Arteriosclerosis, Thrombosis, and Vascular Biology

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.

BackgroundThe pathways linking lipoprotein(a) (Lp[a]) to atherosclerotic cardiovascular disease (ASCVD) are unclear. This study aimed to discover Lp(a)-associated plasma proteins and estimate their associations with incident ASCVD. MethodsWe analyzed 48,859 UK Biobank participants with measured Lp(a) and proteomic profiles, with replication in 9,416 individuals in the Atherosclerosis Risk in Communities (ARIC) study cohort utilizing a separate proteomic platform. Linear models assessed associations between Lp(a) and protein concentrations adjusted for age, sex, cigarette smoking, diabetes diagnosis, body mass index, systolic blood pressure, hypertension, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, triglycerides, estimated glomerular filtration rate, statin prescription, and the first 10 components of genetic ancestry. Multiple testing correction was performed using the Benjamini-Hochberg FDR method (P < 0.05). We examined how the protein effect sizes from the primary analysis using the outcome of Lp(a) aligned with those for the outcomes of an LPA genetic risk score (GRS) and LDL-C. Cox proportional hazards models quantified hazard ratios (HRs) for protein associations with incident ASCVD. ResultsParticipants were a mean age of 57 years (SD 8.22), 93.9% European, and 53.8% male, with median follow-up of 8.9 years (IQR 8.3-9.7). Of 1,459 circulating proteins, 164 were significantly associated with Lp(a) after FDR correction, with enrichment for lipid degradation, metabolism, and insulin secretion. In the ARIC study, 10 proteins were replicated with consistent effect estimates. Of these replicated proteins, there were no significant associations observed with an LPA GRS. Only REG4 and VWC2 showed concordant associations with LDL-C (P < 0.001), consistent with their association with Lp(a). Five proteins exhibited concordant associations with Lp(a) and incident ASCVD (ITIH3, DLL1, REG4, VWC2, CBLN4). ITIH3 was positively associated with coronary artery disease (HR 1.13, 95% CI 1.04-1.23), peripheral artery disease (HR 1.42, 95% CI 1.19-1.69), major adverse limb events (HR 1.65, 95% CI 1.14-2.40), carotid stenosis (HR 1.45, 95% CI 1.13-1.85), and ischemic stroke (HR 1.33, 95% CI 1.13-1.55). CBLN4 uniquely showed inverse associations with Lp(a) and disease: higher levels were linked to lower risk of CAD (HR 0.88, 95% CI 0.80-0.96), PAD (HR 0.78, 95% CI 0.64-0.96), and ischemic stroke (HR 0.72, 95% CI 0.60-0.85). ConclusionUsing high-throughput proteomics, we discovered and replicated 10 proteins associated with circulating Lp(a), several of which were independent of genetically-predicted Lp(a). While Lp(a) is highly heritable, these atherogenic proteins represent a non-heritable Lp(a) axis. Clinical PerspectiveO_ST_ABSWhats New?C_ST_ABSO_LITen proteins associated with circulating lipoprotein(a) levels were identified and independently replicated in an external cohort, with associations independent of a genetic risk score. C_LIO_LIFive proteins (ITIH3, CBLN4, FLL1, REG4, VWC2) were concordantly associated with lipoprotein(a) and incident atherosclerotic cardiovascular disease. C_LI Clinical ImplicationsO_LIThese proteins may represent pathways through which lipoprotein(a) drives atherosclerosis beyond traditional lipid mechanisms. C_LIO_LIFuture mechanistic studies should investigate the roles of these proteins to guide the development of targeted strategies for preventing atherosclerotic cardiovascular disease. C_LI

