevenfold higher in the LLT group. C_LI What are the clinical implications?O_LICarotid plaque morphology is a dynamic event, and 6 and 12-month shifts from low to high-risk morphological types were significantly higher in the LLT group, thus suggesting that evolocumab added to LLT may prevent morphological deterioration. C_LIO_LIThe absence of evolocumab was the only independent predictor of MAVE; according to our results, ELLT might become the standard treatment for patients with carotid plaques [&ge;]50% and LDL-C not at target. C_LIO_LIFuture larger studies are warranted to validate our findings, assess long-term adherence to therapy, and identify subgroups with higher probability of achieving MS and PR. C_LI

Gout is driven by an interleukin-1{beta}-mediated intense innate immune reaction to monosodium urate (MSU) crystals (MSUc). In cell culture models of inflammatory gout there is a synergistic effect of phagocytosis of MSUc and TLR2 and TLR4 activation by agonists such as free fatty acid and lipopolysaccharide (LPS) in NLRP3-inflammasome activation and IL-1{beta} secretion. A substantial number of gout patients do not report a dietary trigger, and observational studies associate airborne particulate matter with incident gout and flares. Airborne particulate matter contains LPS and airborne-derived particulate matter stimulates IL-1{beta} secretion in cell culture. We hypothesized that air-borne particulate matter could co-stimulate, with MSUc, IL-1{beta} secretion and inflammation. We tested the hypothesis using MSUc with extracted airborne PM4 in human cells (the THP-1 monocyte cell line, primary human monocytes and PBMCs) or carbon black particles with ozone (CB+O3) in a murine foot-pad injection model of gout. There was strong NLRP3-inflammasome-dependent co-stimulation of IL-1{beta} secretion in THP-1 cells with PM4+MSUc and a moderate additive effect in primary human PBMCs. However, there was no added effect on IL-1{beta} secretion of PM4 in isolated primary human monocytes. Inhalation of CB+O3 persistently exacerbated MSUc-induced murine paw inflammation, with an increase of alveolar/lavage macrophages that contained CB+O3 particles and increased lavage expression of IL-1{beta}. In conclusion, airborne-derived PM4 particulate matter enhanced MSUc-induced IL-1{beta} secretion in THP-1 cells and PBMCs. Combined with exacerbation of MSUc-induced inflammation by fine particulate matter in in vivo experiments, these data provide evidence that exposure to fine particulate matter may play a role in the etiology of gout.

ObjectiveAutoimmune diseases (ADs) markedly elevate venous thromboembolism (VTE) risk, yet the shared genetic architecture and tissue-specific regulatory mechanisms of this "Autoimmune-Thrombotic Axis" remain poorly defined. We aimed to characterize the genomic landscape of immunothrombosis to identify causal links and therapeutic targets. Approach and ResultsWe integrated large-scale GWAS data for VTE and 16 ADs using a multi-omics framework, including pleiotropy scanning, local genetic correlation, and summary-based Mendelian randomization (SMR). We identified 21 Immunothrombotic Shared Loci (ISLs) and 274 pleiotropic genes enriched in complement and coagulation cascades. Mendelian randomization (MR) analysis revealed a robust causal effect of genetically predicted systemic lupus erythematosus (SLE) on VTE risk (OR = 1.018, 95% CI: 1.008-1.029, P = 0.0003). Mechanistically, IL6R and PLCL1 emerged as central mediators with distinct tissue-specific regulatory partitioning. Colocalization confirmed that shared genetic susceptibility is primarily driven by expression in arterial tissues (aorta and coronary) rather than exclusively in immune cells. Furthermore, the lead SNP rs4129267 was identified as a potential predictor for VTE in rheumatoid arthritis patients, and drug prioritization nominated TNF inhibitors as promising candidates for mitigating thrombotic burden. ConclusionThis study establishes the first genomic atlas of the autoimmune-thrombotic axis, demonstrating that vasculature-specific gene regulation drives immunothrombosis. These findings provide a biological basis for VTE risk stratification and suggest that genotype-guided therapy may optimize vascular outcomes in AD patients.

