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PubMed2026

Bridging genotype, phenotype, and clinical insight: the role of multi-omics in cardiovascular disease.

INTRODUCTION: It is increasingly evident that the multifactorial nature of cardiovascular disease requires the combination of different omics approaches for improving our mechanistic understanding, identifying novel drug targets, and developing accurate diagnostic, predictive, and prognostic biomarker panels. AREAS COVERED: We review the current state and the potential of multi-omics in cardiovascular disease, with a specific focus on plasma-, spatial-, and single-cell approaches. We discuss lipidomics as a genotype‑to‑phenotype bridge, the utility of remote longitudinal monitoring via microsampling/dried blood spots, and emerging clinical‑trial integrations of multi-omics approaches. We outline critical gaps in standardization and how to overcome these, pre‑analytical challenges and constraints that are often neglected, and data‑integration methods spanning from canonical correlation analysis to modern machine learning approaches. EXPERT OPINION: Multi‑omics can shape cardiovascular care by identifying drug targets in diseased tissue and by yielding small, usable biomarker panels.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.

Cardiovascular-kidney-metabolic (CKM) syndrome is an emerging clinical entity that highlights the complex, bidirectional interplay among cardiovascular disease, chronic kidney disease, and metabolic disorders, representing a substantial and growing global health burden. This conceptualization marks a paradigm shift from viewing these conditions in isolation to understanding them as an interconnected disease continuum. Traditional biomarkers face significant limitations in the early detection, risk stratification, and precise management of CKM, necessitating a transition towards an integrated framework that captures its multisystem nature. This review systematically outlines an emerging multidimensional biomarker system encompassing key pathological axes such as metabolism, immuno-inflammation, oxidative stress, and biological aging, offering refined risk assessment beyond conventional metrics. The development of this system is propelled by revolutionary platforms, including accessible sampling techniques (e.g., dried blood spots), advanced in vitro models (e.g., multi-organ-on-a-chip), and multi-omics technologies. These platforms not only facilitate a deeper dissection of the heterogeneous origins and inter-organ crosstalk in CKM but also accelerate the discovery and validation of novel biomarkers. Concurrently, artificial intelligence serves as a pivotal tool for clinical translation, effectively integrating high-dimensional data to transform complex molecular profiles into actionable clinical insights. By enabling the construction of dynamic risk prediction and decision-support systems, this review charts a pathway toward proactive, individualized, and precise prevention and management of CKM syndrome.

Humans↗

PubMed2025

Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes.

The eye is a recognised source of biomarkers for cardiovascular and neurodegenerative disease risk. Here, we characterise the breadth of these associations and identify biological axes that may mediate them. Using UK Biobank data, we developed a multi-omic analysis pipeline integrating physiological, radiomic, metabolomic, and genomic information. We trained adversarial autoencoders (Ret-AAE) to represent optical coherence tomography (OCT) images and colour fundus photographs as 256-dimensional embeddings. Ret-AAE derived embeddings were associated with a range of cardiovascular and neurodegenerative diseases, including ischaemic heart disease, cerebrovascular disease, Parkinson's disease, and dementia. Examining associations across diverse omics datasets, we provide evidence linking ophthalmic imaging features to neurological and cardiovascular anatomy and function, lipid metabolism, and gene sets associated with neurodegenerative pathology. Collectively, our findings demonstrate that ophthalmic features reflect complex, multisystem biological processes, and reinforce the role of the eye as a composite indicator of systemic health.

Journal Article↗

PubMed2024

Cohort Studies and Multi-omics Approaches to Low-Dose Ionizing Radiation-Induced Cardiovascular Disease: A Comprehensive Review.