BackgroundEpigenetic mechanism underlying susceptibility to abdominal aortic aneurysm (AAA) remain poorly understood. Identifying causal DNA methylation markers for AAA can elucidate the regulatory processes that drive aneurysm formation and would accelerate translational applications. We leveraged the VA Million Veteran Program (MVP) to identify methylation biomarkers and delineate underlying pathways. MethodsWe first conducted an epigenome-wide association study (EWAS) of incident AAA (1,324 cases; 42,065 non-cases), performed stratified analyses by population group and smoking status, and conducted Mendelian randomization (MR) to facilitate casual inference of the CpG-AAA association. Chromatin state, island context, and TF binding were implicated through functional annotation of identified CpGs. To identify genes impacted by change in methylation state, we aligned associations with transcriptional data obtained in blood, aorta, and liver. We performed expression quantitative trait methylation (eQTM) to capture CpG-gene-expression links across the genome. Network MR was used to test cardiometabolic mediation. Finally, we developed a risk predictor using methylation data using a penalized regression model, evaluating its performance against a comprehensive clinical model. ResultsEWAS identified 1,253 CpGs associated with incident AAA, and MR supported a putative causal role for 151 of these associations. Functional annotation pointed to predominantly distal, enhancer-centered regulation and enrichment of inflammatory transcription factor programs (e.g., AP-1). This distal architecture was consistent with eQTM results, which showed a larger number of trans associations. Network MR identified 231 putative mediation pathways linking CpGs to AAA, including 179 via cardiometabolic traits and 52 via immune/inflammation-related traits. Among cardiometabolic mediators, blood lipids accounted for >40% of mediation effect linking LDLR-associated CpGs to AAA risk. Among immune/inflammation-related mediators, platelet count and circulating proteins including NEXN, IL1RN, ADH1B, and MMP12 emerged as key intermediates. Genetic colocalization highlighted an aorta-specific cg17511968-WNT6-AAA axis, and network MR implicated IL1RN and MMP12 as downstream protein mediators of association between WNT6-proximal CpGs and AAA. Finally, a methylation risk score improved discrimination when added to a clinical model (AUC 0.775; 95% CI, 0.749-0.801) for incident AAA prediction. ConclusionsThis study identified putative causal DNA methylation markers for AAA, and multi-omics analyses implicate AP-1-linked inflammatory transcriptional programs, blood lipids, platelet count, and multiple immune/inflammation-related proteins as key pathways underlying methylation-associated AAA risk.

BackgroundCarbohydrate-restricted diets are gaining popularity, including among lean individuals. In these populations, a lipid phenotype often emerges comprising elevated LDL cholesterol (LDL-C), alongside elevated HDL-C and low triglycerides, termed the lean mass hyper-responder (LMHR). ObjectiveTo evaluate one-year coronary plaque progression in LMHRs and near-LMHRs. MethodsThis prospective study followed 100 participants who developed the triad of high LDL-C, high HDL-C, and low triglycerides after adopting a ketogenic diet over one year. Coronary plaque changes were assessed using coronary CT angiography and analyzed using the prespecified QAngio(R) methodology (Leiden, the Netherlands), with AI-enabled coronary plaque analysis (AI-CPA; HeartFlow(R) Inc., Mountain View, CA) used as an independent, blinded confirmatory analysis. Plaque burden and plaque progression predictors were examined using linear regression. ResultsAll 100 participants with elevated LDL-C and a mean BMI of 22.5 {+/-} 2.7 kg/m2 completed the study. At baseline, 57 (57%) had zero CAC. After follow-up, most participants remained with low-risk plaque burden markers: 81% of participants had a CAC score <100, and 54% had a CAC of 0. The median increase in non-calcified plaque volume was 5.6 mm3 (37% relative increase). Notably, 15% of participants exhibited plaque regression despite sustaining elevated LDL-C (mean 242 mg/dL) and ApoB (mean 180 mg/dL). Additionally, 78% had percent atheroma volume (PAV) below the high-risk threshold of 2.6%, and 93% had total plaque volume (TPV) below the high-risk threshold of 254 mm3. Baseline plaque metrics were consistently predictive of plaque progression. By contrast, neither ApoB levels nor cumulative LDL-C exposure predicted plaque progression in this population of LMHR and near-LMHR individuals. ConclusionThese findings suggest that over one year, progression was modest and heterogeneous in this population, with baseline coronary plaque emerging as the strongest predictor of subsequent plaque progression in LMHRs, whereas traditional lipid markers such as ApoB and LDL are not.