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration with data-driven machine learning approaches for growth prediction. The proposed arterial G&R model incorporates key mechanisms influencing aneurysm progression, including elastin degradation and stress-mediated collagen production. A modified elastin degradation formulation was introduced to generate realistic aneurysm geometries exhibiting clinically relevant features such as asymmetry and tortuosity. By systematically varying parameters governing elastin damage and collagen production, 200 distinct G&R simulations were performed to produce a diverse set of AAA geometries. The dataset was further expanded using kriging-based spatial interpolation to construct a large in silico cohort. The synthetic dataset, combined with longitudinal imaging data from 25 patients, was used to train and validate four machine learning models: Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). A two-step training strategy was adopted to predict maximum aneurysm diameter and growth rate based on prior geometric characteristics. The LSTM model achieved the highest performance for maximum diameter prediction (R{superscript 2} = 0.92), while the RNN demonstrated strong overall performance (R{superscript 2} = 0.90 for maximum diameter and 0.89 for growth rate). The DBN and GRU models also showed competitive predictive capability. Overall, this study demonstrates that integrating physics-based G&R simulations with machine learning enables accurate prediction of AAA growth and maximum diameter. The proposed framework provides a scalable strategy for augmenting limited clinical datasets and offers a promising tool to support personalized risk assessment and treatment planning.

BackgroundSepsis is a major public health challenge, and reliable biomarkers are essential for distinguishing sepsis from other conditions. Neutrophil Gelatinase-Associated Lipocalin (Neutrophil gelatinase-associated lipocalin (NGAL)) has shown promise as a diagnostic marker due to its role in the immune response. This study evaluates plasma NGAL as a diagnostic tool at the time of ICU admission. MethodsWe analysed plasma NGAL and C-reactive protein (CRP) levels in 4732 adult patients admitted to four ICUs between 2015 and 2018. All patients were retrospectively screened for Sepsis-3 criteria at ICU admission. The discriminative performance of NGAL and CRP for sepsis was assessed using receiver operating characteristic (ROC) analysis, with NGAL levels adjusted for Chronic kidney disease (CKD) and age. Patients were stratified by renal function. ResultsPlasma NGAL levels were significantly higher in septic patients (p<0.001). For the whole cohort, NGAL alone yielded an Area under the curve (AUC) of 0.67 (Confidence interval (CI) 0.66-0.69), CRP yielded an AUC of 0.72 (CI 0.71-0.73, p<0.001), and combining NGAL with CRP nominally improved discriminative performance (AUC 0.74 vs 0.72, p<0.001). Stratified analyses indicated that NGAL, together with CRP, significantly outperformed CRP alone in patients with no kidney injury and those with Acute Kidney Injury (AKI) only. In contrast, differences were not significant in patients with CKD only or CKD and AKI. ConclusionIn this large cohort, NGAL showed modest discrimination for sepsis, with a nominal improvement when combined with CRP. These findings do not indicate that NGAL meaningfully improves sepsis diagnosis in the ICU.