The effect of low-dose ionizing radiation exposure on the risk of cardiovascular disease (CVD) represents a significant concern in the field of radiation protection. The prevailing approach to mitigating the adverse effects of low-dose or low-dose-rate radiation does not currently incorporate the potential risk of CVD, despite the possibility that such risk may be a substantial contributor to overall health hazards. Current evidence suggests a potential association between radiation exposure and CVD; however, the overall findings remain inconclusive. This is particularly due to the uncertainty surrounding the influence of significant non-radiation risk factors on the associations reported in epidemiological studies. It is difficult to discern the underlying connection in observational epidemiology when there is substantial variation in baseline risk factors. The paucity of epidemiological research in this domain is being partially offset by the advancement of multi-omics approaches. These methods assist in identifying radiosensitive targets, comprehending underlying biological processes, and pinpointing biomarkers. This, in turn, fortifies the evidence gleaned from epidemiological studies. In this review, we delve into the body of epidemiological research pertaining to CVD induced by low-dose ionizing radiation and the application of multi-omics techniques. The integration of these two methodologies holds the promise of identifying specific molecules or biological pathways that can be employed to validate endpoints related to radiation risk assessment.

Humans↗

PubMed2025

Soluble Immune Checkpoint Protein and Lipid Network Associations with All-Cause Mortality Risk: Trans-Omics for Precision Medicine (TOPMed) Program.

Adverse cardiovascular events are emerging with the use of immune checkpoint therapies in oncology. Using datasets in the Trans-Omics for Precision Medicine program (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study [JHS], and Framingham Heart Study), we examined the association of immune checkpoint plasma proteins with each other, their associated protein network with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), and the association of HDL-C- and LDL-C-associated protein networks with all-cause mortality risk. Plasma levels of LAG3 and HAVCR2 showed statistically significant associations with mortality risk. Colocalization analysis using genome wide-association studies of HDL-C or LDL-C and protein quantitative trait loci from JHS and the Atherosclerosis Risk in Communities identified TFF3 rs60467699 and CD36 rs3211938 variants as significantly colocalized with HDL-C; in contrast, none colocalized with LDL-C. The measurement of plasma LAG3, HAVCR2, and associated proteins plus targeted genotyping may identify patients at increased mortality risk.

Journal Article↗

PubMed2026

Omics Profiling of Patients with Obstructive Sleep Apnoea Reveals Risks of Diabetes Mellitus and Cardiovascular Diseases.

Obstructive sleep apnoea (OSA) constitutes a multisystemic disorder often associated with cardiovascular and metabolic disorders. Thus, far, the underlying pathophysiological processes are not fully understood. In total, 142 plasma samples were acquired: 50 from controls (CON), 45 from mild/moderate OSA (M-OSA) patients, and 47 from severe OSA (S-OSA) patients. Proteomic and metabolic signatures significantly differed among S-OSA, M-OSA, and CON samples. A novel plasma biomarker panel including two proteins (ACTR2 and ENO1) and three metabolites (2-aminobicyclo[2 2·1], heptane-2-carboxylic acid, 1-O-[2r-hydroxy-hexadecyl]-sn-glycerol, and 1-pentadecene) was developed to identify S-OSA (AUC: 1.000) and distinguish severe cases from nonsevere cases (AUC: 0.813). An independent cohort was used to validate the model by distinguishing S-OSA samples from M-OSA (AUC: 0.729) and CON (AUC: 0.990) samples. Glycolysis pathway activation was identified as a characteristic of OSA; it may contribute to diabetes mellitus onset in OSA patients. Dyslipidaemia, foamy macrophage formation, platelet activation, and actin cytoskeleton might collectively play a key role in vascular damage in OSA patients, contributing to the development of atherosclerosis. These findings reveal molecular bases for OSA-related cardiometabolic complications and provide new diagnostic biomarkers for OSA and the identification of severe cases.

Humans↗

PubMed2026

Large-scale multi-omics enhance risk prediction for type 2 diabetes.