BackgroundScreening criteria for abdominal aortic aneurysm (AAA) are based on clinical factors, such as age and smoking history, but do not include biological factors that may better reflect disease pathogenesis. ObjectivesWe sought to determine whether a proteomic risk score (ProRS) incorporating plasma protein abundance could improve prediction of AAA. MethodsWe performed a cross-sectional analysis of nearly 37,000 participants in the UK Biobank Pharma Proteomics Project with plasma protein abundance data for 274 cardiometabolic proteins. ProRS models were developed using regularized regression. ResultsThe generated sparse ProRS contained well-established clinical risk factors as well as a single protein - matrix metalloproteinase 12 (MMP12). Overall performance and discriminatory utility of this model was higher than an identical model without MMP12 (difference in Brier score 2.1 x 10-4, 95% CrI 2.0-2.3 x 10-4; difference in AUROC 0.021, 95% CrI 0.020 - 0.022). Within the cohort, current AAA screening recommendations applied to 4.6% of the population and captured 30% of cases, whereas screening 4.6% of the population at highest risk by ProRS captured 52% of cases. Among individuals with incident AAA, MMP12 abundance was independently associated with time to rupture or repair (HR 1.86, 95% CI 1.39-2.50, p < 0.001). Additionally, MMP12 level improved discrimination of AAA in an external cohort (SIMPLER; difference in AUROC 0.084, 95% CrI 0.081 - 0.087). ConclusionsA biologically-plausible ProRS incorporating a single matrix metalloproteinase improved prediction of AAA over clinical factors alone. These results may be used to enhance screening strategies for AAA.

ObjectiveDiabetes mellitus (DM) approximately doubles the risk of atherosclerotic cardiovascular disease (ASCVD) events, but the molecular basis is poorly understood. We aimed to define arterial differentially expressed genes (DEGs) associated with DM, validate hits as plasma proteins, and ascertain whether these complement ASCVD risk prediction tools. Research design and methodsRNA-sequencing data from the Genotype-Tissue Expression (GTEx) cohort was used to define DEGs associated with DM in two arterial sites in >90 people with DM and >330 controls. UK Biobank (UKB) was used to corroborate that DEGs in their plasma protein form were differentially abundant in people with DM and associated with ASCVD events. Finally, we assessed if including these plasma proteins improved performance of the SCORE2 and SCORE2-Diabetes ASCVD risk models. Results619 and 356 DEGs were associated with DM in the thoracic aorta and tibial artery, respectively. Of these, 22 were common to both arteries, all of which were directionally concordant. Of these, 5 were included in the UKB plasma proteomics dataset and we corroborated 4 (ACP5, LEFTY2, LILRA5 and PSME2) as showing concordant differential abundance in people with DM; all demonstrated associations with a range of incident ASCVD events. Addition of the 4 proteins to SCORE2 and SCORE2-Diabetes (for people without and with DM, respectively) improved the population-level discrimination, classification and calibration of these models. ConclusionsDM is associated with a distinct arterial gene expression profile, hits from which are associated with ASCVD events and add value to risk prediction. Visual abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/26345847v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@19e0228org.highwire.dtl.DTLVardef@9fd086org.highwire.dtl.DTLVardef@3315f0org.highwire.dtl.DTLVardef@1e5770f_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundAortic aneurysm and dissection (AAD) are highly lethal conditions for which hypertension serves as a primary risk factor. The limited efficacy of conventional antihypertensive treatments suggests an inadequate understanding of the molecular mechanisms that connect these conditions. Despite their pivotal role in regulating biological functions, the specific proteomic signatures associated with both hypertension and AAD have not been extensively investigated. This study sought to conduct a comprehensive mapping of the plasma proteome to identify novel biomarkers and therapeutic targets for AAD within a hypertensive cohort. MethodsWe analyzed 2,923 plasma proteins in 26,690 hypertensive individuals without a prior history of AAD from the UK Biobank. LASSO and Cox regression analyses were employed to identify proteins associated with AAD, while a LightGBM algorithm was utilized to construct predictive models. Causal inference was conducted using two-sample Mendelian randomization (MR). Further mechanistic exploration included colocalization, single-cell RNA sequencing, functional enrichment, and drug-target analysis. ResultsAmong the core hypertension proteins, we identified 186 proteins independently associated with AAD risk, with MMP12 showing the strongest association. A streamlined model, which included the top five proteins alongside age and sex, exhibited superior predictive performance (AUC: 0.791) compared to traditional risk models. MR analysis confirmed causal relationships for 21 proteins with AAD, and colocalization provided high-confidence evidence for shared genetic architecture for MMP7, CCN3, and COL6A3. Mechanistically, single-cell analysis verified cell-type-specific aortic expression of candidate genes, functional enrichment implicated extracellular matrix (ECM) pathways. Furthermore, we identified 95 FDA-approved drugs targeting 11 of these causal proteins. ConclusionThis study presents the first comprehensive plasma proteomic landscape of AAD within a large hypertensive cohort, offering a high-performance predictive model, validating novel causal proteins, and identifying actionable drug targets. These findings provide crucial molecular insights into the pathogenesis of AAD and establish a solid foundation for developing early-detection strategies, improving risk stratification, and guiding precision medicine.