PurposeIntracranial atherosclerotic disease-related large vessel occlusion (ICAD-LVO) presents distinct challenges, particularly regarding the high risk of reocclusion and the need for specific management strategies. While several prediction scores exist to differentiate ICAD-LVO from embolic LVO (EMB-LVO), their external validity remains unproven. We aimed to externally validate six established prediction scores for differentiating the two. MethodsWe analyzed data from a prospectively maintained, two-center stroke registry (June 2021-March 2025). Consecutive patients who underwent mechanical thrombectomy and had complete clinical and imaging data necessary for calculating six scores (ISAT, REMIT, ABC2D, ATHE, ICAS-LVO, and Score-ICAD) were included. LVO etiology was defined based on angiographic findings during endovascular treatment. The discriminative performance of each score was assessed using the area under the receiver operating characteristic curve (AUC). ResultsOf 1,288 screened admissions, 91 patients met the inclusion criteria (ICAD-LVO, n = 18; embolic occlusion, n = 73). The AUCs (95% confidence interval) for differentiating etiology were: ISAT, 0.870 (0.664-1.000; P = 0.064); REMIT, 0.793 (0.676-0.911; P <0.001); Score-ICAD, 0.707 (0.582-0.833; P = 0.013); ABC2D, 0.627 (0.504-0.751; P = 0.095); ATHE, 0.600 (0.451-0.749; P = 0.230); and ICAS-LVO, 0.465 (0.301-0.630; P = 0.650). ConclusionIn this external validation, REMIT demonstrated the most robust and statistically significant discrimination between ICAD-LVO and EMB-LVO. Overall, scores incorporating imaging features outperformed those relying on clinical variables. These findings support the concept that ICAD-LVO represents a distinct pathophysiological entity from embolic occlusion and that accurate mechanism inference requires comprehensive imaging assessment of intracranial atherosclerotic disease beyond the occlusion site.

BackgroundAdvances in wearable devices and machine-learning-based ECG analysis enable highly accurate detection of atrial fibrillation (AF) outside traditional clinical settings, leading to increasing identification of asymptomatic AF. However, the prognostic significance of AI-detected asymptomatic AF and its implications for downstream cardiovascular risk remain unclear. In contrast to clinically diagnosed AF, evidence guiding risk stratification and further evaluation in this population is limited. We therefore investigated the association between AI-detected asymptomatic AF and incident cardiovascular outcomes in a large population-based cohort. MethodsWe applied a validated open-source ECG-based deep learning model for atrial fibrillation detection (AI-AF) to 12-lead ECG recordings from participants in the UK Biobank. Participants with AI-detected AF on ECG and no prior clinical AF diagnosis were classified as asymptomatic AF (c). Kaplan-Meier curves and log-rank tests were used to compare the incidence of ischemic stroke and major adverse cardiovascular events (MACE: myocardial infarction, ischemic stroke, or cardiovascular death) across AF subgroups. Cox proportional hazards models were used to evaluate the association between AI-AF risk and incident MACE, adjusting for age, sex, current smoking, systolic blood pressure, total and HDL cholesterol, and prevalent type 2 diabetes. Follow-up was administratively censored at 6 years. ResultsThe study included 96,531 participants with mean [SD] age of 65 [8] years; 52% female; median follow-up [IQR] of 4.7 [1.6-7.2] years. ECG data were available for 64,029 participants and an additional 32,502 participants with clinically diagnosed atrial fibrillation (AF) without ECG recordings were included. Among participants without prior clinical AF and with available ECGs, 2,399 were classified as asympAF based on AI detection, while 58,879 were AF-free. Over 6 years of follow-up, the incidence of ischemic stroke was significantly higher in participants with asympAF compared with AF-free individuals (1.5% vs 0.52%, p = 7x10-7) and significantly lower than in participants with clinically diagnosed AF (1.5% vs 3.4%, p = 2x10-5). Similar patterns were observed for myocardial infarction and cardiovascular death. Using a more liberal AI-AF threshold corresponding to a 15% false-positive rate (asympAF15) yielded consistent findings: participants classified as asympAF15 had a 62% higher risk of incident MACE in adjusted Cox PH models (hazard ratio 1.6, 95% CI 1.2-2.2) over six years. ConclusionAI-detected asymptomatic AF identified individuals at elevated risk of ischemic stroke and major adverse cardiovascular events. As ischemic stroke is a hallmark complication of atrial fibrillation, these findings support the hypothesis that AI-ECG models may capture subclinical AF-related risk not detected by conventional clinical assessment. This approach may help extend the window for preventive interventions in populations without clinically diagnosed AF.