BACKGROUND: Polygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. METHODS: We analysed data from 42,840 UK Biobank participants without diagnosed diabetes at baseline. The study population was split into a derivation set (Phase 1 metabolomics release, N = 23,108) to fit models and an independent validation set (Phase 2 release, N = 19,732) to evaluate performance. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrell's C-index and the net reclassification index (NRI). RESULTS: During 10 years of follow-up, 1090 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.862 (clinical CDRS) to 0.884 (ΔC-index; + 0.022; P < 0.001), with a continuous NRI of 42.0%. The full multi-omics model further significantly increased the C-index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.891; ΔC-index; + 0.007; P < 0.001). CONCLUSION: Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. Several of the selected proteins and metabolites are on cardiovascular disease pathways, highlighting the link between diabetes and cardiovascular risk. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins. External validation and cost-effectiveness analyses are needed to support clinical adoption.

Humans↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗

PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗

PubMed2026

BIOCARD framework: integrating fecal bile acids, lipids, and metabolites to assess response to a cardiovascular health intervention.

Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality, particularly in under-resourced populations. Although nutritional interventions are important for CVD prevention, their outcomes are commonly evaluated using conventional clinical and behavioral indicators, which may not fully capture early molecular responses. In this study, we developed the BIOCARD framework, an exploratory fecal multi-omics platform integrating bile acids, lipids, and metabolites to evaluate intervention outcomes related to cardiovascular health. Fecal samples were collected from caregiver-child participants enrolled in a 10-week randomized controlled trial comparing a multicomponent garden-based intervention (SHA) with an education-only control group (MSP). Fecal polar metabolites, lipids, and bile acids were analyzed by UHPLC-HRMS-based approaches and integrated with conventional health indicators. Traditional clinical indicators in the present study showed limited sensitivity for detecting intervention-related differences. In contrast, fecal multi-omics analyzes revealed intervention-associated differences in metabolites, lipids, and bile acids, with children showing more apparent molecular variation than parents. Network analysis further revealed associations between selected molecular features and cardiovascular-related indicators, including blood pressure, body fat, skin carotenoids, and Healthy Eating Index scores. Together, these findings suggest that the BIOCARD framework may serve as an exploratory molecular approach to complement traditional outcome measures and improve the evaluation of nutritional interventions for cardiovascular health.

Humans↗

PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗

PubMed2026

Genomic and proteogenomic insights into Spontaneous Coronary Artery Dissection (SCAD): A systematic review of emerging multi-omic evidence.

BACKGROUND: Spontaneous coronary artery dissection (SCAD) is a major cause of myocardial infarction in young women without traditional cardiovascular risk factors (Hayes et al., 2018; Adlam et al., 2018 [1, 2]). Despite growing awareness, its biological underpinnings remain incompletely understood, and clinical management is largely based on observational evidence rather than mechanistic insight (Saw et al., 2014; Lettieri et al., 2015; Steg et al., 2024 [3-5]). OBJECTIVES: To systematically integrate genomic, epitranscriptomic, proteomic, and metabolomic data in order to characterize the multi-omic architecture of SCAD and identify potential biomarkers and therapeutic targets. METHODS: A systematic review was conducted in accordance with the PRISMA 2020 statement (Arbelo et al., 2023 [6]). PubMed/MEDLINE was searched for original studies investigating genomic and multi-omic features of SCAD. Data were extracted on study design, patient characteristics, identified variants, circulating biomarkers, and implicated biological pathways. Functional enrichment analysis was performed using the DAVID bioinformatics resource (Page et al., 2021 [7]). RESULTS: A total of 16 studies were included. Genome-wide association studies consistently identified susceptibility loci related to arterial structure and extracellular matrix integrity, including ADAMTSL4, PHACTR1/EDN1, LRP1, and FBN1 (Huang et al., 2009; Saw et al., 2020; Turley et al., 2020 [8-10]). Rare variant analyses further supported the role of genes involved in extracellular matrix remodeling and vascular smooth muscle cell function, including COL3A1, COL4A1/2, SMAD3, and TLN1 (Adlam et al., 2023; Turley et al., 2021, 2019; Carss et al., 2020; Zekavat et al., 2022; Wang et al., 2022 [11-16]), while ancestry-specific signals such as TSR1 variants were observed in distinct populations (Turley et al., 2023 [17]). Proteogenomic approaches linked genetic susceptibility loci to circulating proteins involved in matrix remodeling and inflammation, including cathepsin B and ECM1 (Maioli et al., 2010 [18]). Epitranscriptomic analyses identified differential microRNA expression profiles associated with vascular injury and repair pathways (Sun et al., 2019 [19]). CONCLUSIONS: SCAD is characterized by a complex, multi-layered biological architecture involving genetic susceptibility, extracellular matrix dysregulation, and vascular signaling pathways. Integration of multi-omic data provides novel insights into disease mechanisms and highlights potential biomarkers and targets for precision medicine approaches in SCAD.