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.

BackgroundChronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking systemic inflammation to cardiac remodeling remain incompletely understood. We investigated associations between circulating inflammatory biomarkers and cardiac phenotypes in a population-based cohort and examined how environmental exposures and genetic susceptibility influence inflammatory responses. MethodsWe analyzed subsets of 488,079 UK Biobank participants with metabolomic and proteomic profiling, cardiac magnetic resonance (CMR) imaging, and longitudinal outcomes. Chronic inflammation was quantified using glycoprotein acetyls (GlycA) by nuclear magnetic resonance spectroscopy. Machine learning-based analysis extracted CMR phenotypes. Multivariable linear regression assessed GlycA-cardiac associations. Mediation analysis tested 80 inflammatory proteins as potential mediators. Cox models evaluated GlycA levels and major adverse cardiovascular events (MACE). An exposome-wide association study identified environmental determinants of inflammation, and gene-environment interactions were assessed using multi-ancestry polygenic risk scores. ResultsHigher GlycA levels were associated with restrictive cardiac remodeling: reduced left ventricular indexed end-diastolic volume ({beta} = -2.09) and stroke volume ({beta} = -1.12) with compensatory increased heart rate ({beta} = 1.38; all P < 10-228). Interleukin (IL) -1 receptor antagonist mediated 27% of the GlycA effect on end-diastolic volume (average causal mediated effect -0.51 [95% CI, -0.53 to -0.64]; P < 10-16). The highest GlycA quintile had 43% higher MACE risk versus the lowest (adjusted HR, 1.43 [95% CI, 1.38-1.49]). Trunk fat mass ({beta} = 0.35), current smoking ({beta} = 0.39), psychological distress, and low socioeconomic status were the strongest GlycA determinants (all P < 10-50). Cardiovascular polygenic risk scores modified associations between environmental exposures, inflammation, and MACE. ConclusionsChronic systemic inflammation is associated with restrictive cardiac remodeling and increased cardiovascular risk mediated by circulating cytokines and growth factors. Individual inflammatory responses are shaped by gene-environment interactions, highlighting the complex interplay between genetic susceptibility, environmental exposures, and their cumulative impact on cardiovascular health.