Animals↗

PubMed2025

Cardiovascular Complications Are Increased in Inflammatory Bowel Disease: A Path Toward Achievement of a Personalized Risk Estimation.

Background/Objectives: The global burden of inflammatory bowel diseases (IBDs) continues to rise, with up to 50% of patients experiencing extraintestinal manifestations. Cardiovascular diseases (CVDs) are of particular concern, ranking as the second leading cause of mortality in this population. Despite a comparatively lower prevalence of traditional cardiovascular (CV) risk factors, the persistent inflammatory milieu and immune dysregulation inherent to IBD may contribute to heightened CVD risk. In this study, following a review of the current literature, an ongoing prospective trial designed to clarify CV risk profiles in IBD patients is detailed. Methods: A cohort of patients with IBD is being enrolled for comprehensive baseline evaluation of CV risk factors, lifestyle metrics, and disease characteristics. The incidence of major adverse cardiovascular events (MACEs) will be tracked and contrasted with a gender- and age-matched non-IBD cohort over a 2-year follow-up period. In cases of MACE occurrence, a multi-omics analysis-including genomic, proteomic, transcriptomic, and microbiome profiling-will be performed, along with a parallel evaluation in matched IBD controls without MACE. An artificial intelligence (AI) framework will support the analysis of this complex dataset. Results: To date, over 150 patients with IBD have been enrolled, and detailed phenotypic data and biological samples have been collected. Conclusions: We aim to introduce an IBD-specific correction factor for existing CV risk scores upon study completion. This is particularly relevant for individuals under 40 years of age, who are often inadequately assessed by current risk stratification models.

Crohn’s disease↗

PubMed2026

Multi-omics Mendelian Randomization Prioritizes Neutrophil Extracellular Trap-related Genes Associated with Atrial Fibrillation Risk.

BACKGROUND: Neutrophil extracellular traps (NETs) participate in thrombosis, inflammation, and cardiovascular remodeling, yet whether NET-related genes (NRGs) are associated with atrial fibrillation (AF) risk across multiple molecular layers remains unclear. This study used a multiomics Mendelian randomization framework to prioritize NRGs supported by methylation, expression, and protein quantitative trait loci (QTL) data. METHODS: Genome-wide significant cis instruments (P < 5 × 10-8) were obtained for 90 methylation QTLs (mQTLs), 100 expression QTLs (eQTLs), and 38 protein QTLs (pQTLs) mapped to 137 literature- curated NRG entries. Summary-data-based Mendelian randomization (SMR) coupled with the heterogeneity in dependent instruments (HEIDI) test was applied using whole-blood mQTL data (n = 1,980), eQTLGen blood eQTL data (n = 31,684), and deCODE plasma pQTL data (n = 35,559). AF outcome data were obtained from a meta-analysis including 60,620 cases and 970,216 controls of European ancestry. RESULTS: At the methylation level, 21 CpG-feature associations across 13 genes remained significant after HEIDI filtering and false discovery rate (FDR) correction. Expression-level analysis identified eight significant gene-AF associations, whereas protein-level analysis identified seven significant features representing five unique proteins. Cross-omics integration prioritized C3, MAPK3, and STAT3 as Tier 1 genes, CTSC, LPAR3, and THBD as Tier 2 genes, and fourteen additional genes as Tier 3 candidates. C3 showed risk-increasing protein-level associations together with multiple significant CpG signals, whereas MAPK3 and STAT3 showed directionally protective expression/protein or methylation/protein patterns. DISCUSSION: The cross-omics convergence on C3, MAPK3, and STAT3 is consistent with complement activation, immune-fibrotic signaling, and cytokine-regulatory pathways implicated in AF biology, but the findings should be interpreted as genetic prioritization rather than definitive intervention-ready causality. CpG-level heterogeneity at the C3 locus and the blood/plasma origin of the QTL resources further support a cautious interpretation. Modest colocalization support and the unresolved possibility of pQTL sample overlap further support this cautious, hypothesis-generating interpretation. CONCLUSION: Multi-omics SMR prioritizes C3, MAPK3, and STAT3 as the most consistently supported NET-related genes associated with AF risk. These findings provide a framework for atrialtissue replication and mechanistic validation of NET-related pathways in AF.