Peripheral artery disease (PAD) is a global health burden affecting over 200 million individuals and is frequently complicated by limb-threatening ischemia, leading to major amputations. Despite known clinical risk factors, the genetic basis underlying amputation risk in PAD remains poorly defined. In this study, we performed a multi-pronged genome-wide association study (GWAS) to identify genetic variants associated with lower extremity amputation in patients with PAD, using data from the All of Us Research Program. Two analytical strategies were employed: a targeted GWAS using ClinVar variants on the full cohort and a comprehensive genome-wide association study using Allele Count/Allele Frequency (ACAF) data on a balanced subset. The ClinVar analysis of 118,871 variants in 14,771 PAD patients (613 with amputation, 14,158 without) identified 3 suggestive associations with a genomic inflation factor of 1.046. The ACAF analysis of 7,784,837 quality-controlled variants in 804 balanced samples (399 cases, 405 controls) yielded 35 suggestive associations (p < 1x10{square}{square}) with a genomic inflation factor of 1.017. No variants achieved suggestive significance in both analyses. These findings highlight candidate loci for further validation and may inform future development of risk prediction tools and targeted interventions to reduce limb loss in PAD.

Apolipoprotein B (apoB) has emerged as a more accurate predictor of coronary artery disease risk relative to standard lipid measurements like low-density lipoprotein cholesterol (LDL-C). Here, we sought to characterize the clinical associations and genetics underlying apoB and LDL-C discordance. We derived a residualized-apoB phenotype, defined as observed minus expected apoB for an individual given their LDL-C level, in 239,144 individuals in the UK and Mass General Brigham Biobanks. Higher residualized-apoB was independently associated with increased risk of myocardial infarction (hazard ratio = 1.30 per standard deviation change, {Delta}R2 = 10%). Genome-wide association analyses identified 137 significant loci for residualized-apoB, including 16 unique genomic loci significant only in residualized-apoB in our study and 2 loci not previously described in lipid studies. Key loci implicated genes involved in lipid metabolism and cardiovascular disease (i.e. NPR2, RORA) and were also linked to other metabolic traits, suggesting broader metabolic relevance.

BackgroundPlatelet morphological indices, such as mean platelet volume and distribution width, reflect platelet turnover and thrombo-inflammatory activity relevant to acute coronary syndrome (ACS), but their individual prognostic value remains inconsistent. This study aimed to derive an Integrated Platelet Index (IPI) using principal component analysis (PCA) to capture a multidimensional platelet morphological phenotype influenced by cardiometabolic and inflammatory stress and to examine its association with long-term major adverse cardiovascular and cerebrovascular events (MACCE) in ACS. MethodsThis prospective observational study included 1,467 ACS patients from the Beijing Hospital Atherosclerosis Study. Five routinely measured platelet indices--platelet count, mean platelet volume, platelet distribution width, plateletcrit, and platelet large-cell ratio--were integrated via PCA to construct the IPI. The primary endpoint was MACCE. Cox models, restricted cubic splines, Kaplan-Meier curves, and prespecified subgroup analyses assessed the prognostic relevance of the IPI and its consistency across metabolic and clinical strata. ResultsOver a median follow-up of 55.0 months, 141 patients (9.6%) experienced MACCE. Lower IPI values were associated with higher risk. Each unit increase in IPI was independently associated with a 27% lower MACCE risk after full adjustment (HR 0.73; 95% CI: 0.59-0.90). A significant L-shaped nonlinear relationship was observed, with steep risk reduction at lower IPI values. Event-free survival increased progressively across IPI tertiles. Associations remained consistent across cardiometabolic subgroups, with no significant interactions. ConclusionsThe PCA-derived IPI represents an integrated platelet morphological phenotype associated with long-term atherothrombotic risk in ACS. Its nonlinear behavior and robust performance across metabolic backgrounds support its potential as an accessible, phenotype-based marker for refining secondary prevention risk stratification. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/25342328v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@7a6e84org.highwire.dtl.DTLVardef@1eda7a3org.highwire.dtl.DTLVardef@198df1forg.highwire.dtl.DTLVardef@160722b_HPS_FORMAT_FIGEXP M_FIG C_FIG What Are the Clinical Implications?Platelet morphology and turnover are influenced by cardiometabolic and inflammatory stress, yet routine platelet indices are rarely incorporated into ACS risk assessment because each parameter reflects only a single dimension of platelet biology. By integrating five commonly available platelet indices into a single PCA-derived morphological phenotype, the Integrated Platelet Index (IPI) provides a multidimensional representation of platelet alterations relevant to atherothrombotic risk. In this study, lower IPI values consistently identified patients at higher long-term risk of MACCE, and the L-shaped nonlinear association suggests that relatively small reductions in this phenotype may reflect disproportionately adverse platelet remodeling. Because the IPI relies solely on automated complete blood count parameters, it is low-cost, universally available, and feasible for implementation in secondary prevention pathways, including in resource-limited settings. Clinically, the IPI may improve risk stratification in ACS by capturing thrombo-inflammatory and metabolic influences not detected by traditional predictors. Its stability across cardiometabolic subgroups supports broad applicability. Future studies should determine whether serial IPI monitoring can identify patients with persistent thrombotic risk and whether incorporating the IPI into validated ACS risk models enhances individualized management. HighlightsO_LIWe derived an Integrated Platelet Index (IPI) representing a PCA-based morphological phenotype from five routine platelet indices. C_LIO_LILower IPI values identified patients with heightened long-term atherothrombotic risk after acute coronary syndrome. C_LIO_LIThe IPI captures multidimensional platelet alterations shaped by thrombo-inflammatory and cardiometabolic stress. C_LIO_LIThe association between IPI and adverse outcomes remained consistent across major cardiometabolic subgroups. C_LIO_LIThe IPI provides an accessible, low-cost phenotype with potential utility for risk stratification in secondary prevention. C_LI

BackgroundHypertension affects over 30% of adults and is the leading risk factor for cardiovascular disease. It often presents without obvious symptoms, meaning that, although effective therapies exist, hypertension remains widely undiagnosed and insufficiently treated. Genomics-based prediction methods have shown only modest benefits for these disorders, but proteomic markers have demonstrated potential for greater predictive and clinical value. MethodsWe applied a novel machine-learning based patient stratification analysis pipeline to proteomics data for 7,086 hypertension patients from UK Biobanks Pharma Proteomics Project cohort (2,911 proteins). We evaluated the contribution of each protein to the output of a tree-based risk model to explore the combinations of protein expression values that naturally separate hypertension cases into clusters and assessed the prevalence of cardiovascular and renal complications within each obtained cluster. ResultsWe identified 10 clusters of hypertension patients segregated by differential expression of HAVCR1, PLAT, PTPRB, REN and RTN4R. Four of these clusters showed statistically significant enrichment for cardiovascular and renal complications, and three of them had significantly lower prevalence of complications than expected among hypertension patients. ConclusionWe hypothesize that the hypertension clusters identified may represent distinct mechanistic subtypes. With further study this could help focus studies on subgroups of hypertension patients with a shared disease etiology, identify more personalized precision medicine treatment options for each subgroup, and develop mechanism-based biomarker tests to support enriched clinical trial recruitment.

BackgroundClonal hematopoiesis of indeterminate potential (CHIP) is associated with cardiovascular disease (CVD) in the general population and is more common among people with HIV (PWH). The mechanisms by which CHIP contributes to atherosclerosis in PWH are unknown. We hypothesized that CHIP is associated with carotid atherosclerosis, arterial inflammation, and hematopoietic activity among PWH. MethodsIn a cohort study, we studied PWH ages 31-74 years. CHIP mutations were detected with a validated targeted sequencing assay. Carotid intima-media thickness (IMT) was measured longitudinally with ultrasound. Aortic inflammation and lymph node activity were assessed cross-sectionally using 18F-FDG-PET. Inflammatory biomarkers were measured using multiplex electrochemiluminescence assay. Linear regression was employed, with adjustments for traditional and HIV-related factors. ResultsWe included 230 PWH (52{+/-}9 years, 7% female); 32 (14%) had CHIP with median variant allele fraction of 2.8%. Common mutations were in DNM3TA (n=21) and TET2 (n=6). Age was associated with CHIP (OR 2.0 per decade older, 95% CI 1.3-3.01; p=0.002). Among 166 participants with IMT measurements (CHIP=23), CHIP was not associated with IMT (p=0.21; unchanged after adjustment). Among 80 with FDG-PET, CHIP (n=12) was not associated with arterial inflammation (p=0.89), but was associated with higher lymph node metabolic activity (p=0.03) that was attenuated in reference to background activity and adjusted for risk factors. CHIP was not associated with soluble inflammatory markers or viral persistence markers. ConclusionsAmong PWH, CHIP mutations were not associated with subclinical atherosclerosis, arterial inflammation, or soluble inflammatory markers but were related to hematopoietic activity. The mechanism by which CHIP increases HIV-associated atherosclerosis may preferentially involve lymph nodes and merits additional evaluation.