Atrial fibrillation↗

PubMed2026

State of Cardiovascular Disease and Stroke in Hispanic/Latino Adults in the United States: A Scientific Statement From the American Heart Association.

Cardiovascular disease became the leading cause of death among Hispanic individuals in the United States in 2022. Hispanic adults experience a disproportionate burden of cardiometabolic risk factors, including obesity, diabetes, and dyslipidemia. Hispanic populations are highly heterogeneous, with substantial variations in genetic ancestry and sociocultural influences that shape cardiovascular disease risk and outcomes. The "Hispanic paradox," describing lower cardiovascular disease mortality despite higher risk factor burden, is increasingly recognized as an oversimplification that does not apply uniformly across Hispanic heritage groups, sexes, or disease types. Disaggregated data reveal substantial differences in risk profiles and disease burden among Hispanic heritage groups, emphasizing the limitations of treating this population as a monolithic unit. Recent evidence demonstrates widening disparities in hypertension control, obesity, diabetes, and metabolic diseases among Hispanic populations, threatening this prior mortality advantage. Advancing cardiovascular and equitable health will require developing a deeper understanding of the unique drivers of cardiovascular disease within diverse Hispanic communities, addressing barriers such as language and insurance access, and implementing culturally tailored interventions and policies. This scientific statement summarizes current cardiovascular disease epidemiology in Hispanic populations, emphasizing heritage group variation and social and structural determinants of health, and presents strategies to improve prevention and healthcare delivery. Key priorities for advancing cardiovascular health in Hispanic adults include expanding disaggregated data collection, increasing representation in research, and ensuring equitable implementation of precision medicine approaches, including genomics, multi-omics, and artificial intelligence, while addressing environmental exposures, psychosocial stressors, and policy-related drivers of risk in order to achieve the American Heart Association's 2028 Impact Goals to advancing health and hope for everyone, everywhere.

AHA Scientific Statements↗

PubMed2026

2025 Donald Seldin Lecture: Leveraging Diverse Population Genomics and Multiomics Integration for Gene Discovery of Cardiovascular and Kidney Diseases.

This review discusses the implications of frameworks leveraging genetic admixture and multiomics data for advancing gene discovery in cardiovascular and kidney disease research. By broadening gene discovery efforts to additional populations that have a disproportionately high risk of disease and leveraging genetic diversity in admixed populations, studies can identify population-enriched risk variants that traditionally have been missed in genome-wide association studies. The use of multiomics approaches, including the transcriptome, proteome, and metabolome, advances a mechanistic understanding of disease beyond associations. As single-cell omics technologies continue to improve, their integration into gene discovery may help uncover cell-type-specific regulatory pathways and more precise biological contexts. The full potential of these approaches depends on sustained investment in diverse, well-characterized omics data sets, methodological innovation in multiancestry statistical approaches, and interdisciplinary collaboration bridging genomics, epidemiology, and clinical medicine. These efforts will need to be translated into clinically actionable insights, including ancestry-informed risk stratification and targeted therapeutics, to improve outcomes for cardiovascular and kidney diseases.

Humans↗