BackgroundHDL particles can carry microRNAs (miRNAs), capable of regulating gene expression connected to HDL functions. Despite links to some cardiovascular risk factors, miRNA association with incident acute myocardial infarction (AMI) remains unclear. ObjectivesOur aim was to elucidate the association between HDL-bound miRNAs (HDL-miRNAs) and incident AMI using a non-targeted approach in a population-based study. MethodsWe conducted a case-cohort study including 247 participants from the REGICOR cohort in northeastern Spain (51 AMI cases and a random sample of 196 participants, including seven overlapping AMI cases). HDL-miRNAs were isolated from apolipoprotein B-depleted serum and quantified by whole-genome miRNA sequencing. Associations between HDL-miRNAs and incident AMI were assessed using multivariable Cox proportional hazards model. For AMI-associated HDL-miRNAs (p-value <0.10), we retrieved their experimentally validated targets and assessed pathway enrichment of these targets via over-representation analysis. ResultsTwo HDL-miRNAs were associated with incident AMI after FDR correction: miR-628-3p (HR 1.69, 95% CI 1.30 to 2.19) and miR-28-3p (HR 1.58, 95% CI 1.21 to 2.06). Nine additional HDL-miRNAs were nominally associated with AMI incidence (p-value <0.05), eight with a direct association (miR-93-5p, miR-26b-5p, miR-106a-5p, miR-126-3p, miR-15b-5p, let-7a-5p, let-7e-5p, and let-7f-5p) and one with an inverse association (miR-361-5p). These miRNAs regulate the expression of genes in pathways involved in cholesterol regulation, particularly cholesterol efflux and homeostasis. The AMI group exhibited higher variance and a greater number of significant and strong correlations. ConclusionsTwo HDL-miRNAs (miR-628-3p and miR-28-3p) were significantly associated with AMI incidence. A tighter coregulatory network in cases was observed, underscoring their potential clinical utility in risk prediction and cardiovascular prevention. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIIn a population-based case-cohort study we profiled the HDL-bound miRNome and found two miRNAs (miR-628-3p and miR-28-3p) associated with incident AMI. C_LIO_LIThe use of HDL-enriched serum fractions provided a focused analysis on HDL functionality. These miRNAs regulate the expression of genes in pathways involved in cholesterol efflux and homeostasis (ABCA1, ARL4C, SIRT1, NFKBIA, ANXA2, LRP6) and show a tighter coregulatory network among significant miRNAs among cases, supporting biological coherence. C_LI What Are the Clinical Implications?O_LIHDL-miRNA signatures may complement traditional risk factors to refine AMI risk stratification and provide a rationale for HDL-guided, miRNA-targeted preventive interventions using HDL-like delivery platforms. C_LI

BackgroundSex hormone alterations, such as estrogen deficiency or testosterone excess, substantially increase cardiovascular disease (CVD) risk in females. Dietary fibre and its microbial by-products, short-chain fatty acids (SCFAs), have cardioprotective effects, but it remains unclear whether these benefits extend to females with an altered sex hormone profile. In this study, we aim to investigate whether dietary fibre intake, measured via plasma acetate--the most abundant SCFA--is associated with improved cardiovascular outcomes in females with altered sex hormone profiles. MethodsThis cohort study included 116,235 female participants from the UK Biobank and Biobank Japan with up to 10 years of follow-up. We analysed early menopause (as a surrogate for estrogen insufficiency) and plasma free testosterone (in a subset). The primary outcome was major adverse cardiovascular events (MACE). Secondary outcomes were blood pressure. Proteomics analyses explored potential mechanisms. ResultsAcetate levels were associated with lower 10-year MACE incidence (-0.618/1000 woman-year, HR=0.900, p=0.002) and systolic blood pressure (-0.231 mmHg per 1 SD, p<0.001) in the UK Biobank. High acetate levels attenuated the increased MACE risk associated with early menopause (HR=1.158, p=0.057) compared with low acetate (HR=1.425, p<0.001), with similar patterns replicated in Biobank Japan (high: HR=1.322, p=0.090; low: HR=1.385, p=0.042). Proteomics analyses suggested a mechanism involving pro-inflammatory proteins. Moreover, high acetate levels attenuated the increased MACE associated with elevated free testosterone in the UK Biobank (high: HR=1.238, p=0.024; low: HR=1.056, p=0.666). A significant interaction between acetate and free testosterone on systolic blood pressure indicated that the effect of rising testosterone on blunting acetates effect ({beta}=0.167, 95% CI: [5.212x10-2-2.818x10-1], p=0.004) was partially mediated by central obesity (waist-to-hip ratio). ConclusionsHigher plasma acetate levels were associated with lower cardiovascular risk, particularly in females with early menopause or elevated free testosterone, potentially via inflammatory pathways. These findings underscore the importance of hormonal context in shaping cardiometabolic resilience and support personalised CVD prevention strategies for females with altered sex hormone profiles, including increasing dietary fibre intake.

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.

Neurological complications frequently impact morbidity, mortality, and quality of life in patients with cardiovascular disease, yet the biological mediators connecting cardiovascular and neurological disease are poorly understood. Leveraging data from 53,014 individuals with plasma proteomic profiles and 50,228 with cardiac and brain MRI from the UK Biobank, we systematically identified circulating proteins correlated with MRI imaging-derived phenotypes (IDPs) (404 proteins with cardiac IDPs; 76 with brain IDPs; 37 with both). Identified proteins were remarkably enriched for biomarkers and mediators of disease in one or both organs. Expression analyses suggested these proteins largely originate from fibroblasts, smooth muscle cells, and macrophages in the arterial vasculature. Pathway analyses highlighted cytokine and vasculature-related processes for cardiac IDPs-associated proteins and extracellular matrix pathways in brain IDPs-associated proteins. Mendelian Randomization and genetic co-localization supported causal roles for most (>63%) of the proteins in disease pathogenesis in one or both organs. Over 90% of the implicated candidates have not previously been established as clinical biomarkers or therapeutic targets. These studies underscore the value of large-scale integrated multi-organ datasets, including plasma proteomics, imaging-derived endophenotypes, and genetics, in unraveling complex disease pathobiology, highlight the close connections between heart and brain disease, and provide a catalog of hundreds of novel candidate biomarkers and therapeutic targets.

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.

Metabolic malfunctions are commonly observed in cardiometabolic diseases (CMDs), yet their genetic connections are not fully explored. We leverage multi-omics to decode the genetic crosstalk between 17 cardiometabolic diseases and 16 metabolic traits. Through genomic structural equation modeling, we identify 7 disease clusters anchored on 7 distinct metabolic axes, revealing subtype-specific variants, candidate genes, pathways, tissues and cell types. Among these are four metabolically distinct subtypes of arterial disorders, two adiposity subtypes with opposing associations with CMDs, and a heart-brain-kidney subtype characterized by blood pressure and BMI with dysregulated muscle-vessel coupling. Additionally, we uncover the genetic links between cardiometabolic processes and female health diagnoses. Finally, we leverage the shared risk genes to discover candidate drugs for CMDs, offering potential to improve comorbidity treatments. Overall, our study reveals the genetic basis of the metabolic networks underlying CMDs and extends their relevance into the female health. These results provide a foundation for future mechanistic studies and precise stratification of complex human diseases.